ArticlePDF Available

Best-in-class maintenance benchmarks in Chilean open pit mines

Authors:

Abstract and Figures

In 2001, the Catholic University of Chile undertook a maintenance benchmarking study of six open pit copper mines having mill capacities varying between 18,000 t/d and 156,000 t/d, and collectively responsible for 58% of Chilean copper production. Key performance indices were selected to measure the effectiveness, efficiency, and development of the maintenance process. Key effectiveness indices used in the mining industry are equipment availability, reliability, and maintainability and safety indices. Reliability is often measured by mean time between failures (MTBF) and maintainability as the mean time to repair or restore service (MTTR). At the time of undertaking the benchmarking study (2001), only two of the six participating mines routinely measured these parameters. For this reason, the study focused on comparing fleet availabilities. Accident rate and accident severity (expressed as the number of lost time incidents and lost time hours per million man hours, respectively) were solicited for all permanent and contracted maintenance personnel. Financial data provided by the participating companies included total annual maintenance and mining costs, as well as a breakdown of labour, materials, and contractor costs. This data, when combined with ore and waste production data, enabled the unit costs of maintenance to be calculated. Internal efficiency indicators were solicited for: * the percentage of planned maintenance (preventive, predictive, and programmed major component replacement) carried out on each equipment fleet (blasthole drills, hydraulic and cable shovels, wheel loaders, haul trucks, and auxiliary equipment); * scheduled maintenance compliance (actual planned hours versus scheduled planned hours); * organizational efficiency in terms of the ratio of supervisors to maintenance technicians; * investment in maintenance planning as evident from the ratio of planners to maintenance technicians (planning clerks and statisticians were also considered to be maintenance planners); and * efficiency in inventory practices as evident from the stores turnover ratio and service level. Development indices were solicited for the educational level of maintenance employees and the level of commitment to maintenance training by both the companies and their collaborators, in terms of annual training hours and investment per employee. The principal conclusions of the study were as follows: * For the six open pit mines that participated in the study, maintenance costs were found to average 44% of mining costs. This highlights the importance of the direct cost of maintenance to the financial performance of mines. * Percentage planned maintenance of equipment fleets were low by world standards, averaging 35%, 56%, and 44%, respectively, for blasthole drill, shovel, and haul truck fleets. * Fleet availabilities were significantly influenced by the percentage of planned maintenance achieved. For example, haul truck fleet availabilities were found to increase on average by 1% for every 4% increase in planned maintenance over the range examined. * Maintenance cost per equipment was found to decrease non-linearly with increases in percentage planned maintenance. An empirical relationship was determined that can be used to estimate potential cost savings as a result of improved planned maintenance practices (see figure). * The ratio of maintenance planning staff to maintenance technicians varied between 1:10 and 1:24, indicating improvement opportunities for some of the participating companies. Investment in technical training (including planned maintenance practice) was found to be low by global industry standards. It was concluded that fleet availability and maintenance costs in Chilean open pit operations could be considerably improved by improving and/or developing maintenance planning standards, and training personnel in planned maintenance techniques
Content may be subject to copyright.
Maintenance/
Engineering
Best-in-class maintenance benchmarks
in Chilean open-pit mines
P. F. Knights*,The University of Queensland, and CRCMining, Brisbane, Australia, and
P. Oyanader, IGT, Santiago, Chile
KEYWORDS: Maintenance, Benchmarking,
Performance, Efficiency.
Paper reviewed and approved for publication
by the Maintenance/Engineering Division of
CIM.
ABSTRACT
In 2001, the Catholic University of Chile
undertook a maintenance benchmarking study
of six open pit copper mines having mill capac-
ities varying between 18,000 t/d and 156,000
t/d, and collectively responsible for 58% of
Chilean copper production. This paper
describes the methodology used to conduct the
study, as well as the overall results. Key per-
formance indices were selected to measure the
effectiveness, efficiency, and development of
the maintenance process. Using a balanced
scorecard philosophy, these indicators were
divided into client satisfaction, financial, inter-
nal process, and learning and growth indices.
