Theoretical Issues in Ergonomics Science, 2015
http://dx.doi.org/10.1080/1463922X.2015.1028507
Managing risk dormancy in multi-team work: application of
time-dependent success-and-safety assurance methodology
Emad Farag
*†, Dov Ingman and Ephraim Suhir
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Faculty of Industrial Engineering and Management, Technion Israel Institute
of Technology, Haifa 32000, Israel
(Received 13 October 2014; accepted 9 March 2015)
The success and safety of many of today’s industrial activities, such as constructing
power plants, transmission lines and civil engineering objects, is often influenced by
situations, when successful and safe work completion is associated with the
implementation of various more or less complex multi-team (MT) layouts. Multi-team
effort is characterised by the presence and interaction of numerous, not necessarily
concurrent time- and space-constrained interfering activities, as well as by dormant
risks. Such risks might threaten the fulfilment of the general task carried out by the
main team and/or by other teams on the construction site. This analysis addresses
the role of the dormant risks during the fulfilment of a non-simultaneous MT work.
The objective of the analysis is to suggest an effective and physically meaningful
probabilistic predictive model. The model is aimed at the understanding,
quantification and effectively managing the dynamics of the system of interest. The
emphasis is on the role of possible dormancies. The study is an extension of
the authors’ earlier research on spatial and time dimensions in the addressed problem.
The study extends the risk management approach to a holistic level.
Relevance to human factors/ergonomics theory
This article characterises the risk dormancy phenomenon to be considered as a proper way
of taking into account multi-team (MT) aspects in the occupational safety research.
Furthermore, a holistic perception of MT functional complexity allows for a generalised
view of MT mutual interaction instead of focusing in the single team behaviour.
Keywords: risk dormancy; team work; multi team; time-dependent probability;
expected RD time
1. Introduction
Modern infrastructure and industrial activities are typically executed by several different
on-site teams: ‘most human work is performed by teams rather than by individuals’
(Sasoua and Reason 1999). Each team performs distinct and designated activities over
time. At the same time, today’s technologies require better understanding and increasingly higher quality than in the past. Sophisticated work methods, tools and equipment
have been developed for, and became available to, the workers in multi-team (MT) systems. Such methods are crucial to achieve the optimum and safe outcome of a particular
project. Tight schedules are implemented today to meet customers’ requirements, and to
*Corresponding author. Email: [email protected]
†Present address: Department of Electrical Engineering, University of California, Santa Cruz, CA,
USA.
Ó 2015 Taylor & Francis
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E. Farag et al.
ensure the demands for increased productivity. This imposes additional pressure on workers. Handling of hazardous materials and energy sources, such as electricity or radiation,
requires the use of highly qualified labour, as well as customised safety procedures and
equipment. Dynamic physical conditions, such as noise, vibrations and working at
heights, contribute also to the likelihood of hazardous situations.
Teams often operate independently, with no explicit functional linkage between them.
In other cases, more or less close professional collaboration is required to perform a particular task. For example, repair work in an electrical utility typically requires involvement of
several teams. One team of electricians disconnects the power, another team repairs the
damage and a third team is deployed outside the secluded site, often on a standby basis, to
assist, if necessary, the workers inside the worksite. In this example, three different teams
perform various aspects of the work aimed at a particular mutual goal. A mistake, error or
a failure in one team’s actions affects other teams involved in sequential large-scale activity, and has a potential to create a hazard. The hazard might remain dormant for some
time, but eventually can become or generate a risk. This risk might have an immediate
impact on the team member who caused it, and/or threaten another team continuing the job
at the given site. This scenario can be identified as risk dormancy (RD). When occurring in
a MT situation, it becomes a MT risk dormancy (MTRD). It is this type of RD that is the
main concern and the main subject of this analysis.
Several researchers have addressed the MTRD lately. Mitropoulos, Howell, and
Abdelhamid (2005) stated that ‘errors by one crew may create unpredictable conditions
for a following crew’ and suggested that ‘future research should focus on better understanding the effect of task unpredictability and on developing error management strategies’. Reason (2000) wrote ‘Different actors’ decisions and actions can produce latent
conditions or pathogens in a system. These might lie dormant for a time until they combine with local circumstances and active failure and penetrate the system’s many layers
of defences, and an accident occurs’. Despite the recognised importance of the MTRD,
there are no studies that suggest effective solutions to the MTRD problems.
