Integrated Management of Physical Mining Assets:
Operational Performance and Productivity
Peter Grundy
PGA Estrategia y Capacitación, Chile
ABSTRACT
The operational management of physical assets at mining sites rests on long-standing assumptions,
particularly performance equivalence of assets, independence of equipment and operational
performance, and sufficiency of evaluations through fleet averages, which are formally incorrect;
this paradigm however leads to the functional segregation of responsibilities between Operations
and Maintenance standard at most sites, which obscures real performance factors, hides causal
origins of behavior, and distorts improvement initiatives.
This paper reviews these fundamentals, yielding an alternate approach keyed on combinations of
equipment and operational parameters which drive performance and the existence of scatter effects
on the behavior of equipment and operational sequences. The equipment scatter effects can
however be estimated via statistical inference techniques, thereby determining detailed quantitative
maintenance measures, and operational scatter is defined by design considerations on mine
profiles. The result is then a full, integrated, quantitative performance model, reflecting all relevant
parameters, tagged to asset productivity indicators, and amenable to objective optimization
algorithms to maximize operational performance and site business results.
The model is applied through an integrated operational planning and performance management
rule, based on quantitative capture of equipment and operations parameters, and optimized
through the assignment of operating units which exhibit optimum performance by operational and
cost considerations. The planning and management system yields, by documented sample
application to mine transport operations, a 15-20% increase in operational effectiveness of assets, a
30-50% reduction of operational costs, and thus an average 64% increase in asset productivity at the
site.
It is noted that these improvements are the result of simple changes to planning and management
strategies, which can be directly implemented via limited affected staff, and do not involve
expectations of performance improvements in maintenance, operations, labor, or other practices at
a site.
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INTRODUCTION
The productivity of mining site operations is a clear and present threat to the sustainability of the
industry, and requires innovative approaches to mitigate its effects; traditional continuous
improvement efforts have not been enough, but rather require a fundamental rethinking of the way
to conduct the business. Fortunately, mining is characterized by high concentration of costs in
specific operations, which thus constitute candidates for study; typically, 70-80% of operational
costs are associated with physical asset management at sites, and, further, approximately 50% of
mine costs are incurred in transport through standard haul-truck operations. Physical asset
management has traditionally rested on long standing paradigms on asset equivalence,
independence of equipment and operations, and sufficiency of evaluations through fleet averages;
this paper challenges those assumptions both in theory and in documented cases of mine transport
operations performance at sample sites. This alternate vision is then developed through a fullfledged approach based on differentiated root-cause performance parameters, integrated planning
through joint selection of operational assets, and objective optimizations of global performance in
both operational effectiveness and cost efficiency. The integrated planning methodology is then
presented, and applied to real operations in two sites in northern Chile, documenting substantial
performance increases in asset productivity. The application of the approach to other sites is
discussed, offering further potential improvements depending on site configurations and marginal
costs of assets.
The revision of paradigms opens up new management concepts and opportunities for productivity
and profitability of physical assets, in particular in the economic management of activities, with a
focus of attention in where and how costs are incurred and the potential to reduce them.
ASSET PERFORMANCE
The practical application of physical asset management on site entails three assumptions:
1.
2.
3.
Assets are generically equivalent in performance;
Performance of equipment and operational tasks are independent;
Functional performance is evaluated by global fleet averages.
Critical review of these assumptions leads to discrepancies and alternate models from traditional
developments.
1st Paradigm Revision: asset performance is a function of inseparable combinations of individual
behaviors of equipments and tasks, which are not independent nor bound by fleet averages.
Proof: the classical expression for operational effectiveness (OEE) defines:
OEE = Ave (A x U x PR x QR) = Ave ((A x PREq) x (U x PROp) x QR)
(1)
where A, U, PR, QR are the availability, utilization, production rate or yield, and quality rate of
equipment in operations. Additionally, the production rate is factored in yield of the equipment in
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itself (mechanical yield) and of the operational tasks (operational yield), and given that in mine
operations there is no rejection of product, the quality rate is taken as one.
On the other hand, the expression for production cost (in transport) entails:
PC = Ave (PCF + PCV) = Ave (PCF + (FE + (FC + FM)) x CR)
(2)
where PCF, PCV are fixed and variable operating costs and the variables entail cost factors for
energy, operational consumables, and maintenance as a function of realized productive capacity.