Maintenance was found, on average, to
be responsible for 44% of mine production
costs. Percentage planned maintenance of
equipment fleets was found to be low by world
standards, averaging 35%, 56%, and 44%,
respectively, for blasthole drill, shovel, and haul
truck fleets. Fleet availabilities were found to be
significantly influenced by the percentage of
planned maintenance achieved, while mainte-
nance cost per equipment was found to
decrease non-linearly with increases in percent-
age planned maintenance. Investment in tech-
nical training (including planned maintenance
practice) was found to be low by global stan-
dards. The paper concludes that fleet availabil-
ity and maintenance costs in Chilean open pit
operations could be considerably improved by
improving and/or developing maintenance
planning standards, and investing in training
personnel in planned maintenance techniques.
Introduction
In today’s capital-intensive mining indus-
try, maintaining high equipment availability,
utilization, production, and quality (or yield)
rates is vital to the financial performance of
mining companies. Equipment maintenance
and repair (referred to hereafter as mainte-
nance) play a vital role in assuring productive
capacity and equipment capability.
Due to a lack of publicly available bench-
marks, in order to identify opportunities for
improving current equipment management
strategies, mining companies should partici-
pate in maintenance benchmarking studies.
These studies involve comparing the mainte-
nance performance of mines operated by the
same owner (internal benchmarking); compet-
ing companies with a mutual interest in sharing
data (competitive benchmarking); or compa-
nies operating in different industry sectors gen-
erally acknowledged to be industry leaders in
maintenance (functional benchmarking). Since
equipment operating parameters can vary
markedly from one mine to another, an essen-
tial element of benchmarking studies is to find
ways for eliminating bias in the data to enable
accurate comparison, the so called comparison
of “apples with apples.
In 2000, the Mining Council of Chile
(Consejo Minero a.g.) agreed to assist the Min-
ing Centre of the Catholic University of Chile in
undertaking a competitive benchmarking study
of key maintenance performance indices in
Chilean mines and concentrator plants. The
chief executives of the 17 companies that then
formed the membership of the Mining Council
were approached with regard to participating
in the benchmarking study; eight mines
decided to participate. All eight mines pro-
duced copper as the principal product. Since
one mine exclusively used underground extrac-
tion methods and another mine submitted data
in an aggregate manner that made it impossi-
ble to separate mine and mill performance, the
maintenance performance of the six remaining
mines employing open pit mining methods
were benchmarked. In order to maintain the
commercial confidentiality of the participants,
the names of these mines cannot be published.
In the study, they are referred to by letters as
mines A to F inclusively. The milling capacities
of these mines varied between 18,000 t/d and
156,000 t/d, and in 2000, the six mines were
collectively responsible for 58% of Chilean
copper production.
Study Objectives
The objectives of the benchmarking study
were fourfold:
Ascertain the relative importance of main-
tenance as a percentage of mine production
costs;
Identify the leaders in mine maintenance
performance and determine their associ-
ated best-in-class performance indices;
Identify global improvement opportunities
for the Chilean mining industry; and
•Identify specific improvement opportunities
for participating companies.
This paper outlines the methodology used
and principal findings associated with the
benchmarking study.
Benchmarking Methodology
The first task in any benchmarking study
(beyond identifying the potential participants)
is to determine which performance indices to
compare (Watson, 1993;Camp, 1994). Consid-
erable differences exist in the number and type
of indices measured by companies. Some com-
panies measure only the basic indices, whereas
others have determined additional perform-
ance indices to be useful. It is therefore neces-
Paper 30 June/July 2005 CIM Bulletin TECHNICAL PAPER
Peter F. Knights
holds a Ph.D. in mining engineering
from McGill University, a M.Eng.in
systems engineering from the Royal
Melbourne Institute of Technology, and
a B.Eng. in mechanical engineering
from the University of Melbourne. He
was recently appointed Principal
Research Fellow with the Division of
Mining and Mineral Processing
Engineering at the University of
Queensland and program leader,smart
mining systems, for CRCMining in
Brisbane,Australia.
Patricio Oyanader
holds a M.Sc. degree in mining
engineering and a B.Sc. in industrial
engineering from the Catholic
University of Chile. He is a principal
consultant with the management
consulting firm IGT,in Santiago, Chile.
His current work focuses on process
and product innovation in a wide range
of industries, including mining,
manufacturing, energy, and
transportation.
June/July 2005 1
*This study was undertaken while Dr. Knights was employed at
the Pontificia Universidad Católica de Chile.
sary to determine which performance indices
are commonly used by all benchmarking partic-
ipants; and, to narrow the focus of the study,
which of these indices should be solicited for
inclusion in the benchmarking study.
