EVALUATING THE NORMAL ACCIDENT THEORY IN COMPLEX SYSTEMS AS
A PREDICTIVE APPROACH TO MINING HAULAGE OPERATIONS SAFETY
by
Michael Duc Do
_____________________
Copyright © Michael Duc Do 2012
A Dissertation Submitted to the Faculty of the
DEPARTMENT OF MINING, GEOLOGICAL, AND GEOPHYSICAL
ENGINEERING
In Partial Fulfillment of the Requirements
For the Degree of
DOCTOR OF PHILOSOPHY
In the Graduate College
THE UNIVERSITY OF ARIZONA
2012
2
THE UNIVERSITY OF ARIZONA
GRADUATE COLLEGE
As members of the Dissertation Committee, we certify that we have read the dissertation
prepared by Michael D. Do entitled, ―Evaluating the Normal Accident Theory in
Complex Systems as a Predictive Approach to Mining Haulage Operations Safety‖ and
recommend that it be accepted as fulfilling the dissertation requirement for the Degree of
Doctor of Philosophy.
______________________________________________Date: 12/7/12
Moe Momayez
______________________________________________Date: 12/7/12
Mary Poulton
______________________________________________Date: 12/7/12
Raymond Yost
Final approval and acceptance of this dissertation is contingent upon the candidate’s
submission of the final copies of the dissertation to the Graduate College.
I hereby certify that I have read this dissertation prepared under my direction and
recommend that it be accepted as fulfilling the dissertation requirement.
_______________________________________________Date: 12/7/12
Dissertation Director: Mary Poulton
3
STATEMENT BY AUTHOR
This dissertation has been submitted in partial fulfillment of requirements for an
advanced degree at the University of Arizona and is deposited in the University Library
to be made available to borrowers under the rule of the Library.
Brief quotations from this dissertation are allowable without special permission,
provided that accurate acknowledgment of source is made. Requests for permission for
extended quotation from or reproduction of this manuscript in whole or in part may be
granted by the head of the major department or the Dean of the Graduate College when in
his or her judgment the proposed use of the material is in the interests of scholarship. In
all other instances, however, permission must be obtained from the copyright holder.
SIGNED: _________Michael D. Do___________
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ACKNOWLEDGEMENTS
There is nothing better than challenging ourselves and then taking great satisfaction in
overcoming our challenges. Of course, success does not come without much personal
sacrifices – that is little time spent with our families, limited social life, and limited
personal time to enjoy the finer things in life. However, through self-sacrifice and
discipline, anything is possible to overcome and achieve.
Pursuing the doctoral program has been by far the greatest challenge of my life,
however, as a result this experience; I have gained much in terms of growth – intellectual,
professional, and personal. With regards to intellectual growth, I have learned a great
deal of knowledge in terms of my ability to research, think, and analyze information in
ways which benefits and prepares me well in dealing with the challenges of life in
general. As for professional growth, the interactions with academia and industry
professionals during the course of this study allowed me to appreciate their time and
expertise in guiding and mentoring me. I truly understand the importance of building
relationships in order to be successful. Lastly, with regards to personal growth, I learned
how to exploit my strengths while working to improve my weaknesses and became a
better person (professionally and personally) because of it.
With that said I want to personally thank my committee members, Dr. Moe Mayomez
and Dr. Ray Yost for their time in volunteering to be on my committee and guided me
through my dissertation phase. Your reviews and feedbacks have greatly made the
dissertation a much better product than the initial draft. Ray Yost provided much
encouragement and shared experiences along the way. Moe Mayomez offered good
advices and experiences during our many lunch meetings at Miss Saigon and Pei Wei
5
restaurants. Lastly, a whole hearted thanks to my advisor, Dr. Mary Poulton, for taking
the time to serve as my graduate and dissertation advisor from the beginning to the end of
this journey. It was a journey through this process that I have come to know her very
well professionally and personally throughout the years. She was certainly very critical
in her reviews and comments of my dissertation which at times left me with brief periods
of euphoria and long periods of frustration. But in the end, her candid comments ensured
a well thought out and storytelling of my dissertation. As she comments, ―you must be
able to tell the story and it needs to have a logical flow of ideas.‖ I am certain she is now
ready to get rid of me. Thanks Mary and in the end, this product tells a GREAT STORY!
Finally, a personal thank you and debt of gratitude is reserved for my civilian
employer (a major defense contractor and because of the nature of their business, their
name shall be withheld) and the scholarship committee that approved my academic
fellowship; thank you so much for financing my education and allowing me to achieve
the impossible. The financial and work schedule flexibility made it possible for me to
pursue my academic goals. Without your unwavering support, the end of this process
would not have been possible and for that, I am truly grateful and will forever be in debt
to you.
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DEDICATION
Of course, no product goes without some sort of dedication to those individual(s) or
things that have made any piece of art work truly a masterpiece. For my masterpiece, I
dedicate my dissertation to the two most important people who I considered to be my
heroes in life – my parents (Robert Vinh and Barbara Cuc Do). They are my heroes not
because I happen to be their son by birth, but because they were two loving parents who
decided more than three decades ago (shortly after the fall of Saigon) that the only future
for their children – a future where their children can experience freedom and
opportunities, a future where their children can grow up to become adults, a future where
their children can live in peace, but most of all, a future where their children can grow up
to become whoever they want to become – was to escape their native homeland, the
former Republic of South Vietnam, and journey to the United States of America with the
hope of pursuing the ―American Dream‖. Recognizing that there was only a 50% chance
of survival, my parents never wavered in their decision and sacrifices to escape Vietnam
with the hope that their children’s future would be secured in America. Every time I
reflect back to my family’s struggles during those terrible years after the fall of Saigon
and our journey to the United States of America as ―boat‖ refugees, I often wondered that
if I was ever in my parent’s situation, would I have the courage to make such a tough
decision to gamble with 5 precious human lives. Many times I often wondered how they
were able to draw the strength to begin and continue a journey during those tough years
when there was so much uncertainty (perhaps my choosing complexity as my research
topic was appropriate because this topic was about uncertainty and the nature of complex
systems much like the uncertain years of my early life) and chaos. How did they manage
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to keep hope to continue when there seemed to be little for hope, especially during those
many days and nights spent at sea on a crowded small boat with little food and water.
Where did they draw the strength when there seem little strength left to continue,
especially during a time when we were stranded on an island for a whole month and had
to live off of seaweed, snails, saltwater, and any other edible foods that we could find.
Again, as I reflect back on those years, I can only conclude that the will to survive and
succeed during times of adversities could only be overcome by those who exhibit strong
character along with determination, resilience, and a will to succeed. I can truly attest
that my parents are people of character. Their self-sacrifice and determination to give
their children a better life in America is the reason why I considered them to be my
heroes and role models. I am who I am today and all that I ever dreamed of becoming in
life, I tremendously owed them to my parents. In writing this dedication I want everyone
who comes across this masterpiece to know why I dedicate this dissertation to my parents
and how grateful I am for their unconditional love and support since birth.
Finally, to my Dearest Mom and Dad – as I wrote in my acknowledgements, this
doctoral endeavor has been by far the toughest challenge of my life, yet I know it is
nothing compared to the challenges that you have undertaken back in 1979. The
attributes of hard work, determination, hope, optimism, courage, and strength that you
have embodied and taught me is well reflected in the completion of this research study
and as such, I dedicate my dissertation to you. You’ve given me so much in life but most
of all, you’ve given me a second chance of life to achieve those beyond my wildest
dreams, and I hope that I have turned out to become the son that you had nurtured, cared
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for, and raised in life. My success is YOUR success. With all my LOVE, RESPECT,
and ADMIRATION – Your Son, Michael.
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TABLE OF CONTENTS
LIST OF FIGURES ...........................................................................................................15
LIST OF TABLES .............................................................................................................17
ABSTRACT .......................................................................................................................19
CHAPTER 1 INTRODUCTION .......................................................................................20
1.1 Background of the Normal Accident Theory.....................................................21
1.2 Haulage Accidents in the Mining Industry ........................................................22
1.3 Haulage Accidents and the Normal Accident Theory .......................................23
1.4 Historical Perspective of the Normal Accident Theory in the
Mining Industry .......................................................................................................24
1.5 Purpose and Significant Contributions of the Research ....................................24
1.6 Research Questions ............................................................................................26
1.7 Research Scope and Boundary...........................................................................26
1.8 Research Structure .............................................................................................27
1.9 Research Data ....................................................................................................28
1.10 Conclusion .......................................................................................................28
CHAPTER 2 MINE ACCIDENTS, THEORIES, SAFETY, REGULATIONS,
PRACTICES, AND TRAINING ......................................................................................29
2.1 Mine Accidents ..................................................................................................29
2.1.1 Types of Accidents ...................................................................................30
2.1.1.1 Definitions.....................................................................................30
2.1.1.2 Accident Fatality Summary ..........................................................33
2.1.2 Haulage Accidents ....................................................................................38
2.1.2.1 Loader and Truck Fatalities ..........................................................43
2.1.2.1.1 Loader-related Fatalities ...............................................45
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TABLE OF CONTENTS – Continued
2.1.2.1.2 Truck-related Fatalities ..................................................47
2.2 Accident Theories ...............................................................................................49
2.2.1 Domino Theory ..........................................................................................50
2.2.2 Multiple Causation Theory ........................................................................51
2.2.3 Human Error Theory ..................................................................................53
2.3 Safety Systems ....................................................................................................56
2.3.1 Safety Systems Implemented by Mining Companies to Reduce
Accidents ............................................................................................................56
2.3.2 Existing Safety Management Systems Implemented by Major Mining
Companies to Reduce Accidents ........................................................................57
2.3.2.1 Freeport-McMoRan ......................................................................58
2.3.2.2 Barrick Corporation .......................................................................60
2.3.2.3 Newmont Corporation ...................................................................66
2.3.3 Critiques of Existing Safety Management Systems Implemented by
Major Mining Companies to Reduce Accidents ................................................69
2.3.3.1 Freeport-McMoRan .....................................................................69
2.3.3.2 Barrick Corporation ......................................................................71
2.3.3.3 Newmont Corporation ..................................................................73
2.4 Safety Regulations, Practices, and Training .....................................................75
2.5 Conclusion .........................................................................................................77
CHAPTER 3 COMPETING ORGANIZATIONAL ACCIDENT THEORIES ................79
3.1 The Origins of the Normal Accident Theory and High Reliability
Theory ......................................................................................................................79
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TABLE OF CONTENTS – Continued
3.2 Proponents and Opponents of the Normal Accident Theory and High
Reliability Theory ....................................................................................................82
3.3 Mining Haulage Accidents within the Context of the Two Competing
Theories....................................................................................................................85
3.4 Conclusion .........................................................................................................91
CHAPTER 4 THE NORMAL ACCIDENT THEORY .....................................................93
4.1 Origins of the Normal Accident Theory ............................................................93
4.2 Definitions..........................................................................................................95
4.2.1 What is a system? ......................................................................................95
4.2.1.1 Complex (Non-Linear) and Linear Systems .................................98
4.2.1.2 Tight and Loose Coupling ..........................................................100
4.2.1.3 Interactive Complexity and Tight Coupling ...............................102
4.3 The Normal Accident Theory and Its Potential Applicability in the Mining
Industry ..................................................................................................................104
4.3.1 Haulage Operation Defined within the Framework of the Normal Accident
Theory ....................................................................................................................105
4.3.1.1 Complexity Definitions.....................................................................105
4.3.1.2 Haulage Operation Complexity and Tight Coupling ........................107
4.4 Conclusion .......................................................................................................108
CHAPTER 5 INTERACTIVE COMPLEXITY ANALYSIS .........................................109
5.1 Accident Models ..............................................................................................109
5.1.1 Energy Model..........................................................................................110
5.1.2 Models Based on Systems Theory ..........................................................112
5.1.3 Accident Triangle Model ........................................................................114
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TABLE OF CONTENTS – Continued
5.2 Advantages and Disadvantages of Accident Models ......................................117
5.3 Application of Accident Models ......................................................................118
5.4 Interactive Complexity Analysis......................................................................119
5.5 Conclusion .......................................................................................................123
CHAPTER 6 TIGHT COUPLING ANALYSIS .............................................................124
6.1 Tight Coupling .................................................................................................124
6.2 Assumptions of the Tight Coupling Analysis ..................................................125
6.3 Tight Coupling Analysis ..................................................................................125
6.4 Conclusion .......................................................................................................130
CHAPTER 7 NAT STATISTICAL ANALYSIS AND ASSESSMENT ........................131
7.1 Dependent and Independent Variable Definitions ..........................................131
7.1.1 Dependent Variable – Haulage Accidents .............................................131
7.1.2 Independent Variables – Haulage Operations Complexity and
Haulage Operations Tight Coupling ................................................................132
7.2 Statistical Analysis ...........................................................................................136
7.3 Assessment .......................................................................................................138
7.4 Conclusion .......................................................................................................144
CHAPTER 8 THE SYSTEMS APPROACH TO DEFINING AND ILLUSTRATING
MINING HAULAGE OPERATIONS SYSTEM ............................................................145
8.1 System Elements .............................................................................................145
8.2 System Nodes...................................................................................................146
8.3 System Hierarchy .............................................................................................147
8.4 System Modeling .............................................................................................149
8.5 System Interface and Interaction .....................................................................150
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TABLE OF CONTENTS – Continued
8.6 Purpose, Definition, and Structure of the Mining Haulage
Operations System .................................................................................................151
8.7 Conclusion .......................................................................................................155
CHAPTER 9 COMPLEXITY MEASUREMENTS – A LITERATURE REVIEW .......156
9.1 Existing Complexity Measurement Approaches .............................................156
9.1.1 Petrochemical Plants and Refineries Complexity ...................................156
9.1.2 Supply Chain Complexity .......................................................................159
9.1.3 Project Complexity Based on Analytic Hierarchy Process .....................162
9.1.4 Elementary System Components Complexity ........................................164
9.2 Advantages and Disadvantages of the Existing Measurement Approaches ....167
9.3 Conclusion .......................................................................................................168
CHAPTER 10 PROPOSED MINING HAULAGE OPERATIONS SYSTEM
COMPLEXITY MEASUREMENT ................................................................................169
10.1 System Elements, Nodes, Hierarchy, and Modeling .....................................169
10.2 Heuristic Approach to Measuring Complexity ..............................................170
10.3 Application of the Literature Review of Complexity Measurements ............171
10.4 Proposed Mining Haulage Operations System Complexity Index ................172
10.5 Strengths and Weaknesses of the Proposed Mining Haulage Operations
System Complexity Index .....................................................................................174
10.6 Conclusion .....................................................................................................176
CHAPTER 11 SYSTEM MODELING AND COMPLEXITY CALCULATION
METHODOLOGY, RESULTS, AND ANALYSIS ........................................................177
11.1 Application of the System Modeling Concepts .............................................177
11.1.1 Development of the Mining Haulage Load, Transfer, and Unload
Operations System Models and Hierarchy ....................................................178
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TABLE OF CONTENTS – Continued
11.1.2 Development of the Mining Haulage Load, Transfer, and Unload
Operations System Nodes ..............................................................................186
11.2 Application of the Proposed Complexity Index.............................................191
11.2.1 Complexity Measurements .................................................................191
11.2.2 Complexity Measurement Analysis ....................................................193
11.3 Results and Analysis Discussion ...................................................................194
11.4 Conclusion .....................................................................................................195
CHAPTER 12 SUMMARY AND CONCLUSIONS ......................................................196
12.1 Summary ........................................................................................................197
12.2 Normal Accident Theory versus High Reliability Theory.............................197
12.3 Research Questions ........................................................................................200
12.4 Limitations of the Research ...........................................................................203
12.4.1 Normal Accident Theory ....................................................................203
12.4.2 Research Data .....................................................................................205
12.5 Recommendations ..........................................................................................205
12.5.1 Mitigating Mining Haulage Operations Accidents ............................206
12.5.2 Future Research Work .......................................................................207
12.6 Conclusions ....................................................................................................210
APPENDIX A TABLE OF TERMINOLOGIES ............................................................211
APPENDIX B INTERACTIVE COMPLEXITY ANALYSIS .......................................212
REFERENCES ................................................................................................................224
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LIST OF FIGURES
Figure 2-1 Haulage and Stationary Equipment Related Accidents, 2000-2007 ................39
Figure 2-2 Mobile Surface Mining Machines or Vehicles Involved in Fatal Accidents
Attributed to Loss of Control, 2000–2007 .........................................................................41
Figure 2-3 Mobile Surface Mining Machines or Vehicles Involved in Fatal Accidents
Attributed to Visibility Issues, 2000–2007 ........................................................................42
Figure 2-4 Direction of Travel during the Fatal Accident Involving Visibility around
Mobile Surface Mining Machines, 2000–2007 .................................................................43
Figure 2-5 Number of Fatalities Involving Loaders and Trucks .......................................44
Figure 2-6 Number of Fatalities Involving Loaders and Trucks per Type of Mining
Operations ..........................................................................................................................45
Figure 2-7 Proportion of Loader Fatalities to Total Fatalities by Category ......................46
Figure 2-8 Root Causes of Loader Fatalities .....................................................................47
Figure 2-9 Proportion of Loader Fatalities to Total Fatalities by Category ......................48
Figure 2-10 Root Causes of Truck Fatalities .....................................................................49
Figure 2-11 Barrick Safety System Methodology .............................................................62
Figure 4-1 System Concept ................................................................................................96
Figure 4-2 Catastrophic Potential Model .........................................................................104
Figure 5-1 Energy Model of Accidents ...........................................................................111
Figure 5-2 Accident Triangle Model ...............................................................................116
Figure 7-1 Dependent Variable Definition ......................................................................132
Figure 7-2 Haulage Operations Complexity ....................................................................133
Figure 7-3 Independent Variables Definition ..................................................................134
Figure 7-4 Complexity/Coupling Matrix Classification .................................................137
Figure 8-1 System Hierarchy ...........................................................................................147
Figure 8-2 System Interface and Interaction ....................................................................151
Figure 8-3 System Architecture .......................................................................................153
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LIST OF FIGURES – Continued
Figure 8-4 Mining Haulage Operations System ..............................................................154
Figure 9-1 Elements, Possible Relationships, and States as a Measure of
Complexity.......................................................................................................................165
Figure 11-1 Mining Haulage Load Operations System Model and Hierarchy ................182
Figure 11-2 Mining Haulage Transfer Operations System Model and Hierarchy ..........183
Figure 11-3 Mining Haulage Unload Operations System Model and Hierarchy ............184
Figure 11-4 Mining Haulage Operations System Model and Hierarchy .........................185
Figure 11-5 Load Operations System Node.....................................................................187
Figure 11-6 Transfer Operations System Node ...............................................................188
Figure 11-7 Unload Operations System Node .................................................................189
Figure 11-8 Mining Haulage Operations System Node ...................................................190
Figure 12-1 NAT Lines of Evidence ...............................................................................199
Figure 12-2 NAT Linkages with Mining Haulage Operations System ...........................200
Figure 12-3 NAT Approach versus Regulatory Approach ..............................................207
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LIST OF TABLES
Table 2-1 Classification of Mine Accidents ......................................................................31
Table 2-2 5-Year Mine Accident Fatality Summary .........................................................34
Table 2-3 M/NM 5-Year Fatality Summary ......................................................................36
Table 2-4 Coal 5-Year Fatality Summary..........................................................................37
Table 3-1 Competing Perspectives of HRT versus NAT...................................................81
Table 4-1 System Attributes ..............................................................................................97
Table 4-2 System Types.....................................................................................................98
Table 4-3 Complex (Non-Linear) versus Linear Systems ..............................................100
Table 4-4 Tight and Loose Coupling Characteristics ......................................................102
Table 4-5 Various Definitions of Complexity .................................................................106
Table 5-1 Assumptions of the Systems Theory and Accident Triangle Models .............118
Table 5-2 Interactive Complexity Analysis .....................................................................121
Table 6-1 Assumptions of the Tight Coupling Analysis ................................................125
Table 6-2 Tight Coupling Analysis..................................................................................126
Table 7-1 Complexity/Coupling Definition .....................................................................134
Table 7-2 Haulage Operations Complexity and Coupling Factors ..................................135
Table 7-3 Complexity and Coupling Accident Statistics .................................................136
Table 7-4 Complexity/Coupling Matrix Summary ..........................................................137
Table 7-5 Loader Equipment Assessment .......................................................................139
Table 7-6 Truck Equipment Assessment .........................................................................139
Table 7-7 Probability of Accident Occurrence ................................................................141
Table 7-8 Complexity Assessment ..................................................................................143
Table 8-1 Haulage Operation Definitions ........................................................................152
Table 9-1 Qualifiers for each Parameter ..........................................................................158
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LIST OF TABLES – Continued
Table 11-1 Modeling Assumptions..................................................................................178
Table 11-2 Haulage Operations System Complexity Measurement Calculation ............192
Table 11-3 Haulage Operations System Complexity Measurement Summary ...............193
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ABSTRACT
The Normal Accident Theory (NAT) attempts to understand why accidents occur in
systems with high-risk technologies. NAT is characterized by two attributes: complexity
and coupling. The combination of these attributes results in unplanned and unintended
catastrophic consequences. High-risk technology systems that are complex and tightly
coupled have a high probability of experiencing system failures.
The mining industry has experienced significant incidents involving haulage
operations up to and including severe injuries and fatalities. Although the mining
industry has dramatically reduced fatalities and lost time accidents over the last three
decades or more, accidents still continue to persist. For example, for the years 1998 –
2002, haulage operations in surface mines alone have accounted for over 40% of all
accidents in the mining industry.
The systems thinking was applied as an approach to qualitatively and quantitatively
evaluate NAT in mining haulage operations. A measurement index was developed to
measure this complexity. The results from the index measurements indicated a high
degree of complexity that exists in haulage transfer systems than compared to loading
and unloading systems. Additionally, several lines of evidence also point to the
applicability of NAT in mining systems. They include strong organizational management
or safety system does not guarantee zero accidents, complexity is exhibited in mining
systems, and they are interactive and tightly coupled systems. Finally, the complexity of
these systems were assessed with results indicating that a large number of accidents occur
when there are between 4 or 5 causal factors. These factors indicate the degree of
complexity necessary before accidents begin to occur.
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CHAPTER 1
INTRODUCTION
Have you ever wondered why catastrophic accidents such as the space shuttle
Columbia explosion, Bhopal chemical release, or Chernobyl nuclear plant explosion
occurred? More importantly, could these accidents have been prevented? These
accidents, according to the Normal Accident Theory (NAT), are failures that result from
interactive complexity and tightly coupled systems (Perrow, 1999).
The space shuttle Columbia explosion that occurred in 2003 is a classic example of a
complex and tightly coupled system. The Columbia explosion resulted in a tragic
accident that killed seven astronauts during re-entry to Earth. This explosion resulted
from a combination of faulty designs and organizational failures (Aftosmis et al, 2004).
Although it was not known at the time, the foam-debris that struck the left wing of the
shuttle during liftoff caused enough damage to the wing that it resulted in the shuttle’s
explosion during re-entry (Aftosmis et al, 2004). The design of the space shuttle
Colombia was a complex system in itself and when integrated with NASA’s mission
control to monitor the shuttle during liftoff and re-entry, it created a much broader
complex system. Additionally, the organizational management failures of NASA’s
mission control team to give due attention to analyze the damaged wing added to the
―interactive‖ complexity of the shuttle system. This interaction between the different
elements: NASA mission control, engineers, shuttle, earth, atmosphere, and gravity
comprised a broader complex system that failed to recognize the severity of damage to
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the shuttle. This accident points out the importance of understanding the nature of
complex systems, especially in environments when human lives are at stake.
1.1 Background of the Normal Accident Theory
NAT was originally conceived by sociologist named Charles Perrow. Perrow
suggests that system accidents pertaining to high-risk technologies occur as a direct
consequence of interactive complexity and coupling and are considered to be ―normal‖
accidents (Perrow, 1999). NAT is also termed ―system‖ accident since interactive
complexity and tight coupling are characteristics of a system (Perrow, 1999). A system is
defined as ―those interrelated elements that interact between man, machine, and the
environment operating together within a set of boundaries‖ (Ossimitz, 1997).
Catastrophic industrial accidents such as the Chernobyl nuclear plant explosion in 1986,
the Bhopal chemical release in 1984, and the space shuttle Columbia explosion in 2003
illustrate the potential magnitude of deaths and injuries associated with system accidents
involving high-risk technological systems. The environmental, economic, and social
costs of industrial accidents can be enormous and as such, their impacts can be global and
their resulting damage may be irreversible (Wolf, 2000).
NAT provides a basis for understanding catastrophic events and the risks involved
with such high-risk technology systems (Wolf, 2000). Industries today, whether defense,
mining, chemical, nuclear, or construction, are constantly working to improve safety yet
accidents continue to occur despite continuous improvements in safety through improved
designs, controls, processes, and procedures. Preventing accidents may sound like the
22
proverbial pipe dream – ―no matter how diligent we are, things just happen; and not long
ago this was exactly what was heard from many people in reference to safety - you
cannot achieve zero accidents; no matter how careful you are accidents happen‖ (Brush
et al, 2007). Although industries may not achieve zero accidents, however,
understanding NAT based on system complexity might yield engineering solutions and
controls that will reduce accidents, thus save lives and cost of accident and failure
investigations, and liabilities.
NAT has had limited applications in industries because of the difficulty in
understanding the nature of complex systems with tight couplings. However, NAT
appears to have some validity because in any industry involving high-risk technology,
there is a high probability that accidents are likely to occur. The documented accidents
(Columbia, Chernobyl, and Bhopal) in our global history imply that there is no such thing
as ―zero accidents‖ and any technology-based system contains some inherent risk.
Therefore, investigating NAT in the mining industry may provide an understanding of
why mining accidents continue to persist and potentially looking at ways to engineer
solutions and controls which will mitigate and reduce these accidents.
1.2 Haulage Accidents in the Mining Industry
The mining industry has experienced significant incidents involving haulage
operations up to and including severe injuries and fatalities. Haulage operations are
defined as any operation that involves the horizontal transport of ore, coal, supplies, and
waste (MSHA, 2007). Since the early 1990s, haulage accidents have become a leading
23
cause of fatal and nonfatal accidents in the mining industry. For example, for the years
1998 – 2002, haulage operations in surface mines alone have accounted for over 40% of
all accidents in the mining industry (MSHA, 2003).
Beyond the safety issues, such incidents also create significant costs to the industry.
For example, cost data from 1994 collected from surface haulage accidents showed that
six haulage truck fatalities cost an estimated $2.58M and 519 lost-time injuries cost
$3.27M for a total of $5.58M (Boldt and Randolph, 1996). The magnitude of these costs
drives the mining industry to continue to look for possible ways to reduce incident
injuries and fatalities in mining operations, either through innovative engineering
technology solutions or better and improved safety practices and procedures.
1.3 Haulage Accidents and the Normal Accident Theory
The basis of argument for haulage operations system accidents based on Perrow’s
NAT is the premise that interactive complexity combined with tight coupling of system
elements result in accidents. Given that haulage operations accounted for more than 40%
of all fatalities in mining operations even though policies, procedures, and controls have
been developed to reduce these incident rates, one can deduce that there must be some
degree of complexity and coupling that exists in mining operations that have not yet been
understood (Aldinger and Keran, 1994). Perhaps this has to do with the fact that systems
such as mining haulage operations are considered socio-technical systems, i.e. technical
systems operated by people (Wolf and Berniker, 2001). By this we mean that these
systems are anchored in engineering, natural sciences, and organizational sciences; and
24
therefore will experience accidents because of the nature of their interactive complexity
between machine, people, and the environment (Wolf and Berniker, 2001). Many new
hazards are related to increased complexity (both product and process) of systems which
makes it difficult to identify the hazards as a function of interactions (Leveson, 1995).
Often these complex systems (with tight coupling between various parts of their
operations) are carried out under time pressure or other resource constraints which results
in accidents (Woods et al., 1994).
1.4 Historical Perspective of the Normal Accident Theory in the Mining Industry
A literature search thus far has found no evidence of Perrow’s NAT applied to the
mining industry. Although Perrow did some research into the mining industry and
reviewed accident data from sources such as Occupational Safety and Health
Administration (OSHA) and mine safety journals, no true evaluation of his theory to
quantify system complexity and tight coupling was ever operationalized to the mining
industry (Perrow, 1999). This research attempts to further advance Perrow’s NAT in
mining operations as a means to mitigate and prevent future haulage accidents from
occurring while improving mine safety as well.
1.5 Purpose and Significant Contributions of the Research
The intent of this research is to use haulage accident data to conduct a qualitative and
quantitative evaluation of NAT. The evaluations should demonstrate that there are lines
25
of evidence that suggest NAT is applicable in the mining industry, in particular, haulage
operations system. The qualitative evaluation analyzes interactive complexity and tight
couplings pertaining to mining haulage operations. The quantitative evaluation applies a
systems approach by modeling mining haulage operations system and developing a
complexity index metric that quantifies system complexity. In order to model haulage
operations system, the haulage aspect of mining operations is divided into three
subsystems (load, transfer, unload) and modeled separately as each subsystem represents
a unique combination of variables that contribute to the overall complexity of the haulage
operations system. Breaking this system down into three subsystems allows for a limited
degree of empirical evaluation of the model since accident data can be used as a guide to
identify the relative probability of incidents occurring in each sub-system.
The significant contributions of this research are that it is the first true evaluation of
NAT’s applicability in the mining industry which enables an understanding of interactive
complexity and coupling of mining haulage operations system. The significant of
contributions is summarized below:

Demonstrates that NAT is relevant and applicable in the mining industry by
evaluating and analyzing accident data in support of accident prevention and
improving mining safety.

Applies a systems approach by use of modeling techniques that models the load,
transfer, and unloads operations.

Develops a complexity index metric that provides a way to quantify complexity
for mining haulage operations.
26

Represents the first application of NAT which acknowledges system complexity
as a contributing factor of accidents rather than viewing accidents as a single
independent variable with a cause-and-effect relationship.