The existing studies suggesting various safety models employ quite a few of diverse
approaches. Some models focus on actions or processes and examine the time and space of the
occurred accidents that led to personal injury or damage to some assets. These are scilicet (s.c.)
active failures. Other models are system oriented, e.g. organisation models related to the management policy, actions and decision-making (Rasmussen, Pejtersen, and Goodstein 1994,
149). Interdisciplinary models ‘focus on cause-effect relationships close in time and space to
the accident sequences’ (Reason 1997; La Coze 2005). Akinci et al. (2002) examined the
time–space management aspect of accident prevention. He indicated that ‘lack of management
of activity space requirements during planning and scheduling results in timespace conflicts
in which an activity’s space requirements interfere with another activity’s space requirements
or work-in-place’ (Rosenfeld et al. 2006). Rozenfeld, Sacks, and Rosenfeld (2010) addressed
this situation by expanding the safety model to MT problems that involve mutual risk exposure
of two or more teams sharing the same timespace domain.
In the analysis that follows a rather general holistic approach is used to address dormant
risks encountered during consecutive MT work activities. This approach treats the problems
of interest from a rather general point of view, regardless of a specific nature of a particular
risk or a possible outcome. Our approach provides a probabilistic assessment of the management’s dynamic response at the organisational level. Here are several typical examples.
Example 1. A scaffold is required for certain tasks performed on a public utility (PU)
construction site by teams working at heights, such as, say, plasterers and painters
Figure 1.
Theoretical Issues in Ergonomics Science
Bricklayer team,
Plasterer team
3
Painter team
Scaffold builder team
Plumber
Project Schedule
(a)
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Scaffold builder team
Bricklayer team,
Plasterer team
Plumber
Painter team
Project Schedule
(b)
Figure 1. Illustration of the scaffold example (a) Prior of lean scheduling. (b) 1b lean scheduling
timespace dependent model (Rozenfeld, Sacks, and Rosenfeld 2010).
A scaffold building team erects the scaffold, while a plumbing team is scheduled to
subsequently lay sewer pipes. In the meantime, the scaffold is being used by other teams.
In such a situation, the timespace sharing is implemented as illustrated in Figure 1(a).
In the safety assessment of the project in question risk exposure in team activities performed with timespace overlapping is not considered. All the teams the plasterers,
the painters and the plumbers worked on the site simultaneously. The Lean Scheduling
Time and Space Dependent Model (Rozenfeld, Sacks, and Rosenfeld 2010) illustrates the
time segregation.
This means that the plumbing team’s work is rescheduled to avoid the risk of dealing
with falling objects or tools from the plastering or bricklaying teams. In space segregation, on the other hand, the plumbers would be assigned to work at the other side of the
site, where no teams would be working above them. The risk posed by the scaffolding
team is eliminated, as illustrated in Figure 1(b). The planner’s instructions indicate, however, that the plumbing team must lay the pipes in trenches at the foot of the building,
where the scaffold is assembled.
Although the plastering team could be temporarily repositioned and the excavation could
be rescheduled, this still might destabilise the scaffold and increase the probability of its collapse at a later time (Figure 2). Rozenfeld’s timespace-dependent model does not address
this aspect of scaffold destabilisation risk. The risk leads to an additional risk, namely to the
RD. In this example, other scaffold users, such as the painting team, are exposed to an underestimated risk, which, however, has been identified beforehand. This example illustrates the
RD phenomenon, i.e. a risk that is dormant, while awaiting for other teams that might use
the hazardous scaffold. Figure 2 emphasises the progress that could be achieved by developing tools and methods to deal with such underestimated or misidentified risks that stem from
the dynamic changing of the site (in this case, the excavation).
Example 2. Power grid works are inherently created serially. A utility lineman team
replaces an insulator on a high-voltage power line after obtaining permission from the
responsible electrician. The electrician operates according to a written checklist issued by
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E. Farag et al.
Underestimation of the identified RD in MT work
or misidentification of RD
Scaffold building
Excavation for sewer
Plastering team
Painting
Project schedule
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Figure 2. RD exposure in MT example risk of scaffold collapse, threat to painter team.
the engineering department. The professional teams work in a serial manner with respect
to both time and space: first, the engineering department writes the instruction checklist;
then the responsible electrician shuts down the power at a circuit box mounted near the
work site (along the power line) and linemen replaces the insulator further down the line
(not even necessarily at the same site). In such situations, several teams are active at the
site, and communication and coordination of their interactions might be quite complex.
Any error in these activities could create RD, thereby increasing the likelihood of an electrical shock accident. Examples of this type of error are failure to check for current, misidentification of the appropriate line connector and/or the specification of the wrong
transformer or pole number.
The above examples (scaffold collapse or electrician’s error) represent dormant risks
caused by MT activities that have no time or space overlapping. This means that accidents
caused by the MTRD activities regardless of their simultaneity and space have not been
addressed. MT activities create dynamic work sites (environments) with constant changes
depending on the needs for the complex system, in/for which the work is being performed.
Such complex systems require a tight control, i.e. appropriate risk management, which
should be the main component of a safety management system (SMS).