Traditional management postulates that the OEE expression is separable by functional factors
OEE = Ave (A x PREq) x Ave (U x PROp)
(3)
This postulate however, when applied to populations (i.e., fleets), is valid if and only if the factors
are statistically independent, which is not the case due to implicit correlations arising from:
1.
2.
3.
4.
5.
Poor operation, damaging both maneuvers as well as mechanical equipment condition;
Poor maintenance, which detracts from the yield and availability of the equipment;
Failures, detracting from availability and eventual yields until regaining regular operation;
Low power, braking, etc. which detract from yield in maneuvers;
Others.
These correlations imply that the management postulate is formally incorrect, and that the impact
of the error is significant; model deviations as a function of standard deviations of equipment and
tasks (assumed equal), qualified with correlation factors involved, reach significant values (>2x)
even in the face of modest correlation factors (0.2). Thus, management by functional segregation is
both formally and in practice incorrect, and evaluation of performances requires joint
consideration of equipment and operational task behaviors in the field.
2nd Paradigm Revision: both equipment and operational tasks exhibit intrinsic performance
scattering per units, which are dependent on installed infrastructure and not affected by
operational or maintenance tactics.
Proof: long term (2 years) tracking of equipment performance statistics highlights the existence of
random scatter per units of the order of 20-30% in basic performance indicators; Figure 1 presents
cumulative maintenance intervention frequencies for fleets of haul-trucks in two sites in Chile.
No. Maintenance Interventions per Unit 930-E4 Fleet
No. Maintenance Interventions per Unit 793B Fleet
680
950
660
900
640
850
620
800
600
750
580
700
560
650
540
600
520
550
500
500
CA-58
CA 59
CA60
CA61
CA62
CA63
CA64
CA65
CA66
CA67
CA68
CA69
Figure 1 Maintenance Interventions per Units in Haul-truck Fleets
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These scatterings are generic over unit performance measures, in non-availability, reliability,
maintenance costs, etc., as illustrated in Figure 2 relative to fleet maximums.
MTBF
Maintenance Costs (Matls & Servs)
100%
100%
80%
80%
60%
60%
40%
40%
20%
20%
TKD0180
TKD0179
TKD0178
TKD0171
TKD0170
TKD0169
TKD0168
TKD0166
TKD0165
TKD0164
TKD0162
TKD0149
TKD0148
TKD0147
TKD0146
TKD0144
TKD0145
0%
0%
Figure 2 Scatter per Unit in Equipment Behaviors
Under long term averages, performance of units should tend to homogeneous, reflecting restitution
standards established for services at the sites; deviations thus constitute reflections of nonuniformity of the elements of fleets. The scatter is intrinsic to configurations of components, spares,
and parts currently installed in the equipment, and cannot therefore be reduced through
maintenance tactics normally applied on site.
On the other hand, operational tasks are also non-homogeneous, and are subject to scattering due to
maneuvers, mine infrastructure (e.g.., grades), and environment conditions (e-g-, congestion,
visibility); suffice it to consider that, in a 500 mt. pit, stripping ratio 2:1, and distance to dumps of 2
km., haul-trucks assigned to the lip of the pit will have operational effectiveness 36% greater than
those assigned to the bottom.
Unit performance scatter is therefore intrinsic to operational activities, both in equipment and in
operational tasks, and require joint actions to mitigate their effects.
METHODOLOGY
In this multi-variable scenario, the goal is to organize activities (equipment & tasks) so as to
maximize operational effectiveness and efficiency. Given the problem statement, the solution
factors are reduced to:
1.
2.
Unit performance (equipment & tasks) specifications;
Joint performance optimization algorithm.
In a planned maintenance environment, such as that of the mining industry where indices typically
reach levels of 60-70%, determination of performance metrics rests on separation of scatter from
random effects of damage and free failure modes. Individual equipment scatterings are however
quasi-constant, that is, slowly varying through component, spares, and parts replacement in
ordinary maintenance tasks, and thus are amenable to recovery.
Note that these variations on equipment imply that the optimum fleet is automatically updated
through migration of performance indices of the different units as they are subject to rotation of
their configurations. Given the slow variations, the unplanned performance remnant can then be
quantified in first instance as “history commands” through sufficient time (6-9 month) moving
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averages, or estimated with greater accuracy through statistical inference techniques that separate
the concurrent effects.