NAT introduces a proactive and predictive systems approach to mine safety;
serves as a decision making tool which aids in accident prevention.
1.6 Research Questions
Three (3) important questions provide the basis for this research investigation. Each
question provided an understanding into the applicability of NAT concerning interactive
complexity and tight coupling. These questions were framed in ways that permitted
qualitative and quantitative evaluation of NAT. The three research questions are:
1. Does mining haulage operations system classify as a complex and tight coupling
system?
2. Is mining haulage operations system an interactive complex system?
3. Does mining haulage operations system complexity increase with greater
interaction of system elements?
1.7 Research Scope and Boundary
The scope and boundary of this research was limited to haulage operations in surface
mines. The study bounds this research by analyzing the load, transfer, and unload
27
systems of haulage operations. This research was applied to surface mine haulage
systems of metal/non-metal mines as these incidents account for the majority of mining
related fatalities.
1.8 Research Structure
The following describes the specific structure of this research investigation which
guided this study. This structure ensured all aspects of the research study in terms of
sequential and logical flow of ideas were well thought of and undertaken in order to
achieve the research purposes. The structure of this research is outlined as follows:
Chapter 1: Introduction
Chapter 2: Mine Accidents, Theories, Safety, Regulations, Practices, and Training
Chapter 3: Competing Organizational Accident Theories
Chapter 4: The Normal Accident Theory – Defining System Complexity and Tight
Coupling
Chapter 5: Interactive Complexity Analysis
Chapter 6: Tight Coupling Analysis
Chapter 7: NAT Statistics Analysis and Assessment
Chapter 8: The Systems Approach to Defining and Illustrating Mining Haulage
Operations System
Chapter 9: Complexity Measurements – A Literature Review
Chapter 10: Proposed Mining Haulage Operations System Complexity Measurement
Chapter 11: System Modeling and Complexity Calculation Methodology, Results, and
Analysis
Chapter 12: Summary and Conclusions
28
1.9 Research Data
The data used for this research work consists of MSHA reports for mining haulage
related fatalities for the years 1999-2010. The data used for this research was limited to
haulage accidents for loading, transfer, and unloading processes of haulage operations.
1.10 Conclusion
NAT presents a holistic approach of viewing accidents from a ―systems‖ perspective
and attempts to explain why accidents occur based on system complexity and tight
coupling. By investigating NAT’s applicability in the mining industry, NAT may be able
to help the mining industry achieve some predictability in determining the likelihood of
accident occurrences which will inform engineering solutions and controls that further
reduce or mitigate incident injuries and fatalities in mining operations. Finally, NAT
introduces a proactive and predictive system based approach to mine safety and serves as
a decision making tool which aids in accident prevention.
29
CHAPTER 2
MINE ACCIDENTS, THEORIES, SAFETY, REGULATIONS, PRACTICES, AND
TRAINING
This chapter discusses the literature review of accidents, theories, regulations,
practices, and training relevant to the mining industry. Also discussed are different types
and definitions of mine accidents which include relevant statistics illustrating incidents
relating to mining, in particular, haulage accidents. Safety theories, regulations,
practices, and training are discussed as well along with current safety systems
implemented by major mining companies. Finally, this chapter concludes with a
discussion of two historical case studies that resulted in catastrophic accidents and their
root causes present compelling arguments for studying NAT and its potential
applicability in the mining industry.
2.1 Mine Accidents
The mining industry is a vital global economic sector which comprises the mining of
coal, metal, and non-metal minerals (Kecojevic et al, 2007). As much as mining is a vital
economic industry, historically, mining has also been labeled as one of the most
hazardous industry in the world (Kecojevic et al, 2007). According to the U.S.
Department of Labor, mining is one of 17 hazardous occupations in which employment is
restricted to persons age 18 or older (DoL, 2011). Additionally, due to the severity and
frequency of mining injuries, illnesses, and fatalities; mining is among the costliest, i.e.
30
lignite and bituminous coal mining rank second in the U.S. for the average cost per
worker for fatal and all nonfatal injuries and illnesses (Leigh et al, 2004).
2.1.1 Types of Accidents
In this section, the definitions and different types of accidents occurring in the mining
industry are discussed. A 5-year accident fatality summary with different categories of
accidents is presented. Additionally, a 5-year summary of metal/non-metal and coal is
provided for comparison of the number of accident occurrences among the different types
of mining accidents. Finally, this section discusses haulage accidents, different types of
haulage accidents, and root causes.
2.1.1.1 Definitions
This section discusses different classifications of mine accidents. The classification
and definitions are provided in Table 2-1. Knowing these different classifications
provide a basic understanding of all the different type of accidents related to mine
operations. It also provides background knowledge of mining accidents.
31
Table 2-1 Classification of Mine Accidents (MSHA, 2011)
Classification
Electrical
Entrapment
Exploding Vessels Under Pressure
Explosives and Breaking Agents
Falling, rolling, or sliding rock or material
of any kind
Fall of Face, Rib, Side or Highwall
Fall of Roof or Back
Fire
Handling Material
Hand Tools
Hoisting
Definition
Accidents in which electric current is most
directly responsible for the resulting
accident.
In accidents involving no injuries or
nonfatal injuries which are not serious,
entrapment of mine workers takes
precedence over roof falls, explosives
accidents, inundations, etc.
These are accidents caused by explosion of
air hoses, air tanks, hydraulic lines,
hydraulic hoses, and other accidents
precipitated by exploding vessels.
Accidents involving the detonation of
manufactured explosives, Airdox, or
Cardox, that can cause flying debris,
concussive forces, or fumes.
Injuries caused directly by falling material.
Accidents in this classification include falls
of material (from in-place) while barring
down or placing props; also pressure
bumps and bursts.
Underground accidents which include falls
while barring down or placing props; also
pressure bumps and bursts.
In underground mines, an unplanned fire
not extinguished within 10 minutes of
discovery; in surface mines and surface
areas of underground mines, an unplanned
fire not extinguished within 30 minutes of
discovery.
Lifting, pulling, pushing, shoveling
material. The material may be in bags or
boxes, or loose sand, coal, rock, timber,
etc. The accident must have been most
directly caused by handling material.
Accidents related to non-powered tools
when being used as hand tools.
Damage to hoisting equipment in a shaft or
slope which endangers an individual or
32
Ignition Or Explosion Of Gas Or Dust
Impoundment
Inundation
Machinery
Non-Powered Haulage
Powered Haulage
Slip or Fall of Person
Stepping or Kneeling on Object
interferes with use of the equipment for
more than 30 minutes. The accident results
from the action, motion, or failure of the
hoisting equipment or mechanism.
Accidents resulting as a consequence of the
ignition or explosion of gas or dust.
Included are exploding gasoline vapors,
space heaters, or furnaces.
An unstable condition at an impoundment,
refuse pile, or culm bank which requires
emergency action in order to prevent
failure, or which causes individuals to
evacuate an area. Also the failure of an
impoundment, refuse pile, or culm bank.
An unplanned inundation of a mine by a
liquid or gas. The mine may be either a
surface or underground operation.
Accidents that result from the action or
motion of machinery or from failure of
component parts. Included are all electric
and air-powered tools and mining
machinery such as drills, tuggers, slushers,
draglines, power shovels, loading
machines, compressors, etc.
Accidents related to motion of nonpowered haulage equipment. Included are
accidents involving wheelbarrows,
manually pushed mine cars and trucks, etc.
Haulage includes motors and rail cars,
conveyors, belt feeders, longwall
conveyors, bucket elevators, vertical
manlifts, self-loading scrapers or pans,
shuttle cars, haulage trucks, front-end
loaders, load-haul- dumps, forklifts, cherry
pickers, mobile cranes if traveling with a
load, etc. The accident is caused by the
motion of the haulage unit. Include
accidents that are caused by an energized
or moving unit or failure of component
parts.
Includes slips or falls from an elevated
position or at the same level while getting
on or off machinery or haulage equipment
that is not moving.
Accidents are classified in this category
only where the object stepped or kneeled
33
Striking or Bumping
Other
on contributed most directly to the
accident.
This classification is restricted to those
accidents in which an individual, while
moving about, strikes or bumps an object
but is not handling material, using hand
tools, or operating equipment.
Accidents not elsewhere classified.
2.1.1.2 Accident Fatality Summary
A 5-yr summary of mine accident fatalities related to powered haulage accident is
provided and describes different categories and statistics of haulage accidents. Table 2-2
provides a 5-yr summary (2006-2010) showing the most percentages that contribute to
mining haulage accidents within each of the categories and their root causes. Analysis of
the data shows that for the 5-yr period, most of the accidents were caused by mine
employees (versus contractors) with a majority of the accidents occurring at mine sites
with over 100 employees. Most of these accidents occurred within metal/non-metal, in
particular, ―stone‖ and ―other‖ commodities.
Looking at the 5-yr period, most of the accidents that occurred were from powered
haulage with most of the accident activities related to maintenance. With regards to
occupation, most of the accidents were committed by repairman, technicians, laborers,
and mobile equipment operators. Additionally, the accidents were committed by
employees with less than 5 years of mining experience and whose ages were on the
average of 25-40 yrs old. Finally, root causes for these accidents were a result of no
proper risk assessments being done, inadequate procedures and policies, failure to
34
conduct pre-operational inspections, failure to received proper training, equipment
failure, and failure to wear appropriate personal protective equipment. The main
contributors of these accidents were from inadequate procedures, failure to conduct
proper risk assessments and pre-operational inspections, equipment failure, and lack of
training.
Table 2-2 5-Year Mine Accident Fatality Summary (MSHA, June 2011)
Category
Employee
Type (Mine
vs
Contractors)
Commodities
Mine Size
(Employees)
Classification
Activity
Occupation
Mining
Experience
(Yrs)
Age (Yrs
Old)
Root Cause
2006
2007
2008
2009
2010
72% (Mine)
67% (Mine)
77% (Mine)
62% (Mine)
56% (Mine)
36% (Stone)
28% (100+)
22% (Other)
26% (100+)
23% (Stone)
36% (100+)
57% (Other)
32% (100+)
27% (Other)
31% (100+)
28% (Powered
Haulage)
56%
(Maintenance)
36%
(Laborer/Utility)
27% (Powered
Haulage)
42%
(Maintenance)
30%
(Repairman/Tec
hnician/Mobile
Equipment
Operator)
35% (15-30)
23% (Powered
Haulage)
42%
(Maintenance)
32% (Mobile
Equipment
Operator)
31% (Powered
Haulage)
56%
(Maintenance)
30%
(Repairman/Tec
hnician)
31% (1-5)
25% (2-5)
31% (Powered
Haulage)
57%
(Maintenance)
30%
(Repairman/Tech
nician/Mobile
Equipment
Operator)
35% (2-5)
32% (45-50)
27% (25-40,
60+)
1. Inadequate
Procedures
2. Risk
Assessment
3. Pre-Op
Checks/Insp
ection
4. Equipment
5. Training
56% (51-60)
43% (20-40)
1.
1.
32% (10-15)
24% (19-25, 4050)
1. Inadequate
Procedures
2. Risk
Assessment
3. Pre-Op
Checks/Insp
ection
4. Equipment
5. Training
1.
2.
3.
4.
5.
Inadequate
Procedures
Risk
Assessment
Pre-Op
Checks/Insp
ection
Equipment
Training
2.
3.
4.
5.
Inadequate
Procedures
Risk
Assessment
Pre-Op
Checks/Insp
ection
Equipment
Training
2.
3.
4.
5.
6.
7.
Inadequate
Procedures
Risk
Assessment
Pre-Op
Checks/Inspe
ction
Equipment
Training
PPE
Procedures
not Followed
35
Note: Data only lists greatest percentage contributing to the accident.
Next, Tables 2-3 and 2-4 illustrates an accident summary (5-Yr) of metal/non-metal
and coal mining. The accident summary is illustrated to provide a general overview of
powered haulage accidents and how they rank compared to the other accident
classifications. Based on metal/non-metal data, powered haulage had the most accidents
out of all of the other accident classifications with 32 accidents occurring during the five
year (2006-10) period. As for coal mining, data showed a total of 177 fatalities during
the 5-yr period with powered haulage committing a total of 39 fatalities. Combining the
5-yr accidents for metal/non-metal and coal mining, data showed that powered haulage
had the highest number of accidents making it a leading cause of all mining accidents.
36
Table 2-3 M/NM 5-Year Fatality Summary (MSHA, 2011)
2006
FATALITIES CHARGEABLE TO
S
THE MNM MINING INDUSTRY U/G
0
5
ELECTRICAL
2007
2008
2009
2010
U/G
S
U/G
S
U/G
S
U/G
S
TOTAL
0
1
1
1
0
1
0
1
10
EXP VESSELS UNDER PRESSURE
0
0
0
0
0
0
0
0
0
1
1
EXP & BREAKING AGENTS
0
0
0
0
0
0
0
0
1
0
1
FALL/SLIDE MATERIAL
0
6
0
2
0
3
0
3
3
4
21
FALL OF FACE/RIB/HIGHWALL
0
1
0
0
0
0
0
0
1
0
2
FALL OF ROOF OR BACK
0
0
3
0
3
0
1
0
1
0
8
FIRE
0
0
0
0
0
0
0
0
0
0
0
HANDLING MATERIAL
0
0
0
1
0
1
0
0
0
0
2
HAND TOOLS
0
0
0
2
0
0
0
0
0
0
2
NONPOWERED HAULAGE
0
0
0
1
0
0
0
0
0
0
1
POWERED HAULAGE
1
6
3
5
3
2
1
4
0
7
32
HOISTING
0
0
0
0
0
0
0
0
0
0
0
0
IGNITION/EXPLOSION OF GAS/DUST
0
INUNDATION
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
MACHINERY
0
4
1
6
1
2
0
4
1
1
20
SLIP/FALL OF PERSON
0
3
1
5
0
5
0
1
0
1
16
STEP/KNEEL ON OBJECT
0
0
0
0
0
0
0
0
0
0
0
STRIKING OR BUMPING
0
0
0
0
0
0
0
0
0
0
0
OTHER
0
0
0
2
0
1
0
1
0
1
5
1
25
8
25
8
15
2
14
7
16
121
YEAR TO DATE TOTAL
COMBINED END OF YEAR
TOTAL
26
33
23
16
23
121
37
Table 2-4 Coal 5-Year Fatality Summary (MSHA, 2011)
2006
FATALITIES CHARGEABLE TO
S
THE COAL MINING INDUSTRY U/G
0
2
ELECTRICAL
2007
2008
2009
2010
U/G
S
U/G
S
U/G
S
U/G
S
TOTAL
0
0
1
1
0
0
0
0
4
EXP VESSELS UNDER PRESSURE
0
1
0
0
0
0
0
0
0
0
1
EXP & BREAKING AGENTS
1
0
0
1
0
0
0
0
0
0
2
FALL/SLIDE MATERIAL
0
0
1
0
0
0
0
0
0
1
2
FALL OF FACE/RIB/HIGHWALL
3
1
9
2
0
1
1
0
3
0
20
FALL OF ROOF OR BACK
7
0
3
0
4
0
2
0
3
0
19
FIRE
2
1
0
0
0
0
0
0
0
0
3
HANDLING MATERIAL
0
0
1
0
0
0
0
1
0
0
2
HAND TOOLS
0
0
0
0
0
0
0
0
0
0
0
NONPOWERED HAULAGE
0
0
0
0
0
0
0
0
0
0
0
POWERED HAULAGE
6
3
1
3
7
3
1
7
4
4
39
HOISTING
0
0
0
0
0
0
1
1
0
0
2
17
IGNITION/EXPLOSION OF GAS/DUST
0
INUNDATION
0
0
0
0
0
0
0
29
1
47
0
0
0
0
0
0
0
0
0
0
MACHINERY
1
2
2
2
3
7
0
1
2
1
21
SLIP/FALL OF PERSON
0
0
0
8
0
1
1
1
0
0
11
STEP/KNEEL ON OBJECT
0
0
1
0
0
0
0
0
0
0
1
STRIKING OR BUMPING
0
0
0
0
0
0
0
0
0
0
0
OTHER
0
0
0
0
0
2
1
0
0
0
3
37
10
18
16
15
15
7
11
41
7
177
YEAR TO DATE TOTAL
COMBINED END OF YEAR
TOTAL
47
34
30
18
48
177
38
2.1.2 Haulage Accidents
Tables 2-3 and 2-4 show powered haulage equipment as a leading accident from the
different accident classifications. This should not be a surprise to the mining community
considering that powered haulage equipment such as trucks travels at great speed, has
poor visibility, weighs a considerable amount, and travels long distances (Aldinger and
Keran, 1994). Many of these haulage accidents involved front-end-loaders which have
the second highest number and severity of accidents (31% of the accidents and 21% of
the fatalities) (Karmis, 2001).
Aldinger and Keran (1994) conducted a study of more than 2,800 lost-time injuries
involving surface mine haulage equipment. Their study concluded that more than 40% of
all reported injuries were classified as sprains or strains with about 1,200 of the injuries
resulted while operating haulage trucks (Aldinger and Keran, 1994). The leading cause
of lost time injuries (about 35%) was jarring or jolting of the operator when the vehicle
encountered bumps, rocks, or potholes during normal operation, and during dumping or
loading (Karmis, 2001). The second major cause of injury was loss of control which
resulted in serious accidents or rollovers (Karmis, 2001). The third most frequent injury
was passive strain caused by repeated shocks and vibrations (Karmis, 2001).
On average, severe equipment related accidents classified as struck-by or caught-in
accounted for 41% of all severe accidents in mining operations (Coleman, Martini, and
Ruff, 2010). Furthermore, in order to determine the most common equipment involved in
severe accidents, data from the Mine Safety and Health Administration (MSHA) were
obtained for 562 accidents from 2000-2007. Figure 2-1 shows the top 20 equipment
39
types and the breakdown of mine types (surface or underground) as well. Of the top 20
equipment types, eighty (14%) of the 562 accidents involved conveyors with most of the
accidents occurring at surface operations (Coleman, Martini, and Ruff, 2010). Next, roof
bolting machines, haul trucks and front-end loaders were most frequently involved in
accidents involving haulage equipment (Coleman, Martini, and Ruff, 2010). Clearly, the
analyzed data shows that haulage equipment warrants much attention.
Figure 2-1 Haulage and Stationary Equipment Related Accidents, 2000-2007 (MSHA,
2000-2007)
40
There are other types of haulage accidents which include loss-of-control of haulage
equipment and other mobile machines and are a leading source of machine-related
fatalities in surface mining (Kecojevic & Radomsky, 2004; MSHA, 2000–2007). Their
root causes vary greatly and include mechanical failure, failure to set brakes, weatherrelated issues, operator fatigue, and travelling at high speeds (Coleman, Martini, and
Ruff, 2010). In many cases, after losing control of the equipment, the operator jumped
from the vehicle or simply did not wear a seat belt resulting in a fatality (Coleman,
Martini, and Ruff, 2010). MSHA fatal grams and fatal investigation reports revealed
these accidents were focused on struck-by and caught-in mobile machinery fatalities at
surface mining operations (MSHA, 2000-20007). Sixty-one fatalities were attributed to
brake failures, other mechanical problems, or operator errors resulting in a roll-over or
collision (Coleman, Martini, and Ruff, 2010). Figure 2-2 illustrates a breakdown of
fatalities by machine type. Based on Figure 2-2, dump trucks (27) led the group of
mobile equipment accidents followed by ―other‖ trucks (13). These statistics suggest that
haulage equipment accidents involve dump trucks which occur during loading,
transferring, and unloading operations between sites.
41
Figure 2-2 Mobile Surface Mining Machines or Vehicles Involved in Fatal Accidents
Attributed to Loss of Control, 2000–2007 (MSHA, 2000-2007)
Another type of haulage accident involved operator visibility issues such as collisions
or backing over an unseen edge (Coleman, Martini, and Ruff, 2010). Twenty-seven
fatalities fit this criteria and Figure 2-3 shows a breakdown of these accidents by machine
type (Coleman, Martini, and Ruff, 2010). Of the twenty-seven fatalities, 16 involved
collisions with workers on the ground or with other vehicles, and 11 involved driving
over an unseen edge (Coleman, Martini, and Ruff, 2010). The majority (67%) occurred
while the machine or vehicle was in reverse motion (Figure 2-4) (Coleman, Martini, and
42
Ruff, 2010). The analyses reveal that visibility is one of the leading causes of haulage
accidents involving mobile equipment.
Figure 2-3 Mobile Surface Mining Machines or Vehicles Involved in Fatal Accidents
Attributed to Visibility Issues, 2000–2007 (MSHA, 2000-2007)
43
Figure 2-4 Direction of Travel during the Fatal Accident Involving Visibility around
Mobile Surface Mining Machines, 2000–2007 (MSHA, 2000-2007)
2.1.2.1 Loader and Truck Fatalities
There are many different types of powered haulage accidents; however, loader and
truck fatalities are accidents that occur frequently with haulage operations. An analysis
of loader and truck fatalities indicate that for mining operations, there were a total of 121
fatalities that involved wheel loaders and haul trucks for the years 1995-2002 (Kecojevic
and Radomsky, 2004). Figure 2-5 shows the number of fatalities per year, while Figure
2-6 shows the number of fatalities per type of mining operations. Based on Figure 2-5,
there were a total of 32 loader-related fatalities that occurred over the 8-year period, an
average of 2.66 per year and 89 truck-related fatalities, an average of 7.41 per year
44
(Kecojevic and Radomsky, 2004). There were 34 loader-related fatalities in coal mining,
21 in metal, and 66 in aggregate production (Kecojevic and Radomsky, 2004).
Figure 2-5 Number of Fatalities Involving Loaders and Trucks (Kecojevic and
Radomsky, 2004)
45
Figure 2-6 Number of Fatalities Involving Loaders and Trucks per Type of Mining
Operations (Kecojevic and Radomsky, 2004)
2.1.2.1.1 Loader-related Fatalities
According to MSHA accident investigations of fatalities involved with loader-related
accidents, the percentage distribution of each category is shown in Figure 2-7. Incidents
where mine and non-mine personnel were hit, struck, or run over by a wheel loader were
the most common and represent the highest (41%) of the total loader-related fatalities
(Kecojevic and Radomsky, 2004). These accidents were followed by the second highest
fatality incidents (34%) involving rollovers from the elevated edge of the pit, haul road,
waste dump or elevated stock pile ramps (Kecojevic and Radomsky, 2004). The
remainder of the proportional distribution of fatality incidents by category is illustrated in
Figure 2-7. In most of the fatality cases, they involved the operator as the victim.
46
Figure 2-7 Proportion of Loader Fatalities to Total Fatalities by Category (Kecojevic and
Radomsky, 2004)
Based upon analysis of MSHA accident investigation reports, the root causes of
loader fatalities are illustrated in Figure 2-8. These root causes are classified by the
following categories: 1- failure of mechanical components (28%); 2- inadequate
maintenance procedures (28%); 3- failure to recognize adverse geological conditions
(6%); 4- failure to obey the loader’s working area (9%); 5- failure to maintain adequate
berms (6%); 6-lack of warning signs and appropriate mine maps (3%); 7-inadequate
provisions for secure travel (3%); and 8- failure to adjust to poor weather conditions
(17%). Additionally, further analysis revealed several other contributing factors to these
fatalities which are categorized as: lack of adequate training (37%); failure to wear seat
47
belts (31%); lack of efficient communications (19%); and failure to maintain the haul
roads (13%) (Kecojevic and Radomsky, 2004).
Figure 2-8 Root Causes of Loader Fatalities (Kecojevic and Radomsky, 2004)
2.1.2.1.2 Truck-related Fatalities
Truck-related fatalities include rollovers, direct collision with a pedestrian, collision
with another vehicle, equipment repair, contact with public utility lines, and other
(Kecojevic and Radomsky, 2004). The proportional distribution of each truck fatality
category is shown in Figure 2-9. Incidents involving ―rollovers‖ were from elevated edge
of the pits, waste dumps, or elevated haul roads which are the most common and
represent the highest (47%) of the total fatalities (Kecojevic and Radomsky, 2004).
These accidents were followed by the second highest fatality incidents (28%) which
48
include fatalities when mine and non-mine personnel were hit, struck, or run over by a
truck (Kecojevic and Radomsky, 2004). The remainder of the proportional distribution
of fatality incidents by category is illustrated in Figure 2-9.
Figure 2-9 Proportion of Loader Fatalities to Total Fatalities by Category (Kecojevic and
Radomsky, 2004)
Again, based upon analysis of MSHA accident investigation reports, the root causes
of truck fatalities are illustrated in Figure 2-10. These root causes were classified into the
following categories: 1- failure of mechanical components (22%); 2- lack of and/or
failure to obey warning signals (20%); 3- failure to maintain adequate berms (13%); 4failure to recognize adverse geological conditions (10%); 5- inadequate hazard training
(10%); 6- inadequate maintenance procedures (7%); 7- failure to respect the truck’s
working area (7%); and 8- failure to set the parking brake (7%); and 9- operator’s health
49
condition (4%) (Kecojevic and Radomsky, 2004). The accident data shows that a
majority of truck accidents were caused by failure of mechanical components of haulage
equipment and failure to obey warning signs. Given these revealing statistics, mining
companies should focus their resources and efforts towards design component failures
and implementing policies that enforce the abeyance of warning signals.
Figure 2-10 Root Causes of Truck Fatalities (Kecojevic and Radomsky, 2004)
2.2 Accident Theories
There have been a number of accident theories in occupational industries that attempt
to explain accidents and their causation. Understanding these theories allows industries
to determine root causes. Knowing these root causes, industries can develop effective
50
measures such as policies, procedures, or controls to mitigate or prevent accidents. This
can also lead to the development of effective safety systems as well. The following
accident theories are discussed as a way of gaining insights into what causes accidents
and what can be done to prevent them.
2.2.1 Domino Theory
In 1930, William H. Heinrich pioneered research in accident causation. Heinrich
(1959) discussed accident causation with regards to the interaction between humans and
machines, the relation between severity and frequency, the reasons for unsafe acts,
management role in accident prevention, the costs of accidents, and finally, the effect of
safety on efficiency. As such, these different relationships led to the development of the
Domino Theory of causation in which an accident is presented as one of five factors in
sequences that result in an injury (Abdelhamid and Everett, 2000). The name was chosen
because it describes the sequence of events which Heinrich believe exists prior to and
after the occurrence of accidents. Additionally, the name was intuitively ―appealing
because the behavior of the factors involved was similar to toppling of dominoes when
disrupted: if one falls (occurs), the others will too‖ (Abdelhamid and Everett, 2000).
Heinrich’s five dominoes in this theory were: ancestry and social environment, fault
of person (human error), unsafe act and/or mechanical or physical hazard, accidents, and
injury (Abdelhamid and Everett, 2000). This five-domino theory suggested that through
inherited or acquired undesirable traits, people may commit unsafe acts or cause the
existence of mechanical or physical hazards, which in turn cause injurious accidents
51
(Abdelhamid and Everett, 2000). Heinrich defined an accident as ―an unplanned and
uncontrolled event in which the action or reaction of an object, substance, person, or
radiation results in personal injury or the probability thereof‖ (Abdelhamid and Everett,
2000). Hence, Heinrich’s work can be summarized in two points: people are the
fundamental reasons behind accidents and management is responsible for the prevention
of accidents (Petersen, 1982).
In terms of NAT, the Domino Theory could be described from this perspective.
Heinrich’s five ―dominoes‖ constitute the different factors that comprise the complexity
of the system involved. When any one of these factors is interrupted, the system
experiences a ―failure‖ which Heinrich defines as an accident event resulting from an
―unplanned and uncontrolled‖ event. According to NAT, this ―unplanned and
uncontrolled‖ event is associated with complex systems and is consistent with Heinrich’s
theory. These five dominoes represent the ―interactive‖ connection between these factors
or elements and per NAT, failure of a system results from the interactive complexity of
the system.
2.2.2 Multiple Causation Theory
Daniel Petersen introduced a different theory which is non-domino based in his book
titled, Technique of Safety Management, and believed that many contributing factors,
causes, and sub-causes are the main root causes in an accident scenario and hence, named
this theory, ―multiple causation‖ (Petersen, 1971). Under the multiple causation theory,
52
these factors combined together in random fashion which causes accidents. Petersen
maintained these factors are the ones to be targeted in an accident investigation.
Petersen’s theory slightly differs from the Domino Theory in that his theory only
looks at one act and/or one condition that would be identified by using investigative
means (Abdelhamid and Everett, 2000). To explain his theory, Petersen provides an
example of a common accident scenario, that of a man falling off a defective stepladder.
The one act, climbing a defective ladder, and/or one condition, the defective ladder, was
identified as the problem. The corrective action to the problem would be to get rid of the
defective ladder in order to prevent the accident from happening. This would be the root
cause of the investigation if the domino theory was used.
By using multiple causation questions during investigations, Petersen claimed that the
surrounding factors to the ―incident‖ would be revealed. Investigative questions to the
stepladder would be, why the defective ladder was not found in normal inspection; why
the supervisor allowed its use; whether the employee knew not to use the ladder; etc.
(Abdelhamid and Everett, 2000). Petersen believed that these types of questions would
lead to improved inspection procedures, improved training, better definition of
responsibilities, and pre-job planning by supervisors (Abdelhamid and Everett, 2000).
Additionally, Petersen asserted that trying to find the unsafe act or the condition is
dealing only at the symptomatic level because the act or condition might be the
―proximate cause‖ but invariably it is not the ―root cause‖ (Abdelhamid and Everett,
2000). Like others have concluded, Petersen also concluded that root causes must have
been found to have permanent improvement and indicated that root causes often relate to
53
the management system such as policies, procedures, supervision, effectiveness, training,
etc. (Abdelhamid and Everett, 2000).
The Multiple Causation Theory could be explained in terms of NAT. Under this
theory, Petersen explains that multiple factors combined together in ―random‖ fashion
which causes accidents. This is similar to NAT in that NAT explains accidents resulting
from an increase in complexity. This increase in complexity causes systems to behave in
―unplanned‖ ways which results in accidents. The more factors that are injected into the
system, the more the system behave in complex ways. Often times, the behavior of the
system is so complex that they result in accidents. Mining haulage operations could be
described in this manner.
2.2.3 Human Error Theory
In accidents involving machines, there is the one element that contributes to the
accident. This one element is the human error aspect that interacts with machines to
perform some function. As such, a theory known as the Human Error Theory was
developed to study human errors involved in accidents. The Human Error Theory studies
the tendency of humans to make errors under various situations and environmental
conditions where most of the fault lies with the human characteristic (Abdelhamid and
Everett, 2000). As defined by Rigby (1970), human error is ―any one set of human
actions that exceed some limit of acceptability.‖ Human beings by their very nature
make mistakes; therefore it is unreasonable to expect error-free human performance
(Shappell and Wiegmann, 1997). Therefore, it is no surprise then that human error has
54
been implicated in a variety of occupational accidents which includes the mining industry
as well (Shappell and Wiegmann, 1997).
There are different distinct types of human error. By knowing the different types of
error involved in a given accident, it is easy to identify its cause(s) and also the best
approaches to removing the potential error or to mitigate the effects of the
consequence(s) (Simpson, Horberry, and Joy, 2009). The four known types of human
error are skill, rule, and knowledge-based; slips/lapses, mistakes, and violations; errors of
commission and omission; and input, decision, and output errors.
Skill, rule, and knowledge-based human error relates to the ―mental context‖ in which
the error occurs (Simpson, Horberry, and Joy, 2009). Skill-based errors occur when
operators work on ―auto-pilot‖, working on tasks in which they are very familiar and can
complete without conscious thoughts (Simpson, Horberry, and Joy, 2009). Rule-based
errors occur when the operation is defined by a series of known rules (Simpson,
Horberry, and Joy, 2009). Knowledge-based errors occur when the situation has gone
beyond that covered by the person’s training and/or experience (Simpson, Horberry, and
Joy, 2009). These ―mental‖ errors are what drive the operators to commit the human
errors that they do.
Slips/lapse, mistakes and violations are primarily based on the nature of the error
itself (Simpson, Horberry, and Joy, 2009). Slip/lapse errors are characterized by
situations where operators start with the correct intention but end up taking the wrong
action (Simpson, Horberry, and Joy, 2009). Mistake errors are where operators choose to
do the wrong thing but when they make a decision, it is with the belief that it is, in fact,
the correct decision (Simpson, Horberry, and Joy, 2009). Violation errors occur when
55
operators deliberately choose an action which deviates from that which is required
(Simpson, Horberry, and Joy, 2009).
Errors of commission are basically where operators do something wrong (Simpson,
Horberry, and Joy, 2009). For example, pressing a wrong button, read the wrong
information, or giving wrong instruction. Errors of omissions are where operators fail to
do something which they should have done (Simpson, Horberry, and Joy, 2009).
Examples of these errors include failing to report a known hazard, failing to conduct a
safety check, or failing to mark an identified hazard.
Human information processing consists of three steps – input (receiving information);
decision (deciding what it means and what action); and output (taking action) (Simpson,
Horberry, and Joy, 2009). These type of errors can be useful in helping to identify the
causes of an error and where to apply error mitigation techniques (Simpson, Horberry,
and Joy, 2009). These techniques can help mitigate or prevent accidents from occurring.
Finally, human errors are best captured in behavior models since these models depict
human operators as the main cause of accidents (Abdelhamid and Everett, 2000). The
most predominant means of investigating the causal role of human error in accidents are
to analyze post-accident data (Shappell and Wiegmann, 1997). With regards to the
mining industry, these multiple factors involved the interaction between the operator,
equipment, and the operating environment. But clearly, if accidents are to be reduced in
the mining industry, more emphasis needs to be placed upon human error as it relates to
accident causation (Shappell and Wiegmann, 1997).
In the context of NAT, human error is one element of the complex interaction that
exists between humans, machines, and the work environment. The mining industry
56
operates within this interactive context. Any disconnection between these elements
results in a system failure or accident and supports NAT’s assertion that accidents result
from the interactive behavior of complex systems. Therefore, rarely, if ever, is the
human the sole cause of an accident but rather, most accidents involved a complex
interaction of multiple factors (Shappell and Wiegmann, 1997).
2.3 Safety Systems
This section discusses general safety systems implemented across the mining industry
to reduce accidents. Examples of safety systems implemented by major mining
companies such as Freeport-McMoRan, Barrick, and Newmont are discussed as well.
These implemented safety systems are specific to their respective organizations.
2.3.1 Safety Systems Implemented by Mining Companies to Reduce Accidents
Most large mining companies have standard operating procedures (SOPs) in place
within the mine, mill, and preparation plant sites to encourage safe work practices
(Karmis, 2001). The SOPs cover safety indoctrination, road design and maintenance,
traffic control standards, accepted or best practices for conducting various operations,
regular maintenance schedules for equipment, conveyor guards and crossovers, task
safety analysis and training, communications systems, dispatchers, and safety personnel
(Karmis, 2001). Many smaller operations have fewer full-time safety professional and
57
standardized procedures, thus may be less prepared to deal with conditions that contribute
to accidents (Karmis, 2001).
The Mine Safety and Health Administration (MSHA) train miners, inspect mines, and
issues citations for violations of safety regulations (Karmis, 2001). MSHA is also
involved in the development of training materials such as safety videos, best-practices
pamphlets, and on-and-offsite miner training; all of which hopes to mitigate safety
incidents (Karmis, 2001). Although these activities encourage safety, however, they are
not the total answer for safe operations at mines. Mining companies must continue to
develop, revise, and update their SOPs in order to mitigate or reduce incidents in mining
operations.
2.3.2 Existing Safety Management Systems Implemented by Major Mining
Companies to Reduce Accidents
This section discusses existing safety systems that have been implemented by major
mining companies such as Freeport-McMoRan, Barrick, and Newmont. Each of these
companies implemented a safety system that was specifically developed for their
organization. Therefore, no single safety systems are uniform across the industry. Of
course, the end-state and goal is to ensure the safety of the mining workforce and
reduction of accidents.
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2.3.2.1 Freeport-McMoRan
Freeport-McMoRan Copper & Gold Inc. (FCX) is a leading international mining
company and producer of copper, gold and molybdenum (Freeport-McMoRan, 2012).
FCX is also the world’s largest producer of copper and molybdenum (FreeportMcMoRan, 2012). The company’s portfolio of assets include mining complexes in
Indonesia, the world’s largest copper and gold mine in terms of recoverable reserves, the
large scale Morenci and Safford minerals districts in North America and the Cerro Verde
and El Abra operations in South America, and the highly prospective Tenke Fungurume
minerals district in the Democratic Republic of Congo (Freeport-McMoRan, 2012). FCX
had proven and probable copper reserves of 120.5 billion pounds as of December 31,
2010 (Freeport-McMoRan, 2012).
With regards to their safety program, FCX has a ―strong commitment to safety
performance to the local communities where it operates‖ (Freeport-McMoRan, 2012).
As stated in their corporate safety policy, FCX is committed to the philosophy of ―zero
incidents, injuries, fatalities and any number other than zero are simply not acceptable‖
(Freeport-McMoRan, 2012). A fundamental tenet of FCX’s safety policy is that there
will be compliance with applicable internal and external safety standards and that it must
actively be supported by management (Freeport-McMoRan, 2012). Given these policies,
FCX has implemented a safety system that is based on a combination of developed
General Code of Safe Practices, Risk Management Practices, and Standard Operating
Procedures (SOPs).
59
FCX implemented a General Code of Safe Practices which is a code that governs the
conduct and safety practice of employees. The code states that ―all employees are
responsible and accountable for working safely and productively while remaining aware
of the hazards of their jobs and following recognized safe job procedures‖ (FreeportMcMoRan, 2012). The purpose of this code is to provide all employees and contract
employees with proven safe practices that are common to all Freeport-McMoRan
Operations (Freeport-McMoRan, 2012). The code details specific safe practices involved
with mining operations such as in pit traffic safety, pre-operation vehicle/equipment
inspection, operating vehicles/equipment, and working around machinery or equipment
(Freeport-McMoRan, 2012).
In addition to the general code, FCX also implemented a Risk Management Practice
that integrates employees into this management practice. FCX’s safety program looks at
every project and does a risk inventory. Each mine site goes through each risk with the
employees and looks at how they are controlling risks and what gaps may exist (Melfi,
2012). Risk assessment is an important part of this process.
Adding to the robustness of FCX’s safety program, the company established and
implemented Standard Operating Procedures (SOP). One way of correcting gaps or
controlling risks is by establishing and implementing standard operating procedures in
the event engineering solutions to the problem could not be solved (Melfi, 2012). SOPs
serve as added controls as part of the safety program to prevent accidents. Although risk
assessment is an integral part of the risk management process, what is more important is
that FCX must ensure that they have controls (SOP) in place that are working to prevent
accidents from occurring.
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Finally, FCX conducts regular audits on their site projects and also has a Corporate
Safety Team that oversees the entire corporate mine site safety programs. This safety
team is independent of the mine site management. FCX also includes regular audits from
outside independent firms on OSASH 18001 and ISO 14001 plus independent Board
Mandated Audits (Melfi, 2012). All these things are done to insure a uniform process
because each mine site has their own safety program.
2.3.2.2 Barrick Corporation
Barrick Corporation is considered the world’s largest producer of gold with a
portfolio of 26 mines operating across five continents (Barrick, 2011). The company also
has the largest reserves in the industry with about 140 million ounces of proven and
probable gold reserves, 6.5 billion pounds of copper reserves, and 1.07 billion ounces of
silver contained within gold reserves as of December 31, 2010 (Barrick, 2011).
With regards to a safety program, Barrick is ―committed to achieving a zero-incident
work environment with a safety culture based on teamwork and safety leadership‖
(Barrick, 2011). The company’s safety and health policy states ―that nothing is more
important to Barrick than the safety, health and well-being of workers and their families‖
(Barrick, 2011). Barrick’s safety culture places safety as a priority for every employee
making them an integral part of what they believe and the way they approach their work
every day (Barrick, 2011).
At Barrick, the company implemented a Safety Management System which contains
the following major elements of the system (Barrick, 2011):
61