The term ‘risk management’ includes the notion of mitigating risks to an adequate and
achievable level acceptable by the organisation. This can be done by the application of
two major methods:
(1) appropriate and effective risk control and
(2) the use of the most suitable accident causation models.
The obvious challenge in the assurance of the occupational safety is the development
of the ability to effectively control problematic situations, identify hazards and assess and
control risks. The actual down-to-earth and practical on-site work is more complicated,
however, than the models that attempt to predict and simulate the effort. Proactive
approaches, such as risk assessment and control, can never be entirely accurate, complete
and successful when applied alone. ‘Effective risk management depends crucially on
establishing a reporting culture. Without a detailed analysis of mishaps, incidents, near
misses and “free lessons”, we have no way of uncovering recurrent error traps or of knowing where the edge is until we fall over it’ (Reason 2000). Risk management should be
therefore implemented both proactively and reactively, and its complete success
Theoretical Issues in Ergonomics Science
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necessitates reactive approaches such as accident investigation/causation. A description
of the process of investigating accidents in the context of the concept presented in this
paper, namely, the underlying cause RD, is set forth below.
The general strategy of pursuing risk management approach in the problem in question includes the following major items.
1.1. Hazard identification
Before quantifying the probability of failure, hazards related to the system operation must
be identified. Several identification techniques are available (Carter and Smith 2006),
some of which are based on brainstorming among people familiar with the installations at
a work site, while other techniques are of a more systematic nature. According to Cuny
and Lejeune (2003), recognising hazards can be a rather complicated task. Many different
kinds of uncertainty factors contribute to the challenge of recognising hazards and a
clear-cut determination of the source and level of the risk for each hazard is next to
impossible. Furthermore, many observations are needed to accurately estimate the likelihood of an accident, particularly, if it is of rare occurrence. One of the paradigms in the
Accident Root Causes Tracing Model (Abdelhamid and Everett 2000) reveals that workers, more often than not, fail to identify hazardous situations. This leads to the conclusion
that the hazard identification process only partially covers the hazards that should be identified on site. Multi-team hazards are even more difficult to identify.
1.2. Risk assessment
Hazards become a problem only when they could possibly result in an accident whose
occurrence is preceded by a sequence of events that may cause a hazardous situation.
After a hazard is identified, all possible sequences of events that can be triggered by that
hazard must be studied and checked to determine whether or not they might lead to an
accident. With the relevant scenarios in hand, it is possible to calculate the two elements
of a risk: the probability of the events occurring, and their consequences. Reason (1997)
presents three models for safety management: the Pearson model, the engineering model
and the organisational model. Each of these models has a different perspective on human
error. ‘Workplaces and organisations are easier to manage than the minds of individual
workers. You cannot change the human condition, but you can change the conditions
under which people work. In short, the solutions to most human performance problems
are technical rather than psychological’. This concept can be better understood by considering the work of Papadopoulos et al. (2009), who concluded that ‘risk assessment must
be conducted for each task and for each worker. This risk assessment must consider all
hazards and their interactions and must be revised when changes occur’. In addition they
wrote ‘However, frequent changes regarding workforce, working hours and working conditions, as well as time pressure, result in insufficient time for conducting a complete and
effective risk assessment, determining training needs, setting up, applying and monitoring
the corresponding OSH measures. Furthermore, the methodological tools used in risk
assessment up to now are not sufficient for this complex situation’. In an earlier paper on
this subject, Drivas and Papadopoulos (2004) pointed out that risk assessment needs to
consider all hazards and their collaborations and must be reviewed when changes occur.
Risk assessment will be even more difficult to identify for risks arising from MT work,
which is characterised by difficulty in identifying or the lack of the researcher capacity to
evaluate risks.
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1.3. Risk control
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Risk management is basically a control problem (Rasmussen and Svedung 2000). The
review focuses on the most suitable methods of risk control.
(a) Control hierarchy is achieved by applying four main levels of action: (1) the hazard is eliminated; (2) a physical barrier is erected between the hazard and the performer (worker); (3) personal protective equipment is used; and (4) workers
comply with written safety instructions.
(b) Root cause analysis addresses accident causation according to four basic categories: management factors (safety and risk control), intermediate factors (procedures, work design, training), performance (behavioural and technical) factors
and external (environmental) factors.
(c) Comparative analysis consists of measurements for risk prevention using the following four dimensions: effectiveness, applicability, efficiency and influence
(Griffel 1999).
Moreover, since most serious accidents are apparently caused by the operation of hazardous systems outside the design envelope, the basic challenge in the development of
improved risk management strategies is essentially to ensure improved interaction
between the decision-making and planning strategies at the various levels of the organisation (Rasmussen and Svedung 2000).
Thus, despite the currently implemented risk management strategy, the control of
MTRD appears to be unsolved yet.