On the other hand, scatter of operational tasks constitute design conditions of the operating
context, which may currently lack formal structuring but that in principle are known a priori.
Integrated operational planning thus involves the recovery of equipment and operational
parameters and their joint application by unit selection to maximize global performance in both
operational effectiveness and economic efficiency, as illustrated in Figure 3.
Operational Prod.
(tasks)
Operational Prod.
(equipment)
Eq. Effectiveness
Oper. Effetiveness
Asset Management
Oper. Cost
Economic
Dispatch
Utilization
Op. Yield.
Eq. Cost
Effective
Dispatch
Availability
Operations
Management
Maintenance
Management
Unit
Cost
Reliability
Eq. Yield.
Process
Capacity
Figure 3 Integrated Operational Planning
Given such specification of equipment and task performance, optimization algorithms for
operational effectiveness and economic efficiency entail:
1. Operational Effectiveness: the function to be optimized involves the products of
participating equipment and tasks effectiveness as documented in expression (1):
OEE = Ave ((A x PREq) x (U x PROp))
Mathematics then states that the maximum of the function is the result of ordered products
of the maximums of the participating series, as illustrated in Figure 4.
Optimization Logic
Equipment
Tasks
Max
Max
X
Min
Min
Figure 4 Operational Effectiveness Optimizations
Thus, the optimal assignment criterion of equipment to tasks corresponds to “most effective
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equipment” to “most effective tasks”.
2. Economic Efficiency: the corresponding function entails the fixed and variable cost factors
of a mining transport operation as given in expression (2)
PC = Ave (PCF + (FE + (FC + FM)) x CR)
Maximum efficiency then results from minimizing the scattering impacts on operational
variable cost factors.
RESULTS AND DISCUSSION
Operational effectiveness management then seeks to maximize OEE of the fleet of operational
assets by joint selection and the assignment of equipment units to operational tasks. In a case of
transport operations at Escondida site, involving eighteen (18) CAT 793B haul-trucks of nominal
280 tons load capacity, the quantified availability profile of the fleet is presented in Figure 5.
Availability due to Failures.
TKD0164
TKD0147
TKD0169
TKD0168
TKD0183
TKD0166
TKD0179
TKD0162
TKD0178
TKD0182
TKD0145
TKD0181
TKD0165
TKD0171
TKD0144
TKD0170
TKD0146
TKD0180
TKD0163
TKD0149
100,0%
90,0%
80,0%
70,0%
60,0%
50,0%
40,0%
30,0%
20,0%
10,0%
0,0%
Figure 5 Estimated Fleet Availability Profile
The required mine plan dictates two hauling operations, one at mid-depth of the pit and the other
at upper levels, with transport profiles of Table 1.
Table 1 Haul-trucks Transport profiles
Haul Profiles
Travel
Grade in Pit Cycle Time
Yield
Profile 1
4.4 kms
9°
41.5 mins
52,810 ton-km/hr
Profile 2
6.0 kms
0°
19.8 mins
221,049 ton-km/hr
The OEE optimality criterion then distributes the hauling units based on truck availability and
transport profiles so as to maximize operational effectiveness performance, yielding a 7.5%
improvement relative to the case of random assignments of trucks; additionally, truck selection by
yield would add a comparable improvement.
Economic management on the other hand seeks to minimize variable cost factors, based on the on
non-homogeneity of assets with quantified scatter as planning guide. Operational variable cost
factors, in fuel consumption, consumables, and maintenance, are tracked by management
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information systems normally available on site. Thus, Figure 6 presents yields and variable cost
factors for units of a fleet of low-profile AD30 haul-trucks in a site in northern Chile.
Dumper Energy Efficiencies
Dumper Yields (Ton-Km/hr)
Yield
Hrs/Refill
Op Hrs
600,00
500,00
Dumpers Maintenance Costs
Op Hrs
$/Hr
1400
1400
14,0
1200
1200
12,0
Op Hrs
$50,00
1.400
$45,00
1.200
$40,00
1000
1000
10,0
800
800
8,0
600
600
6,0
400
400
4,0
200
200
2,0
0
0,0
400,00
$35,00
$30,00
1.000
800
$25,00
300,00
200,00
$20,00
$15,00
600
400
$10,00
100,00
0,00
0
200
$5,00
$0,00
0
Figure 6 Yields, Energy Efficiency, Maintenance Cost per Unit, AD30 Fleet
The figure also plots truck operating hours, which in general do not show a relationship to an
economic efficiency criterion at the site.