Leadership and Personal Commitment

Training and Competence

Risk Management

Operational Controls and Procedures

Contractor Controls

Incident Investigation

Emergency Preparedness

Performance Measurement and Assessment
Figure 2-11 illustrates Barrick’s Safety System Methodology which implements the
elements of the safety management system. This methodology shows how the nine
elements of the Barrick Safety System work together to ensure high performance and
facilitate continuous improvement for safety.
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Figure 2-11 Barrick Safety System Methodology (Barrick, 2011)
Leadership and personal commitment at Barrick requires leaders to commit to safety
by establishing clear roles, responsibilities, and accountabilities for individuals and teams
at all levels of the organization. Barrick’s system recognizes that all employees can play
a leadership role for safety. Barrick wants their leaders to lead by example and to
effectively communicate a positive safety message to all employees. Barrick recognizes
that leaders must take action in creating and promoting a safe workplace. At Barrick,
safety responsibility and accountability includes the selection, training, and appraisals of
workers, supervisors, and management (Barrick, 2011).
Training and competence at Barrick is part of the safety management system which
means providing opportunities for learning as well as reinforcing and monitoring the
63
application of learned skills and knowledge on the job to employees (Barrick, 2011).
Barrick is committed to providing their employees with a safe work environment and
ensuring they have the skills and knowledge which includes knowledge of applicable
regulations and procedures to work in a safe and reliable way (Barrick, 2011). Barrick
implemented training programs that include (Barrick, 2011):
 Company safety philosophy, expectations, and personal responsibilities
 Employees and contractor orientation
 Skills and knowledge of assigned tasks
 Hazard recognition and control
 Risk and change management skills
 Emergency procedures and basic first aid
 Regulatory requirements
 Refresher training
Having a risk management at Barrick can greatly reduce the risk of accidents
occurring, thus improving safety in the work environment. Barrick implemented a risk
management program that identifies hazard, assessing risks, and management of changeprocess which are pro-active approaches to dealing with concerns and issues that have the
potential to create unplanned, unexpected, or undesirable consequences (Barrick, 2011).
Barrick’s risk management program includes (Barrick, 2011):
 Hazard identification
 Annual site risk assessments
 Processes, procedures, and guidelines
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They monitor their risk management program through annual assessments to determine
the level of compliance and effectiveness of the implemented changes.
Barrick implemented operational controls and procedures to ensure work activities
are performed safely and regulatory compliance is maintained. Regular monitoring and
accountability are necessary to ensure the controls are effective. These controls and
procedures include (Barrick, 2011):
 Conduct regular assessment of the effectiveness of controls and procedures to
achieve safety performance objectives
 Appropriate controls, including operational, process, maintenance, and safety
involvement in the review of design and development of new projects and
facilities
 Appropriate operating procedures for all work activities, including the
identification of training, equipment, manpower, and logistical requirements
Barrick also implemented contractor controls for a wide range of activities including
administrative support, construction, mining operations, equipment repair, and
maintenance (Barrick, 2011). As such, Barrick places an importance on ensuring an
effective management system is in place to ensure the safety of all of their employees.
They require all their contractors to provide and maintain a safe work environment and
are responsible as a minimum for performing work to Barrick’s safety standards (Barrick,
2011). Barrick manages their contractors to ensure safety performance which includes
(Barrick, 2011):
 Each contractor is provided information about the company’s safety program
and its requirements
65
 A process is in place to review safety performance, systems, and plan as part of
the contractor selection process
 Each contractor provides an adequate safety plan for the required work based
on a risk assessment for the scope of work
 The work is conducted in a safe and responsible manner in compliance with
standards and applicable regulations
 There is timely, effective reporting, investigation, and review of all incidents
 Provide ongoing monitoring of the contractor’s safety performance review,
evaluation, and corrective action
 Review and document safety performance at the close of each contract
Finally, Barrick implemented an incident investigation element for proper incident
reporting followed by a thorough investigation and root-cause analysis to develop, apply
and monitor effective remedial actions. They are key components to control risks and
prevent accidents. The key components to this incident investigation program include
(Barrick, 2011):
 Ensuring a system that encourages incident reporting
 Ensuring investigations identify root causes, including systemic failures and
provide remedial actions
 Providing incident investigation training
 Personally participating in investigation of lost-time or high-risk incidents
 Ensuring the quality and completeness of incident reports
 Ensuring results of investigations, flash reports, and external incidents are
communicated to all site workers
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 Ensuring regulatory and company reporting requirements are met
 Regularly reviewing incident trends and analyzing root causes to correct
systemic failures
 Monitoring follow-up and remedial actions to ensure completion and
effectiveness
The elements discussed above describe the different components that Barrick has
implemented as part of their Safety Management System. This system is designed to
prevent, reduce, or mitigate accidents from occurring at mine operations across all
locations within Barrick Corporation.
2.3.2.3 Newmont Corporation
Newmont Mining Corporation is primarily a gold producer with significant assets or
operations in the United States, Australia, Peru, Indonesia, Ghana, Canada, New Zealand,
and Mexico (Newmont, 2011). Founded in 1921 and publically traded in 1925,
Newmont is one of the world’s largest gold producers and is the only gold company
included in the S&P 500 Index and Fortune 500 (Newmont, 2011). Headquartered near
Denver, Colorado, the company has over 34,000 employees and contractors worldwide
(Newmont, 2011).
With regards to safety programs, Newmont implemented a companywide system
called Health, Safety and Loss Prevention (HSLP) Management System which includes
detailed standards and procedures (Newmont, 2011). Together, these programs and
systems form the cornerstone of safety at Newmont and ensure that employees have the
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―tools‖ they need to work safely. A more specific safety management system
implemented by Newmont is called the Rapid Response system. Rapid Response is a
global crisis communication system with trained teams at each of the mine sites, regional,
and corporate levels (Newmont, 2011). This structure provides for an immediate
response capability to address potentially sensitive issues in such areas as health and
safety, security, environment, social and political (Newmont, 2011). Activating these
teams quickly helps minimize the incident's impact, prevent escalation, and allows the
company to resume normal operations (Newmont, 2011).
Rapid Response is also a process that mitigates and prevents the escalation of adverse
consequences in the event that existing risk management controls fail, particularly in the
event of an incident that may have the potential to seriously impact the safety of
employees, the community, or the environment (Newmont, 2011). This process includes
(Newmont, 2011):

Provides appropriate support to an affected site and/or region to complement
their technical response to an incident.

Minimizes the impact on the company by considering the environmental,
strategic, legal, financial and public image aspects of the incident.

Ensures communications are being carried out in accordance with legal and
ethical requirements.

Identifies actions that need to be taken on a broader scale than can be
predicted by those involved in overcoming the immediate hazards.
Additionally, the Rapid Response system was designed to (Newmont, 2011):

Allow for risk profiling, evaluation, and monitoring of a potential threat;
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
Prevent undue loss of control or escalation of a situation by triggering
response teams and business continuity management processes;

Ensure compliance with Newmont, legal and regulatory requirements;

Drive leading practices in emergency and crisis response across the
company;

Dovetail site and regional emergency plans with corporate systems to
ensure a coordinated response;

Ensure the right message is delivered to the right audiences at the right time
using a secure system;

Offer a resource library of information (e.g., maps, photos);

Require personnel be trained at Newmont locations worldwide;

Drive accountability by monitoring the effectiveness of a rapid response
incident; and