1.4. Accident causation
Accident investigation/causation analyses enable one to better understand the factors and
processes leading to accidents. Our analysis that follows explores further accident causation or aggravating factors, i.e. RD in MT work.
The primary objective of this paper is to develop a holistic safety model, in which
both management and labour respond to various dormant MT risk situations. Management is responsible for making decisions concerning the handling of such risks and accident causation in accordance with a SMS. Disorder might result in a potential for unsafe
actions. Such actions are characterised by the following major attributes.
Deviation of a process from its planned time schedule, requiring corrective action to
remove the source of non-conformity in order to prevent recurrence. The corrective
action is aimed at ensuring that the existing potentially hazardous situations do not
lead to accidents.
Time lags in the sequenced work of the various teams require communication and
mutual reporting.
Continuing random changes to the physical sites, thereby necessitating continuous
risk assessment.
Uncontrolled multiple degrees of freedom, instead of a tight and narrow path to a
successful outcome.
The lack of quality methodology principles being applied to monitor safe performance, so as to reduce or even prevent accidents, especially those with casualties.
These attributes can create hazards that might be difficult to identify properly.
Theoretical Issues in Ergonomics Science
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Table 1. Number of employees by division.
Division
Number of employees
North district
South district
Logistic
Generation
Construction
800
1300
700
2100
1700
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Two major problems with MT risk management have been identified.
(1) RD identified in MT work is often underestimated. For example, after excavating
trenches intended for power or communication lines or for drainage pits, the
installation of the cables of pipes is often delayed and the trenches and pits are
marked using yellow caution tape only. Despite the identification of an open
trench or a pit as a hazard, such trenches and pits are sometimes left open for
days and even weeks, constituting a threat to other teams working at the site.
(2) The potential threat of risk situations in an MT work is often misidentified. This
leads to a dynamic risk situation. For example, a maintenance technician places a
rag on the floor to absorb the condensation from a faulty office air conditioner
and leaves to get his tools. A secretary inadvertently steps on the wet rag, slips
and falls and injures her ankle.
The analysis that follows addresses the occupational accident data of the integrated
electrical PU in the State of Israel between 2004 and 2011. As of 2011, the PU company
employed 12,687 workers and maintained and operated several power station sites with
an aggregate installed generating capacity of 13,133 MW, supplied to customers via a
national grid transmission and distribution (T&D) system. The following segmentation of
the employee roster into five operational divisions reflects the company’s main areas of
activity relevant to this study.
The works and expertise of the first and the second divisions, the north and south District ones, are the T&D of electrical power. The third division is engaged in building
power plants and substations. The fourth division is in charge of logistics, and provides
transportation services, cranes, heavy vehicles and workshop works to the other divisions.
The fifth division is the Generation Division, which operates the power stations. The
number of the employees in these five divisions is shown in Table 1.
The reported accidents of five PU’s divisions during 20042011 were compiled by its
safety department. The data presented in this paper were collected in accordance with the
Israel Institute for Occupational Safety and Hygiene classification system. Each of the
accidents was examined to determine its causation in the context of the RD in MT work.
The results were subdivided into categories using two main factors: RD and non-risk dormancy. The criteria described in Section 1.3 were applied. The results, as they relate to
the above five PU divisions, are shown in Table 2. These results reveal as much as 9.85%
RD-related accident rate for all the accidents.
2. Analyses
2.1. Risk dormancy analysis
2.1.1. Multi-team risk dormancy
RD is the time delay between the occurrence of a failure (hazard event) in the action of
one team (Team A) that affects another team (Team B) involved in the process (Figure 3).
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Table 2. RD and non-RD accidents in MT work.
Division
N.MTRD.A
MTRD.A.
TOTAL
% MTRD.A
North district
South district
Logistic
Generation
Construction
783
45
828
5.4%
860
81
941
8.6%
474
64
538
11.9%
1391
134
1525
8.78%
715
133
848
9.85%
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N.MTRD.A non-MTRD accidents.
MTRD.A MTRD accidents.
Such a failure has the potential to produce a hazard that is underestimated or undetected
by Team B and eventually becomes or generates a risk that lies dormant for a period of
time (risk dormancy). This risk has no immediate effect on the Team A member who
caused it, but might be a threat to another team (referred to as Team B) that will later continue the same job or will be engaged in a different job at the site. This situation is
referred to as RD in MT work.
An analysis of the RD time path reveals the following stages leading to an accident
(Figure 3):
T0 beginning of Team A activity that could possibly generate a hazard event that
becomes a risk and could threaten the Team B work.
TR hazard event time.
Td RD time, which is equal to the time interval from the hazard event caused by the
Team A activity until the accident occurs, injuring the next team. The time is estimated
by the professional safety officer teams, based on their experience and knowledge.
Tacc the time the accident occurred.