Furthermore, companies have adopted
widely different definitions when it comes to
calculating performance indices. For example,
equipment availability may be calculated on
the basis of calendar hours or programmable
hours (calendar hours minus unavoidable
losses due to energy cuts and natural phenom-
ena) in a given time period. In any benchmark-
ing study it is essential to understand and
locate these differences, otherwise, apples will
be compared with oranges or bananas!
Maintenance Performance Indices
Maintenance is essentially a support
process, as distinct from a core production
process. As Figure 1 shows, the maintenance
system responds to mine operations, which in
turn must respond to the business environment.
Maintenance can be modelled as a transforma-
tion process that transforms resources (labour,
materials, spares and repairables, support
equipment and tools, knowledge, finance, and
external services) to a set of measurable out-
comes designed to maintain productive capacity
and capability assurance. These outcomes are:
equipment availability, reliability, maintainabil-
ity, product throughput, product quality (or
yield), compliance with safety and environmen-
tal legislation, and most importantly, company
profitability. If we accept that this model accu-
rately portrays the objectives of a maintenance
organization, three classes of performance
indices can be identified. These are: effective-
ness indices (how the achieved outcomes com-
pare with target outcomes); efficiency indices
(how efficiently the resources are utilized in
producing the outcomes); and development
indices (how much is being invested to improve
maintenance service levels and efficiency).
Effectiveness Indices
Much has been published concerning
maintenance performance indices (e.g. Camp-
bell, 1995; De Groote, 1995; Wireman, 1998;
Arts et al., 1998; Dwight, 1999; Duffuaa et al.,
1999). The key maintenance effectiveness
indices used in the mining industry are equip-
ment availability,reliability, and maintainability.
Reliability is often measured by mean time
between failures (MTBF), and maintainability
as the mean time to repair or restore service
(MTTR). At the time of undertaking the bench-
marking study (2001), only two of the six par-
ticipating mines were routinely measuring
these parameters. For this reason it was
decided that the study should focus on com-
paring fleet availabilities and forego the collec-
tion of fleet reliability and maintainability
data1.Fleet availabilities were solicited in the
form of available hours/(calendar hours – lost
hours), where lost hours include energy shut-
downs and stoppages due to storms and other
natural phenomena.
Safety statistics, namely accident rate and
severity (expressed as the number of lost-time
incidents and lost-time hours per million man
hours, respectively) were solicited for mainte-
nance personnel. This included both company
employees and contractors working on non-
capital projects. Production data, in terms of
tons of ore and waste mined per year, were
solicited from the participating mines. As well
as ensuring fleet availability and reliability,
maintenance contributes to a mine’s profitabil-
ity via its cost efficiency. Total annual mainte-
nance costs for the mines were solicited which,
combined with the production data, enabled
the unit costs of maintenance (US$/ton) to be
calculated. These costs include the direct and
indirect costs of maintaining the mobile equip-
ment and work shops. In one case (mine B), it
was not possible to separate the primary
crusher maintenance costs from aggregate
data submitted. This is an acknowledged
source of bias in the financial results.
Efficiency Indices
Efficiency indicators can effectively be
divided into two groups: financial and internal
process efficiency indicators. The following
financial indicators were solicited from the
companies participating in the study:
maintenance costs as a percentage of min-
ing costs;
annual cost of mine maintenance, including
the indirect costs associated with supervi-
sion and planning;
labour costs as a percentage of annual
mine maintenance costs;
•spares and repairables cost as a percentage
of annual mine maintenance costs;
contractor costs as a percentage of annual
mine maintenance costs; and
overhead costs as a percentage of annual
mine maintenance costs.
Internal process efficiency indicators were
solicited for:
the percentage of planned maintenance
(preventive, predictive, and programmed
major component replacement) carried out
on each equipment fleet (blasthole drills,
hydraulic and cable shovels, wheel loaders,
haul trucks, and auxiliary equipment);
scheduled maintenance compliance (actual
planned hours vs scheduled planned hours);
organizational efficiency in terms of the
ratio of supervisors to maintenance techni-
cians (the inverse of this ratio is known as
the span of control);
investment in maintenance planning as evi-
dent from the ratio of planners to mainte-
nance technicians (planning clerks and
statisticians were also considered to be
maintenance planners); and
efficiency in inventory practices as evident
from the stores turnover ratio (value of
spares and repairables consumed vs aver-
P. F. KNIGHTS and P. OYANADER CIM Bulletin June/July 2005
2CIM Bulletin Vol. 98, N° 1088
1It is interesting to note that, in the three years that have
passed since undertaking this study, most Chilean mines now
routinely report these variables. Considerable difficulty still exists
in benchmarking MTBF and MTTR values between mines, since
no industry standard has been developed for the measurement
of these variables. For instance, some mines include all stop-
pages, including PMs, in their calculation, while others incorpo-
rate only those events which they classify as failures.