Measure the effectiveness of staff awareness of their roles and
expectations.
Newmont conducts regular training exercises in Rapid Response to ensure it is ready to
respond to serious events. Rapid Response provides a corporate-wide, common and
tested procedure that will allow an appropriate response to any circumstance, in any
geographic location, in a predictable and measurable manner (Newmont, 2011).
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2.3.3 Critiques of Existing Safety Management Systems Implemented by Major
Mining Companies to Reduce Accidents
In this section, a critique of the previously discussed safety systems is analyzed to
determine the effectiveness of their programs in preventing mining accidents. Critiquing
these systems provide an understanding of the shortfalls that exist for each of these safety
systems. It also helps to identify gaps in these systems.
2.3.3.1 Freeport-McMoRan
FCX stated that they are ―strongly committed to safety performance.‖ As stated in
their corporate safety policy, FCX is committed to the philosophy of ―zero incidents,
injuries, fatalities and any number other than zero are simply not acceptable‖ (FreeportMcMoRan, 2012). An analysis of FCX’s safety program using haulage accident data was
conducted in order to determine the effectiveness of their program and commitment to
safety.
An analysis of the accident report (MSHA Report dated October 3, 2001) showed that
the accident occurred because of maintenance problems with the braking systems and the
use of an undersized tractor. The miner was operating a Mack semi-tractor and lowboy
trailer moving an excavator when he lost control of the truck while descending a grade
and collided with the pit high wall. The report revealed several factors that contributed to
the fatality. The accident report concluded that the root cause of the accident was the
failure of FCX to establish procedures that required the proper capacity tractor to
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transport the excavator. At the time of the accident, the trailer was loaded with 168,230
pounds which required the tractor to accommodate approximately 112,000 pounds hitch
load. The Mack, DM800 tractor, attached to the trailer was used beyond its
manufacturer's design capacity of 60,000 pounds. Additional factors revealed that
maintenance problems with the braking systems and the use of an undersized tractor also
contributed to the cause of the accident.
The accident revealed gaps that exist within FCX’s safety program. First, nowhere in
the code or the SOP did FCX discussed procedures to take precaution or working with
hauling oversized equipment such as an excavator. Second, although the code discussed
pre-operation and equipment inspection, there was no section in the code that discussed
maintenance and equipment defects. As a result of the lack of procedures in dealing with
maintenance and equipment defects, the braking systems were not detected for defects.
Third, the code stated that ―pre-shift inspections are required before operation and defects
that limit safe usage are to be noted, and the vehicle is not to be operated until repaired‖
(Freeport-McMoRan, 2012). Therefore, the defects in the parking brakes should have
been detected but was not which contributed to the accident.
Other gaps that existed within FCX’s safety program were their risk management and
risk assessment processes. Although FCX claimed that their risk management practice
and risk assessment was integral to the company’s mine operations, there appears to be a
gap in terms of how their risks were assessed. If the program was intended to integrate
employees into the process, the defective parking brakes should have been captured as a
risk during inspections and noted as a hazard or risk but were not. This failure or lack of
71
risk management renders ineffectiveness in the process and FCX’s safety program as a
whole.
In conclusion, analysis of FCX’s safety system using haulage accident data revealed
several gaps in their program. There were too many factors that contributed to the
accident which were unplanned and unpredicted. Human error was one of these
unpredicted factors. Additionally, redundancies in the safety program were implemented
to prevent accidents, yet, accidents still occurred. These accident occurrences lend
support to NAT and the difficult nature of understanding complex systems such as
mining haulage systems. This is why zero accidents are difficult to achieve.
2.3.3.2 Barrick Corporation
Barrick has implemented a strong safety system ―committed to achieving a zeroincident work environment with a safety culture based on teamwork and safety
leadership‖ (Barrick, 2011). Furthermore, Barrick’s safety and health policy states that
―nothing is more important to Barrick than the safety, health and well-being of workers
and their families‖ (Barrick, 2011). Their safety culture places safety as a priority for
every employee making it an integral part of what they believe and the way they
approach their work every day (Barrick, 2011). An analysis of the major eight elements
of Barrick’s safety system using haulage accident data was conducted in order to
determine the effectiveness of their program and commitment to safety.
An analysis of the accident report (MSHA Report dated August 24, 2004) showed
that the accident occurred when a miner was attempting to load his haulage unit. He had
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backed his vehicle under a load-out chute and exited the cab to operate the chute controls.
Apparently the haulage unit drifted forward and pinned him against the concreted rib.
The report revealed several factors that contributed to the fatality. One of these
contributing factors was that management policies and controls were inadequate and
failed to ensure that the victim had received training in the health and safety aspects and
safe operating procedures regarding the DUX, Model TD-26, haulage unit and the batch
plant conveyor load out. Other factors were the failure to repair the defective park brake
switch along with the failure to either chock the wheels or turn them into the rib was
contributory. The victim had received training in accordance with 30 CFR, Part 48;
however, he had not received all of the required Part 48.7 new task training. From the
eight elements of Barrick’s safety management system, Leadership and Personal
Commitment, Training and Competence, and Operational Controls and Procedures gaps
occurred that failed to prevent this accident.
From a Leadership and Personal Commitment perspective, Barrick’s leaders should
assess how effective was their leadership in their commitment to ensure safety at their
organization. Did Barrick’s leaders lead by example? Did Barrick’s leaders effectively
communicate a positive safety message to their employees? Did Barrick’s leaders create
and promote a safe work place for their employees? If Barrick’s leaders were committed
to safety as they claim they were then perhaps the accident that occurred on August 2004
which fatally wounded a miner should not have happen.
From a Training and Competence perspective, perhaps Barrick did not provide
adequate training to their employees. The accident report revealed that Barrick
inadequately provided the necessary training required for the miner. Again, the victim
73
had received training in accordance with 30 CFR, Part 48; however, he had not received
all of the required Part 48.7 new task training and went to work with a deficient amount
of training. Clearly, there was a flaw in the system ―to monitor the application of learned
skills and knowledge on the job to employees‖ as part of Barrick’s safety management
system that they’ve implemented across all levels of organizations (Barrick, 2011). The
inadequateness of Barrick’s safety system to ―ensuring they have the skills and
knowledge of assigned tasks‖ contributed to the miner’s fatality (Barrick, 2011).
From an Operational Controls and Procedures perspective, there was a flaw in the
system to ―ensure work activities are performed safely and regulatory compliance is
maintained‖ (Barrick, 2011). The accident report revealed that Barrick did not apply the
appropriate controls to properly maintaining their equipment (defective brakes) which
contributed to the miner’s fatality. Barrick’s ppolicies and controls were simply
inadequate in terms of safe operating procedures required to perform the miner’s duty.
The miner was fatally injured as a result of these inadequate procedures. Clearly, the
accident report revealed several flaws in Barrick’s safety management system. These
flaws indicate a need to review their safety system for areas of improvements in order to
prevent future fatalities.
2.3.3.3 Newmont Corporation
At the core of Newmont’s implemented safety system is the Health, Safety and Loss
Prevention (HSLP) Management System, which includes detailed standards and
procedures. This system forms the cornerstone of safety at Newmont and ensures that
74
employees have the ―tools‖ they need to work safely. An analysis of Newmont’s safety
system using haulage accident data is conducted in order to determine the effectiveness
of their program and commitment to safety.
An analysis of the accident report (MSHA Report dated February 16, 2001) showed
that the accident occurred because the parking brake had not been set while tests were
being performed on the unattended truck. The miner was fatally injured when the truck
(Caterpillar, Model 789) he was performing maintenance on suddenly lunged forward
and crashed through the shop door. The report revealed several factors that contributed
to the fatality. The root cause of the accident was the failure to have the parking brake
set during the test procedure. Other contributing factors were the failure of the
company's written procedures to include caution statements alerting the mechanics that
the truck will move forward if the downshift harness wire was connected to the up shift
solenoid; no instructions defining the locations of the solenoids or the color-coding and
numbering of the harness wires; and no instructions directing the mechanics to reset the
parking brake after performing the previous tests.
The accident also revealed several other gaps with Newmont’s maintenance test
procedures. Their procedures were not in alignment with the haul truck’s manufacturers
procedures (Caterpillar Model 789) because the Caterpillar, Model 789, utilized the EMS
Monitoring System. The stored codes were examined to determine if any existing fault
codes were related to the accident. As such, two active fault codes, "Down Solenoid
Open Circuit, Code No. 708-05", and Up Solenoid Open Circuit, Code No. 707-05" were
found. These codes showed that the only two solenoids disconnected at the time of the
accident were the downshift solenoid and the up shift solenoid. The lockup solenoid was
75
not disconnected even though it should have been disconnected.
Additionally, Newmont's test procedure known as "Park Brake Release with Lockup
Engaged (Low Idle)" was not a test procedure shown in Caterpillar's Service Manual for
the Model 789 haul truck. Caterpillar's manual (Power Train Testing & Adjusting
Manual No. SENR6327) specified a test called "Lockup Clutch Leakage" to determine if
there are bad seals in the lockup clutch. Also, Newmont's PRE-ALPM Inspection
narrative procedure on page 2 for "Park Brake Release With Lockup Engaged (Low
Idle)" required that the park brake be released, caution statements alerting the mechanics
of the danger of connecting the downshift harness wire to the up shift solenoid be written,
and instructions defining the locations of the solenoids or the color-coding and
numbering of the harness wires be included in the test procedures. Clearly, Newmont’s
test procedures contained gaps that were not in accordance with the truck’s manufacture
procedures. In conclusion, the analysis from the accident report revealed several gaps in
Newmont’s safety system. As such, their safety system indicates a need to be reviewed
for areas of improvements.
2.4 Safety Regulations, Practices, and Training
The first step in managing safety in mining operations, in particular haulage safety, is
having a knowledge, an understanding, and an enforcement of the applicable government
safety regulations and standards (Title 30, Code of Federal Regulations, Part 56 and 77)
(USG Printing Office, 2002). These regulations detail the standards to which all mine
companies must adhere and operate accordingly. The standards are quite comprehensive.
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For example, most of the relevant standards for loader and truck safety for surface noncoal operations can be found in Part 56, Subpart H (Loading, Hauling, and Dumping) and
Subpart M (Machinery and Equipment) (Kecojevic and Radomsky, 2004). The standards
in these subparts encompass, but are not limited to safe operating speeds, necessity of
adequate berms, safety of truck spotters, unstable ground and dumping locations, horns
and back-up alarms, correction of safety defects, adequate brakes and tire repair
(Kecojevic and Radomsky, 2004).
Safety practices must also be considered and consistently followed in order to
prevent accidents from occurring in mine operations. For example, operators must read
and understand operator’s manuals and safety instructions prior to working on any
equipment. The adoption of safe work procedures by mining companies ensures that
workers are not exposed to hazards when performing their assigned tasks (Kecojevic and
Radomsky, 2004). Adopting safe work procedures mitigate the potential of accidents
occurring. Implementing safety rules such as requiring operators to wear seat belts or
prohibiting foot traffic in loading and haulage areas mitigates the potential for accidents
and avoids unnecessary fatalities (Kecojevic and Radomsky, 2004). Adhering to
prescribed safety practices in mine operations requires everyone in the workforce to have
a working knowledge of the safe practices and procedures.
The mining industry has long recognized training as a critical element in an effective
health and safety programs designed to protect mine workers (Carter, 2002; Peters,
2002). Three general types of safety training that have been recognized in the workforce:
1- skills training; 2- management training; and 3- motivational training (Kecojevic and
Radomsky, 2004). Skills training are used mostly to address skill and knowledge gaps in
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worker-machine interactions (Swanson, 1982). Management training focuses on workerworker and worker-idea relationships (Swanson, 1982). Motivational training seeks to
influence worker attitudes and beliefs (Swanson, 1982).
Finally, if the mining industry has a well established safety regulations, practices, and
training to provide a safe working environment and protect miners, the question then is
why does the industry continue to experience accidents? What are the driving factors that
contribute to these accidents? Is it possible that mine operations are so complex that the
industry is not able to understand the complexity? These are the questions that this
research study seeks to answer.
2.5 Conclusion
This chapter provided general background knowledge of mine accidents, theories,
regulations, practices, and training in the mining industry. Accident data was presented
in ways that illustrated the seriousness of the problem of powered haulage accidents in
the mining industry. This problem involves stakeholders from every aspect of the
industry such as mine management, miners, government regulators, trainers and safety
consultants. Powered haulage operations is inherently dangerous and the causes of their
related accidents are complex, encompassing management planning, monitoring and
control activities, as well as issues such as commitment to safe production, design and
engineering, safety standards, enforcement of standards, training, and safety behavior
(Kecojevic and Radomsky, 2004).
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Finally, this chapter presented the arguments for studying NAT and the need to
understand complex systems in order to prevent future catastrophic accidents from
occurring in the mining industry. Analysis of haulage accidents revealed that although
mining companies such as FCX, Barrick, and Newmont have implemented robust safety
systems, however, these systems are not flawless because they have yet to achieve zeroaccidents. This explains why haulage accidents, at least in surface mines, account for
40% of the industry’s accidents (MSHA, 2003). Mining operations much like space
systems and nuclear systems are so complex that it is difficult to anticipate accidents.
The study of NAT might help explain why unanticipated behaviors associated with
complex systems result in accidents. By understanding system complexity, NAT
provides an understanding for designing and building a more reliable and effective safety
system with the potential to add some predictability to prevent mining haulage accidents.
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CHAPTER 3
COMPETING ORGANIZATIONAL ACCIDENT THEORIES
This chapter discusses two competing organizational accident theories - the Normal
Accident Theory (NAT) and its alternative, the High Reliability Theory (HRT). NAT
and HRT both describe system accidents in opposing ways. They both represent very
different perspectives on how modern organizations function to prevent accidents of
high-risk technologies. NAT’s perspective focuses on the structure and claims that
complex and tightly coupled structures inevitably cause system-wide accidents while
HRT’s perspective focuses on the processes and identifies organizational initiatives that
can prevent such accidents (Pazzaglia, Shrivastava, and Sonpar, 2009).
3.1 The Origins of the Normal Accident Theory and High Reliability Theory
Prior to the publication of his own book, Charles Perrow was a contributor to research
that was published in 1981 which discussed the human aspects of the nuclear accident at
Three Mile Island in 1979 (Sills, Wolf, and Shelanski, 1981). Another contributor to this
work was Todd La Porte, who founded a research group at Berkeley and whose group
conducted research on highly reliable organizational performance under difficult
conditions (Rijpma, 1997). Both of these authors laid the foundation for two different
ways of thinking about accidents and reliability which subsequently resulted in their
competing perspectives against each other and has evolved into two separate theories of
NAT and HRT (Rijpma, 1997).
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NAT was first thought of by Charles Perrow, whose work later culminated in his
book called Normal Accidents, Living with High Risk Technologies (1984, 1999). The
basic premise of Perrow’s NAT holds that accidents are inevitable in complex systems
that are tightly coupled such as nuclear power or chemical plants. Complexity inevitably
yields unexpected interactions between independent failures and combined with tight
coupling, these interactions escalate rapidly and almost unobtrusively into a system
failure (Rijpma, 1997). The combination of complexity and tight coupling makes these
accidents inevitable which is why Perrow called such accidents ―normal‖ accidents.
The Berkeley school on HRT, on the other hand, purports to have discovered
organizational strategies with which organizations facing complexity and tight coupling
have achieved outstanding safety records (Roberts, 1993). The Berkeley school claims
that highly reliable organizations (HRO) centralize the design of decision premises in
order to allow decentralized decision making (Weick, 1987). HROs use redundancy in
their organization in order to back-up failing parts and persons (La Porte and Consolini,
1991). HROs also maintained several theories – conceptual slack – on the technology
and the production processes in order to avoid blind spots and hasty action (Schulman,
1993). Finally, HROs learned to comprehend the complexities of the technology and the
production processes (Rochlin, La Porte, and Roberts, 1987). Table 3-1 outlines the two
competing perspectives of NAT and HRT.
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Table 3-1 Competing Perspectives of HRT versus NAT (Sagan, 1993)
High Reliability Theory
Normal Accident Theory
Accidents can be prevented through good
organizational design and management
Accidents are inevitable in complex and
tightly coupled systems
Safety is the priority organizational
objective
Safety is one of a number of competing
objectives
Redundancy enhance safety: duplication
and overlap can make ―a reliable system
out of unreliable parts‖
Redundancy often causes accidents: it
increases interactive complexity and
opaqueness and encourages risk-taking
Decentralized decision-making is needed to Organizational contradiction:
permit prompt and flexible field-level
decentralization is needed for complexity,
responses to surprises
but centralization is needed for tightly
coupled systems
A ―culture of reliability‖ will enhance
safety by encouraging uniform and
appropriate responses by field-level
operators
A military model of intense discipline,
socialization, and isolation is incompatible
with democracy values
Continuous operations, training, and
simulations can create and maintain high
reliability operations
Organizations cannot train for unimagined,
highly dangerous, or politically unpalatable
operations
Trial and error learning from accidents can
be effective, and can be supplemented by
anticipation and simulations
Denial of responsibility, faulty reporting,
and reconstruction of history cripples
learning efforts
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3.2 Proponents and Opponents of the Normal Accident Theory and High Reliability
Theory
HRT argues that accidents occur because engineers and organizations failed to follow
and implement prescribed safety procedures or processes to prevent such accidents and
not as a result of interactive complex systems as Perrow’s NAT would suggest. HRT
focuses on the ―processes and identifies organizational initiatives that can prevent such
accidents‖ (Pazzaglia, Shrivastava, and Sonpar, 2009). In essence, the degree of
―reliability‖ of the system built by organizations results in system accidents and has
nothing to do with complex systems and tight coupling. These high reliability theorists
believe high-risk technologies can safely be controlled by organizations if design and
management techniques were followed based on four conditions (Sagan, 1993):
● Organizational leaders placing a high priority on safety and reliability
● Incorporating significant levels of redundancy and allowing backup or
overlapping units to compensate for failures
● Failure rates are reduced through decentralization of authority, strong
organizational culture, and continuous operations and training
● Organizational learning to reduce failure through a trial-and-error process
supplemented by simulation
HRT places an importance on the need to build an organizational culture that puts safety
first in order for the organizational processes and management to be effective in
preventing accidents to complex systems (Pazzaglia, Shrivastava, and Sonpar, 2009).
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Without a strong organizational culture that places safety as a high priority, HRT is not
validated.
NAT argues that when a system is interactively complex, independent failure events
can interact in ways that cannot be predicted by the designers and operators of the system
(Dulac, Leveson, and Marais, 2004). Additionally, if the system is tightly coupled,
resulting effects can get out of control before operators are able to understand the
situation and perform appropriate corrective actions which HRT suggest is mandated by
organizational processes or procedures. Furthermore, HRT stipulates that if systems fail,
then it’s because redundancy or multiple safety measures were not built into the system
through robust organizational processes. Perrow counters this argument by stating that if
additional redundancies were added to the system, then it would result in the system
becoming more complex with additional tight coupling and therefore, accidents are even
more likely to occur (Dulac, Leveson, and Marais, 2004).
Perrow provides many examples of how redundant safety devices may not only be
ineffective in preventing accidents but can even be a direct cause of accidents (Dulac,
Leveson, and Marais, 2004). For example, Perrow mentions that ―a near meltdown at the
Fermi demonstration reactor in Michigan in 1966 occurred when a piece of zirconium
installed inside the reactor as an additional safety feature broke off and stopped the flow
of coolant to the reactor core. The core was partially melted and the reactor was
permanently disabled‖ (Dulac, Leveson, and Marais, 2004). Many high-tech systems that
utilize new developed technology often do not understand the physical phenomena that it
causes, which may eventually result in system failures. For example, defense companies
that design and build weapon systems often cannot wait for a complete understanding of
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the systems before testing them since these complex systems have unresolved technical
uncertainty. If it were necessary to resolve all uncertainty before operating these systems
as required by HRT, then all high-risk systems would need to be shut down and
advancement of these technologies would come to a halt (Dulac, Leveson, and Marais,
2004). Therefore, the best way to mitigate or eliminate system accidents is to reduce
complexity and tight coupling. Interactive complexity and tightly coupled designs were
developed because they often allow greater functionality and efficiency to be achieved
which simpler designs do not afford such opportunity. Reducing system accidents can be
done through the ―most effective approaches involve eliminating hazards or significantly
reducing their likelihood by means other than redundancy; for example, substituting nonhazardous materials for hazardous ones, reducing unnecessary complexity, decoupling,
designing for controllability, monitoring, etc.‖ (Dulac, Leveson, and Marais, 2004).
Both NAT and HRT present compelling arguments from each of their perspectives.
―If a tightly coupled complex systems were to succeed in avoiding an accident, NAT
proponents would attribute the safe outcome to the system in question being not
complicated enough and similarly, in the event of an accident in a high reliable
organization, HRT proponents would argue that the accident occurred because the
organization had ceased being reliable in that it had not followed recommended
processes‖ (Pazzaglia, Shrivastava, and Sonpar, 2009). The arguments can be for or
against either of these two theories. However, Perrow’s theory goes beyond the
organizational processes and procedures of designing a system, but also looks into other
areas such as the political, social, human factors, and environmental aspect that makes up
the overall ―interactive complex system.‖ Finally, it’s been noted that the opposing HRT
85
―do not dispute the basic logic of Perrow’s argument that the structural conditions of
interactive complexity and tight coupling should, in theory, lead to accident-prone
organizations‖ (Sagan, 1993). This acknowledgement by HRT presents a favorable
argument in support of NAT.
3.3 Mining Haulage Accidents within the Context of the Two Competing Theories
In many ways surface mining haulage operations could be described from both
perspectives of NAT and HRT. From the perspective of NAT, haulage accidents result
from interactive complexity and tight couplings. Likewise, from the perspective of HRT,
haulage accidents can be prevented by having a robust organizational management that
addresses all the redundancies involved to prevent haulage accidents. A review of these
competing perspectives is described by Scott Sagan, author of ―The Limits of Safety:
Organizations, Accidents, and Nuclear Weapons,‖ in Table 3-1 (Sagan, 1993). Here the
arguments in support of NAT are discussed within the context of HRT perspectives
(reference Table 3-1).
1- Accidents can be prevented through good organizational design and management.
The stipulation that accidents can be prevented through good organizational design
and management is not entirely true because not all organizations have or can maintain a
robust design and management. Sagan ties this perspective to NAT by arguing that
accidents are inevitable in complex and tightly coupled systems (Sagan, 1993). As such,
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Perrow also argued that although accidents can be prevented through good organizational
design and management, however, not all accidents can be avoided in systems that are
interactively complex with tight coupling (Perrow, 1999). It is the complexity and
interactions of these systems that make it difficult to prevent their ―unanticipated‖
accidents.
It could also be argued that there will always be some sort of competing resources
(budgetary, personnel, etc.) in ways that will not allow organizations to develop good
designs and improve management techniques. For example, with limited resources, even
an organization with excellent design processes will not be able to design an error-free
system or continuously make improvements to existing systems. For surface mining
operations, limited resources could prevent the purchase of new equipment to replace old
existing equipment, thus making it difficult to alleviate some of the accidents that are
caused by equipment failures. This explains why from 2006-2010, equipment failure was
one of the top five leading causes of haulage accidents (MSHA, 2011). In many cases,
these equipment failures include mechanical and brake failures (Coleman, Martini, and
Ruff, 2010).
2- Safety is the priority organizational objective.
While it is beneficial to have leaders who place safety as a priority organizational
objective, however, safety priority will only have a limited effect on the behavior of the
entire organization (Sagan, 1993). In any organization ―conflict over organizational
goals such as desires to maximize production, maintain autonomy, and protect personal
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reputations can severely impair efforts to improve safety,‖ and compromises the safety of
these systems even if safety is recognized as a priority (Sagan, 1993). Furthermore,
Sagan ties this perspective to NAT by arguing that safety is one of a number of
competing objectives (Sagan, 1993). For example, other competing objectives such as
production pressures can create an organizational conflict over its goals which can cause
them to influence behavioral changes in their leaders to compromise safety. This
compromise as Perrow states, ―they (production pressures) contributed to a decision that
increased the proximity of subsystems and reduced the amount of slack available, moving
it towards the complex, tight coupled‖ systems which can lead to accidents (Perrow,
1999).
3- Redundancy enhances safety: duplication and overlap can make ―a reliable system out
of unreliable parts‖.
Redundancy does enhance safety and can make ―a reliable system out of unreliable
parts‖ however; there are potential negative consequences of redundancy. Sagan ties this
perspective to NAT by arguing that redundancy often causes accidents because it
increases interactive complexity which leads to unanticipated behaviors, makes the
system more opaque, and encourages risk taking, hence increasing the likelihood of even
more accidents (Sagan, 1993). This was also argued by Perrow in which he states that
―redundancy does constitute a safety device if it was in the original design and its
interactions with other components can be envisioned, but since redundancy is often
added after problems have been recognized, too frequently it creates unanticipated
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interactions with distant parts of the system that designers would find it hard to
anticipate,‖ thus leads to system accidents (Perrow, 1999).
4- Decentralized decision-making is needed to permit prompt and flexible field-level
responses to surprises.
Sagan ties this perspective to NAT by arguing that this perspective offers a
contradiction in that decentralization is needed for complexity but centralization is
needed for tightly coupled systems because preventing accidents require that the
complexity of systems be reduced while centralization is required to manage tightly
coupled systems (Sagan, 1993). Perrow makes these similar arguments by arguing that
―complex but loosely coupled systems are best decentralized; linear and tightly coupled
systems are best centralized; but complex and tightly coupled systems can be neither –
the requirements for handling failures in these systems are contradictory‖ (Perrow, 1999).
These concepts, complex (non-linear) versus linear systems and loose versus tight
couplings, are further discussed in Chapter 4.
5- A ―culture of reliability‖ will enhance safety by encouraging uniform and appropriate
responses by field-level operators.
Sagan ties this perspective to NAT by arguing that this perspective follows a military
model of intense discipline, socialization, and isolation which is incompatible with
democracy values because ―democratic societies are unwilling to isolate and control all
89
aspects of the lives of such organizations’ members‖ (Sagan, 1993). There is no way that
organizations would be able to maintain a consistent culture of reliability because ―their
leaders cannot know how operators should respond in all contingencies‖ (Sagan, 1993).
Perrow adds to this argument by arguing that ―even the best of cultures may not be
sufficient‖ and they often do so at the expense of production and profits because such
culture of reliability would eventually be compromised by operators under production
pressures (Perrow, 1999).
6- Continuous operations, training, and simulations can create and maintain high
reliability operations.
Sagan ties this perspective to NAT by arguing that organizations cannot train for
unimagined, highly dangerous, or politically unpalatable operations because in high risk
technologies or operations, continuous training cannot address all the ―unanticipated‖
failure modes produced by high interactive complexity (Sagan, 1993). There is no
possible way that any organization, even with skilled management, can develop training
that will allow them to achieve maximum reliability operations because frankly, no
organization can anticipate any potential failures in a complex system in any
environment. Any potential accident scenarios that have not been imagined obviously
will not be practiced and some scenarios that are dangerous cannot be practiced (Sagan,
1993). Additionally, while Perrow does agree with HRT that ―good training is effective
and sufficient for systems that are linear and loosely coupled systems, however, in
systems involving complexity and tight coupling, there is a probability for system
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accidents because we can never try hard enough, but such systems make trying harder,
and thus we get more occasions for multiple error which can interact in mysterious or
unpredictable ways‖ (Perrow, 1999).
7- Trial and error learning from accidents can be effective, and can be supplemented by
anticipation and simulations.
Sagan ties this perspective to NAT by arguing that a denial of responsibility, faulty
reporting, and reconstruction of history cripples learning efforts due to the following
factors: uncertainty about the causes of accidents, the political interests and biases of
organizational leaders and low-level operators, and compartmentalization within the
organization and secrecy between organizations (Sagan, 1993). These factors severely
constrain the process of trial-and-error learning in the following ways; First, the causes
of ―accidents and near-accidents are often unclear, and in such situations even wellmeaning organizational leaders are likely to reconstruct history to conform to their
preconceptions, to attribute success to their own actions, and to develop lessons to fit into
their sense of mission and unless the causes of an incident are relatively clear and cannot
be ignored, biased interpretations will therefore reign‖ (Sagan, 1993). Second, ―analyses
of real or potential accidents often take place in extremely politicized environments in
which blame for failures and credit for successes must be assigned to someone within the
organization and if this is the case, such analyses are likely to be designed to protect the
parochial interests of the most powerful actors instead of promoting more objective
learning‖ (Sagan, 1993). Third, ―faulty reporting will make it extremely difficult for
91
organizations to assess their performance accurately‖ (Sagan, 1993). Finally, the
―constraint on organizational learning is secrecy inside complex organizations and
between organizations‖ (Sagan, 1993).
Perrow adds to this argument by arguing that there is little opportunity for trial and
learning in systems with catastrophic potential. He even argues that learning might easily
be erroneous, thus raising the possibility of missing the root causes as a result of learning
from trial and error (Perrow, 1999). For example, in the case of mining haulage
operations, no trial and error learning can anticipate or predict every accident that occur
because we just don’t know when or how accidents will occur in order to permit a trial
and learning environment. This is why mining accidents have continued to persist in the
mining industry with surface mining haulage operations accounting for over 40% of all
accidents (MSHA, 2003).
3.4 Conclusion
This chapter presented two competing accident theories, NAT and HRT. NAT argues
that accidents involving high-risk technologies occur because these technologies are
complex and tightly coupled. HRT on the other hand argues that high-risk technology
accidents occur because of a lack or failure of a strong organizational management to
prevent these accidents. Clearly, the two diverge in that NAT focuses on system
complexity while HRT focuses on the organizational management of high-risk systems.
Understanding the differences between these two theories provide distinct perspectives
and background knowledge of the two theories. These competing theories continue to be
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the subjects of debate and are one of the motivations for conducting this research study to
evaluate complexity and NAT’s applicability in the mining industry.
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CHAPTER 4
THE NORMAL ACCIDENT THEORY
This chapter discusses the background and development of NAT. NAT provides a
framework for managing high-risk technologies. Much of NAT originated from research
done by Perrow with regards to accidents in high-risk technologies such as defense,
nuclear plants, air transport, DNA research, and chemical plants (Yale, 2009). High-risk
technology systems are those that have the potential to exhibit a catastrophic event with
the ability to take the people’s lives in one incident or accident (Perrow, 1999).
4.1 Origins of the Normal Accident Theory
The Normal Accident school of thought was rooted in organizational theory. It
started in 1967 when Perrow developed a questionnaire-based survey of salaried
employees working in 14 manufacturing firms from two industrial sectors located in two
parts of the country (Perrow, 1967). Perrow’s purpose was to identify important
characteristics of technology and organizational structure. A subsequent interpretation of
Perrow’s survey results led to the development of a two-by-two taxonomic frame work
consisting of a technology description of either ―routine‖ or ―non-routine‖ which was
used to classify operational ―exceptions‖ (Perrow, 1967). According to Perrow, if the
number of ―exceptions‖ was large and the search for solutions not ―logical and
analytical,‖ the technology was classified as ―non-routine‖ (Perrow, 1967). The second
classification was ―task structure‖ which was a composite variable derived from
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―employee discretion, power, coordination, independence and supervision‖ (Perrow,
1967).
Perrow discussed technology in the context of transformations and tasks. He stated
that ―organizations have two characteristics that might provide a basis for a typology: raw
materials (things, symbols, or people) which are transformed into outputs through the
application of energy; and tasks or techniques of effecting the transformations (Perrow,
1986). Perrow wrote that raw materials vary in a number of ways, such as uniformity and
stability (Perrow, 1986). Additionally, he wrote that tasks also varied in a number of
ways, including the difficulty in learning them or executing them, their simplicity or
complexity, whether they are repetitive or not, and whether they are structured or ill
defined‖ (Perrow, 1986).
From his research involving technology in organizations, Perrow formulated his two
by two model of interactive complexity and coupling which is the core of NAT (Perrow,
1999). He called it NAT because of his belief that when dealing with high-risk
technologies, there is an increased risk of an inevitable catastrophic event happening due
to the nature and characteristics of these high-risk technologies. Perrow argued that the
―characteristics of high-risk technologies suggest that no matter how effective
conventional safety devices are; there is a form of accident‖ with a good probability of
occurring (Perrow, 1999). The good news is that ―if we can understand the nature of
risky enterprises better, we may be able to reduce or even remove these dangers‖
(Perrow, 1999).
Most high-risk systems have special characteristics that make accidents in them
probable, which is why they are considered to be ―normal‖ accidents because it has to do
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with how systems interact together and how failures are tied to the systems (Perrow,
1999). It is possible to analyze system characteristics in order to gain a much better
understanding of why accidents occur and why they will always occur in these systems
(Perrow, 1999). Understanding these high-risk technology systems, ―we will be in a
better position to argue that such systems should be abandoned or eliminated and others
in which we cannot abandon ought to be modified for safer usage‖ (Perrow, 1999).
4.2 Definitions
The subsequent sections define what is meant by a system, linear and complex (or
non-linear) systems, tight and loose coupling systems, and system interactions.
Understanding these terminologies provide background knowledge of NAT. These
terminologies establish a general understanding for studying NAT and its potential
applicability in mining haulage operations.
4.2.1 What is a system?
In order to investigate NAT and its applicability to mining haulage operations, the
term ―system‖ must first be defined. What does system really mean to those who have
never used this terminology in their daily life or profession? This study defines a system
as ―a composite, at any level of complexity, of personnel, procedures, materials, tools,
equipment, facilities, and software in which the elements of this composite entity are
used together in the intended operational or support environment to perform a given task
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or achieve a specific purpose, support, or mission requirement‖ (Ericson, 2005). Figure
4-1 illustrates a generic concept of a system. The diagram describes a system which is
comprised of many subsystems interfacing together. The input to the system produces a
desired output of the system.
Figure 4-1 System Concept (Ericson, 2005)
System
INPUT
Subsystem
A
Subsystem
B
System
Interfaces
Subsystem
C
OUTPUT
System
Interfaces
System & Subsystems Functions
Systems have many different attributes. It is necessary to understand different
attributes because they provide the framework for designing, building, analyzing, and
operating systems (Ericson, 2005). All of the system attributes and their
interrelationships must be considered during the development and design of the system.
Table 4-1 provides examples of system attributes.
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Table 4-1 System Attributes (Ericson, 2005)
Hierarchy
Elements
Domains
Operations
Types
Systems
Hardware
Boundaries
Functions
Static
Subsystems
Software
Complexity
Tasks
Dynamic
Units
Humans
Criticality
Modes
Robotic
Assemblies
Procedures
Phases
Process
Components
Interfaces
Weapon
Piece part
Environments
Aircraft
Facilities
Spacecraft
There are different systems that exist in our society. Each system type has its specific
function and performs according to its intended purpose in which these systems were
design to operate. For example, an automobile system was design as a means for
transporting people and equipment. The automobile system contains subsystems (i.e.
steering, brakes, lights, wheels, etc.) that when interacted together perform an intended
purpose. Table 4-2 lists an example of different types of systems that are utilized in
today’s society.
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Table 4-2 System Types (Ericson, 2005)
System
Objective
Subsystems
Ship
Transport people/deliver
cargo/weapons
Engines, hull, radar,
humans
Aircraft
Transport people/deliver
cargo/weapons
Engines, airframe, radar,
humans
Missile
Deliver ordinance
Guidance, rocket motor,
warhead
Automobile
Transport people/deliver
cargo
Engine, frame, brakes,
humans
Mine
Extract minerals
Haulage trucks, crushing,
milling, humans
4.2.1.1 Complex (Non-Linear) and Linear Systems
This section defines the differences between complex (or non-linear) and linear
systems. Differentiating the two provides further insights into understanding NAT and
why accidents occur. Complex systems are defined as ―systems composed of many parts,
elements, or components which maybe the same or different and interconnected together
in a more or less complicated fashion‖ (Haken, 2006). Linear systems are defined as
―single purpose systems with less functionality and feedback loops‖ (Perrow, 1999).
Complex systems allow for more multi-functionality with common mode connections
and interconnected subsystems. Complex systems also allow for feedback loops with
multiple and interacting controls. Linear systems on the other hand are less
multifunctional with dedicated connections and single purpose, segregated controls.
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Additionally, linear systems have few feedback loops and provide direct information.
Which are considered the best? The numerous problems associated with complex
systems and the advantages of linear systems might suggest the latter are much preferable
and complex systems should be made linear (Perrow, 1999). However, this is not the
case as complex systems are more efficient than linear systems. There is less slack, less
underutilized space, less tolerance of low-quality performance, and more multifunction
components (Perrow, 1999). Table 4-3 describes the differences between complex
systems versus linear systems. Complex systems present a more complicated and
structured system than linear systems. Thus, from a design and hardware efficiency
standpoint, complex systems are more desirable.
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Table 4-3 Complex (Non-Linear) versus Linear Systems (Perrow, 1999)
Complex (Non-Linear) Systems
Linear Systems
Tight spacing of equipment
Equipment spread out
Proximate production steps
Segregated production steps
Many common-mode connections of
components not in production sequence
Common-mode connections limited to
power supply and environment
Limited isolation of failed components
Easy isolation of failed components
Personnel specialization limits awareness
of interdependencies
Limited substitution of supplies and
materials
Less personnel specialization
Unfamiliar or unintended feedback loops
Few unfamiliar or unintended feedback
loops
Many control parameters with potential
interactions
Control parameters few, direct, and
segregated
Indirect or inferential information sources
Direct, on-line information sources
Limited understanding of some processes
Extensive understanding of all processes
Extensive substitution of supplies and
materials
4.2.1.2 Tight and Loose Coupling
Before the characteristics of tight coupling are discussed, the terms ―tight‖ versus
―loose‖ coupling systems must first be defined. Tight coupling is a term that means there
is no slack or buffer and what happens in one directly affects what happens in the other
(Perrow, 1999). Tightly coupled systems are more time dependent and cannot wait or be
put off until attended to at a later time (Perrow, 1999). The system doesn’t allow for
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adjustment and re-adjustment of the event that is taking place within the system. The
sequences in tightly coupled systems are more invariant; for example, B must follow A,
because that is the only way for the system to make the product (Perrow, 1999). Partially
finished products cannot be re-routed to have B done before A; hence B depends upon A
having been performed in that sequence of the system (Perrow, 1999). In tightly coupled
systems, not only are the specific sequences invariant, the overall design of the system
allows only one way to produce the final product (Perrow, 1999). For tightly coupled
systems, buffers and redundancies must be designed and thought of in advanced (Perrow,
1999).
Loosely coupled systems allow for flexibility. The system is not lacking in
connections, but they are ―loose‖ and can respond to external demands without causing
catastrophic failure in the system (Perrow, 1999). In loosely coupled systems, there is a
better chance that expedient buffers and redundancies can be added during failures even
though they were not planned ahead of time. Fail-safe mechanisms can be designed and
added to loosely coupled systems while tightly coupled systems cannot be altered. Loose
coupling allows certain parts of the system to function and express themselves according
to their own logic or interests while tight coupling restricts this form of expression
(Perrow, 1999). Table 4-4 summarizes the characteristics of tight versus loose coupling.
These couplings are merely considered ―tendencies‖ since not every system will exhibit
all of these characteristics.
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Table 4-4 Tight and Loose Coupling Characteristics (Perrow, 1999)
Tight Coupling
Loose Coupling
Delays in processing not possible
Processing delays possible
Invariant sequences
Order of sequences can be changed
Only one method to achieve goal
Alternative methods available
Little slack possible in supplies, equipment, Slack in resources possible
personnel
Buffers and redundancies are designed-in,
deliberate
Buffers and redundancies fortuitously
available
Substitutions of supplies, equipment,
personnel limited and designed-in
Substitutions fortuitously available
4.2.1.3 Interactive Complexity and Tight Coupling
Perrow theorized that accidents are highly probable with interactive complexity and
tightly coupled systems because the complexity enabled unexpected interactions within
the system, which leads to system accidents (Perrow, 1999). ―Interactive complexity‖ is
a measure, not of the system’s overall size or makeup, but rather of the way in which
their parts are connected and interact within the system (Sagan, 1993). In a more general
definition, defined interactive complexity is defined as a measure of the degree in which
we cannot foresee all the things that can go wrong. This may have to do with the fact
that there are simply too many interactions within the system to keep track and maintain.
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Next, "tightly coupled" is defined as a measure of the degree in which an impending
disaster cannot stop once it starts. This may be because there isn’t enough time, it is
physically impossible, or there are no known solutions on how to stop it. The greater the
degree of interactive complexity the system exhibits, the less the capacity to prevent the
disaster. Additionally, the greater the degree of tight coupling the system contains, the
less the capacity to alleviate the impending disaster. Therefore, it can be re-phrased to
say that the greater the degree of interactive complexity and tightly coupled the system
exhibits, the greater the likelihood that the system will experience an accident. Figure 42 illustrates a descriptive model of a potential catastrophic failure based on the degree of
interactive complexity and tight coupling. This model was drawn from Perrow’s two by
two complexity/coupling model. The ―most at risk‖ quadrant in the model predicts the
greatest risk of accidents occurring because systems in this quadrant exhibit the greatest
complexity and coupling. The ―least at risk‖ quadrant in the model predicts less
accidents occurring because systems in this quadrant exhibit the least complexity and
coupling.
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Figure 4-2 Catastrophic Potential Model (Perrow, 1999)
INTERACTION
Complex
Tight
Linear
Loose
COUPLING
MOST AT RISK
1
2
3
4
LEAST AT RISK
4.3 The Normal Accident Theory and Its Potential Applicability in the Mining Industry
Given that accidents occur in the mining industry, it is worth analyzing these
accidents in order to understand the potential applicability of NAT in the mining industry.
With 40% (Aldinger and Keran, 1994) of accidents occurring from mining haulage
operations, it should prompt the industry to continue to determine how mining companies
can operate more effectively but safely. Mining is a dangerous occupation and
understanding the complexity and coupling that exists in mining operations can help the
industry develop better ways to mitigate accidents and reduce fatalities by either reducing
complexity or tight coupling.
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4.3.1 Haulage Operation Defined within the Framework of the Normal Accident
Theory
In order to evaluate NAT’s applicability for haulage operations, haulage operations
are defined within the framework of NAT. This research looked at two characteristics of
NAT, complexity and tight coupling. As such, haulage operations are defined by these
two characteristics. The concept of complexity has been researched by others for years;
however, the difficulty is that complexity has many definitions. As Sinha et al (2001)
states, ―there is no single concept of complexity that can adequately capture our intuition
notion of what the word ought to mean‖ (Sinha et al, 2001). Complexity is understood in
many different ways and not only in different fields, but has also different connotations
within the same field (Morel and Ramanujam, 1999).
4.3.1.1 Complexity Definitions
This section provides examples of the definition of complexity from different
industrial perspectives. The Webster’s English dictionary defined the word ―complex‖ as
being ―difficult to understand or explain‖ (Kim, 1999). Based on this English definition,