This paper addresses the time aspects of RD in MT work and proposes a model for
risk evaluation and management based on time-dependent probability (TDP) methodology. In this context, classification criteria for RD are related to risks generated by MT
activities.
Figure 3. MT risk dormancy pathway.
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2.1.2. Probabilistic analysis of risk dormancy time
The analysis of RD in MT work requires collecting information regarding an accident and
establishing the sequence of events that led to the accident. This includes identifying the
team affected by the accident and the accident occurrence time, as well as the team that
most probably generated the risk that ultimately materialised (risk-causing team) and the
time when the risk was actually generated. The affected team and the time, Tacc, of accident occurrence are easily determined in most cases since accidents are usually investigated and documented. It is, however, often quite difficult to identify the risk-causing
team and determine the time at which the risk was generated (Figure 3).
Still, identifying the team that generated the risk is quite complicated and requires
efforts of a team of professional analysts, such as the safety officer’s team, which is supposed to be familiar with the stages and layout of the work. The accident investigation
determines not only the active causes leading to the accident, but also includes the attributes of the schedule itself, as well as tools, places, personnel, etc. involved in the risk creation. As a result, the safety officers can determine with high confidence the commencing
time T0 of the Team A activity that most likely created a hazard event TR, which became
a risky one and threatened the Team B. Finally, a team of safety experts analyses all
actions that preceded the occurrence of the accident, since the range of the RD time
uncertainties is basically the risk creation time until its materialisation.
One can therefore calculate the statistics of the RD duration from its creation, TR, until
accident occurrence, Tacc, regardless of the teams involved in the hazard generation. RD
time is a random variable; hence a probabilistic analysis should apply. Actually, Tacc
could be established rather accurately due to the time recording of an accident’s occurrence, while T0 and TR should be considered as best estimates made by the professional
safety officers.
RD is clearly a positive value, and its range is between the RD generating point TR on
one side and the time of the accident occurrence Tacc, where RD terminates at the other.
The RD time Td is a random variable extending between the beginning of Team A
activity T0, which could possibly generate a hazard event TR, and the accident time Tacc.
When TR is close to T0, then the time Td reaches its maximum value. Similarly, when the
time TR is close to Tacc, then Td approaches zero. Thus, the entire range of RD time uncertainty can be expressed as
Td D Tacc ¡ TR :
(1)
In accordance with the maximum entropy principle, we choose a uniform distribution
for RD time (Figure 4) regardless of which particular team caused it.
Figure 4. Risk dormancy time distribution.
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E. Farag et al.
The probability density function (pdf) is therefore a constant, as expressed by the
equation:
f ðTd Þ D f
ðTacc ¡ T0 Þ
:
ðTacc ¡ T0 Þ
(2)
Here,f is the Heaviside step function:
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f D 1 T0 < Td < Tacc
:
fD0
Tacc < Td
(3)
f (Td) is the normalised pdf of RD time (normalisation of pdf by Tacc to achieve an
integral equal to 1) and Td is the random RD time uniformly distributed.
Our observations are based not only on a single event but also on the distribution of
RD time generated by a number of accident occurrences. Thus, the distribution should be
averaged for all events. The average is the arithmetic mean of all the RD times:
(
f D ðTd Þ D
Xnk
iD0
f ½Td ; Tacc i
(4)
n
(
Here f D average of RD time of each division, i index of time to accident on each
division, n RD accidents number on each division, k division index.
The RD time probability of all accidents at the PU (in the five divisions, to be precise)
is expressed in Equation (5):
X5
njk ðf ðTd ÞÞk
k D 1X
f ðTd Þ D
nk
:
(5)
Here, f ðTd Þ average probability of RD time of all divisions
Z
Fexp ðTd Þ D
Td
f ðTd ÞdTd :
(6)
0
Fexp experiment cumulative distribution function (CDF) of RD time of all divisions
3. Results
As shown in Equation (7), the CDF fit function is specified by the five divisions of the PU.
This fit function positively predicts the experimental CDF function Fexp in Equation (6) of
RD time for MTRD accidents data:
FðTd Þ D 1 ¡ ae
¡
b1
Td
u1
¡ ð1 ¡ aÞe
¡
b2
Td
u2
(7)
F (Td) CDF fit function,
u1 scale parameter, short-term expected time,
u2 scale parameter, long-term expected time,
b1 shape parameter, short-term expected time,
b2 shape parameter, long-term expected time,
a partition parameter, dividing the data into short a part and long (1 a) content.
Theoretical Issues in Ergonomics Science
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Table 3. Distribution characteristic parameters.
Division/parameter
North district
South district
Logistics
Generation
Construction
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a
u1
u2
b1
b2
0.47
0.68
0.69
0.9
0.8
3.98
4
19
20
51
570
539
622
660
750
0.92
0.92
0.67
0.66
0.49
1.4
0.96
1.32
1.55
2.32
Majority of RD time appearances of short-term character compared to a smaller long-term RD appearance.