Fig. 1. The maintenance resource transformation process.
Labour
External
services
Finance
Knowledge
Tools
Spares &
Repairables
Materials
Availability
Reliability
Maintainability
Throughput
Product
Quality
Safety
Environmental
Norms
Profitability
age annual on-hand inventory value) and
service level (percentage of requisitions that
the store is able to meet without delay).
An efficiency index commonly used to
compare the efficiency of maintenance across
different industry sectors is the annual cost of
maintenance as a percentage of replacement
asset value. Due to difficulties in determining
equipment replacement values, it was decided
not to include this index in the study.
Development Indices
The development indices solicited con-
cerned:
the level of training commitment to mainte-
nance employees (both contractor and com-
pany workers), in terms of annual training
hours and investment per employee; and
•a breakdown of the educational status of
maintenance employees, in terms of the
highest level of educational certification
achieved.
The Balanced Scorecard Approach
The management of any large enterprise
involves the allocation of resources where they
will best create value. Different entities within
the same company may have competing objec-
tives requiring compromises to be sought. The
balanced scorecard, first developed by Kaplan
and Norton (1992), professors at the Harvard
Business School, is a means of balancing
opposing goals that also effectively links per-
formance indices with company strategy.
Although it has been successfully implemented
by many Fortune 500 companies, the balanced
scorecard has to date had limited application in
the management of mine equipment mainte-
nance (Tsang, 1998).
The balanced scorecard identifies four
classes of performance indices: client satisfac-
tion; financial performance; internal process;
and learning and growth (innovation and
development) indices. The first class of indices
equates to process effectiveness. The second
and third classes (financial and internal process
indices) are efficiency indices, and the last class
corresponds to the development indices. It was
therefore determined to structure and present
the results of the benchmarking study using the
balanced scorecard format.
The Structured Questionnaire
Benchmarking studies should ideally be
conducted by an independent team that visits
each of the participating mines. However, due
to financial restrictions, it was decided to con-
duct the benchmarking study via the use of a
structured questionnaire. A benchmarking
questionnaire was devised to capture the nec-
essary performance data. An annex to the
questionnaire was also prepared which speci-
fied the form in which the performance indica-
tors should be measured. A copy of the
questionnaire and annex was sent by email to
the maintenance managers of all participating
companies. In addition to maintenance per-
formance data, information was solicited con-
cerning mine operating parameters (altitude
above sea level, average haulage distances,
rock hardness, and abrasivity) and fleet charac-
teristics (fleet size, composition, and capacity).
Data Collection and Quality Assurance
Data collection and revision took place
over a period of six months. Five of the mines
participating in the study submitted data via
the questionnaire, and a visit was made to one
mine in order to directly collect data (mine C).
Quality assurance of the data was undertaken
by a three-step process. The completed ques-
tionnaires were scrutinized for missing, incom-
plete, or inexact data before being re-sent to
each participating company with a list of
queries. When the revised questionnaires were
re-submitted, a preliminary analysis of the per-
formance data was undertaken. This involved
constructing histograms of each of the per-
formance parameters, and in some cases, x-y
plots of key performance indicators vs mine
operating parameters. This process enabled
outlier data to be identified, resulting in addi-
tional queries being sent to the corresponding
companies. Following the receipt of explana-
tions and/or revisions for this data, a prelimi-
nary report was prepared and submitted to
each mine. This in turn generated queries from
some of the participating mines, resulting in
further revision of the data.
Results
The aggregate results of the benchmark-
ing study are shown in Table 1. All data are for
maintenance performance registered in 2000.
Discussion
Fleet Availabilities
Equipment availabilities are a complex
function of many factors. For example, haul
truck availability depends on planned mainte-
nance practices (preventive, predictive, and
programmed); mine altitude above sea level;
haul route distance and profile; haul route
maintenance practices; equipment type
(diesel-electric or mechanical) capacity and
age (cumulative operating hours); climatic
conditions and truck/shovel operating prac-
tices. In order to determine the best-in-class
availability for a specific mining fleet, it is
important to quantify the influence of each of
these variables.