complexity has evolved into many different forms of definitions according to the areas of
interest. Some of these various forms of definitions are listed in Table 4-5.
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Table 4-5 Various Definitions of Complexity
Areas of Interest
Definition
Computer (Peliti and
Vulpiani, 1987/1988)
The complexity of an object (pattern, string, machine, and
algorithm) is the difficulty of the most important task
associated with this object.
Manufacturing (Klir, 1985)
A manufacturing system may make thousands of part types
during a year. There may be hundreds of machines. At
each moment, the managers are faced with hundreds of
decisions, such as: which part should be loaded onto each
machine next? The consequences of each decision are hard
to predict.
Biology (Snyder and Jervis, A complex system has a multitude of partial simple
1993)
descriptions but we cannot construct from them a single
―largest‖ description that is also simple. In this sense, the
reductionistic paradigm fails for complex systems.
Physics (Peliti and
Vulpiani, 1987/1988)
A complex system is a complicated system, composed of
many parts, whose properties cannot be understood.
Project (Baccarini, 1996;
Edmonds, 1999; Marle,
2002; Austin et al., 2002 ;
Vidal et al., 2008)
Project complexity is the property of a project which makes
it difficult to understand, foresee, and keep under control
its overall behavior, even when given reasonably complete
information about the project system.
Knowing that there are many different forms of definitions for complexity, the definitions
of haulage operation complexity and tight coupling are defined from the perspective of
mining haulage operations.
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4.3.1.2 Haulage Operation Complexity and Tight Coupling
For this research, haulage operation complexity is defined as the interactive
components of operators, roads and road conditions, equipment, open pits, work
environment conditions, traffic flow and signs, loading, transfer, and unloading
processes, equipment functions such as maintenance and inspections, and policies,
controls, and procedures all integrated and operating together in a haulage operational
environment. Each of these ―components‖ makes up the mining haulage operations
complex system. With each one of these elements interacting together, new ―unwanted
and unintended interactions‖ result in an increasing likelihood of accidents occurring.
Further defining operational complexity of haulage operations, ―interactive‖ is added
to this definition by defining interactive operation complexity as the presence of
―unfamiliar or unplanned and unexpected events in a system that are either not visible or
not immediately comprehensible‖ within the haulage operational environment (Dulac,
Leveson, and Marais, 2004). As haulage operations expand and the number of
components increase as part of the operation, they become more interactively complex.
Understanding any one of these components enables the mining industry to manage
haulage operations complexity better, thus allowing them to mitigate future accidents.
Finally, haulage operation tight coupling is defined as multiple factors that manifest
itself in the working environment, equipment, humans, organizational management, and
processes which offers little slack or redundancy in a haulage operational environment.
Defining haulage operation complexity and tight coupling establishes baseline definitions
which is necessary for evaluating NAT and its applicability in the mining industry.
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4.4 Conclusion
This chapter discussed the origins of NAT and how NAT can provide insights into
understanding complex systems. NAT’s potential applicability in the mining industry is
a positive step towards reducing incident injuries and fatalities. By gaining insights into
mining operations complexity, the mining industry will be in a better position to
determine ways to mitigate incidents, thus reducing increased costs relating to lost-time
injuries, law suits, regulation fines, investigations, and potential mine closures. For
example, cost data collected from surface haulage accidents showed that six haulage
truck fatalities cost an estimated $2.58M while 519 lost-time injuries cost $3.27M for a
total of $5.58M in 1994 (Boldt and Randolph, 1996). The magnitude of these costs
drives the mining industry to continue to look for ways to reduce incident injuries and
fatalities in mining operations, either through innovative engineering technology
solutions or better and improved safety practices and procedures. Therefore, application
of NAT may provide insights into these incident rates and at the same time, find ways to
improve mine safety.
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CHAPTER 5
INTERACTIVE COMPLEXITY ANALYSIS
This chapter analyzes interactive complexity involved with mining haulage
operations. Accidents involving haulage operations are not attributable to a single cause,
but more to a number of contributory complex factors. By analyzing the many
interactions between human, machine, and environment components, NAT’s applicability
in the mining industry is evaluated. Also, different accident models are discussed to
facilitate analysis of interactive complexity in haulage operations. These models provide
a way to evaluate and understand haulage operations complexity and their accidents.
5.1 Accident Models
In order to evaluate interactive complexity in mining haulage operations, accident
models are discussed because they are best suited for this type of analysis. Accident
models are the basic philosophy of accident occurrence and prevention shared by the
people involved in accident prevention (Huang, 2007). As Leveson (1995) writes,
―models provide a means of understanding complex phenomena.‖ Accident models serve
two purposes: to understand past accidents and to learn how to prevent future ones.
Accident models are important in accident prevention because they provide a tool for
those involved in accident prevention work (Huang, 2007). The model contains a
common pattern which specifies the causes of accidents and links between causes and
consequences. Accident data is analyzed and correlated based on the patterns between
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the accident data analyzed and countermeasures generated to prevent future accidents
(Huang, 2007).
Another use for these models is in accident prediction which looks at factors that
might be involved in future accidents so that they can be eliminated or controlled. The
models aid in helping to understand complexity associated with haulage accidents and its
common patterns in accidents. In essence, these models support NAT’s applicability in
the mining industry from the perspective of a predictive approach to preventing
accidents. This can be achieved by investigating an accident that has not yet occurred in
order to determine how to prevent it.
Finally, different accident models serve different industrial applications. Many
models have been proposed with, often differing only slightly. There isn’t any single
model that is ―one-size‖ fits all; therefore, it is prudent to select the best applicable
available model that serves an organization’s purposes. Several models are discussed as
a way to determine which model is best suited to analyze interactive complexity in
mining haulage operations system.
5.1.1 Energy Model
The energy model is the oldest of all accident models and views accidents as a result
of uncontrolled and undesired release of energy (Leveson, 1995). The idea is that if
accidents are a result of uncontrolled energy flow, then an obvious way to reduce them is
to use barriers or other control mechanisms as a way to prevent this energy release. In
this way, accidents are prevented by altering or controlling the path (energy flow)
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between energy source and the at-risk object (potential victim) (Leveson, 1995). This
model, however, does not account for many types of accidents. Figure 5-1 illustrates a
simple energy model of accidents. Energy flows from a source until it reaches an object.
Once the energy flow reaches an object, this energy whether in the form of chemical,
thermal, electrical, acoustic, or radiological can have the potential to harm the object
(victim) (Leveson, 1995). This energy source, unless prevented by a barrier from flowing
to the object, constitutes the direct cause of accidents according to this model.
Figure 5-1 Energy Model of Accidents (Leveson, 1995)
Barrier
Energy
Source
Energy Flow
Object
Equating this model from the perspective of NAT is that the energy source is the
system of interest. The uncontrolled energy flow constitutes the complexity of the energy
source and its flow is equivalent to the system’s behavior that results in the object’s
accident. The only way to reduce or prevent these accidents is to implement a safety
system (barrier) to prevent the flow of energy from reaching the object (victim). This
model is described in the context of NAT.
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5.1.2 Models Based on Systems Theory
Accidents can be viewed in terms of interactions among humans, machines, and the
environment based on the systems theory approach (Leveson, 1995). This approach is
discussed further in Chapter 8 where it is used to define mining haulage operations
system. Based on the systems approach, the components (humans, machine, and
environment) of the system is interrelated where each part affects the others either
directly or indirectly, thus responsible for accidents. As such these relationships between
causal factors can be seen as a complex net with factors at different distances from the
accident along proximal-distal axis (Leplat, 1987). An accident is then the coincidence
of factors related to each other through this network and stemming from multiple,
independent events (Leplat, 1987).
Models based on systems theory consider accidents resulting from the interactions
between components of a system. In this approach, safety is viewed as an emergent
property that arises when the system component interact within an environment.
Emergent properties are controlled or enforced by a set of constraints related to the
behavior of the system components. Accidents result from the interactions among the
components that violate these constraints, in other words, from a lack of appropriate
constraints on the interactions.
In a system, different type of levels of subsystems that comprise the system can be
identified by accident models. For example, using haulage operations systems from this
research study, the systems are divided into three subsystems – load, transfer, and unload.
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The physical systems include inanimate objects such as equipment, operators, materials,
and environment. Haulage operations include the interactions between humans, machine,
and the environment. Models based on systems theory describe accidents in terms of
―dysfunctional‖ interactions. Webster’s dictionary (2012) defines the term dysfunctional
as ―the state of being unable to function in a normal way.‖ Since accidents arise from the
interactions between components or subsystems, one way to eliminate these accidents is
by controlling the ―dysfunctional‖ interactions. These dysfunctional interactions are
divided into two types: 1- deficiencies in the articulation of subsystems and 2- lack of
linkup between elements of a system (Leplat, 1987).
Leplat’s first type of dysfunctional interaction suggests that accidents arise from
problems in articulating and coordinating subsystems. The operation of the entire system
depends not only on the operation of the subsystems, but also on the way in which the
subsystem operations are coordinated to meet the aims of the system as a whole (Leplat,
1987). For example, some root causes of haulage accidents include equipment
maintenance failure, lack of communication, design errors, etc. are considered
―dysfunctional‖ interactions because these accidents result from problems in articulating
and coordinating operations between these subsystems. The second type of dysfunctional
interaction is a lack of linkup between system elements. Examples of this type is poor
circulation of information within a component, lack of communication between
individual capacity and the task requirements, and incompatibility between the system
operation and mental models, thus resulting in accidents due to human errors. This is
certainly experienced in haulage accidents. For example, with haulage operations,
accidents result from human errors due to lack of communication on completing tasks
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requirements or different mental models perceived by operators versus the requirements
to complete the specified tasks.
This model approach based on systems theory is very much similar in concept to
NAT. NAT views accident from the perspective of complexity and coupling of systems.
Applying the systems approach to modeling, accidents could be viewed in a similar
context because the model describes accidents based on ―dysfunctional‖ interactions. As
such accidents viewed by NAT is also based on complexity of ―interactions‖ as well.
Increasing interactions result in an increase in dysfunctions to the system. This is similar
to NAT because according to NAT, increasing interactions result in an increase in
complexity which creates unwanted and unplanned accident occurrences, since an
accident is an undesired – hence unplanned – consequence of the system functioning. An
accident is therefore a consequence of dysfunction in the system which does not work as
planned (Leplat and Cuny, 1979). Finally, much like NAT, this model approach
underlines the limitations of all interpretations in terms of a unique cause. Accidents do
not have a single cause but a network of causes.
5.1.3 Accident Triangle Model
The accident triangle model is a well known accident model that has been used for
decades and was developed by H.W. Heinrich in the 1930s (Anderson et al, 2010). This
model served as a fundamental cornerstone of safety philosophy. As such this
philosophy has driven the approach and techniques used by all companies actively
engaged in reducing injuries or accidents in the workforce for over 70 years (Anderson et
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al, 2010). Heinrich argued that the accident occurrence of any event for any situation has
three components that interact together to create the risk of accidents occurring. These
three components formulate the ―accident triangle‖.
The three components of the accident triangle are the host, agent, and environment.
The host is the affected person and its behavior (TPH, 2010). The host refers to the
person that is affected by the accident. The agent is the vector causing the accident
(TPH, 2010). The agent is transmitted to the host through a vehicle (inanimate object) or
vector (Runyan, 1998). The environment is the physical, social and economical factors
surrounding the event of the accident (TPH, 2010). The environment includes all the
characteristics of the setting in which the accident event takes place (Runyan, 1998).
Figure 5-2 illustrates the accident triangle model.
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Figure 5-2 Accident Triangle Model
Host
Environment
Agent
The accident triangle describes accidents consisting of three necessary and coupled
components, each of which forms the side of a triangle (Ericson, 2005). All three sides of
the triangle are essential and required in order for the accident to occur. All of these
components are inter-connected and interact together; a change in one without related
change in the others will increase the risk of an accident occurring (TPH, 2010). The
concept of the accident triangle model is useful when determining where root causes of
the accident and interaction of the system takes place. For mining haulage operations
system, this model looks at the interconnections between the human, machine, and
environment components. Based on analyses using this method, it may reveal the
disconnected components in the model.
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5.2 Advantages and Disadvantages of Accident Models
The advantages of accident models are that these models provide a means of
understanding ―complex phenomena‖ in a way that can be communicated to others.
Models are basically abstractions that allow for understanding of a system by abstracting
away irrelevant details and focuses on the features that are assumed to be most relevant to
the system. Accurate models can provide an effective way to understand the system in
which they are applied. In order to design an effective safety system and select an
appropriate set of procedures and techniques, it is important to understand the models that
underlie any options and assumptions about accidents they represent. Finally, models
serve to understand past accidents and to learn how to prevent future ones.
The disadvantages of accident models especially energy models is that their scope is
limited to energy processes and flows, and so may not include accidents involving
nonphysical losses caused by logical errors in operations. If accidents are defined as
including loss of mission, then energy models will not be sufficient. No simple
mechanism can be defined for this very general definition of loss; more sophisticated
models of causal factors are needed. Another disadvantage of accident models is that the
results depend on how good the model is — a poor model will give unreliable results.
Therefore, models must carefully be researched and selected for specific purposes in
order to achieve the desired end results.
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5.3 Application of Accident Models
Based on the accident models presented previously, the systems theory and accident
triangle models are most applicable to evaluate interactive complexity in mining haulage
systems. Both models are essentially similar in that both view accidents in terms of the
interactions between the human, machine, and environment components. The only
difference with the accident triangle is that the model refers to the human component as
the host and machine component as the agent. Regardless of how these models are
represented, each of the components is interrelated where each part affects the others
either directly or indirectly. A disconnection between any one of these components
constitute an accident.
Prior to applying these accident models, three assumptions are made. These
assumptions enable the models to evaluate interactive complexity in mining haulage
operations system. Table 5-1 outlines the following assumptions of these models.
Table 5-1 Assumptions of the Systems Theory and Accident Triangle Models
Assumption #
1
2
3
Description
Models are appropriate in evaluating haulage operations
system based on the human (host), machine (agent), and
environment components
Haulage operations are interconnected between the human
(host), machine (agent), and environment components
Disconnection between the human (host), machine (agent),
and environment components result in haulage accidents
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Assumption 1 assumes that the systems theory and accident triangle models are
appropriate to analyze interactive complexity in mining haulage operations system. This
assumption is made in order to evaluate interactive complexity associated with haulage
operations. NAT attributes accident occurrences as a result of complex systems being
highly interactive. The accident models evaluate this attribute. Assumption 2 assumes
that haulage operations are interconnected between the human (host), machine (agent),
and environment components, thus allowing the models to evaluate these component
interactions that exist in haulage operations.
Finally, assumption 3 assumes that any disconnection between the interactive
components associated with haulage operations result in accidents. Since complex
systems are considered highly interactive, their elements are interdependence, therefore a
failure of one element results in a failure of another, thus resulting in an accident of the
system as a whole. The accident models evaluate the interdependence of these elements
in mining haulage operations system.
5.4 Interactive Complexity Analysis
Based on the accident models and assumptions discussed, the systems theory and
accident triangle are appropriate models used to analyze haulage accident data. The
models provide a means to analyze interactive components associated with the
complexity of the load, transfer, and unload haulage operations system. These models
analyze the interdependence between the human (agent), machine (agent), and
environment. Any disconnections between these components result in accidents. As
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such an interactive complexity analysis was conducted by applying these models to
determine the interdependence components that are responsible for haulage accidents.
This analysis evaluated the complex interactions that exist in haulage operations.
Mining systems, similar to other industrial systems have significant interactions.
Failure to identify and mitigate these interactions often results in accidents. Haulage
accident data used for this analysis was obtained from MSHA database for the year 19992010. As discussed previously, the three components: human (host), machine (agent),
and environment interact together to create the probability of an accident occurrence.
For example, analyzing one of the 49 accident data set from Appendix B (#46), the
accident report dated September 27, 2009 describes how a miner was fatally injured when
a loaded haul truck he was operating left a haul road and drove onto a berm, causing it to
overturn. Factors that contributed to the miner’s death were that he was not wearing a
seatbelt and he did not maintain control of the haul truck (1997 Komatsu 830E) that he
was operating.
If we analyzed this accident in terms of the three components; the human component
is the miner, the machine component is the haul truck, and the environment component is
the road and berm. Since the operator lost control of his truck, the truck could not
maintain a steady direction but instead, went off the road and hit a berm causing it to
overturn. Based on the models’ definitions, any ―disconnections‖ between these
components result in accidents. The disconnection here is the operator (human) who is
the interface between him, the haul truck (machine), and the road and berm
(environment). Additionally, had the operator wore seatbelts, the injury might not have
been fatal. Again, the disconnection links back to the operator.
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Applying these models provides a qualitative analysis of the interactions between
these components and how they affect haulage accidents. Tables 5-2 summarize the
results of the interactive complexity analysis conducted for the load, transfer, and unload
systems. A detailed analysis and description is referenced in Appendix B.
Table 5-2 Interactive Complexity Analysis
Summary
Interactive Component
Human (Host)
Machine (Agent)
Environment
Accident (n=49)
17
6
26
%
34.7
12.2
53.1
From the results summarized in Table 5-2, haulage accidents result mostly from the
operational environment (53% of total haulage accidents with 26 of 49 accidents
occurring) with many contributing factors that include unknown hazards, unsafe working
area, unstable terrain, and inclement weather. These accidents reveal the interactive
complexity nature of haulage accidents with several of these contributing factors
resulting from multiple combinations of operator, equipment, and the environment rather
than from a single isolated factor. These interactive complex factors include failures in
design error, equipment and parts, policy, administrative, and controls, operator errors,
and the operating environment.
A design error, if not identified, can propagate through other systems resulting in
system accidents. Design can include equipment, parts, slope, or any components of
haulage operations. The equipment and parts (front-end-loader, braking system, seat
122
belts, etc.) must ―fit‖ together for a purpose, conform to design specifications, quality
assured, inspected and tested (Raman, 2010). The equipment includes vehicle and parts
equipment. Governing the rules or regulations for the safety and operation of equipment
are the established policies, administrative, and controls for any mine operations. The
operating policies, administrative, and controls include procedures and work instructions
that are required for safe operations and maintenance of the equipment (Raman, 2010).
Failures in any of these factors contribute to haulage accidents.
Next, operators are required to operate and maintain the equipment. These factors
include qualified personnel for the appropriate duty, training in the operating and
maintenance procedures, training in the identification of workplace hazards, quality
control systems, and emergency drills and exercises to reinforce the response plan
(Raman, 2010). The mining industry is highly dependent on operators doing many things
right at the right time, and human error is a significant contributor to safety risk (Raman,
2010). Haulage operations are one of those operations that are dependent on operators.
Finally, the operating environment is an important contributing factor of haulage
system accidents. The operating environment includes both the workplace environment
(ergonomics) and the external environment (regulatory) (Raman, 2010). In summary,
mining haulage operations system contains many complex interactions (non-linear) and
problems in one area have the potential to affect one or more other areas. The main
characteristics of complex interactions are illustrated by common cause failures, human
error, and hidden interactions (Raman, 2010). These interactions have made it difficult to
identify all of the hazards associated with mining haulage operations.
123
5.5 Conclusion
This chapter discussed several accident models used to analyze root causes of
accidents. Accident models provide a way of understanding why accidents occur and
how to prevent them from occurring in the future. The accident triangle model was
introduced as a model that has been determined appropriate for analyzing haulage
accidents. The model provided a means to analyze interactive components between
humans, machine, and the environment which are integral in haulage operations.
Analyzing these interactive components provide an understanding of the complex factors
contributing to haulage accidents. The results have shown that haulage operations system
are highly interactive and lends further support to Perrow’s NAT in which accidents
result from the interactive nature of complex systems.
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CHAPTER 6
TIGHT COUPLING ANALYSIS
This chapter qualitatively analyzes tight couplings in mining haulage operations.
According to Perrow, tight couplings are one of two attributes of NAT that are
responsible for system accidents. Complex systems are usually designed with couplings
which enable them to optimize their functional objectives. Since couplings increase the
likelihood of system accidents, redundancies are normally built in as mechanisms to
guard against and mitigate these accidents. Identifying these couplings may aid in future
efforts to reduce mining haulage accidents.
6.1 Tight Coupling
As defined by Perrow, tight coupling systems are systems with ―little slack or buffer‖
and are ―more time dependent, more invariant‖ which leaves little or no opportunities for
redundancies to be built into the system (Perrow, 1999). Complex systems with tight
couplings are likely to experience system failures because these systems have a tendency
to exhibit behaviors that are unplanned and unintended. These unplanned behaviors
result in unintended consequences. In order to evaluate NAT’s applicability, part of this
research is to identify tight couplings associated with mining haulage operations. Once
these couplings are identified and understood, future solutions could be developed to
mitigate haulage accidents.
125
6.2 Assumptions of the Tight Coupling Analysis
Two assumptions are made in order to enable an analysis of tight couplings in mining
haulage operations system. Table 6-1 outlines these assumptions.
Table 6-1 Assumptions of the Tight Coupling Analysis
Assumption #
1
2
Description
Mining haulage operations accidents result from multiple
coupling factors
Mining systems are complex systems
Assumption 1 assumes that mining accidents result from multiple coupling factors in
mining haulage operations system that offers little slack or buffer. As a result, accidents
occur. Assumption 2 assumes that mining haulage operations system is complex because
according to Perrow’s NAT, accidents are a result of tights couplings present in complex
systems.
6.3 Tight Coupling Analysis
Mining haulage operations system much like other complex systems experiences
accidents because of the presence of tight couplings exhibiting in these systems. These
couplings result in a lack or absence of ―availability of buffers, resources, time, and
126
information that can enable recovery from failures‖ of the system (Wolf and Berniker,
2001). Identifying these couplings is a step forward in understanding haulage operations
complexity and determining the driving factors that are responsible for haulage accidents.
Table 6-2 identify ―tight couplings‖ present in haulage operations. This was done by
conducting an analysis of forty-nine haulage accidents that occurred from 1999-2010.
These couplings were verified by cross-referencing each mining companies’ accident to
each identified coupling. The specific name of each mining company is not identified but
for ease of this analysis, a generic naming convention is used to identify the companies
for each coupling in Table 6-2. These identified couplings provide an understanding of
the underlying complexity factors that are responsible for haulage operations accidents.
Table 6-2 Tight Coupling Analysis
1
Coupling
"Unknown" Hazards
2
"Human Error‖
3
―Unable‖ Changes to the
Operational Environment
4
5
6
Mine Company
A, B, G, H, J, M, S, Y, Z, AA, EE, KK,
MM, QQ, RR
E, G, N, S, U, W, Z, AA, EE, FF, NN, OO,
PP, QQ, TT, UU, VV
B, G, Y, Z, AA
―Failure To Follow"
A, C, D, F, L, N, O, P, Q, R, T, V, X, BB,
Established Safety, Training, DD, FF, GG, HH, OO, PP, TT
& Maintenance Procedures
―Inadequate‖ Policies,
Procedures, and
Administrative Controls
CC, EE, GG, HH, II, JJ, LL, RR, SS, UU,
WW
"Organizational Management D, F, H, I, J, K, M, O, P, Q, R, T, U, V, X,
Failure" To Establish Safe
BB, FF, II, KK, MM, NN, OO, UU, WW
Working Environment
127
The first identified coupling which is difficult to predict is called ―unknown‖ hazards.
Unknown hazards will always exist in any operations such as mining haulage operations
and is not within an organization's control no matter how hard the organization tries to
control every single aspect of the operational environment. These unknowns have the
potential for creating an unsafe working condition which can result in accidents. For
example, accident data from mining company RR resulted in a fatality during unloading
operations because the ground failed and collapsed where a load of material was
stockpile (MSHA, 2008). Policies and procedures were in placed to prevent such
accidents from occurring yet this unknown hazard resulted in a fatality in which mining
company RR could not have predicted.
The second identified coupling is ―human error‖. Humans are not perfect and will
make inadvertent errors even under the most established policies, procedures, and
controls. These errors are even made by the most experienced operators with knowledge
of operating the equipment. For example, accident data from mining company VV
resulted in a fatality during transfer operations because the victim approached the haul
truck (35 ton Euclid Model R-35 end dump) and did not notify the haul truck operator of
his presence (MSHA, 2010). As a result, the operator ran over the victim with his truck
fatally injuring him. The operator was an experienced equipment operator with 20 years
of mining experience.
The third identified coupling is the ―unable‖ changes to the operational environment.
For the most part, there will always be some operations that organizations are unable to
change because of the complex nature of the operational environment. Some operations
are inherently dangerous and cannot be eliminated entirely, although the operation can be
128
evaluated and good risk management procedures can be applied to ensure it is conducted
as safely as possible. For example, accident data from mining company AA resulted in a
fatality during unload operations when a raised bed on a haul truck (2002 Terex 25 ton,
Model TA25) lowered, pinning the operator against the frame of the truck (MSHA,
2003). The operator had dumped a load of material and pulled the truck forward with the
bed in the raised position. He went under the raised bed to add brake fluid in the master
cylinder because the brake light on the dashboard came on inside his truck when
suddenly, the raised bed of the Model TA 25 truck lowered pinning him against the truck.
This example shows the complex nature and inherent danger of working in mining
operations.
The fourth identified coupling is ―failure to follow‖ established safety, training, and
maintenance procedures. Accidents occur because operators simply fail to follow
established safety, training, and maintenance procedures. This failure, regardless of
whatever reasons, results in accidents that could have been prevented. For example,
accident data from mining company TT resulted in a fatality during transfer operations
when a loaded haul truck the miner was operating left a haul road and drove onto a berm,
causing it to overturn (MSHA, 2009). The accident occurred because the truck driver did
not maintain control of the haul truck he was operating. The failure of the driver to wear
the provided seat belt contributed to the severity of his injuries.
The fifth identified coupling is ―inadequate" policies, procedures, and administrative
controls. These inadequacies failed to provide a safe operational work environment for
operators. For example, accident data from mining company WW resulted in a fatality
during loading operations where the operator was fatally injured when a spotter truck was
129
backing a partially loaded 53-foot long box trailer into a bay hitting the operator and
pinning him between the trailer and wall of the loading dock (MSHA, 2010). This
accident occurred because management policies, procedures, and controls were
inadequate and did not protect persons at the loading dock. The bays at the loading dock
were not sufficiently illuminated. The loading docks were not monitored to ensure that
foot traffic was adequately controlled. Site-specific hazard training was not effectively
provided to the truck drivers. Consequently, they were not made aware of specific mine
hazards.
The sixth identified coupling is ―organizational management's failure" to establish a
safe working environment for operators. As a result of organizational management’s
failure to establish and ensure the safety of equipment, facilities, personnel, and work
environment through policies, procedures, and administrative controls, incidents occur
which leads to fatalities. For example, accident data from mining company UU resulted
in a fatality during unloading operations when the operator apparently stepped between
the trailers of an over-the-road tandem trailer truck to get out of the way of another
tandem trailer truck exiting the temporary dump site and got run over by the left front
wheels of the rear trailer of the unit (MSHA, 2010). The accident occurred because
management did not have policies and procedures that provided for the safe movement of
mobile equipment in an area with pedestrian and vehicular traffic. Mine management
also failed to ensure that mobile equipment operators sounded a warning that was audible
above the surrounding noise level prior to moving to warn all persons who could be
exposed to a hazard from the equipment. Additionally, mine management did not ensure
that the roadway in this area was maintained at a width sufficient to allow for safe
130
operation. The site-specific hazard awareness training did not protect persons by
addressing the appropriate subjects regarding the hazards associated with mobile
equipment operating near pedestrians.
6.4 Conclusion
This chapter analyzed tight couplings in order to understand complexity and how
couplings are exhibited in mining haulage operations. Tight coupling is one attribute of
NAT responsible for system accidents because according to Perrow’s NAT, systems
accidents result from complexity and tight couplings. By analyzing tight couplings, the
analysis reveals the complex nature of mining operations and the multiple coupling
factors that are present in haulage operations. Understanding these couplings and how
they are responsible for haulage accidents can lead to engineering controls and solutions
that will mitigate future fatalities. Finally, the results of this analysis provide plausible
support of Perrot’s NAT and its applicability in the mining industry.
131
CHAPTER 7
NAT STATISTICAL ANALYSIS AND ASSESSMENT
This chapter discusses the statistical analysis and assessment used to evaluate NAT’s
applicability. The analysis and assessment attempt to quantify and assess complexity and
coupling for mining haulage operations system. Having an ability to measure complexity
may provide insights and explain why haulage accidents occur. As Lord William
Thomson Kelvin once said, ―One does not understand what one cannot measure‖
(Sammarco, 2001).
7.1 Dependent and Independent Variable Definitions
In order to evaluate NAT, the dependent and independent variables must first be
defined. The dependent variable is the response that is measured (I&DV, 2011). The
independent variable is the variable that is varied or manipulated and is the presumed
cause (I&DV, 2011).
7.1.1 Dependent Variable – Haulage Accidents
Haulage accidents, according to Perrow’s NAT, result from complexity and tight
coupling. As such, haulage accidents are defined as the dependent variable. A dependent
variable is what is being measured and what is being affected. The dependent variable
responds to the independent variable. Figure 7-1 illustrates this simple definition.
132
Figure 7-1 Dependent Variable Definition
Dependent
Haulage
Variable
Accidents
7.1.2 Independent Variables – Haulage Operations Complexity and
Haulage Operations Tight Coupling
Since haulage accidents result from complexity and tight coupling, haulage
operations complexity and tight coupling are defined as the independent variables.
Additionally, haulage operations complexity is further defined as high and low
complexity. High complexity is defined as multiple complexity factors that contribute to
haulage accidents because according to Perrow, an increase in the complexity of a system
increases the likelihood of accidents occurring (Perrow, 1999). High complexity
signifies an increased complex system.
Low complexity is defined as a single complexity factor that contributes to haulage
accidents. Systems that are least complex are less prone to experience accidents or
failures. Although low complexity is less prone to accidents than high complexity,
nevertheless, these systems can and do experience accidents. Haulage operations
complexity is further defined as high and low because it provides a means to quantify the
degree of complexity that is exhibited in haulage operations. Figure 7-2 illustrates
haulage accidents within the framework of NAT.
133
Figure 7-2 Haulage Operations Complexity
Haulage
Operation Complexity
Haulage
Accidents
Haulage Operations
Haulage
Operation Tight Coupling
Next, haulage operations tight coupling is defined as tight and loose couplings. Tight
coupling is multiple coupling factors that contribute to haulage accidents. Loose
coupling is a single coupling factor that contributes to haulage accidents. The more a
system is coupled the higher the likelihood of the system experiencing accidents or
failures. A system with loose coupling experiences few accidents because the system is
able to make adjustments or has a slack built in that makes it less prone to accidents.
Haulage operations tight coupling is further defined as tight and loose because it
provides a means to quantify the degree of coupling that is exhibited in haulage
operations. Figure 7-3 illustrates the independent variables which are defined as haulage
operations complexity and haulage operations tight coupling. As such they are further
defined as high/low complexity and tight/loose coupling. Table 7-1 defines high/low
complexity and tight/loose coupling. Table 7-2 provides an analysis of complexity and
coupling factors (high/low & tight/loose) that contributed to each of the 49 haulage
accidents. These definitions and factors were defined and analyzed as part of the analysis
134
that will be used to quantify complexity and coupling in haulage operations. A detail
description of Table 7-2 is provided in Appendix B.
Figure 7-3 Independent Variables Definition
Independent Variables
High
Haulage Operations
Complexity
Low
Tight
Haulage Operations
Tight Coupling
Loose
Table 7-1 Complexity/Coupling Definition
Definition
Complexity:
Coupling:
High = > 1
Tight = > 1
Low = 1
Loose = 1
135
Table 7-2 Haulage Operations Complexity and Coupling Factors
Mine
Company
A
B
C
D
E
F
G
H
I
J
K
L
M
N
O
P
Q
R
S
T
U
V
W
X
Y
Z
AA
BB
CC
DD
EE
FF
GG
HH
II
JJ
KK
LL
MM
NN
OO
PP
QQ
RR
SS
TT
UU
VV
WW
Accident
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
Haulage Operations
Complexity Factors
High
Low
2
2
3
4
1
4
2
4
2
2
2
1
2
3
3
3
4
1
2
2
3
6
1
3
3
2
4
1
2
1
2
2
2
3
2
1
1
1
2
1
3
2
2
1
1
2
3
1
2
Haulage Operations
Coupling Factors
Tight
Loose
2
2
1
2
1
2
3
2
1
2
1
1
2
2
2
2
2
2
2
2
2
2
1
2
2
3
3
2
1
1
3
3
2
2
2
1
2
1
2
2
3
2
2
2
1
2
3
1
2
136
7.2 Statistical Analysis
For this analysis, NAT complexity and tight coupling were analyzed to evaluate the
degree of complexity and tight coupling that resulted in haulage accidents. This analysis
is based on a sample size of 49 haulage accidents. Table 7-3 summarizes the results of
the analyzed 49 accidents. The summary indicates that 73.5% of haulage accidents that
occur were high in complexity with multiple factors contributing to these accidents.
Conversely, only 26.5% of haulage accidents were low in complexity meaning only a
single factor contributed to the accident. Additionally, 75.5% of haulage accidents that
occur exhibited tight coupling with multiple factors contributing to these accidents.
Finally, only 24.5% of haulage accidents were considered loose meaning only a single
factor contributed to the accident.
Table 7-3 Complexity and Coupling Accident Statistics
Complexity
High
Low
Coupling
Tight
Loose
Summary
Accident (n=49)
%
36
13
73.5
26.5
37
12
75.5
24.5
Additional statistical analysis was conducted on high and low complexity and tight
and loose coupling. Table 7-4 summarizes the results. Additionally, these results are
classified into a 2x2 matrix (see Figure 7-4). From Figure 7-4, 32 of the 49 (65.3%)
137
haulage accidents were classified (Quadrant 2) as having high in complexity and tight in
coupling. According to Perrow’s NAT, systems that fall into this category have a strong
probability of experiencing accidents. This explains why haulage accidents account for
over 40% (Aldinger and Keran, 1994) of the accidents that occur in the mining industry.
Systems such as mining haulage operations exhibit behaviors that are high in complexity,
interactive, and tight in coupling.
Table 7-4 Complexity/Coupling Matrix Summary
Complexity
High
High
Low
Low
Summary
Coupling
Accident (n=49)
Tight
32
Loose
4
Tight
5
Loose
8
%
65.31
8.16
10.20
16.33
Figure 7-4 Complexity/Coupling Matrix Classification
Tight
High
n=5 (10.2%)
n=32 (65.3%)
12
34
Loose
n=8 (16.3%)
n=4 (8.2%)
Coupling
Complexity
Low
138
7.3 Assessment
The complexity of a system can be assessed by measuring the probability that an
accident occurs within a given system. Merriam-Webster (2011) defines probability as
―the chance that a given event will occur.‖ By measuring the probability of haulage
accidents occurring, a complexity assessment of haulage operations is made. The
assessments provide another means to quantify complexity and coupling, thus evaluating
NAT’s applicability in mining haulage operations. The assessments are evaluated for the
loader and truck equipment. Table 7-5 and 7-6 summarizes these assessments.
Results from Table 7-5 (Loader) indicate that complexity factors 1 & 2 are assessed
high because they have the strongest probability of accidents occurring as a result of
mechanical component failure and inadequate maintenance procedures are established to
maintain loader equipment in safe operating conditions. For Table 7-6 (Truck),
complexity factors 1 & 6 are also assessed high much like loader equipment because they
too have the strongest probability of accidents occurring as a result of mechanical
component failure and inadequate maintenance procedures are established to maintain
truck equipment in safe operating conditions.
139
Table 7-5 Loader Equipment Assessment
Complexity Factor
1
2
3
4
5
6
7
8
9
10
11
12
KEY:
Description*
Failure of Mechanical Components
Inadequate Maintenance Procedures
Failure to Recognize Adverse
Geological Conditions
# of Accident
Occurrences**
16
13
Probability
0.20
0.16
Value
3.24
2.14
Assessment
High
High
5
0.06
0.32
Low
4
5
0.05
0.06
0.20
0.32
Low
Low
7
0.09
0.62
Medium
5
0.06
0.32
Low
2
7
8
4
3
79
0.03
0.09
0.10
0.05
0.04
1.00
0.05
0.62
0.81
0.20
0.11
Low
Medium
Medium
Low
Low
Probability
0.30
0.07
0.09
Value
4.74
0.30
0.46
Assessment
High
Low
Low
0.09
0.11
0.24
0.46
0.67
3.13
Low
Medium
High
0.06
0.02
0.02
1.00
0.17
0.02
0.02
Low
Low
Low
Failure to Obey Loader's Working Area
Failure to Maintain Adequate Berms
Lack of Warning Signs and Appropriate
Mine Maps
Inadequate Provisions for Secure Travel
Failre to Adjust to Poor Weather
Conditions
Lack of Adequate Training
Failure to Wear Seat Belts
Lack of Efficient Communications
Failure to Maintain Haul Roads
Total =
* Kecojevic and Radomsky (2004)
** Verified from MSHA (1999-2010)
High = ≥ 1.0
Medium = ≥ .50 ≤ .99
Low = < .49
Table 7-6 Truck Equipment Assessment
Complexity Factor
1
2
3
4
5
6
7
8
9
# of
Accident
Description*
Occurrences
16
Failure of Mechanical Components
4
Lack of/Failure to Obey Warning Signals
5
Failure to Maintain Adequate Berms
Failure to Recognize Adverse Geologial
5
Conditions
6
Inadequate Hazard Training
13
Inadequate Maintenance Procedures
Failure to Respect Truck's Working Area
Failure to Set Parking Brakes
Operator's Health Conditions
Total =
3
1
1
54
140
KEY:
*Kecojevic and Radomsky (2004)
** Verified from MSHA (1999-2010)
High = ≥ 1.0
Medium = ≥ .50 ≤ .99
Low = < .49
By assessing the complexity of haulage operations, the results can be used as a
decision making tool for mine management to consider when looking at ways to mitigate
future accidents and reduce costs. Specifically, management can use these assessments
as a way of helping them make informed decisions that provide them opportunities to
better utilize resources and determine course of actions that allows them to mitigate
haulage accidents. Additionally, informed decision making allows companies to save
costs such as liabilities, equipment damages, and lost-time from work due to injuries that
result from mining accidents. Therefore, a complexity assessment of mining haulage
operations allows for informed decision making that benefits companies in preventing
mining accidents.
In another complexity assessment approach, forty-nine (49) haulage accident data
was analyzed which evaluated the number of factors that contributed to each accident.
By evaluating these factors, the probability of haulage accidents for each causal factor is
calculated by:
Probability of Accident Occurrence (Pao) = # of Accident Occurrence
(Equation7.1)
Total Number of Accidents
Table 7-7 lists the probability of haulage accident occurrences based on the number of
each causal factor. Next, knowing the Pao, the complexity of each accident occurrence is
calculated by:
141
Complexity (C) = Causal Factor * Probability of Accident Occurrence
(Equation 7.2)
= Cf * Pao
Table 7-7 Probability of Accident Occurrence
Causal
Factors
1
2
3
4
5
6
7
8
Total
=
=
=
=
=
=
=
=
=
# Accident
Occurrences
0
8
8
14
11
6
1
1
49
Probability Accident
Occurrences (Pao)
0
0.16
0.16
0.29
0.22
0.12
0.02
0.02
1.00
From the calculation of the complexity for each accident occurrences, an assessment is
made for each of the 49 accidents. Table 7-8 summarizes this assessment. Values
greater than or equal to 1.0 is classified as ―high‖. Any values less than or equal to .50 is
classified as ―medium‖ and values below .50 is classified as ―low‖. Table 7-8 shows that
there are few accidents that occur even though 1, 2 or 3 causal factors are present. It isn’t
until there are 4 causal factors that accidents occur in large numbers. This might be an
indicator of the degree of complexity/coupling that is necessary before many accidents
start to occur. Additionally, it must also be noted that there were 2 accident occurrences
where there were 7 & 8 causal factors yet their values (.14 & .16) were assessed low
when it probably should have been high. According to NAT, the complexity of a system
increases with increasing factors because increasing factors create additional unplanned
and unanticipated behaviors that lead to accidents. The reason that the 7 & 8 causal
142
factors were assessed low results from the fact that their accident occurrences were few in
number taken from the 49 analyzed sample size data. If there were a larger sample size
to evaluate, perhaps the results would have been assessed high. This is a limitation of the
research data.
143
Table 7-8 Complexity Assessment
Mine
Company
A
B
C
D
E
F
G
H
I
J
K
L
M
N
O
P
Q
R
S
T
U
V
W
X
Y
Z
AA
BB
CC
DD
EE
FF
GG
HH
II
JJ
KK
LL
MM
NN
OO
PP
QQ
RR
SS
TT
UU
VV
WW
Accident
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
Definition:
High = ≥ 1.0
Medium = ≥ .50
Low = < .50
# Causal
Factors (Cf)
4
4
4
6
2
6
5
6
3
4
3
2
4
5
5
5
6
3
4
4
5
8
2
5
5
5
7
3
3
2
5
5
4
5
4
2
3
2
4
3
6
4
4
3
2
4
6
2
4
Probability Accident
Occurrences (Pao)
0.29
0.29
0.29
0.12
0.16
0.12
0.22
0.12
0.16
0.29
0.16
0.16
0.29
0.22
0.22
0.22
0.12
0.16
0.29
0.29
0.22
0.02
0.16
0.22
0.22
0.22
0.02
0.16
0.16
0.16
0.22
0.22
0.29
0.22
0.29
0.16
0.16
0.16
0.29
0.16
0.12
0.29
0.29
0.16
0.16
0.29
0.12
0.16
0.29
Complexity
(C)
1.16
1.16
1.16
0.72
0.32
0.72
1.1
0.72
0.48
1.16
0.48
0.32
1.16
1.1
1.1
1.1
0.72
0.48
1.16
1.16
1.1
0.16
0.32
1.1
1.1
1.1
0.14
0.48
0.48
0.32
1.1
1.1
1.16
1.1
1.16
0.32
0.48
0.32
1.16
0.48
0.72
1.16
1.16
0.48
0.32
1.16
0.72
0.32
1.16
Complexity
Assessment
High
High
High
Medium
Low
Medium
High
Medium
Low
High
Low
Low
High
High
High
High
Medium
Low
High
High
High
Low
Low
High
High
High
Low
Low
Low
Low
High
High
High
High
High
Low
Medium
Low
High
Low
Medium
High
High
Low
Low
High
Medium
Low
High
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7.4 Conclusion
The results, analysis, and findings discussed in this chapter analyzed the complexity
of mining haulage operations system by providing a way to measure and assess haulage
accident complexity. By developing ways to quantify complexity, it’s possible to
understand factors that contribute to haulage accidents in order to develop or engineer
solutions to reduce these accidents. Without an ability to measure complexity, it’s
difficult to understand how systems behave and why accidents such as haulage result.
Understanding how complex systems behave allows for a more predictive approach in
preventing mining accidents through better informed decision making.
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CHAPTER 8
THE SYSTEMS APPROACH TO DEFINING AND ILLUSTRATING MINING
HAULAGE OPERATIONS SYSTEM
This chapter discusses the systems approach and its application as a way of defining
mining haulage operations system. The systems approach is ―an approach to thinking
and developing models to promote our understanding of events, patterns of behavior
resulting in the events, and even more importantly, the underlying structure responsible
for the patterns of behavior‖ (Bellinger, 2004). From a systems perspective, haulage
operations are defined as a system. The systems concept such as elements, nodes,
hierarchy, and interface provide a way of defining and illustrating haulage operations
system.
8.1 System Elements
System elements are the analytical building blocks of any system of interest. The
system elements when integrated into an architectural framework form the system
architecture and serve as the key construct for system analysis and design of the system
itself (Wasson, 2006). System elements are important for three reasons. First, they
enable us to organize, classify, and bound the system of interest and their interactions
(Wasson, 2006). It’s also a way of differentiating elements that are part of the system.
Second, the architecture of the system elements establishes a common framework for
developing the logical and physical architectures of each entity within the system
146
hierarchy (Wasson, 2006). Finally, system elements serve as an initial starting point for
the allocation of multi-level performance specification requirements of the system of
interest (Wasson, 2006).
Given the definition of system elements as defined previously, haulage systems are
then defined in terms of system elements. For haulage systems, these elements exist as
operators, facilities, equipment, and the working and physical environment of haulage
operations, natural or created. These elements make up the building blocks of the system
of interest – mining haulage operations system. Without these building blocks, no system
can exist. Therefore, the system elements build the system structure for developing
haulage operations system and its boundaries.
8.2 System Nodes
System nodes are the connection points between different elements or components of
the system. Each added node within the system increases the complexity of the system.
In haulage operations system, the elements within each of their systems are connected to
their next higher systems and are represented by a node (Muduli and Yegulalp, 1996).
Nodes provide a connection between different systems and also within the system itself
in a hierarchy. For example, a shovel used to load ore or waste into haul trucks is
considered a ―system‖ which in turns integrates into its next higher system – equipment
since the ―equipment‖ includes the shovel and any other types of equipment that interact
with other elements in the loading process of haulage operations. The system nodes
147
illustrate the nested interconnections between different ―systems of systems‖ (shovel
system with haul truck system) within any system of interest.
8.3 System Hierarchy
System hierarchy provides a framework for the structure of any system of interest. In