The RD time distribution parameters for the five PU divisions are shown in Table 3.
Each one is also subdivided by two distinctive populations. Sub-data are well described
by the Weibull distribution. Accordingly, each data subset is characterised by a vector of
three parameters as shown in Table 3.
Table 3 distinguishes between two different groups. First, the three divisions: north,
south and logistics, showing a clear distinction between short- and long-term. Expectedly,
the fit function of this group shows a good fit to RD time data, see Figures 5(a)5(c).
However, the second group of the two other divisions, generation and construction, has a
majority of RD time appearances of short-term character, about 0.9 and 0.8, respectively,
compared to a smaller long-term RD appearance. Because of that we ignore the longterm for this group, whose sub-data are well described by the Weibull distribution. Each
subset data are characterised by a vector of two parameters (Table 4).
Accordingly, a modified CDF fit function of the Weibull type is specified:
FðTd Þ D 1 ¡ e
¡
b1
Td
u1
:
(8)
The fit function shows a sufficient fitness to RD time data, see Figures 5(d) and 5(e).
Monte Carlo simulation is used to generate random points from the domain RD time distribution data to determine the validity of the five divisions’ parameters a kind of bootstrap simulation.
The simulation data show rather poor correlation between a, b and u parameters.
Consequently, we are considering them as independent parameters.
3.1. Hazard function
To confirm the results of the effect of RD, one could examine the impact of these parameters by employing the hazard function for each division:
hðTd Þ D ¡
dlnð1 ¡ FðTd ÞÞ
:
dðTd Þ
(9)
The hazard function has resulted in the same groups of CDF functions with respect to
RD time: Group One: north, south and logistics divisions characterised by two shape
parameter b1 and b2 as follows: 0.92, 0.92, 0.67 and 1.4, 0.96, 1.32, respectively (see
Tables 3 and 4). The results for the b value were found to be close to 1, demonstrating
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E. Farag et al.
Figure 5. (a) CDF of RD time of north district division (experimental and fit function). (b) CDF of
RD time of south district division (experimental and fit function). (c) CDF of RD time of logistic
division (experimental and fit function). (d) CDF of RD time of generation division (experimental
and fit function). (e) CDF of RD time of construction division (experimental and fit function)
Table 4. Distribution characteristic parameters.
Division/parameter
Generation
Construction
u1
b1
26
121
0.4
0.45
Theoretical Issues in Ergonomics Science
0.04
0.04
trace 1
Hazard function
Hazard function
trace 1
0.03
0.02
0.01
0
0
200
13
400
600
800
0.03
0.02
0.01
0
3
1×10
0
200
400
(a)
(c)
0.04
0.04
trace 1
0.03
Hazard function
Hazard function
0.02
0.01
200
400
600
0.03
0.02
0.01
0
800
0
200
400
Dormancy time
600
800
3
1× 10
Dormancy time
(b)
(d)
0.04
trace 1
Hazard function
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trace 1
0
800
Dormancy time
Dormancy time
0
600
0.03
0.02
0.01
0
0
200
400
600
800
3
1× 10
Dormancy time
(e)
Figure 6. (a) Hazard function of north district divisions. (b) Hazard function of south district divisions. (c) Hazard function of logistic divisions. (d) Hazard function of generation divisions. Hazard
function of construction division.
almost constant failure rate in time, see Figure 6(a)6(c). However, we are unable to
explain the volatility behaviour of the two models presented in Equation 7, which brings
one to the three choices of Weibull. Similarly, one could question why these parameter
values were obtained. These questions will be addressed in the future work.
Group Two: generation and construction divisions characterised by single-shape
parameter b1 of 0.4 and 0.45, respectively, see Table 4. The results of b were found to
be less than 1 demonstrating a decreasing failure rate in time as shown in Figure 6(d)
and 6(e).
4. Discussion
The type of organisations considered in this study are quite complicated, as they are characterised by significant and strong interdependence between the management and MT
professional labour, in addition to the effect of interaction among themselves, regardless
of simultaneity. This complexity could lead to problematic aspects in internal company
behaviour, sometimes causing safety problems.
14
E. Farag et al.
The scope of this paper extends the risk management approach from the well-known
management models, with the addition of the important aspect of the MT safety perspective beyond simultaneous situations, i.e. RD. The research provided here extends the
existing approaches to a holistic level.
The obtained data on RD accidents indicate that the RD time distribution is characterised by the Weibull parameters: u1, b1 short-term, u2,b2 long-term, respectively, and
partition parameter a as shown in Tables 3 and 4 above, for situations, in which the very
nature of the work dictates the dormancy time, as supported in the following data
discussion.