These cost factors then allow building of a marginal cost profile for units of the fleet, that is, the cost
per operating hour of each equipment in the operation, as presented monotonically in Figure 7.
Marginal Costs per Unit ($/Ton-KM)
$0,60
$0,50
$0,40
$0,30
$0,20
$0,10
$0,00
Figure 7 Marginal Costs per Unit, AD30 Fleet
Economic management proposes that, for an equal equipment productive capacity (AxPREq), units
of lower cost are to be preferred; the resulting redistribution of unit operating hours is presented in
Figure 8 for the AD30 fleet, with an associated 31.8% cost reduction in site transport operations.
Economic Redistribution of Operating Hours
Op Hrs
Rev Hrs
1800
1600
1400
1200
1000
800
600
400
200
0
Figure 8 Redistribution of Operating Hours per Unit, AD30 Fleet
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This concept is equally applicable to parallel equipment configurations in fixed plant; in the case of
a set of six ceramic filters in a concentrator plant; the redistribution of operating hours on economic
efficiency considerations returns a 20% saving in operating costs for this case.
These results illustrate in concrete terms the possible cost reductions through leveraging of
marginal cost profiles for sets of operating units.
It should be noted however that these results are restricted by the assumption of preservation of
operational effectiveness of the fleet involved, and that loosening that restriction yields additional
savings potential. Given operational capacities in excess of optimal effectiveness requirements,
stoppage of excess units of higher marginal costs leads to corresponding reductions in operational
costs of the site; Figure 9 presents those reductions as a function of remaining unit OEE increments
for the example AD30 fleet...
Cost Reduction by OEE Increase
105%
100%
95%
Reduction %
90%
85%
80%
75%
70%
65%
60%
42%
50%
60%
70%
80%
OEE
Figure 9 Cost Reductions by OEE Increases, AD30 Fleet
Thus, in a quasi-linear relationship, a 38% OEE increase results in a 30% reduction in operating
costs; the utilization of excess units represents not only sunken capital costs, but a direct increase of
operating costs at the site.
CONCLUSIONS
The stated paradigm revisions pertaining to physical asset management in the mining industry
establish alternative solutions to conventional approaches, specifically in the relevance of
individual behaviors of equipment and operational tasks and the scattering signature effects
involved. These behaviors are quantifiable a priori, thus constituting an objective and documented
basis for optimization of the operational effectiveness and economic efficiency of activities at the
site.
Particularly as pertains to economic management, quantification of unit marginal costs highlights
significant opportunities for cost reductions in the order of 30-50% through equipment assignments
to operations. These approaches, since they entail derivation of management first principles, are not
subject to limitations or approximations of applicability and in fact can be extended to the variety of
equipment populations at a mining site.
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REFERENCES
Grundy, P.A., 2015, Rentabilización de Activos Físicos (Physical Asset Rentabilization), 1st Edition, PGA
Estrategia y Capacitación S.A., Santiago, Chile.
Grundy, P.A., 2015, Productividad Operacional (Operational Productivity), 1st Edition, PGA Estrategia
y Capacitación S.A., Santiago, Chile.
Grundy, P.A., 2015, “Operational Cost Reduction Through Integrated Management of Physical
Assets”, Proceedings Maplemin 2015, 1-3 Julio 2015, Lima, Perú
Grundy, P.A., 2015, “Integración en la Gestión de Activos”, Proceedings Mantemin-Mapla 2015, 11-13
Septiembre, Santiago, Chile.
Grundy, P.A., 2015, “Gestión de Costos en Configuraciones Paralelas – Caso Mina”, Proceedings
Mantemin-Mapla 2015, 11-13 Septiembre, Santiago, Chile.
Grundy, P.A., 2015, “Gestión de Costos en Configuraciones Paralelas – Caso Planta”, Proceedings
Mantemin-Mapla 2015, 11-13 Septiembre, Santiago, Chile.
Grundy, P.A. / PGA Estrategia y Capacitación S.A., 2015, Gestión de Activos, webpage of PGA
Estrategia, www.pga.cl
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