complex systems, it is necessary to define the general scope and structure of the system in
order to understand its make up (Kossiakoff and Sweet, 2003). System hierarchy
provides an understanding of different levels of aggregation of complex interacting
elements from the highest to the lowest levels of any system of interest (Kossiakoff and
Sweet, 2003). By their nature, complex systems have a hierarchical structure, generally
called a system, subsystem, component or assembly, and part levels. Figure 8-1
illustrates a generic system hierarchy.
SYSTEM
SUBSYSTEM
COMPONENT/ASSEMBLY
System Integration
System Hierarchy Decomposition
Figure 8-1 System Hierarchy
Where:
= Composition
PART
= Consists of
148
From Figure 8-1, the system level is the overall highest level of the system. It is a
level of abstraction that describes the top-level representation of the system of interest
(Wasson, 2006). This level of abstraction is referred to as a Level 1 or Tier 1 system
(Wasson, 2006). At the system level, it’s where the ―whole‖ of the system
collaboratively functions and integrates together to produce a specific intended purpose.
The subsystem level is the next intermediate level of the system. The subsystem(s) is
considered the main elements that make up the overall system. This level refers to the
system entities at the first level of decomposition below the system level (Wasson, 2006).
This level is referred to as a Level 2 or Tier 2 system (Wasson, 2006). The subsystem(s)
control all the lower level elements below them.
The component or assembly level is another intermediate level below the subsystem.
This level refers to system entities at the first level of decomposition below the subsystem
level (Wasson, 2006). This level is referred to as a Level 3 or Tier 3 system (Wasson,
2006). This level comprises much of the elements that make up the ―system‖. This is
where the ―nuts and bolts‖ of the system is made up.
The lowest level of the system is the part level. It is the lowest level of
decompositional element of a system (Wasson, 2006). This level is referred to as a Level
4 or Tier 4 system (Wasson, 2006). This level is where the elements form the assembly,
subsystem, and system levels. Since this level is the lowest level of the system, its
interactions are the least and increases in complexity as the elements integrate up the
system hierarchy.
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8.4 System Modeling
System modeling is a tool commonly used to understand complexity and uncertainty
of complex systems (Kossiakoff and Sweet, 2003). In general terms, modeling is used to
focus on particular key attributes of a complex system and to illustrate their behavior and
relationships apart from less important system characteristics (Kossiakoff and Sweet,
2003). The Department of Defense (DoD) defines modeling as ―a physical,
mathematical, or otherwise logical representation of a system entity, phenomenon or
process‖ (Kossiakoff and Sweet, 2003). The intent of modeling is to reveal critical
system issues in order to understand the behavior of any system of interest. The use of
models allows for an understanding of system complexity and any issues that may arise.
More importantly, models help organizations manage large cost of having to develop,
build, and test complex systems. Modeling also supports decision making and related
activities.
Examples of system modeling include schematic, mathematical, and physical models
(Kossiakoff and Sweet, 2003). Schematic models are diagrams or charts representing the
system of interest (Kossiakoff and Sweet, 2003). Block diagrams are examples of
schematic models used to model a system. It is also referred to as ―descriptive models‖.
Mathematical models use mathematical notation to represent a relationship or function
(Kossiakoff and Sweet, 2003). An example of a mathematical model is using statistical
methods to model a system. Another model is physical models which directly reflect
physical characteristics such as airplanes, full-scale mockups, or prototypes (Kossiakoff
150
and Sweet, 2003). For haulage operations system, modeling is applied to haulage
systems as a way of illustrating and analyzing haulage operations behaviors.
8.5 System Interface and Interaction
The system interface and interaction occurs between two or more individual elements
of the system. These interactions between different elements occur at various levels and
boundaries of the system. Such levels and boundaries are called the system’s external or
internal interfaces (Kossiakoff and Sweet, 2003). Outside of the system, boundaries
between system elements and the environment constitute the system’s external interfaces.
Inside the system, boundaries between system elements constitute the system’s internal
interfaces. Figure 8-2 illustrates a generic interface and interaction of the system. These
interfaces and interactions between the elements internal and external illustrate the
complexity of the system of interest. One element can have an affect on another element
which then causes an affect to the integrated system as a whole. For mining haulage
operations, such operations certainly do have external and internal boundaries from
which it operates. For example, in haulage operations, the surroundings include the
operators, equipment, and work environment from which ore and waste is mined and
transported to its final destination for processing. The boundary from which haulage
operations take place is from within the premises of the mine sites.
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Figure 8-2 System Interface and Interaction
8.6 Purpose, Definition, and Structure of the Mining Haulage Operations System
The mining haulage operations system serves three purposes. First, haulage allows
miners to travel from the mine entrance to the place of work quickly and efficiently
(Foster, 1997). This is particularly important in mine operations because of the excessive
distances involved in transporting minerals or overburden (waste) from the surface or
underground mine to its dump sites or collection points (Foster, 1997). Second, haulage
allows movement of materials to the place of operation at the time and point required
with the minimal amount of transfer and unloading (Foster, 1997). Third, transporting
minerals from the point of mining to the point of processing is a function of haulage
systems (Foster, 1997).
For this research study, the load, transfer, and unload systems of the mining haulage
operations system is the system of interest. These systems have been selected for
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simplicity in terms of modeling haulage systems and their elements could be analyzed
without creating difficulty in achieving the research objectives. Table 8-1 references
general definitions of haulage operations. These definitions are provided to avoid any
confusion of what constitutes loading, transfer, and unloading in mining haulage
operations versus other industrial applications of haulage operations.
Table 8-1 Haulage Operation Definitions
Terminology
Haulage
Load
Definition
Horizontal transport of ore, coal, supplies, and waste
Operation where ore/waste is loaded onto trucks at the loading
site
Transfer
Operation where ore/waste is being transported by trucks from the
loading site to the unloading site
Unload
Operation where ore/waste is being unloaded at the unloading site
In general, for open pit operations, a load, transfer, and unload system is defined as a
―truck goes to a shovel, gets loaded, goes to transfer ore or waste to an unload site and
comes back to the same or different shovel‖ (Muduli and Yegulalp, 1996). Establishing a
basic definition of these systems facilitates an understanding of how haulage systems
operate.
From previous discussions of system elements, nodes, hierarchy and interface; these
systems approach concepts are applied in order to develop the system architecture for the
mining haulage operations system. Figure 8-3 illustrates a generic system architecture.
The haulage operations system is illustrated based on this architecture. The architecture
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depicts the basic structure of the haulage system which includes its elements, nodes,
hierarchy, interface, and the boundary from which the system operates. Figure 8-4
illustrates a more specific mining haulage operations system which depicts the three subsystems (load, transfer, unload) that make-up the overall haulage operations system. By
applying the systems approach, haulage operations system is defined and illustrated. This
system serves as a baseline for further application of the systems approach where a
modeling technique is applied in Chapter 11 of this research study in an attempt to
quantify complexity of haulage operations system.
Figure 8-3 System Architecture
Interface
System
Node
Boundary
Sub-System 1
Sub-System 2
Sub-System N
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Figure 8-4 Mining Haulage Operations System
System
(Mining Haulage Operations)
Boundary
Interface
Sub-System 1
(Loading)
Node
Sub-System 2
(Transfer)
Sub-System 3
(Unloading)
155
8.7 Conclusion
This chapter discussed the systems approach used in defining and illustrating the
mining haulage operations system. Definitions such as system elements, nodes,
hierarchy, modeling, interface and interaction were discussed in order to apply them to
developing haulage operations system. Finally, the load, transfer, and unload systems
were selected and defined for their simplicity in analyzing haulage systems. It also
avoids any confusion that may arise in quantifying and evaluating NAT’s applicability in
mining operations.
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CHAPTER 9
COMPLEXITY MEASUREMENTS – A LITERATURE REVIEW
This chapter discusses some existing approaches of measuring complexity along with
their advantages and disadvantages. There are many different ways to measure
complexities and many different industrial bases have used different methods to measure
system complexity. By measuring complexity for different industrial applications,
industries gain better knowledge of its application and how best to maximize their
systems’ outputs without incidents.
9.1 Existing Complexity Measurement Approaches
In the following sections, different complexity measurement approaches are discussed
as a way of introducing different existing methods used to measure complexity for
different industrial applications. These existing approaches provide insights into
understanding systems complexity and how they were applied in measuring complexity
for specific applications.
9.1.1 Petrochemical Plants and Refineries Complexity
Frederick Wolf’s research work studied the complexity of petrochemical plants and
petroleum refineries because ―refineries are an important subset of high-risk technical
systems and sector of the economy of the United States,‖ stated Wolf (2000). As such,
157
Wolf developed what he called the index of complexity (IOC) which measures the
complexity of petroleum plants and refineries (Wolf, 2000). It’s an index that measures
refinery-specific complexity by using process knowledge (Bloom, 1998). Wolf applied
his IOC on thirty-six (36) refineries located in the western United States. His IOC was
calculated as the product of the parameters and states fundamental to the function of the
technology system (Wolf, 2000). The index is illustrated as follows (Wolf, 2000):
n
n
m
n m r
Ciplant = ΠCi = Π [Π(Qij)] = Π Π (ΠQijk)
i=1
i=1 j=1
(Equation 9.1)
i=1 j=1 k=1
Where:
Ciplant = complexity of a specific refinery
n
= number of unit processes in a refinery
m
= number of nodes at unit process i
r
= number of parameters at node j
Qijk
= number possible states of parameter k at node j for unit process i
Qij
= number of possible states of all parameters at node j, unit process
i
Ci
= number of possible states of all parameters of all nodes for unit
process i
The complexity index of the system or plant, Ciplant (Equation 9.1), measures the
value for the maximum number of states in which the system could mathematically exist
(Wolf, 2000). The Ciplant is the product of the Ci values for each of the major unit
158
processes in petroleum refining (Wolf, 2000). Wolf used this method to measure
complexity by illustrating an example of an idealized catalytic cracker technology
common to gasoline refineries (Wolf, 2000). With regards to the catalytic technology,
there are three characteristic nodes in this process: identified on Node A – Catalyst
Regenerator, Node B – Catalytic Reaction Unit, and Node C – Fraction Unit. Node A
has five critical parameters: pressure, temperature, reaction kinetics, flow, and quantity
(Wolf, 2000). Table 9-1 describes the qualifiers for each parameter used to calculate
complexity.
Table 9-1 Qualifiers for each Parameter (Wolf, 2000)
Parameter
Pressure
Temperature
Reaction Kinetic
Quality
Flow
State
Above operating limit
Within operating limit
Below operating limit
Above operating limit
Within operating limit
Below operating limit
Above operating limit
Within operating limit
Below operating limit
Catalyst is regenerated
Catalyst is not regenerated
Above operating limit
Within operating limit
Below operating limit
Partial flow
No flow
Reverse flow
Index k
1
Qijk
3
2
3
3
3
4
2
5
6
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Applying the IOC equation, Node A, the Catalyst Regenerator, has a complexity of
Qij = (3)(3)(3)(2)(6) = 324. When the two other nodes are included, the catalytic
cracking unit has a calculated interactive complexity index of 11,337,408 (Log Ci =
7.0545). Wolf took the logarithm of the Ci value as a way to reduce the calculated large
complexity values to more manageable values for analytic purposes (Wolf, 2000).
In summary, Wolf’s developed index of complexity provides a useful way to measure
complexity for refinery systems and ranking them based on their complexity values.
Although Wolf’s method provides a useful way to measure complexity of refineries,
however, this method is not applicable to mining haulage operations because unlike a
refinery where it doesn’t move and is static, a mine system is dynamic and haulage
operations are constantly moving parts.
9.1.2 Supply Chain Complexity
Kathryn Marley’s research work studied the complexity of supply chain and its
consequences of a disruption. A disruption of the supply chain in any manufacturing
process can greatly increase the production and operating costs to industries. As such,
industries are constantly looking for better ways to improve their supply chain processes
in order to avoid ―disruptions‖ of production systems. In Marley’s dissertation, she
defines a supply chain disruption as a ―stoppage in production at a customer’s plant
caused by activities that occur at a supplier plant upstream‖ (Marley, 2006). Her
dissertation looks at supply chain from a NAT perspective and suggests that tight
coupling and a high degree of interactive complexity led to an increased risk of supply
160
chain disruptions (Marley, 2006). Her research proposes a lean management approach to
mitigating disruptions by measuring the complexity and tight coupling of a production
process.
Marley’s approach to measuring complexity starts with categorizing interactive
complexity of supply chain as – process complexity and product complexity. Process
complexity increases as the number of production steps and technologies increases
(Khurana, 1999). Product complexity increases as the parts increases in the production
process (MacDuffie et al, 1996). As a result of both process and product complexity, this
creates a complex supply chain process susceptible to the risk of unpredictable
interactions which leads to what Marley defined as ―disruptions‖.
Marley developed a complexity measurement for the process complexity of
production process. Her process complexity measurement considers the number of steps
in routing. This measurement is based on the individual job. Her measurements
described the interactive complexity of the process because the greater the number of
steps in a process, the greater the number of potential interactions between resources
(Marley, 2006). Marley captured process complexity in the following equation (Marley,
2006):
Cprocess = number of steps in the process
(Equation 9.2)
Additionally, Marley defined product complexity by the number, complexity, and
novelty of the discrete parts involved (Novak and Eppinger, 2001). Greater numbers of
components and options of these components increases the number of potential
161
interactions that may exist between parts (Khurana, 1999). Marley uses steel as an
example of product complexity with its characteristics – gauge, width, gauge tolerance,
width tolerance, and a ―Rockwell‖ tolerance. As such, Marley captured product
complexity in the following equation (Marley, 2006):
Cproduct = g + w + a + b + c
(Equation 9.3)
Where:
g = gauge
w = width
a = gauge tolerance
b = width tolerance
c = Rockwell tolerance
Marley’s equation is applicable to haulage systems in a sense that haulage operations
are also a process much like Marley’s Cprocess (Equation 9.2). Additionally, by
analyzing the number of components or elements functioning within the boundary of
haulage operations system, haulage system complexity could also be modeled similar to
Marley’s product complexity index (Equation 9.3). Increasing number of elements
increases haulage interactions, thus increasing the complexity of haulage operations.
Since mining systems are dynamic, there are many moving elements involved with
haulage operations.
162
In summary, Marley developed ways to measure process and product complexities.
These measurements were based on Frederick Wolf’s research work (section 9.1.1) with
measuring complexity in petroleum and refinery process plants. Marley developed her
complexity equation from Wolf’s complexity index but with some variations. Marley’s
complexity equations were based on individual jobs in the process and not of the plants
as Wolf’s index were based on.
9.1.3 Project Complexity Based on Analytic Hierarchy Process
Project complexity has been deemed ―ever growing and needs to be understood and
measured better to assist modern project management‖ (Vidal et al, 2010). The ability to
develop a measurement for project complexity will greatly assist decision making. A
project is ―a temporary and unique endeavor undertaken to deliver a result which always
is a change in the organization, whatever it is in its processes, performance, products or
services‖ (Vidal et al, 2010). Project complexity has been researched for years yet lacks
a consensus of what project complexity really means. As such, Vidal et al (2010) states
that ―project complexity is the property of a project which makes it difficult to
understand, foresee and keep under control its overall behavior, even when given
reasonably complete information about the project system.‖
The Analytic Hierarchy Process (AHP) is a multi-criteria decision making process
that permits a relative assessment and prioritization of alternatives (Saaty, 1977, 1980,
1990). The AHP is based on the use of pair-wise comparison which leads to the
elaboration of a ratio scale and uses a model of the decision problem as a hierarchy
163
consisting of an overall goal, a group of alternatives, and a group of criteria which links
the alternatives to the goal (Vidal et al, 2010). Project complexity applies the AHP as a
hierarchical structure. The overall goal is the ranking of alternatives according to their
complexity level, meaning the AHP score which is obtained in the end catches and
aggregates the importance factors on each alternative (Vidal et al, 2010). The first level
criteria corresponds to the four groups of project complexity factors and the second level
criteria then corresponds to the factors of refined project complexity framework (Vidal et
al, 2010).
The hierarchical structure allows for the development of project complexity
measurement. A global score is obtained for each alternative and this score underlies the
relative value of the goal within the set of alternatives studied (Vidal et al, 2010). Given
this score, a relative proposed complexity index is expressed as the following ratio (Vidal
et al, 2010):
CIi = ___S(i)__  0≤CIi≤1
max (S(i))
(Equation 9.4)
Where,
S(i) is the priority score of alternative Ai.
Project complexity is applicable to haulage operations system by applying the
hierarchical structure to haulage systems. In Chapter 8, haulage systems were
decomposed into subsystems of load, transfer, and unload. Additionally, these
subsystems could also further be decomposed down to the lowest level - element level.
164
Therefore, a hierarchical structure of a system - haulage operations, increases in
complexity as each level within the hierarchy integrates into its next higher level until it
reaches all the way up to the system level, thus increasing the complexity of the system.
In summary, project complexity is difficult to understand. The only way to
understand this complexity is to be able to develop a measurement for project
complexity. This is accomplished by applying a hierarchical structure based on different
group levels. These levels are then measured by applying a complexity index (CI).
9.1.4 Elementary System Components Complexity
Elementary system components include equipment, machines, operational
environment, people, etc. The interaction between these system components creates the
complexity of the system of interest. The number of elementary system components
contributes to the system complexity by increasing the number of possible relationships
and states (Kim, 1999). Examples of this relationship are illustrated in Figure 9-1 (Flood
and Carson, 1988):
165
Figure 9-1 Elements, Possible Relationships, and States as a Measure of Complexity
(Flood and Carson, 1988)
(e)
(r )
(s)
1
0
2
2
1
4
3
3
8
number
(s)
15
(r)
10
5
(e)
1 2 3 4 5
Key
-----
Relationship
An element/node
(e)
Number of elements
(r)
Number of relationships
(s)
Number of states
166
Elementary system component considers each node as an element (e), each
connection as a relationship (r) between two of the elements and the possible number of
states (s) where each element maybe in one of two states (Kim, 1999). In a graphical
illustration (Figure 9-1), it can be seen that the rise in the number of potential
relationships grows at a faster rate than that in the number of elements. The number of
possible states rises even faster than the number of potential relationships. In this
context, a large number of elementary system components may indicate a high level of
system complexity as a result of the exponentially increasing in the number of states or
relationships of these system components (Kim, 1999). The elementary system
components complexity equation is illustrated as follows (Kim, 1999):
np
Ne = ∑ mi + si + pi
(Equation 9.5)
i=1
Where,
Ne = number of elementary system components
mi = number of machines that ith process has
si = number of stations at ith operations
pi = number of people at ith operations
Elementary system component complexity is applicable to haulage operations
because the complexity of a system increases with an increasing number of components,
which is what occurs in haulage operations. For haulage systems, an increase in the
number of elements or components also increases the complexity of haulage operations.
167
Additionally, an increase in the number of elements in haulage operations also increases
the relationships among these elements. Therefore, haulage operations system
complexity could also be modeled similar to the elementary system component
complexity index (Equation 9.5).
In summary, complexity is measured in terms of elementary system components.
System complexity increases with an increasing number of elementary system
components by increasing the number of possible relationships and states. As the number
of possible relationships and states increases, the complexity of system components
increases as well.
9.2 Advantages and Disadvantages of the Existing Measurement Approaches
The advantages of the existing measurement approaches are that there are different
available methods of measuring complexity for different systems. They are easy to apply
to any real system meaning they are – easy to collect data, interpret, and quantify systems
for future improvements (Kim, 1999). Researchers and engineers can apply these
methods that are most applicable for their purposes. Measuring complexity for different
systems allows for a clear understanding of which systems are more complex than others.
Understanding complexity of systems help supports decision making. Additionally,
utilization of these different complexity measurement methods allow for comparisons
between them in order to determine their usefulness and applicability. Finally, the
different existing measurement approaches allow researchers and engineers to choose the
best available option for their research or project purposes (Kim, 1999).
168
The disadvantages of the existing measurement approaches are gathering applicable
information to be able to understand the probability of certain tasks or behaviors of the
system which is not always easy (Kim, 1999). The ability to gather appropriate and
sufficient data is important and provides a way to calculate probabilities in order to
accurately understand the complexity of the system of interest. Utilizing incorrect data
can cause a miscalculation of the probabilities which results in an incorrect conclusion
about the systems’ behavior. Additionally, choosing the correct variables for which their
probabilities are calculated is important because incorrect variables may lead to
inaccurate measurements of the involved system complexity (Kim, 1999). Finally, these
methods may not be applicable to other systems; therefore, subjecting them to debates as
to whether or not they reflect the true nature of the system of interests’ complexity (Kim,
1999).
9.3 Conclusion
In conclusion, different existing complexity measurement methods offer alternative
ways to measure system complexity. From these alternative methods, industries gain
better insights into the complexity of such systems and how they are best understood by
calculating probabilities in order to predict their behaviors. Of course, there are
advantages and disadvantages of complexity measurements. An advantage is that these
different methods allow industries a way of measuring complexity for different systems.
A disadvantage is that these different methods may not apply to every system and the
ability to measure complexity depends on the available information gathered.
169
CHAPTER 10
PROPOSED MINING HAULAGE OPERATIONS SYSTEM COMPLEXITY
MEASUREMENT
In Chapter 9, a review of the literature discussed several existing complexity methods
that were applied to different industries. This chapter proposes a complexity index for
measuring the complexity of mining haulage operations system. Again, ―one does not
understand what one cannot measure‖ by Lord William Thomson Kelvin (Sammarco,
2001). Therefore, this index provides a way to measure haulage operations complexity
which is a positive step towards understanding NAT’s applicability in mining operations.
Additionally, the ability to quantify haulage complexity may lead to future development
of engineering solutions as well as aid in decision making processes that will reduce
mining haulage accidents.
10.1 System Elements, Nodes, Hierarchy, and Modeling
In Chapter 8, the systems approach - system elements, nodes, hierarchy, and
modeling was discussed as a way of defining haulage operations complexity. The system
elements were the building blocks that make up the haulage system. This system also
included its subsystems which were the load, transfer, and unload systems. The system
nodes provided a connection point between different systems and subsystems. These
nodes connected the system within a system, which then increases the system levels of
functionality and complexity. The system hierarchy provides a structural framework of
170
the system and its different levels of hierarchy. It depicts different levels of interaction of
elements within the system. Finally, system modeling allows a way of illustrating
elements and components of the system in order to understand its behavior or
performance. By representing the system as a model, its behavior could be depicted and
understood from a complexity point of view.
10.2 Heuristic Approach to Measuring Complexity
A heuristic approach is discussed as a way of measuring complexity in mining
haulage systems. Heuristic is defined as ―a process of gaining knowledge by experience
or some desired result by intelligent guesswork rather than by following some preestablished formula‖ (Whatis.com, 2011). As previously defined heuristics then is the
application of experience-derived knowledge to solving a problem
(SearchSoftwareQuality.com, 2011). Heuristic knowledge is applied to complex as well
as simple systems. Given this approach, heuristic method have an advantage in that they
are easy to apply to real systems such as mining haulage operations and easy to collect
data, interpret, and quantify systems for future improvements (Kim, 1999).
However, as much as this approach can help solve system issues, the disadvantage of
heuristic methods is that they have a deficiency of being subjective to arguments as to
whether or not these methods reflect the system complexity and finally, these methods
may not be applicable to other systems (Kim, 1999). This approach of applying heuristic
knowledge is applicable to haulage operations because haulage systems are considered
171
real systems where haulage accident data could be collected, interpret, and quantify in
order to understand haulage operations system complexity.
10.3 Application of the Literature Review of Complexity Measurements
In Chapter 9, different existing measurements were discussed for measuring system
complexity. From the literature review, supply chain, project, and elementary system
components complexity methods provided useful indices that could be applied to develop
a specific index that measures the complexity of mining haulage operations. Such index
provides a means to quantify haulage system complexity.
Marley’s (2006) supply chain complexity where process and product complexity
measurements were defined by the number of discrete elements or components involved
in a supply chain process. In a supply chain process, the greater number of components
and their options increases the number of potential interactions that may exist between
process components (Khurana, 1999). Therefore, based on Marley’s supply chain
literature, haulage systems could be measured in a similar approach. For haulage
operations system, the greater the number of components that make up and interacts
within the system; the more the system exhibits a high degree of complexity.
Project complexity was developed based on the hierarchical structure. As such,
project complexity is defined by complexity level(s). This complexity measurement is
applicable to haulage operations system because there are different levels of hierarchy
that is evident within haulage systems. Haulage systems increases in complexity as the
hierarchy within each level integrates into its next higher levels. For example, haulage
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operations system is the top level system which is then broken down into separate
subsystems (lower levels) such as the equipment, the road, operator, etc.; all of which
integrates to form the overall haulage system.
The elementary system components method measures the components of machines,
operating stations, people, and the environment; all of which are applicable to haulage
operations system (human, machine, and environment). The number of components
contributes to system complexity by increasing the number of possible relationships and
states (Kim, 1999). As such, a large number of elementary system components would
indicate a high level of system complexity because it exhibits an increasing relationships
and states of the system (Kim, 1999). For haulage operations, an increase in the number
of elements or components also increases the complexity of haulage operations.
Additionally, an increase in the number of elements in haulage operations also increases
the interactive relationships among these elements. Therefore, mining haulage operations
system complexity could also be measured similar to the elementary system components
method.
10.4 Proposed Mining Haulage Operations System Complexity Index
As mentioned thus far, complexity is generally recognized as a main cause of failure
in high risk systems. Although complexity is subjective and relative because it depends
on how complexity is defined, having a method to quantify complexity of systems can
eliminate any subjectivity. The search for a measurement method to quantify complexity
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is an urgent necessity because without a method, complex systems remain a challenge to
understand.
By applying the literature review of the three different approaches – systems thinking,
existing complexity measurements, and knowledge of heuristics; a complexity index for
measuring the complexity of mining haulage operations system is proposed. From
Perrow’s work, he states that ―the greater the number of interactive elements, the greater
the complexity‖ (Perrow, 1984, 1999). Additionally, Peliti and Vulpiani (1987/1988)
defined complexity as a ―built up of a plurality of interacting elements of a variety of
kinds.‖ Therefore, haulage complexity is measured by the number of elements or
components and the interactions between systems and subsystems.
Proposing a complexity index provides a way of measuring the complexity of a real
system – in this case, mining haulage operations system. Measuring haulage complexity
using the proposed index may reveal system complexity of haulage operations that have
not yet been understood. Additionally, understanding haulage complexity may also
provide explanations as to why haulage accidents (over 40% of all fatalities) continue to
persist in the mining industry (Aldinger and Keran, 1994). Finally, haulage complexity
may add plausibility that accidents are best explained by NAT rather than by HRT. The
proposed haulage operations system complexity index is derived as follows:
Haulage Operations System Complexity (HOSC) = ∑ Haulagei= [(E + N)]*H
(Equation 10.1)
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Where,
Haulagei is the complexity of the haulage operations system
E = # of elements or components in system i
N = # of nodes in system i
H = # of hierarchy levels
i = # of system of interest
Therefore,
HC (Total System) = ∑ Haulagei = Haulage1 + Haulage2 +
(Equation 10.2)
Haulage3 + … Haulagei
10.5 Strengths and Weaknesses of the Proposed Mining Haulage Operations
System Complexity Index
There are several strengths with the proposed mining haulage operations system
complexity index. First, the index provides a way of measuring the complexity of mining
haulage operations which has not been done previously in the mining industry. No
literature reviews thus far have provided any research methods that measure the
complexity of mining operations. This index provides a means to quantify real systems
such as haulage operations in order to understand its system behavior and the complexity
involved which causes haulage accidents. This index translates elements, nodes, and
hierarchy of the haulage system into a quantifiable number for data analysis.
Second, the complexity index supports NAT’s applicability in mining operations and
helps to explain why haulage accidents occur based on complexity and coupling. NAT
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suggests that system accidents pertaining to high-risk systems such as mining haulage
operations occur as a direct consequence of interactive complexity and coupling (Perrow,
1999). Finally, the complexity index provides a way to aid in decision-making process.
Having an index that can quantify data serves to better support management with
informed decision-making that works to mitigate haulage accidents and potentially
reduce the 40% (Aldinger and Keran, 1994) haulage fatalities occurring in the mining
industry.
There are several weaknesses with the proposed mining haulage operations system
complexity index. First, the index depends on an accurate modeling of haulage
operations in order to quantify the system. This includes understanding all the elements,
nodes, and hierarchy that comprises the haulage system. An inaccurate modeling of the
system may result in an inaccurate measurement which may misrepresent its system
behavior and complexity.
Second, the index was only developed for haulage operations system. It has not been
developed for measurement with other systems. As such, there is no way to compare
results of different systems in order to make a conclusion on the robustness of the index.
Finally, since the complexity index was only developed for haulage operations system,
the index is subject to debates as to whether or not their measurements reflect the true
nature of the system of interests’ behavior and complexity.
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10.6 Conclusion
This chapter proposes a complexity index as a method for measuring the complexity of
mining haulage operations system. The development of this method is a positive step
towards understanding haulage system complexity and NAT. This measurement index
may reveal insights into why haulage accidents continue to persist in the mining industry.
Having an understanding of haulage operations system complexity may lead to future
development of engineering solutions and aid in decision-making that mitigates and
reduces haulage accidents. Finally, this complexity index may add plausibility that
accidents are best explained by NAT rather than by HRT.
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CHAPTER 11
SYSTEM MODELING AND COMPLEXITY CALCULATION METHODOLOGY,
RESULTS, AND ANALYSIS
This chapter discusses the methodology, results, and analysis used to quantify the
complexity of haulage operations system. The system modeling approach and
complexity calculation provided a means to evaluate NAT’s applicability in mining
operations. Prior to this research study, there was no true evaluation of Perrow’s NAT
using any developed approaches, methods, or techniques for the mining industry. The
modeling approach and complexity calculation methodology were specifically developed
for this study.
11.1 Application of the System Modeling Concepts
In order to model haulage operations system, the systems are simplified into three
separate subsystems – load, transfer, and unload. Simplifying this system of interest
allows for simplicity in modeling and measuring the complexity of haulage operations
system. The load, transfer, and unload operations are all important aspect of mining
haulage operations. These operations are an integral component in the processing of the
final mineral products. By focusing on these three operations, the mining industry might
be able to understand the complexity involved in haulage operations and root causes
behind haulage accidents.
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There are two underlying assumptions that must be stipulated prior to applying the
system modeling approach to measure complexity of haulage operations system. These
assumptions facilitate the evaluation of NAT given the difficulty in measuring complex
systems. Table 11-1 describes two assumptions for haulage systems. The first
assumption is that the load, transfer, and unload are considered systems. Identifying what
is a ―system‖ based on systems thinking allows us to measure their system states. The
second assumption is that haulage operations are a dynamic process. As such their
models may not accurately represent the true nature of haulage operations system.
Table 11-1 Modeling Assumptions
Assumption
Description
1
Load, transfer, and unload are identified as systems
2
Haulage operations are a dynamic process
11.1.1 Development of the Mining Haulage Load, Transfer, and Unload Operations
System Models and Hierarchy
Prior to measuring the complexity of haulage operations systems, the load, transfer,
and unload systems must be modeled in a way that represents the entire haulage system.
Modeling these systems illustrates many different elements that comprise the haulage
operations system. The block diagram concept (based on the systems approach) is a
179
practical method for modeling this system. Block diagrams typically consist of boxes
representing either systems functions or system components connected by arrows or lines
and represent the connection between each element in the system (Wymore, 1993).
An open-pit mine site visit (July 2010) to the Rio Tinto-Boron Operations located in
Boron, California provided an opportunity to discuss haulage safety with Boron engineers
and observe haulage operations. This site visit provided many insights which aided in the
modeling of haulage operations system. Figures 11-1, 11-2, and 11-3 illustrate system
models for the load, transfer, and unload systems. These systems were modeled using
block diagram concepts.
Based on these models, the load, transfer, and unload systems are decomposed into
three levels of hierarchy. The first level is the top level for each of the three systems.
These systems are defined as an ―integrated set of interoperable elements, each with
explicitly specified and bounded capabilities, working synergistically to perform valueadded processing enabling a user to satisfy mission-oriented operational needs in a
prescribed operating environment with a specified outcome and probability of success‖
(Wasson, 2006). The functional elements of these systems include the hardware,
software, people, facilities, data, and services. The haulage operations system includes
any of these elements and also any of the interacting elements of the human, machine,
and environment.
For load operations system, the main components that make up this system are the
equipment used to load ore and waste, loading site where ore and waste are being loaded
into trucks, operator handling the equipment, and weather conditions that influence safety
of loading operations. These components are modeled within Level 2 of the hierarchy
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structure of the system. The loading system is at the top level of the system which is
Level 1 or Tier 1. The third level hierarchy comprises different types of equipment
involved with loading operations. This equipment includes haulage, non-haulage, light
vehicles, shovel, and power lines. Finally, at the loading site, the element that has an
influence on loading is ground conditions. An unstable ground condition can create an
unsafe environment for loading ore and waste.
For transfer operations system, the main components that make up this system are the
equipment used to transfer ore and waste to its processing locations, layout of the road
that provides a route during transfer of ore and waste, operator handling the equipment,
and weather conditions that influence conditions of transfer operations. These
components are modeled within Level 2 of the hierarchy structure of the system. The
transfer system is at the top level of the system which is Level 1 or Tier 1. The third
level hierarchy comprises different types of equipment involved with transfer operations.
This equipment includes haulage, light vehicles, and support equipment. Finally, the
road layout includes many elements that make up the layout enabling haulage transfer
operations to take place. These elements are the traffic flow or patterns, signs, and
intersections that control the movement and safety of haulage operations. The slope and
road conditions also influence safety as well during transfer.
For unload operations system, the main components that make up this system are the
equipment used to unload ore and waste at dumping locations, dumping sites, the
operator handling the equipment, and weather conditions which can influence the safety
of unloading operations. Similar to the other two systems, these main components are
modeled within Level 2 of the hierarchy structure of the system with unloading system at
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the top level which is Level 1 or Tier 1. The third level of hierarchy comprises different
types of equipment (haulage equipment and light vehicles) associated with unload
operations. Road conditions, ramps/berms, and ground stability are important elements
during unloading because the safety of this operation depends on the interactions of these
elements without incident. Finally, all three subsystems integrate together to form the
overall mining haulage operations system (see Figure 11-4).
182
Figure 11-1 Mining Haulage Load Operations System Model and Hierarchy
Level 1
Load
Level 2
Equipment
Level 3
Haulage
Equipment
Non-Haulage,
Large
Equipment
Light
Vehicles
Shovel
Power
Lines
Loading Site
Ground
Conditions
Operator
Weather
Conditions
183
Figure 11-2 Mining Haulage Transfer Operations System Model and Hierarchy
Level 1
Transfer
Level 2
Equipment
Level 3
Haul age
Equipment
Light
Vehicles
Road
Layout
Slope
Conditions
Traf f ic
Flow/
Patterns
Traf f ic
Signs
Support
Equipment
Traf f ic
Intersection
Road
Conditions
Operator
Weather
Conditions
184
Figure 11-3 Mining Haulage Unload Operations System Model and Hierarchy
Level 1
Unload
Level 2
Equipment
Level 3
Haulage
Equipment
Light
Vehicles
Unloading
Site
Road
Conditions
Ramps/
Berms
Ground
Stability
Operator
Weather
Conditions
185
Figure 11-4 Mining Haulage Operations System Model and Hierarchy
Mining Haulage
Operations System
Level 1
Level 2
Transfer
Load
Unload
Level 3
Equipment
Haulage
Equipment
NonHaulage,
Large
Equipment
Loading Site
Ground
Conditions
Operator
Weather
Conditions
Equipment
Haul age
Equipment
Light
Vehicles
Road
Layout
Slope
Conditions
Traffic
Flow/
Patterns
Level 4
Equipment
Haulage
Equipment
Light
Vehicles
Unloading
Site
Road
Conditions
Ramps/
Berms
Ground
Stability
Support
Equipment
Power
Lines
Weather
Conditions
Traffic
Signs
Light
Vehicles
Shovel
Operator
Traffic
Intersection
Road
Conditions
Operator
Weather
Conditions
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11.1.2 Development of the Mining Haulage Load, Transfer, and Unload Operations
System Nodes
Based on the system models of the load, transfer, and unload subsystems, the system
nodes for the mining haulage operations system is also developed. The system nodes
illustrate the connection points between each of the elements within the hierarchy of the
subsystems. These nodes provide a linkage between elements to elements, components
to components, and systems to systems within the mining haulage operations system.
Figures 11-5, 11-6, and 11-7 illustrate system nodes for each of the load, transfer, and
unload subsystems. Figure 11-8 illustrates the integration of all nodes connecting the
lower levels to the top level of the mining haulage operations system. The annotated
alphabet letters in the figures identify each node that corresponds to the elements they
connect within the system hierarchy.
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Figure 11-5 Load Operations System Node
Load
A
Equipment
D
Power
Lines
NonHaulage
Equipment
Loading
Site
Operator
Weather
Conditions
E
Light
Vehicles
Shovel
Haulage
Equipment
Slope
Condition
= Element
= Node
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Figure 11-6 Transfer Operations System Node
Transfer
B
Weather
Conditions
Equipment
Road
Layout
Operator
F
Support
Equipment
G
Haulage
Equipment
Light
Vehicles
Slope
Conditions
Road
Conditions
Traffic
Flow
Traffic
Signs
Traffic
Intersection
= Element
= Node
189
Figure 11-7 Unload Operations System Node
Unload
C
Equipment
H
Haulage
Equipment
Unloading
Site
Operator
Weather
Conditions
I
Light
Vehicles
Ground
Stability
Ramps/
Berms
Road
Conditions
= Element
= Node
190
Figure 11-8 Mining Haulage Operations System Node
Mining Haulage
Operations
System
J
A
D
B
E
F
C
G
H
I
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11.2 Application of the Proposed Complexity Index
In Chapter 10, the complexity index was introduced and proposed as an index used to
measure the complexity of mining haulage operations system. In the previous section
(11.1 Application of the System Modeling Concepts), the systems approach was applied
to develop system models, hierarchy, and nodes for the load, transfer, and unload
systems. From these models, the proposed index is applied to measure haulage system
complexity in mining operations.
11.2.1 Complexity Measurements
Chapter 10 proposed an index to measure the complexity of the load, transfer, and
unload systems for haulage operations. This index calculated haulage complexity based
on system models developed for the load, transfer, and unloads systems. Table 11-2
shows the calculated complexity values using the index. Table 11-3 summarizes these
results which were verified with haulage accidents.
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Table 11-2 Haulage Operations System Complexity Measurement Calculation
System
1. Load Operations System
Measurement Index
Haulage Operations System Complexity (HOSC) = ∑ Haulage i
= [(E + N)]*H
= [(10 + 3)]*3
= (13)(3)
= 39
2. Transfer Operations System
Haulage Operations System Complexity (HOSC) = ∑ Haulage i
= [(E + N)]*H
= [(12 + 3)]*3
= (15)(3)
= 45
3. Unload Operations System
Haulage Operations System Complexity (HOSC) = ∑ Haulage i
= [(E + N)]*H
= [(9 + 3)]*3
= (12)(3)
= 36
4. Haulage Operations System
Haulage Operations System Complexity (HOSC) = ∑ Haulage i
= ∑ Load + Transfer + Unload
= 39 + 45 + 36
= 120
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Table 11-3 Haulage Operations System Complexity Measurement Summary
System
Load
Transfer
Unload
Haulage Operations
Complexity Value Haulage Accident Verification*
39
14
45
23
36
12
120
49
%
28.6
46.9
24.5
100.0
*Data was obtained from MSHA database for 1999-2010 and applies only
to the load, transfer, and unload process of haulage operations.
11.2.2 Complexity Measurement Analysis
Results of the complexity calculation revealed the transfer system (value=45) was the
most complex system followed by the load system (value=39) and then unload system
(value=36). Furthermore, verifying these results with haulage accidents (MSHA, 19992010) showed that haulage accidents (46.9%) occurred mostly during the process of
transferring ore and overburden to and from loading and unloading locations.
These complexity values were consistent and support Perrow’s arguments that system
complexity increases with increasing number of components or elements in the system.
Perrow also argued that the greater the interaction of components, the greater the
complexity that exists in the system and the likelihood that the system will experience
―normal‖ accidents (Perrow, 1999). These values confirm that mining operations are in
fact complex. This may explain why haulage accidents account for over 40% of the
accidents in the mining industry (Aldinger and Keran, 1994).
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11.3 Results and Analysis Discussion
The complexity of the haulage operations system was measured based on the
developed index. The general system, haulage operations, is comprised of specific
subsystems - load, transfer, and unload operations. As each subsystem integrates into its
next higher level, this increases the haulage system’s complexity. Each of the subsystem
was measured for complexity using accident data pertaining to each subsystem
operations. This allowed for uniform measurements rather than measuring ―apples vs.
oranges‖ type of measurement. The accident data was used to measure the complexity of
the subsystems and not used to determine the complexity of each accident events. Each
subsystem operations have different elements involved, thus exhibit different interactions
which create different complexities of the subsystems.
Analysis of the accident data looked at causal factors that resulted in accidents which
were used in measuring the complexity of each subsystem. Based on the complexity
results for each subsystem, a conclusion can be made about the complexity of the general
system which in this case, haulage operations system. Finally, although the results of the
complexity measurements are not entirely conclusive that the transfer operations system
is the most complex of the three systems, however, the results (verified using the 49
accident data) does indicate that we are on the right track in our stipulation that the
transfer system is the most complex of the three haulage systems. Additionally, the
results also indicate that the index is a suitable measurement method to quantify the
complexity of systems.
195
11.4 Conclusion
The results from applying the complexity index suggest that quantifying complexity
is possible for mining haulage operations system. Additionally, the results revealed that
transfer operations system exhibited the most complexity of the three measured systems.
These values were verified using haulage accident data. The ability to develop methods
that can measure complexity provides a means for understanding system complexity and
its behaviors. Therefore, understanding how complex systems behave provides a more
predictive approach in preventing mine accidents.
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CHAPTER 12
SUMMARY AND CONCLUSIONS
This chapter summarizes and discusses the conclusions of this research investigation.
The Normal Accident Theory was the focus of this investigation. From this study, a
qualitative and quantitative analysis was conducted to evaluate NAT’s applicability in
mining operations. The investigation identified several ―lines of evidence‖ that lends
support to NAT in mining operations. Finally, the limitations of this research,
recommendations, and future work are discussed as well.
12.1 Summary
NAT is concerned with two attributes that results in accidents - complexity and tight
coupling. These accidents occur because NAT operates within the boundary of
technology systems that are considered high-risk. As these systems are designed with
advanced technologies, their functionality becomes more complex and coupled in nature.
These complex systems contain numerous elements that interact together in many ways
that makes them difficult to understand. As such the interactions between these systems
become so complex that they have a tendency to experience unintended and unplanned
behaviors. Without redundancies or safety systems designed to protect them, these
complex systems eventually lead to accidents or failures.
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12.2 Normal Accident Theory versus High Reliability Theory
In Chapter 3, the proponent and opponent arguments for NAT and HRT were
discussed. Both theories presented positive and negative perspectives of organizational
accidents. The results and findings of this research investigation do not dismiss NAT or
HRT in its entirety, but rather the findings highlight several lines of evidence that support
NAT’s applicability in mining haulage operations. These lines of evidence are:

Strong organizational management or safety system does not guarantee zero
accidents

Complexity is exhibited in mining haulage operations system

Mining haulage operations are an interactive system

Tight couplings are exhibited in mining haulage operations
HRT’s main argument in contrast to NAT is that accidents occur because
organizations failed to prevent accidents and not a result of the failure of the system
itself. However, our findings have shown that companies with strong organizational
management or safety systems cannot guarantee zero accidents from occurring in the
work place. Mining companies such as Freeport-McMoRan, Barrick, and Newmont are
large companies with robust implemented safety systems (discussed in Chapter 2);
however, data have shown that these companies continue to experience accidents
(MSHA, 1999-2010). Hence, the argument by HRT that accidents occur because
organizational management failed to prevent accidents from occurring is simply not true.
The findings from this research investigation along with several documented accidents
(space shuttle Columbia, Chernobyl, and Bhopal) in our global history indicate several
198
lines of evidence that point towards NAT and that accidents result from interactive
complexity and tight couplings.
A mining haulage system is a complex system. The developed complexity index
(discussed in Chapter 10) provided a means to quantify haulage system complexity. As
such the results indicated that the transfer system was the most complex of the three
systems (load, transfer, and unload) measured. These results were verified and shown to
be consistent with MSHA haulage accident data (1999-2010). Additionally, the results
support Perrow’s arguments that system complexity increases with increasing number of
elements in the system. The transfer system contained the most elements out of the three
systems that were measured. Given that accidents continue to exist in mining companies
even with robust safety systems, complexity is the driving factor that results in haulage
accidents.
Mining haulage systems exhibits interactive complexity. The interactive complexity
analysis (discussed in Chapter 5) demonstrated that haulage systems are highly
interactive. The human, machine, and environment components in mining haulage
operations are very interactive in nature. Furthermore, haulage accidents are a result of
―dysfunctional‖ interactions which comprises of deficiencies in the articulation of
subsystems and lack of linkup between elements of a system (Leplat, 1987). Haulage
operations system much like other industrial systems exhibit significant interactions
which is why these systems are considered an interactive complex system.
Mining haulage systems exhibits tight couplings. The tight coupling analysis
(discussed in Chapter 6) showed that couplings are in fact exhibited in haulage systems.
199
As a result, tight couplings increase the complexity of haulage systems and their ability to
make adjustments during operations to prevent accidents from occurring.
This study showed several lines of evidence that support NAT’s applicability in
mining haulage operations system. Figure 12-1 illustrates these lines of evidence. Figure
12-2 illustrates a linkage between mining haulage operations system and NAT. Haulage
systems are complex systems where multiple causal factors are manifested in haulage
operations. These lines of evidence all point towards NAT in which accidents occur
because of interactive complexity and couplings rather than by strong organizational
management.
Figure 12-1 NAT Lines of Evidence
Mining Haulage System is Complex
NAT
Mining Haulage System Exhibits
Interactive Complexity
versus
NAT
Mining Haulage System Exhibits
Presence of Coupling
HRT
Strong Organizational Management
Do Not Ensure Zero Accidents
200
Figure 12-2 NAT Linkages with Mining Haulage Operations System
Mining
Haulage
Operations
System
Normal
Accident
Theory
Complex
System
Multiple
Accident
Causal
Factors
Multiple
Functions/
Factors
Haulage System
Complexity –
Interaction of Human,
Machines,
& Environment
Increasing
Interactive
Complexity
Drives
System
Behavior
Unintended/
Unplanned
Consequences
Tight
Coupling
System
Accidents
12.3 Research Questions
The research questions that were presented (discussed in Chapter 1) serve as a basis
for this research investigation. Based on the results, analyses, and findings, the research
questions are discussed as follows:
201
Research Question 1:
Does mining haulage operations system classify as a complex and tight coupling
system?
Based on the results of this investigation, mining haulage operations system is
classify as a complex and tight coupling system. A statistical analysis of complexity and
tight coupling was analyzed for the 49 haulage accident data set. From this analysis, 32
were classified as complex and tightly coupled (See Figure 7-4).
Research Question 2:
Is mining haulage operations system an interactive complex system?
Based on the results of this investigation, mining haulage operations system is an
interactive complex system. An interactive analysis was analyzed for the 49 haulage
accident data set. The results of this analysis (discussed in Chapter 5) are summarized:
Summary
Interactive Component
Human (Host)
Machine (Agent)
Environment
Accident (n=49)
17
6
26
%
34.7
12.2
53.1
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Haulage accidents result mostly from the operational environment (53.1%) with many
contributing factors that include unknown hazards, unsafe working area, unstable terrain,
and inclement weather. All of these independent components when interacted together
create the interdependencies within haulage operations such that when any one of these
components becomes disconnected, haulage accidents result.
Research Question 3:
Does mining haulage operations system complexity increase with greater interaction
of system elements?
Based on the results of this investigation, mining haulage operations system
complexity does increased with greater interaction of system elements. The complexity
measurements showed that of the three systems, haulage transfer operations had the
greatest interaction of system elements which resulted in the greatest complexity
(46.9%). These values were verified for each of the 49 haulage accidents obtained from
MSHA (1999-2010) which showed the transfer system resulting in more accidents than
both the load and unload systems. According to Perrow (1999), ―increasing number of
elements‖ introduces an increased in system element interactions. These interactions
create the complex unintended and unplanned behaviors of systems that result in
accidents.
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12.4 Limitations of the Research
This section discusses the limitations of the research investigation. These limitations
defined the boundaries in which this research study was conducted. As such the results,
analyses, and findings are predicated by these limitations.
12.4.1 Normal Accident Theory
NAT presents challenges because the theory has limited applications. The difficulty
and challenges of NAT is that there is little data that entirely supports either its
proponents or its detractors. There have been numerous debates among other researchers
(Sagan, Hopkins, Leveson) about the validity of NAT but none have published any
literatures dismissing NAT in its entirety. Of course, these debates have pointed out the
positives and limitations of NAT. The other challenge in trying to understand NAT is the
limitation of existing appropriate test methods that can measure system complexity in a
uniform way across all industries that could provide some consistent similar results for a
comparative study of NAT’s applicability. The following highlights several limitations
posed by NAT based on this research investigation:
 Limited in its applicability – NAT has limited applications in industry because of
the difficulty and challenges that NAT presents, thus providing little data to
understand system complexity.
 Definition of complexity and tight coupling – This leads to ambiguities because
the definition of complexity and tight coupling has different meaning and
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applicability among different fields of studies and industrial applications. As a
result, this creates challenges in the development of measurement methods to
quantify the degree of ―complexity‖ and ―tight coupling‖ which lends itself for
debates.
 No single method for measuring complexity – Since every industry is different,
different methods exist and developed for specific applications, thus no uniform
acceptable standard exists to assess NAT’s applicability in a uniform method.
 Limitation of the developed system models - The system models developed for
load, transfer, and unload systems may not represent haulage operations system in
its completeness; the system model only modeled the dynamic behavior and
excluded the static behavior of haulage systems which limits a complete
understanding of haulage operations complexity.
 Application of redundancy in systems – There’s no standard way of analyzing or
comparing redundancies built or implemented in systems of different industries,
thus making it difficult to apply NAT across different industries to understand
complexity unless an acceptable standard or criteria was established (it’s like
comparing oranges to apples among different systems).
 Lack of direct information about system state – This lends itself to difficulty in
understanding NAT and the true behavior of systems because it leads to unplanned
and unintended accidents. This is a characteristic of systems with many complex
interactions such as mining haulage operations.
205
12.4.2 Research Data
The research data limits the extent in which this investigation was conducted in an
effort to evaluate NAT’s applicability. The analyses were based on accident data drawn
from Mine Safety and Health Administration (MSHA) database. MSHA provided a
single source of data for mining haulage accidents. As a result, the data lacked content in
its interpretation and reporting of accidents. For example, MSHA only requires
companies to report fatal accidents and not non-fatal accidents. This lack of content
limits the analysis in order to understand the complexity behind haulage accidents.
Furthermore, the difficulty of mining companies to support this research investigation
by providing accident and near miss data at their mine sites limits the extent this study
was conducted. During the course of the investigation, companies such as Newmont and
Freeport-McMoRan were contacted to seek their support in obtaining detailed safety data
and program information, but none were successful. As a result, their safety programs
could not be analyzed to the level required to determine the effectiveness of their
implemented programs. Analyses were conducted based on data and information
provided in MSHA and mine company public websites.
12.5 Recommendations
The recommendations are based on the results, analyses, and findings of this research
study. The discussions suggest several recommended solutions to this research’s
206
findings. The recommendations provide a way ahead for future research in continuing to
advance NAT and the study of system complexity.
12.5.1 Mitigating Mining Haulage Operations Accidents
The research findings revealed that the transfer system was the most complex system
versus the load and unload systems measured. Since 46.9% of haulage accidents
occurred during the transfer process, a recommended solution is to focus and redirect
resources toward investigating the transfer operations and further analyze the interactive
elements associated with these operations and factors that might contribute to these
accidents.
Additionally, another way for mining companies to reduce haulage accidents is to
consider applying a ―NAT-based‖ risk management approach to mine safety as opposed
to a ―regulatory‖ approach that is currently being practiced in the U.S. mining industry.
A NAT-based risk management approach views risk from a system complexity
perspective and not as independent accident events. A regulatory approach prescribes
ways to mitigate risks through compliance. This type of approach practiced by MSHA
has seen very little effects in preventing mining accidents. ―Mine safety regulations in
the U.S. have always been prescriptive but none of this has resulted in preventing
catastrophic accidents from occurring‖ (Lauriski, 2011). Figure 12-3 illustrates the
differences between NAT versus the regulatory approach. By applying a NAT risk
management approach based on systems perspective, accidents are holistically analyzed
and encourage a proactive and predictive approach to preventing haulage accidents from
207
occurring. This recommendation is also suggested as a future work and builds on this
research study.
Figure 12-3 NAT Approach versus Regulatory Approach
NAT Approach
 Proactive
 Preventive
 Take Control
 Predictive
vs.
Regulatory Approach
 Reactive
 Corrective
 Hope for the Best
 Unknown
NAT Encourages a Proactive & Predictive Approach
12.5.2 Future Research Work
This research study evaluated the applicability of NAT in the mining industry with
respect to mining haulage operations. The results and findings suggest there were lines
of evidence that supports the argument that mining haulage operations is an interactive
complex and coupled system. Therefore, in order to build on this research effort and
further evaluate NAT in mining operations, recommendations for future work are as
follows:

Continuous refinement and development of the mining haulage operations
system models

Development and application of simulation tools for mining haulage
operations
208

Development and application of a NAT-based risk management approach to
mining haulage operations