First, u1, b1 short-term expected RD time and a partition parameters.
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(1) North and south divisions T&D districts
Scale parameter u1, whose RD time is 3.9 and 4 hours, respectively. An examination
of the accident investigation data shows that tasks in the T&D districts have the following
characteristics.
(a) Most tasks are scheduled and completed in one day, because the nature of T&D
work requires power supply resumption to customers as quickly as possible. As a
result, the short-term of RD time lasts a few hours.
(b) Tasks are performed sequentially by a professional MT.
(c) Subtasks are performed sequentially.
(d) Similar field of activity, e.g. lineman-teams of differing proficiency levels are
necessary for executing complementary parts of power line work due to the complexity of the work and the existence of risk factors such as electricity, and, as a
consequence of that, have high safety level requirements. For example, erecting a
transformer requires at least two different teams: an electricians’ team to de-energise transformer connections to the power lines and install grounds and an overhead line-work team to install the transformer. Thus, the work teams require the
necessary expertise for each phase of the work.
4.1. Shape parameter b1 of 0.92 for both districts
These b1 values are close to 1, indicating an almost constant accident rate regarding RD
time. This happens if there is maximum entropy, characterised by exponential distribution
process. Remarkably, hazards and risky situations analysed in this paper, and which are
more likely to cause accidents at a constant rate, are related to electrical supply divisions
(south and north divisions as shown in Figure 6(a) and 6(b).
Partition parameter a divides the south and north divisions’ RD time appearances
into two separate categories in which the short-term is 0.47, 0.68, respectively.
Case study 1: a lines work team of PU North division performed an underground cable
connection to an overhead line. According to the safety instructions, the cable to be
worked on must be positively identified by tags and must be isolated from the electric
supply sources. Furthermore, tests must be performed to verify that the cable is de-energised, and grounds of an approved type must be applied to protect workers from all the
energy sources. Earlier in the morning of the same day, an authorised clearance team
should be assigned to identify and de-energise the underground cable, in accordance with
an authorisation provided and documented by a system operator. The clearance team is
supposed to install the protective short-circuiting and grounding equipment required for
Theoretical Issues in Ergonomics Science
15
the protection of the team working on cable connection. The permission to start working
was given at the work site. While the cable-cutting work was in progress, an explosion
occurred. The team cut an energised cable. The authorised clearance team misidentified
the correct cable and gave the work authorisation for the wrong cable. Workers were
injured due to electrical arc flash.
In this case, the above-mentioned scale parameter characteristics apply about 2 hours
of short-term dormancy time.
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(2) Logistic division
Scale parameter u1, whose RD time is 19 hours. The present results revealed a prominent attribute related to work course duration. The large majority of appearances of these
RD accident occurrences are at the end of a working day or a shift. The following theme
is the main characteristic of the RD causation: in the logistic division the main MT activities occurring at the beginning or at the end of the working day/shift were the loading and
unloading of trucks.
Shape parameter b1 a shape parameter value of b1 D 0.67 < 1 indicates that the
accident rates decrease over time. This happens if significant hazards or risky situations
are generated resulting in an accident at a decreasing rate over time.
Partition parameter a, which is dividing the logistic division RD time appearances
into two substantial populations’ 0.69, 0.31 of short- and long-term, respectively.
Case study 2: a workshop employee was on his way to repair a metal processing
machine. A stack of iron bars, delivered the previous day, was still on the workshop floor,
protruding into the employee’s pathway. He stumbled as he passed the stack and injured
his leg. Material deliveries are usually made in the morning and materials are unloaded
from trucks at the workshop yard close to where the machines are placed, pending transfer
to storerooms. These irons bars were unloaded a day before the accident. A 24-hour dormancy time was estimated by the safety officer.
Case study 3: the PU owns a rather big truck fleet, used for truck-mounted work platforms and truck-mounted cranes, which are used for loading/unloading and uplifting
workers to heights. In this case, one of the trucks was sent back to duty from in-house
periodic maintenance service on the morning of that day, the truck driver opened the
engine hood during a routine cleaning and checking procedure at the end of the shift and
was injured while trying to remove a ‘piece of rubber’ that was inadvertently left there by
a garage worker. An eight-hour RD time was estimated by safety department.
(3) Generation and construction divisions with significant short-term expected RD
time
It is obvious from Table (3) that partition parameter a of the magnitude of 0.9 and 0.8,
respectively, indicates the dominant short-term RD time appearances in these divisions.
Therefore, we neglected/ignored the long-term appearance in those two divisions as
shown in Table (4).
Scale parameter u1, whose RD time is 26 and 121 hours, respectively. Task substitute
time, i.e. first task completion and transition to the next task by MT at generation, construction and logistic divisions are longer than in T&D districts.