Figure 2 shows the relationship between
truck fleet availability and percentage planned
maintenance for five of the six participating
mines. Fleet availabilities increase on average
by 1% for every 4% increase in planned main-
tenance over the range examined2.The
increase will be slightly higher or lower for spe-
cific mines and truck fleets when the other fac-
tors listed previously are taken into account. It
should be stressed that the availability values
recorded are for overall fleet availability. The six
June/July 2005 CIM Bulletin P. F. KNIGHTS and P. OYANADER
June/July 2005 3
2Recent non-published work by two mining companies operat-
ing in Chile (2004) suggests that, when planned maintenance
levels exceed 60%, there exists on optimum planned mainte-
nance level beyond which declining fleet availabilities can be
expected. The best fit curve in this case is not linear; a quadratic
fit may be more appropriate.
Fig. 2. Haul truck fleet availability vs percentage planned maintenance.
mines participating in the study operate mixed
haul trucks fleets, and while a new fleet may
have an availability in excess of 90%, the
availability of an older truck fleet will tend to
reduce overall fleet availability.
tenance represents between 40% and 50% of
mining costs (including operating and mainte-
nance costs but excluding general and admin-
istrative costs). The average value is 44%. This
highlights the importance of maintenance in
the overall cost structure of mining.
Unit Maintenance Costs
The cost of maintenance per ton extracted
is dependent on environmental variables and
operational factors that are specific to each
mine. For this reason, a comparison based on
this indicator alone cannot be made. The main
factor affecting unit cost is the size of the total
equipment fleet operated by the mine per tons
milled. An indicator that takes into account
fleet size (a function of haul route distance and
profile, equipment availability and utilization,
and operational factors such as blending) and
that can be better used for comparison is the
maintenance cost per equipment. However, this
indicator does not take into account cost dif-
ferences arising from the use of equipment of
different capacities.
Planned Maintenance
The low levels of planned maintenance
(preventive, predictive and programmed com-
ponent replacement) for the equipment fleets
indicate that company resources are being
used for reactive maintenance, resulting in
higher costs, lower fleet availabilities, and mak-
ing it more difficult to schedule proactive main-
tenance. Analyzing the data per company, it
can be seen that as planned maintenance
increases, the overall maintenance costs are
reduced as a result of a more efficient use of
resources (Fig. 3).
Figure 3 provides a useful empirical
means of estimating potential maintenance
cost savings through improvement in planned
maintenance practices.
Scheduled Maintenance Compliance
A graph prepared for fleet availability as a
function of scheduled maintenance compliance
showed strong positive correlations for shovel
and haul truck fleets. This indicates that not
only is the percentage planned maintenance
important, but it is important to meet targets
set by weekly maintenance plans.
Span of Control
Supervision ratios vary between 1/6 and
1/16 and were shown to be strongly correlated
with the use of contractors. Overall supervision
ratios are higher for companies making more
use of contractors, since not only must a con-
P. F. KNIGHTS and P. OYANADER CIM Bulletin June/July 2005
4CIM Bulletin Vol. 98, N° 1088
Table 1. Maintenance benchmark results
Performance Index Mines Inferior Superior Mean
Client (Effectiveness) Indices
Availability per fleet (%)1
Blasthole drills 6 64% 89% 78%
Shovels 6 68% 93% 84%
Wheel loaders 6 69% 88% 78%
Haul trucks 6 76% 89% 83%
Auxiliary equipment 6 63% 85% 79%
Accident frequency25 8.1 0 4.12
Severity index25 450 0 153
Financial Indices
Maintenance cost3per ton extracted (US$/ton)45 0.46 0.29 0.36
Maintenance cost as per cent of mining cost 5 40% 50% 44%
Maintenance cost per equipment (KUS$/equipment)55 486 684 549
Labour cost as per cent of direct cost of maintenance 5 0% 28% 14%
Spares and repairables as per cent of direct cost of maintenance 5 0% 49% 32%
Contractors as per cent direct cost of maintenance 5 16% 99% 50%
Overhead as per cent direct cost of maintenance 5 1% 7% 4%
Internal Process Indices
Planned maintenance per fleet (%)6
Blasthole drills 5 11% 65% 35%
Shovels 6 45% 67% 56%
Wheel loaders 6 19% 71% 40%
Haul trucks 5 25% 58% 44%
Auxiliary equipment 5 21% 66% 42%
Scheduled maintenance compliance 3 47% 91% 67%
Maintenance man hours as per cent production man hours7537% 54% 45%
Contractor man hours as per cent production man hours8520% 98% 56%