Application of NAT on the static process of mining haulage operations

Conducting sensitivity analysis on the complexity measurements of mining
haulage operations
By continuing to refine and develop the system models for haulage operations, it will
lead to a further understanding of the behavior of haulage systems. Continuous
refinement of these models will increase the fidelity of haulage systems when measuring
its complexity. Additionally, the models developed for this study utilized simple block
diagram concepts. There are other modeling techniques that exist and should be explored
in order to obtain models that add fidelity in understanding system state of haulage
operations. These techniques are left to future researchers to decide on appropriate
modeling techniques that can build on this research study.
The development and application of simulation techniques that can simulate haulage
operations system behaviors is also a recommended future work. Simulation provides a
way to ―imitate or mimic‖ real world haulage operations over time. Simulation shows
the potential real effects of alternative conditions and courses of action which can help
provide insights into haulage accidents. Applying system models that were developed for
this research study along with simulation tools that can simulate system state behaviors,
results may reveal haulage complexity and root causes of accidents in ways that are not
known from MSHA investigation reports. By simulating haulage operations and its
system state behaviors, it may have the potential to determine the true complexity nature
209
behind haulage accidents which can further lead to engineering solutions that will
mitigate future haulage accidents from occurring.
Another recommended future work is to apply a NAT-based risk management
approach to mitigating haulage accidents. Recognizing that mining companies have
implemented some sort of risk management process in their organizations, however, no
existing published literature has shown that the mining industry has implemented a risk
management based on NAT. Developing and implementing a NAT-based risk
management approach helps to holistically identify, analyze, and mitigate haulage
accidents from the perspective of systems complexity and coupling. This NAT-based
risk management approach leads to a proactive and predictive approach to mine safety.
Given that 40% (Aldinger and Keran, 1994) of mine accidents result in haulage fatalities,
perhaps it is time that the mining industry move beyond a reactive and prescriptive
approach of increasing regulations and fines after every accident investigation (which
does little to prevent accidents) and towards a more proactive and predictive approach to
mine safety, which is what NAT aims to achieve (Lauriski, 2011).
This research study only evaluated the dynamic process of haulage operations and did
not consider its static state. Future work should consider evaluating this process (static)
because it may reveal additional understanding of haulage system complexity that was
not understood from only evaluating the dynamic process. Additionally, the static
process may also reveal the degree of complexity necessary before accidents start to
occur. Finally, conducting sensitivity analysis of complexity measurements is also
recommended as a future work in developing additional complexity methods because the
analysis provides a way to validate the robustness of these measurement methods.
210
12.6 Conclusions
This research study investigated the applicability of NAT in the mining industry and
concludes that mining operations are interactively complex and tightly coupled. Mining
operations, which are similar to space and nuclear systems, are so complex that it is
difficult to anticipate accidents. As such every incident cannot be predicted, thus
accidents occur. Recent accidents such as Sago Mine (2004) and Upper Big Branch
(2010) have shown that additional regulatory measures and fines are not enough since
accidents continue to occur in the mining industry (Lauriski, 2011). This makes NAT
relevant in that NAT views mining accidents from a systems perspective and looks at
complexity as a contributing factor to accidents as oppose to accidents resulting from a
single independent event with a cause-effect relationship. This explains why
unanticipated behaviors associated with complex systems result in accidents. The ability
to develop some measure of predictability is essential in managing and mitigating mining
accidents. By understanding system complexity, NAT serves as a basis for designing and
building reliable and effective safety systems to prevent mining haulage accidents.
Therefore, NAT is a plausible theory and is applicable to the mining industry because
NAT introduces a proactive and predictive systems approach to mine safety that is
currently not practiced.
211
APPENDIX A
TABLE OF TERMINOLOGIES
Terminology
Complexity
Element
Human Error
Haulage Operation
Complexity
Haulage Operation Tight
Coupling
Heuristic
Hierarchy
High Complexity
Interactive Complexity
Linear Systems
Low Complexity
Loosely Coupled System
Loose Coupling
Modeling
Node
Non-linear Systems
Tightly Coupled System
Tight Coupling
Subsystem
System
Definition
Systems composed of many parts, elements, or components which maybe
the same or different and interconnected together in a more or less
complicated fashion and are difficult to understand or explain
Building blocks of any system of interest
Any one set of human actions that exceed some limit of acceptability
The interactive components of operators, roads and road conditions,
equipment, open pits, work environment conditions, traffic flow and signs,
loading, transfer, and unloading processes, equipment functions such as
maintenance and inspections, and policies, controls, and procedures all
integrated and operating together in a haulage operational environment
Multiple factors that manifest itself in the working environment,
equipment, humans, organizational management, and processes which
offers little slack or redundancy in a haulage operational environment
A process of gaining knowledge by experience or some desired result by
intelligent guesswork rather than by following some pre-established
formula
A framework for the structure of any system of interest
Multiple complexity factors
Degree in which parts are connected and interact within the system
Single purpose systems with less functionality and feedback loops
Single complexity factor
Flexible systems that can respond to external demands without causing
catastrophic failure in the system
Single coupling factor
A physical, mathematical, or otherwise logical representation of a system
entity, phenomenon or process
Connection points between different elements or components of the
system
Systems composed of many parts, elements, or components which maybe
the same or different and interconnected together in a more or less
complicated fashion
Time dependent systems with no slack or buffer
Multiple coupling factors
A lower level system between or within a system
A composite, at any level of complexity, of personnel, procedures,
materials, tools, equipment, facilities, and software in which the elements
of this composite entity are used together in the intended operational or
support environment to perform a given task or achieve a specific purpose,
support, or mission requirement
212
APPENDIX B
INTERACTIVE COMPLEXITY ANALYSIS
Associated Interactive Component
Year
1999
1999
1999
1999
Mineral
Type
M/NM
M/NM
M/NM
M/NM
Mine Company
Accident (Generic Name)
1
2
3
4
Description of Complexity Factors
Associated
Systems
A
The accident was caused by failure to follow
established rules governing traffic control for
the safe movement of mobile equipment at
the mine. A contributing factor was the
inability of the haul truck driver to see the
pickup due to blind spots.
LOAD
B
The direct causes of the accident were failure
to maintain berms, bumper blocks or other
impeding devices to eliminate the hazard of
over traveling the dump site and failure to
examine conditions as warranted.
UNLOAD
C
The accident occurred because the mine
operator failed to maintain the truck in safe
operating condition. The service braking
system and the parking brake were not
maintained to allow the driver to control the
truck. The transmission linkage was worn and
out of adjustment. Failure to inspect mobile
equipment for safety defects was a
contributing factor. Failure to wear the
seatbelt contributed to the severity of the
accident.
UNLOAD
D
The primary cause of the accident was
management's failure to design and construct
the roadway and dumping facilities with
materials capable of supporting the loads to
which they were subjected. Failure to provide
adequate berms, proper road and dumping
width to accommodate the equipment using
the facilities, and the proper maintenance of
the front-end loader were contributing
factors. Failure to wear a seat belt may have
contributed to the severity of the accident.
UNLOAD
Human (Host) Machine (Agent)
Environment
1
1
1
Haulage Operations
Complexity Factors
High
Low
Haulage Operations
Coupling Factors
Tight
2
2
2
2
3
1
4
Loose
1
2
213
APPENDIX B – CONTINUED
Associated Interactive Component
Year
Mineral
Mine Company
Type Accident (Generic Name)
1999
M/NM
1999
2000
2000
M/NM
M/NM
M/NM
5
6
7
8
Description of Complexity Factors
Associated
Systems
E
The primary cause of the accident was the
failure to set the park brake before exiting the
operator's cab.
TRANSFER
F
The primary cause of the accident was
management's failure to maintain the scraper
in safe operating condition. The service
braking system and the parking brake were
not maintained to allow the driver to control
the scraper. Contributing factors were
management's failure to assure that adequate
pre-operation inspections had been
conducted on mobile equipment to identify
safety defects and failure to assure that
berms of mid-axle height were provided to the
outer edge of the haul ramp. Failure to assure
that seat tether straps were installed
contributed to the severity of the accident.
LOAD
G
The accident occurred when the victim lost
his balance and fell into the water while
dismounting the haul truck. The investigation
was unable to determine why the victim
became disoriented and drove in the wrong
direction into a non-working area of the mine
and into a water-filled sump.
TRANSFER
H
The root cause of the accident was
management's failure to establish effective
traffic control for the safe movement of
mobile equipment. A warning had not been
sounded prior to moving the truck, which
contributed to the accident. Other
contributing factors were impaired operator
visibility and failure to provide all appropriate
Part 48 training.
UNLOAD
Human (Host) Machine (Agent)
Environment
Haulage Operations
Complexity Factors
High
1
Low
Haulage Operations
Coupling Factors
Tight
1
1
Loose
1
4
2
1
2
3
1
4
2
214
APPENDIX B – CONTINUED
Associated Interactive Component
Year
2000
2000
2000
2000
Mineral
Mine Company
Type Accident (Generic Name)
N/NM
N/NM
N/NM
N/NM
9
10
11
12
Description of Complexity Factors
Associated
Systems
Human (Host) Machine (Agent) Environment
Haulage Operations Haulage Operations
Complexity Factors Coupling Factors
High
I
The root cause of the accident was mine
management's failure to enforce traffic
controls at the crossing. A contributing
factor was the lack of proper turning lanes
and warning signs on state highway 114.
TRANSFER
1
2
J
The cause of the accident was the failure to
ensure that the loader had an adequate
service braking system capable of stopping
and holding the loader on the grade it was
traveling. Contributing to the severity of the
injuries was the lack of structural strength of
the ROPS.
TRANSFER
1
2
K
The root cause of the accident was
management's failure to ensure that the
safety equipment installed on the loader was
maintained in safe, operational condition.
Contributing to the accident was
management's failure to ensure adequate
equipment inspections were conducted prior
to placing equipment into service.
TRANSFER
1
2
L
The cause of the accident was failure of the
company to ensure that policy was followed
and customers remained in the cabs of the
trucks at all times while being loaded.
LOAD
1
Low
Tight
Loose
1
2
1
1
1
215
APPENDIX B – CONTINUED
Associated Interactive Component
Year
2000
2001
2001
2001
Mineral
Type
N/NM
M/NM
M/NM
M/NM
Mine Company
Accident (Generic Name)
13
14
15
16
Description of Complexity Factors
Associated
Systems
M
The root cause of the accident was the failure
to establish procedures requiring systematic
examination and maintenance of the truck's
brake systems. As a result, neither the
service brakes nor the parking brakes were
maintained to allow the driver to control the
truck. Failure to remove the water covering
the quarry haul road created a corrosive
environment to the truck's braking
components and affected the truck's ability to
stop effectively.
LOAD
N
The primary cause of the accident was
material being drawn from the bin while the
victim was inside. Contributing to the
severity of the accident was failure to wear a
safety belt and line with a second person
tending the line. Failure of effective
communication between the victim and the
employee lowering the material through the
discharge chute was also a contributing
cause.
LOAD
O
The primary cause of the accident was failure
to properly maintain the truck's service brake
system in operable condition. The following
root causes were identified: failure to
establish procedures requiring safety defects
to be reported, recorded and promptly
corrected and the failure to train equipment
operators in the correct operation of the haul
trucks.
TRANSFER
P
The root cause of the accident was the failure
to establish procedures that required the
proper capacity tractor to transport the
excavator. Maintenance problems with the
braking systems and the use of an undersized
tractor contributed to the cause of the
accident.
TRANSFER
Human (Host) Machine (Agent)
Haulage Operations
Complexity Factors
Environment
High
1
2
2
3
2
1
3
2
1
3
2
1
Low
Haulage Operations
Coupling Factors
Tight
Loose
216
APPENDIX B – CONTINUED
Associated Interactive Component
Year
2002
2002
2002
2002
Mineral
Mine Company
Type Accident (Generic Name)
M/NM
M/NM
M/NM
M/NM
17
18
19
20
Haulage Operations Haulage Operations
Complexity Factors Coupling Factors
Associated
Systems
Human (Host) Machine (Agent) Environment
High
Q
The root cause of the accident was the failure
to establish procedures requiring examination
and prompt maintenance of the loader's
service brake system. The accident occurred
because the loader had defective service
brakes. The following contributing factors
were identified: failure to provide and
maintain seat belts, the lack of adequate
training, failure to maintain the ramp free of
rocks and ruts.
TRANSFER
1
4
R
The root cause of the accident was the failure
to establish procedures that required
examination and prompt maintenance of the
loader's service brake system. The cause of
the accident was failure to maintain the
loader's service brake system in operable
condition.
TRANSFER
1
S
The root cause of the accident was restricted
visibility due to the heavy rain that was
falling at the time the accident occurred.
Contributing to the accident may have been
the inability of the victim to hear the loader
because of the rain hitting the tin roof on the
load-out building.
LOAD
1
2
2
T
The root cause of the accident was the failure
to establish procedures that required prompt
maintenance of the defective power take off
control cable. The cause of the accident was
failure to secure the raised bed of the truck
prior to performing work under it.
TRANSFER
1
2
2
Description of Complexity Factors
Low
Tight
2
1
2
Loose
217
APPENDIX B – CONTINUED
Associated Interactive Component
Year
2002
2002
2002
2002
Mineral
Type
M/NM
M/NM
M/NM
M/NM
Mine Company
Accident (Generic Name)
21
22
23
24
Description of Complexity Factors
Associated
Systems
U
The cause of the accident was the truck
driver's inability to maintain sight of the
victim while backing the truck. Root causes
included failure to establish procedures
requiring truck drivers to stop backing when
they loose sight of persons on foot behind
their vehicles and the failure to require
persons on foot to leave the area when
mobile equipment is backing toward them.
LOAD
V
The accident occurred because the truck's
braking systems had not been maintained in
functional condition. The following root
causes were identified: failure to implement a
safety process; failure to promptly correct
safety defects; and the failure to inspect
mobile equipment thoroughly prior to
operating it. The failure to construct a berm
on the elevated outer edge of the plant
roadway and the failure to require mobile
equipment operators to wear seat belts were
contributing causes.
TRANSFER
W
The victim was walking with her head down
as she approached the scale house and did
not realize she was in the loader operator's
blind spot. These conditions contributed to
her being struck and fatally injured by the
front-end loader. The root cause of the
accident was the failure to make eye contact
with the loader operator.
LOAD
X
The cause of the accident was failure to
maintain adequate berms along the elevated
edge of the roadway above the pit. A
contributing cause was the failure to properly
maintain the front brakes of the loader. Root
causes included the following: failure to
follow established procedures that required
berms to be maintained along the elevated
edges of roadways, failure to identify the
narrow work area as a hazard and restrict
access to it, and failure to establish
procedures requiring periodic examinations of
mobile equipment brakes and the prompt
repair of brake defects.
LOAD
Human (Host) Machine (Agent)
Environment
1
1
Haulage Operations
Complexity Factors
High
Low
Haulage Operations
Coupling Factors
Tight
3
2
6
2
1
1
1
3
Loose
1
2
218
APPENDIX B – CONTINUED
Associated Interactive Component
Year
Mineral
Type
Accident
Mine Company
(Generic Name)
2002
M/NM
25
Y
2003
M/NM
26
Description of Complexity Factors
The accident occurred because the base of
the dumpsite became liquefied and collapsed
under the weight of the truck and its load.
The large amounts of material dumped the
prior week created instability. The root
causes identified during the investigation
included the following: Failure of the fine
sand and clay material to adequately drain,
and the inability to recognize the effect that
10 to 15 feet of water already in the bottom of
the dump area had on the stability of the fine
sand and clay tailings that comprised the
base.
Associated
Systems
UNLOAD
Z
The accident occurred because a loading
ramp being used by a front-end loader was
also used as a walkway. The loader operator
didn't realize that a person was on foot in the
area because his view was restricted.
Pedestrians could access the control booth
from the same ramp that large mobile
equipment used. The layout of roads and
ramps to the plant promoted foot traffic in the
same locations where heavy equipment
traveled.
UNLOAD
UNLOAD
TRANSFER
2003
M/NM
27
AA
The accident occurred because the safety
prop had not been positioned between the
raised bed and the truck frame before the
miner climbed on the trailer frame to add
brake fluid to the master cylinder. The wiring
harness from the tractor to the trailer was
damaged. This caused a malfunction in the
electrical system that prevented the dump
bed control switch from working properly.
Due to the electrical malfunction, when the
control lever was released after raising the
bed, the hydraulic control valve would go to
the float position and not return to the hold
position, allowing the bed to come down. The
pre-operational examination of this haul unit
failed to identify the defective brake line
fitting resulting in the operator having to
routinely add brake fluid.
2004
M/NM
28
BB
The accident was caused by the failure to
maintain all braking systems on the truck in
functional condition. Braking systems on the
truck was defective.
Human (Host) Machine (Agent)
1
1
1
Haulage Operations
Complexity Factors
Low
Haulage Operations
Coupling Factors
Environment
High
1
3
2
2
3
4
3
1
Tight
2
Loose
219
APPENDIX B – CONTINUED
Associated Interactive Component
Year
2004
2004
2005
2005
Mineral
Mine Company
Type Accident (Generic Name)
M/NM
M/NM
M/NM
M/NM
29
30
31
32
Description of Complexity Factors
Associated
Systems
Human (Host) Machine (Agent) Environment
CC
Management policies and controls were
inadequate and failed to ensure that the
victim had received training in the health and
safety aspects and safe operating procedures
regarding the DUX, Model TD-26, haulage
unit and the batch plant conveyor load out.
LOAD
DD
The accident was caused by the failure to
maintain the braking systems on the truck in
a functional condition. Maintenance on the
brake system was deficient and the truck had
not been repaired or removed from operation.
TRANSFER
1
EE
The accident occurred because policies,
standards, and controls were inadequate and
failed to ensure that the defective throttle
control, steering assembly, and improper tire
pressure had been promptly corrected on the
tractor. The roadway grade and S turn, in
conjunction with the speed, contributed to
the operator's failure to maintain control of
the tractor.
TRANSFER
1
FF
The accident occurred because management
did not have any policies, procedures, or
controls in place to ensure that equipment
operators received training before operating
equipment. The victim did not maintain
control of the skid steer loader he was
operating. The victim was not wearing a seat
belt at the time of the accident.
UNLOAD
1
1
Haulage Operations Haulage Operations
Complexity Factors Coupling Factors
High
Low
Tight
2
Loose
1
1
1
2
3
2
3
220
APPENDIX B – CONTINUED
Associated Interactive Component
Year
Mineral
Type
2005
M/NM
2005
2005
2005
M/NM
M/NM
M/NM
Mine Company
Accident (Generic Name)
33
34
35
36
GG
Description of Complexity Factors
The accident occurred because the task
training procedures for the newly hired miner
were inadequate to ensure he could safely
operate the haul truck. The trainee operator
had no previous experience operating heavy
equipment. Operator had not completed his
40 hour new miner training before being
assigned work duties. The operator's manual
for the haul truck and other written
procedures were available but were not used
for task training.
Associated
Systems
Human (Host) Machine (Agent)
Environment
Haulage Operations
Complexity Factors
High
Low
Haulage Operations
Coupling Factors
Tight
TRANSFER
1
2
2
HH
The accident occured because management
policies and controls were inadequate. The
mine operator failed to ensure that a berm or
similar impeding devices were maintained on
the elevated edges of the stockpile. The pan
scraper brakes had not been maintained and
the equipment operator was not wearing the
provided seat belts which contributed to the
severity of the accident.
UNLOAD
1
3
2
II
The accident occurred because the task
training procedures used were inadequate
and did not ensure that the victim could
safely operate the haul truck. He had no
previous experience operating mobile
equipment. Competent trainers were not
provided because the truck drivers who task
trained the victim were unfamiliar with the
safe operating procedures of the haul trucks.
They lacked knowledge of the retarder and
braking systems provided on the haul trucks.
TRANSFER
1
2
2
JJ
The accident occurred because the scraper's
braking systems were not maintained in a
functional condition; policies, standards, and
procedures were inadequate because
equipment examinations had not been
routinely performed and the equipment had
not been repaired or removed from service.
TRANSFER
1
1
Loose
1
221
APPENDIX B – CONTINUED
Associated Interactive Component
Year
2005
2005
2006
2006
Mineral
Type
M/NM
M/NM
M/NM
M/NM
Mine Company
Accident (Generic Name)
37
38
39
40
Description of Complexity Factors
Associated
Systems
Human (Host) Machine (Agent)
Environment
KK
The accident occurred because policies,
standards, and controls were not in place to
ensure that a berm or similar impeding device
was maintained at the dump site; the
dumping location was not visually inspected
to identify signs of possible unstable ground
conditions prior to directing trucks to dump.
UNLOAD
LL
The accident occurred because the task
training procedures for the newly hired miner
were inadequate; the victim did not
demonstrate the skills to safely control the
haul truck where he was assigned to operate
it.
TRANSFER
MM
The accident occurred because safe
operating procedures were not established to
ensure that the extraction area was safe to
resume operations after being flooded and
idle. An examination of the extraction area
was not completed on the previous day or
the day of the accident. There were no
barricades or warning signs installed in mine
extraction area to identify the location of the
water filled pit and warn miners of the hazard.
Management did not conduct a risk
assessment to identify all possible hazards
and establish safe work procedures for the
extraction area after the flood and before
work commenced.
LOAD
1
NN
The accident occurred because mine
management failed to establish procedures
for safe movement of mobile equipment
through a congested area. No risk
assessment was conducted to ensure that
persons could safely perform tasks while
working near mobile equipment traveling in
the area to be paved.
LOAD
1
Haulage Operations
Complexity Factors
High
1
1
Haulage Operations
Coupling Factors
Low
Tight
1
2
1
2
1
2
1
Loose
2
222
APPENDIX B – CONTINUED
Associated Interactive Component
Year
2007
2007
2007
2008
Mineral
Mine Company
Type Accident (Generic Name)
M/NM
M/NM
M/NM
M/NM
41
42
43
44
Description of Complexity Factors
Associated
Systems
OO
The accident occurred because management
policies and procedures failed to ensure that
all braking systems on the truck were
maintained in a functional condition. The
truck was operating on a road where berms
were not maintained along the elevated
portions, the engine brake was defective, and
the truck was traveling on grades steeper
than those recommended by the
manufacturer. The victim was not wearing a
seat belt which contributed to the severity of
his injuries.
TRANSFER
PP
The accident occurred because the victim did
not maintain control of the truck. The victim
was not wearing a seat belt which
contributed to the severity of his injuries.
TRANSFER
QQ
The accident occurred because the truck
driver did not maintain control of the truck as
he drove across the bridge. The right front
tire struck the guard rail causing the tire to
lose air and adversely affected the truck
driver's ability to steer the truck.
TRANSFER
RR
The accident occurred because management
policies and procedures used to dump and
load out material at the stockpile was
inadequate. The angle of repose at the
stockpile was steep because a front-end
loader removed material at the bottom making
the dump location unstable.
UNLOAD
Human (Host) Machine (Agent) Environment
1
Haulage Operations Haulage Operations
Complexity Factors Coupling Factors
High
Low
Tight
3
3
1
2
2
1
2
2
1
1
2
Loose
223
APPENDIX B – CONTINUED
Associated Interactive Component
Year
2009
2009
Mineral
Type
M/NM
M/NM
Accident
45
46
Mine Company
(Generic Name)
Description of Complexity Factors
SS
The accident occurred because management
policies and procedures did not ensure that
foot traffic was adequately controlled. Signs
or signals that warned of hazardous
conditions had not been placed at all
appropriate locations at the mine.
TT
The accident occurred because the truck
driver did not maintain control of the haul
truck he was operating. The failure of the
driver to wear the provided seat belt
contributed to the severity of his injuries.
The accident occurred because management
did not have policies and procedures that
provided for the safe movement of mobile
equipment in an area with pedestrian and
vehicular traffic. Mine management also
failed to ensure that mobile equipment
operators sounded a warning that was
audible above the surrounding noise level
prior to moving to warn all persons who
could be exposed to a hazard from the
equipment. Additionally, mine management
did not ensure that the roadway in this area
was maintained at a width sufficient to allow
for safe operation. The site-specific hazard
awareness training employed at this mine did
not protect persons on site by addressing the
appropriate subjects regarding the hazards
associated with mobile equipment operating
near pedestrians. The victim may also have
been distracted by the cell phone he was
holding when the accident occurred.
Associated
Systems
LOAD
1
Low
Tight
1
2
UNLOAD
1
3
3
1
UU
2010
M/NM
48
VV
The accident occurred because the victim
approached the haul truck and did not
communicate with the haul truck operator of
his presence.
TRANSFER
WW
The accident occurred because mine
management policies, procedures, and
controls were inadequate and did not protect
persons at the loading dock. Site-specific
hazard training to the truck drivers was not
effectively provided to the truck drivers. The
truck drivers were not made aware of specific
mine hazards. The loading docks were not
monitored to ensure that foot traffic was
adequately controlled.
LOAD
TOTAL =
1
1
17
6
26
2
Loose
1
2
47
49
High
1
M/NM
M/NM
Environment
Haulage Operations
Coupling Factors
TRANSFER
2010
2010
Human (Host) Machine (Agent)
Haulage Operations
Complexity Factors
1
2
224
REFERENCES
Abdelhamid, T. S. and Everett, J. G. (2000). Identifying Root Causes of Construction
Accidents. Journal of Construction Engineering and Management. Pages 52-60.
Aftosmis et al. (2004). STS-107 Investigation Ascent CFD Support.
American Institute of Aeronautics and Astronautics (AIAA) Paper 2004-2226.
Aldinger, J.A. and C. Keran. (1994). A Review of Accidents During Surface Mine
Mobile Equipment Operation. In Proceedings: 25th Annual Institute on Mining Health, Safety and
Research. Blacksburg, VA: Department of Mining and Minerals Engineering, Virginia Polytechnic
Institute and State University. Pages 99-108.
Anderson et al. (2010). The Heinrich Accident Triangle—Too Simplistic A Model For
HSE Management in the 21st Century? SPE International Conference on Health, Safety and
Environment in Oil and Gas Exploration and Production, Rio de Janeiro, Brazil.
Austin et al. (2002). Modeling and Managing Project Complexity. International
Journal of Project Management, 20. Pages 191-198.
Baccarini, D., (1996). The Concept of Project Complexity, a Review. International
Journal of Project Management 14(4). Pages 201-204.
Barrick, <http://www.barrick.com/CorporateResponsibility/Safety-Health/default.aspx>,
2011.
Bellinger, Gene. (2004). Systems Thinking, A Disciplined Approach.
<http://www.systems-thinking.org/stada/stada.htm>
Bloom, Mitchel, Wolf’s personal communication, March 18, 1998.
Boldt, C.M.K. and Randolph, Robert. (1996). Safety Analysis of Surface Haulage
Accidents. U.S. Department of Energy, Pittsburgh and Spokane Research Centers.
< http://www.cdc.gov/niosh/mining/pubs/pubreference/outputid145.htm>
Brush, Patricia A, et al. (2007). Preventing Your Next Environmental Incident –
Applying Behavior Based Safety and Pre-Task Analysis Tools to the Environmental Arena. Bechtel
Corporation. Houston, TX. Pages 1-12.
Carter, RA. (2002). Miner Training Turns to Technology. Coal Age. 107:(3):31-34.
Coleman, Patrick, Martini, Laura, and Ruff, Todd. (2010). Machine-related Injuries in
the US Mining Industry and Priorities for Safety Research. National Institute for
Occupational Safety and Health (NIOSH), Office of Mine Safety and Health Research, 315 E.
Montgomery Ave., Spokane, WA 99207, USA
<http://www.cdc.gov/niosh/mining/pubs/pubreference/outputid3341.htm>
Department of Labor, <http://www.dol.gov>, 2011.
225
Dulac, Nicolas, Leveson, Nancy, and Marais, Karen. (2004). Beyond Normal Accidents
and High Reliability Organizations: The Need for an Alternative Approach to
Safety in Complex Systems. MIT. Pages 1- 2, 4, 9.
Edmonds, B. (1999). Syntactic Measures of Complexity, Thesis of the University of
Manchester for the degree of doctor of philosophy in the faculty of arts.
Ericson, Clifton A. II. (2005). Hazard Analysis Techniques for System Safety. John
Wiley & Sons, Inc., Hoboken, NJ. Pages 6-8, 16, 17.
Federal Mine Safety and Health Review Commission. (2005). Civil Penalty Proceeding,
Secretary of Labor, Mine Safety and Health Administration vs. Jim Walter Resources, Inc., November 1,
2005. Docket No. SE 2003-160; AC No.01-01322-00004. Washington D.C.
Flood, R. and Carson, E. (1988). Dealing with Complexity - An Introduction to the
Theory and Application of System Science. Plenum Press, New York.
Foster, Patrick J. (1997). Application of Risk Assessment in the Mining Industry. The
University of Nottingham, Department of Mineral Resources Engineering. Page
235.
Freeport-McMoran, <http://www.fcx.com>, 2011/12.
Haken, Hermann. (2006). Information and Self-organization: A Macroscopic Approach
to Complex Systems. Springer, 3rd Edition. Berlin Heidelberg, Germany.
Heinrich, H. W. (1959). Industrial Accident Prevention. McGraw-Hill, New York.
Huang, Yu-Hsing. (2007). Having a New Pair of Glasses Applying Systemic Accident Models on Road
Safety. Linköping Studies in Science and Technology,
Dissertation 1051, Department of Computer and Information Science, Linköpings
Universitet, SE-581 83. Linköping, Sweden. Pages 39-46.
Independent and Dependent Variables (I&DV),
<http://www.uncp.edu/home/collierw/ivdv.htm>, May 10, 2011.
Karmis, Michael. (2001). Mine Health and Safety Management. Society for Mining,
Metallurgy, and Exploration, Inc., Littleton, Colorado. Page 399.
Kecojevic and M. Radomsky. (2004). The Causes and Control of Loader-and Truckrelated Fatalities in Surface Mining Operations. Injury Control and Safety Promotion, Vol 11, No. 4, pp.
239-251.
Kecojevic et al. (2007). An Analysis of Equipment-Related Fatal Accidents in U.S.
Mining Operations: 1995-2005. Safety Science, Vol. 45, pp. 864-874.
Kim, Yong-Suk. (1999). A System Complexity Approach for the Integration of Product
Development and Production System Design. Massachusetts Institute of
Technology. Pages 23, 44-47, 53-55.
226
Khurana, A. (1999). Managing Complex Production Processes. Sloan Management
Review 40 (2), 85-97.
Klir, G. (1985). Complexity: Some General Observations. Systems Research, 2(2).
Kossiakoff, A. and Sweet, W. (2003). Systems Engineering Principles and Practice.
John Wiley and Sons, Inc. Hoboken, New Jersey. Pages 409-411.
La Porte, T.R. and Consolini, P.M. (1991). Working in Practice But Not in Theory:
Theoretical Challenges of High Reliability Organizations. Journal of
Contingencies and Crisis Management, Volume 2, Number 4, Pages 221-227.
Lauriski, D. and Yang, B. (2011). Is the System of Mine Safety and Health in the U.S.
Broken? Mining Engineering, Society for Mining, Metallurgy and Exploration, Inc., Vol. 63, No. 7, pp.
77-82.
Leigh et al. (2004). Cost of Occupational Injury and Illness across Industries.
Scandinavian Journal of Work Environment and Health, Vol. 30 (3), pp. 199-205.
Leplat, J., and Cuny, X. (1979). Les Accidents du Travail. Coll. ―Que Sais-Je‖ No.
1591, Paris : P.U.F., pg. 126
Leplat, Jacques. (1987). Occupational Accident Research and Systems Approach. New
Technology and Human Error, pages 181-191, John Wiley & Sons, New York,
1987.
Leveson, Nancy G. (1995). Safeware: System Safety and Computers. Addison-Wesley
Publishing Company. Pages 8, 185-188.
MacDuffie et al. (1996). Product Variety and Manufacturing Performance : Evidence
from the International Automotive Assembly Plant Study. Management Science 42 (3), 350-369.
Marle, F. (2002). Modele d’information et methods pour aider a la prise de decision en
management de projects, These en Genie Industriel de l’Ecole Centrale Paris.
Marley, Kathryn A. (2006). Mitigating Supply Chain Disruptions: Essays on Lean
Management, Interactive Complexity and Tight Coupling. ProQuest Information and Learning
Company. Ann Arbor, MI. Pages 1, 64, 66.
Mckinney et al. (2002). Report of Investigation Fatal Underground Coal Mine
Explosions September 23, 2001, No.5 Mine Jim Walter Resources, Inc. Mine Safety and Health
Administration.
<http://www.msha.gov/fatals/2001jwr5/ft101c203light.pdf>
Melfi, John. (2012). Email Correspondence on Safety Program Conducted at FreeportMcMoRan.
Merriam-Webster, <http://www.merriam-webster.com/dictionary/probability>, 2011.
Merriam-Webster, <http://www.learnersdictionary.com/search/dysfunction>, 2012.
227
Mine Safety and Health Administration (MSHA). (1999). Fatal grams and Fatal
Investigation Reports Coal Mines.
<http://www.msha.gov/FATALS/FABC1999.asp>
Mine Safety and Health Administration (MSHA). (1999). Fatal grams and Fatal
Investigation Reports Metal and Non-Metal Mines.
< http://www.msha.gov/FATALS/FABM1999.asp>
Mine Safety and Health Administration (MSHA). (2000). Fatal grams and Fatal
Investigation Reports Coal Mines.
<http://www.msha.gov/FATALS/FABC2000.asp>
Mine Safety and Health Administration (MSHA). (2000). Fatal grams and Fatal
Investigation Reports Metal and Non-Metal Mines.
< http://www.msha.gov/FATALS/FABM2000.asp>
Mine Safety and Health Administration (MSHA). (2000 – 2007). Fatal Alert Bulletins,
Fatal grams, and Fatal Investigation Reports.
<http://www.msha.gov/fatals/fab.htm>
Mine Safety and Health Administration (MSHA). (2003). Quarterly Employment and
Coal Production, Accidents/Injuries/Illnesses Reported to MSHA under 30 CFR Part 50, 1991-2000.
Denver, CO: U.S. Department of Labor, Mine Safety and Health Administration, Office of Injury and
Employment Information.
<http:// www.msha./STATS/PART50/p50y2k/p50y2k.HTM>
Mine Safety and Health Administration (MSHA). (2005). Fatal grams and Fatal
Investigation Reports Coal Mines.
<http://www.msha.gov/FATALS/FABC2005.asp>
Mine Safety and Health Administration (MSHA). (2005). Fatal grams and Fatal
Investigation Reports Metal and Non-Metal Mines.
< http://www.msha.gov/FATALS/FABM2005.asp>
Mine Safety and Health Administration (MSHA). (2006). Fatal grams and Fatal
Investigation Reports Coal Mines.
<http://www.msha.gov/FATALS/FABC2006.asp>
Mine Safety and Health Administration (MSHA). (2006). Fatal grams and Fatal
Investigation Reports Metal and Non-Metal Mines.
< http://www.msha.gov/FATALS/FABM2006.asp>
Mine Safety and Health Administration (MSHA). (2007). Appendix Y Glossary of
Mining Terms.
< http://www.msha.gov/Fatals/2007/CrandallCanyon/AppendixY.pdf>
Mine Safety and Health Administration (MSHA). (2007). Fatal grams and Fatal
Investigation Reports Coal Mines.
<http://www.msha.gov/FATALS/FABC2007.asp>
228
Mine Safety and Health Administration (MSHA). (2007). Fatal grams and Fatal
Investigation Reports Metal and Non-Metal Mines.
< http://www.msha.gov/FATALS/FABM2007.asp>
Mine Safety and Health Administration (MSHA). (2008). Fatal grams and Fatal
Investigation Reports Coal Mines.
<http://www.msha.gov/FATALS/FABC2008.asp>
Mine Safety and Health Administration (MSHA). (2008). Fatal grams and Fatal
Investigation Reports Metal and Non-Metal Mines.
<http://www.msha.gov/FATALS/FABM2008.asp>
Mine Safety and Health Administration (MSHA). (2009). Fatal grams and Fatal
Investigation Reports Coal Mines.
<http://www.msha.gov/FATALS/FABC2009.asp>
Mine Safety and Health Administration (MSHA). (2009). Fatal grams and Fatal
Investigation Reports Metal and Non-Metal Mines.
<http://www.msha.gov/FATALS/FABM2009.asp>
Mine Safety and Health Administration (MSHA). (2009). Coal Daily Fatality Report, 5Yr Summary.
<http://www.msha.gov/stats/charts/coal2009yearend.asp>
Mine Safety and Health Administration (MSHA). (2009). Metal/Non-Metal Daily
Fatality Report, 5-Yr Summary.
<http://www.msha.gov/stats/charts/mnm2009yearend.asp>
Mine Safety and Health Administration (MSHA). (2010). Accident Classification.
<http://www.msha.gov/Fatals/AccidentClassifications.asp>
Mine Safety and Health Administration (MSHA).
<http://www.msha.gov/stats/statinfo.htm#Review>,
<http://www.msha.gov/Fatals/AccidentClassifications.asp>,
<http://www.msha.gov/stats/charts/mnm2010yearend.asp>,
<http://www.msha.gov/stats/charts/coal2010yearend.asp>, 2011.
Morel, B., and Ramanujam, R. (1999). Through the Looking Glass of Complexity: The
Dynamics of Organizations as Adaptive and Evolving Systems. Organization Science 10 (3), 278-293.
Muduli, P. and Yegulalp, T. (1996). Modeling Truck-Shovel Systems as Closed
Queuing Network with Multiple Job Classes. Int. Trans. Opl Res, Vol. 3, No. 1. Pages 89-98.
Newmont, <http://www.newmont.com/about/health-safety>,
<http://www.newmont.com/features/our-values-features/Rapid-Response-System-Continues-toEvolve>, 2011.
Novak, S. and Eppinger, S.D. (2001). Sourcing by Design: Product Complexity and the
Supply Chain. Management Science 47 (1), 189-204.
Ossimitz, Gunther. (1997). The Development of Systems Thinking Skills Using System
Dynamics Modeling Tools. Universität Klagenfurt. Pages 65-67.
229
Pazzaglia, Federica, Shrivaastava, Samir, and Sonpar, Karen. (2009). Normal Accident
Theory versus High Reliability Theory: A Resolution and Call for an Open Systems View of Accidents.
Human Relations, Volume 62. Pages 1358, 1365, 1366, 1368.
Peliti, L. and Vulpiani, A. (1987/1988). Measures of Complexity. Proceedings of the
Conference held in Rome and Springer-Verlag.
Perrow, Charles. (1967). A Framework for the Comparative Analysis of Organizations.
American Sociological Review, 32 (1). Page 198.
Perrow, Charles. (1986). Complex Organizations (3rd edition). New York, McGrawHill. Page 141.
Perrow, Charles. (1994). Accidents in High-Risk Systems. Technology Studies. Pg 4.
Perrow, Charles. (1984, 1999). Normal Accidents: Living with High-Risk Technologies.
Princeton University Press. Princeton, NJ. Pages 3-8, 62-98, 101, 184, 24445, 249-250, 331, 358, 360, 368, and 370-71.
Peters, RH. (2002). Strategies for Improving Miners’ Training. (IC 9463). Washington,
DC: Department of Health and Human Services, NIOSH.
Petersen, Daniel. (1971). Techniques of Safety Management. McGraw-Hill, New York.
Petersen, Daniel. (1982). Human Error. Reduction and Safety Management. STPM
Press, New York.
Raman, Raghu. (2010). Underground Mine Safety – Are We Doing Enough? Kellogg
Brown & Root Pty Ltd, Level 9, 201 Kent Street, Sydney, NSW.
Rigby, L. V. (1970). The Nature of Human Error. Proc., Annu. Tech. Conf. Trans of the
American Society for Quality Control, 4/5-566.
Rijpma, Jos A. (1997). Complexity, Tight Coupling and Reliability: Connecting Normal
Accidents Theory and High Reliability Theory. Blackwell Publishers, Ltd.
Volume 5, Number 1. Pages 15-23.
Rio Tinto, <http://www.riotinto.com/search.asp?q=safety+programs>, 2011.
Rochlin, G.I., La Porte, T.R. and Roberts, K.H. (1987). The Self-Designing HighReliability Organization: Aircraft Carrier Flight Operation at Sea. Naval War
College Review, Volume 40, Number 4. Pages 76-90.
Roberts, K. H. (1993). New Challenges to Understanding Organization. New York:
Macmillan.
Runyan, Carol W. (1998). Using the Haddon Matrix: Introducing the Third Dimension.
Injury Prevention, Vol. 4, Pages 302-307.
230
Saaty, T. (1977). A Scaling Method for Priorities in Hierarchical Structures. Journal of
Mathematical Psychology 15, 234-281.
Saaty, T. (1980). The Analytic Hierarchy Process: Planning, Priority Setting, Resource
Allocation. McGraw-Hill.
Saaty, T. L. (1990). How to Make a Decision: the Analytic Hierarchy Process.
European Journal of Operational Research 48, 9-26.
Sagan, Scott D. (1993). The Limits of Safety: Organizations, Accidents, and Nuclear
Weapons. Princeton University Press. Page 27, 32, 38-45.
Saleh, Joseph and Cummings, Amy. (2011). Safety in the mining Industry and the
Unfinished Legacy of Mining Accidents: Safety Levers and Defense-in-Depth for Addressing Mining
Hazards. Safety Science 49, pages 764-777.
Sammarco, John J. (2001). A Complexity Assessment Methodology for Safety-Critical
Embedded Systems. Dissertation Proposal, Page 6.
Schulman, P.R. (1993). The Negotiated Order of Organizational Reliability.
Administration and Society, Volume 25, Number 3. Pages 353-372.
SearchSoftwareQuality.com,
<http://searchsoftwarequality.techtarget.com/definition/heuristics>, 2011.
Shappell, S. A. and Wiegmann, D. A. (1997). A Human Error Approach to Accident
Investigation: The Taxonomy of Unsafe Operations. The International Journal of Aviation Psychology,
7(4), 269-291.
Sills, D., Wolf, C. and Shelanski, V. (Eds) (1981). The Accident at Three Mile Island:
The Human Dimensions. Westview Press, Boulder.
Simpson, G., Horberry, T., and Joy, J. (2009). Understanding Human Error in Mine
Safety. Ashgate Publishing Company, Burlington, VT. Pages 7-9.
Sinha, S. et al. (2001). A Complexity Index for the Design Process. International
Conference on Engineering Design, ICED’01, Vol.1, Glasgow. Professional
Engineering Publishing, Bury St. Edmunds, pp. 157-163.
Snyder, J., and Jervis, R. (1993). Coping with Complexity in the International System.
Waterview Press, Boulder.
Swanson, RA. (1982). Industrial Training. In Mitzel, HE (ed.), Encyclopedia of the
Educational Research, (5th ed. Vol.2). New York: The Free Press, pp. 864-870.
Toronto Public Health (TPH). (2010). Growing Up Safely Reference Manual.
<http://www.toronto.ca/health/injuryprevention/0to5/pdf/growingupsafely_manual_appx02.pdf>
US Government Printing Office. (2002). Title 30, Code of Federal Regulations.
Washington, DC: US Government Printing Office.
231
Usher, M. J. (1984). Information Theory for Information Technologists. Macmillan,
London.
Vidal et al. (2008). Project Complexity Understanding and Modeling to Assist Project
Management. PMI Research Conference. Warsaw, Poland.
Vidal et al. (2010). Measuring Project Complexity Using the Analytic Hierarchy
Process. International Journal of Project Management.
Wasson, Charles S. (2006). System Analysis, Design, and Development: Concepts,
Principles, and Practices. John Wiley & Sons, Inc. Hoboken, NJ. Pages 18, 70-71, 81-83.
Weick, K. E. (1987). Organizational Culture as a Source of High Reliability. California
Management Review, Volume 29, Number 2, Winter, Pages 112-127
Whatis.com, <http://whatis.techtarget.com/definition/0,,sid9_gci212246,00.html>, 2011.
Windridge, F. (1996). Report on an Accident at Mora No.2 Underground on Sunday, 7
August 1994. Brisbane: Warden’s Court.
Wolf, Frederick G. (2000). Normal Accidents and Petroleum Refining: A Test of the
Theory. Bell & Howell Company. Ann Arbor, Michigan. Pages 1-2, 62-67.
Wolf, Frederick and Berniker, Eli. (2001). Managing Complex Technical Systems
Working on a Bridge of Uncertainty. International Association for the
Management of Technology. Lausanne, Switzerland.
Woods et al. (1994). Behind Human Error: Cognitive Systems, Computers and
Hindsight. SOAR Report 94-01, Wright-Patterson Air Force Base, Ohio:
CSERIAC.
Wymore, A. Wayne. (1993). Model-Based Systems Engineering. CRC Press, Inc.
Boca Raton, Fl. Pages 109-110.
Yale Department of Sociology, Yale University,
<http://www.yale.edu/sociology/faculty/pages/perrow/>, 2009.