Shape parameter b1 of 0.4 and 0.45, values of b1 < 1, indicates that the accidents rate
decreases over time. This happens if there are significant hazards or risky situations generated early and leading to an accident in a decreasing rate over time.
16
E. Farag et al.
Case study 4: To carry out a maintenance job in a turbine building of a power station,
a scaffold was erected and placed on the route of an overhead bridge crane. The crane
consists of parallel runways with a travelling bridge spanning the gap and equipped with
hoist that travels along the bridge. These cranes are electrically operated from ground
level by a control pendant. Three days later, a team from another department used the
crane for lifting heavy valves as part of a job. The crane hit the scaffold, causing extensive
damage. In this case, the above-mentioned generation scale parameter characteristics
apply a RD time of about 72 hours.
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4.2. Second, u2, b2 long-term expected RD time and partition parameters
(1) North, south and logistic divisions
Scale parameter u2 whose RD time is 570, 539 and 622 hours, respectively, the long-term
RD accidents are likely to affect teams with no professional linkage between them. There
is no significant difference observed in the results for all the divisions as seen in Tables (3)
and (4).
Case study 5: a working team of the Southern PU district was sent for carrying out
maintenance work on electric supply line of low voltage network. An employee whose
job included activities installing, constructing, adjusting, repairing, etc. climbed on a
metal pole and started the repair work. An electrical current flow as a result of contact
between transformer cables and the pole causing electrical shake to worker. Investigation
found that a month before another working team of the same district performed different
work on the same transformer, causing faulty cables connection of the transformer. As a
result, loose connection of the cable made contact with the conductive metal pole and
electrical flash of short circuit injured the team.
In this case, different teams, namely maintenance and operation teams of the same
district, performed different tasks without professional affiliation or linkage between
them. The scale parameter u2 characteristic applied a RD time of about 720 hours.
(2) Generation and construction divisions.
(3) Negligible long-term expected RD time.
5. Conclusions
A novel probabilistic risk management model has been introduced to characterise the RD
phenomenon to be considered as a proper way of taking into account MT aspects in the
occupational safety research. Furthermore, a holistic perception of MT functional complexity allows for a generalised view of MT mutual interaction instead of focusing in the
single team behaviour.
The following conclusions can be drawn from the carried out analysis.
The model is innovative in two major ways: First, the identification of RD in sequential MT work, i.e. MTRD. Though, unidentified dormant risks or the underestimation of
identified dormant risks are a ‘ticking bomb’, each such risk represents an unsafe/hazardous event that is certain to happen in the foreseeable future and which threatens other
teams continuing the same or a different job on site. Indeed, the current timespace
approach does not address or offer solutions to such risks. Therefore, we have developed
an RD approach that offers a solution for predicting TDP. Second, the PU accident
Theoretical Issues in Ergonomics Science
17
database enables us to evaluate and determine the above-mentioned significant risk aspect
tendencies. Accordingly, the proposed model defines RD, a new facet of risks generated
by MT work in modern industrial and infrastructure organisations, regardless of the time
frame involved.
Acknowledgements
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The authors would like to thank the Israel Electric Company for allowing us to use their safety data
base which made valuable contribution to the research. This study is dedicated to my friend, late Dr
Majdi Latif, who inspired and encouraged me. “True friendship is the willingness to sacrifice one’s
self for the other.”
Disclosure statement
No potential conflict of interest was reported by the authors.
About the authors
Emad Farag is currently a PhD student in Industrial Engineering and Management at the TechnionIsrael Institute of Technology, under the supervision of professor Dov Ingman. His research deals
with how to use reliability methods (such as time dependent probability, hazard function and modelling) as tools for managing risks in the modern multi-team organisations as part of occupational
safety management, with a special focus on the dormant risks and teams interaction.
Dov Ingman is a staff member of Industrial and Management Engineering faculty at TechnionIsrael Institute of Technology. His research interests include element and system reliability, damage
accumulation processes, physical kinetics, pattern recognition, information theory, neural nets,
measurement theory and instrumentation, desalination technology, non-destructive testing and quality control.
Ephraim Suhir is a fellow of the American Physical Society, the Institute of Physics, UK, Institute
of Electrical and Electronics Engineers, American Society of Mechanical Engineers, Society of
Optical Engineers, International Microelectronics and Packaging Society, and the Society of Plastics Engineers. He is a Fulbright Scholar in information technologies, State Department, USA, and
Foreign Full Member of the National Academy of Engineering, Ukraine. Ephraim has authored
about 300 technical publications (patents, books, book chapters, technical papers) and received
numerous professional awards in various fields of engineering and applied science. Currently, he is
a staff member of Mechanics and Materials Department, Portland State University, Portland, OR,
USA.
ORCID
Emad Farag
http://orcid.org/0000-0002-1595-5833
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