Overtime (%)950%5.3% 2.5%
Ratio of supervisors to technicians (span of control)10 41/6 1/16 1/8
Ratio of maintenance planners to technicians10 41/10 1/24 1/14
Stores turnover11 2 1.7 1.9 1.8
Stores service level11 380% 100% 92%
Learning and Growth Indices
Training cost as per cent total maintenance cost12 4 0.06% 0.39% 0.21%
Annual training hours per maintenance employee 4 27 75 55
Employees in training as per cent total employees 4 32% 100% 77%
Training investment per maintenance employee (US$/person) 4 71 870 497
Education status of the maintenance workers:13
University (%) 4 3% 24% 13%
Technical professional qualification (%) 4 4% 42% 27%
Technical training (%) 4 24% 52% 40%
High school (%) 4 13% 17% 15%
Primary school (%) 4 0% 17% 5%
1Availabilities recorded by mines C, D, and F are on the basis of calendar hours and do not take into account lost hours. Fleet availabilities for the remaining
companies should be marginally lowered for comparison.
2Safety indicators for mines A and D correspond to contractor personnel only,responsible for the majority of man hours.
3Cost data for mine B includes the primary crusher.
4Includes ore and waste (does not include ore from stockpiles). Maintenance and production costs are annual, do not include depreciation and amortization,
and are expressed in US dollars as of December 2000. The conversion factor used considers both Major Price Index (MPI) and Consumer Price Index (CPI):
Factor = 0.68*Variation CPI + 0.32*Variation MPI.
5Number of equipment includes: drills, shovels, loaders, haul trucks,and auxiliary equipment.
6Total maintenance hours are divided into two categories: planned maintenance (under schedule) and unplanned maintenance (reactive).
7Total man hours worked includes operations and maintenance for company and contractor personnel.
8Contracted man-hours include only maintenance tasks and exclude capital improvement projects.
9Mine A and mine D values correspond to contracted personnel representing 84% and 98% of total personnel, respectively.
10 Mine A and mine D values correspond to contracted personnel.
11 Average values for six months (July to December 2000).
12 Training delivered to own personnel, with the exception of mine D, which includes company and contracted personnel.
13 Mine D value corresponds to contracted personnel only.
Maintenance Costs
For the companies participating in the
study, representing 58% of the production of
Chilean fine copper in 2000, the cost of main-
tractor employ on-site supervisors,but the mine
must employ administrators to supervise the
contractors. As Table 2 shows (see subsequent
section), supervision ratios for the mines are
generally higher than those for other global
industries.This reflects the fact that the data in
Table 2 is strongly weighted by process plant
data, and the geographic dispersion of blast-
hole drills, shovels, and associated infrastruc-
ture requires that mines employ proportionally
more maintenance supervisors than is the case
for maintaining fixed process equipment.
Planning Ratio
Planning ratios vary between 1/10 and
1/24. This positions companies between the sec-
ond and fourth quartile results of the global
industry data shown in Table 2. Since mainte-
nance planning has been shown to be a key vari-
able in influencing equipment availability and
maintenance cost per equipment, an opportunity
exists for some of the participating companies to
increase their respective planning ratios.
Training Indicators
Training and development indicators for
maintenance staff show a low investment by
the participating companies. This is further
emphasized by the global industry comparison
in Table 2. Given that the majority of training
hours are dedicated to safety training, it can be
concluded that, on average in 2000, Chilean
mining companies were investing little in main-
tenance technical training. Considerable bene-
fits could be attained by investing more
resources in improving and/or developing
maintenance planning standards, and training
maintenance personnel in the use of planned
maintenance techniques.
Educational Level
The private sector mines participating in
the study employed a higher percentage of uni-
versity and technically qualified maintenance
professionals than did the state-run mine that
participated in the study. In addition, a trend
towards hiring better qualified maintenance
personnel was shown by the two recently com-
missioned mines participating in the study.
Comparison with Other Industries
In 1998, James Humphries, vice president,
manufacturing, for the engineering company
Fluor Daniel published a paper entitled “Best-
in-class maintenance benchmarks” in which
maintenance benchmarks were gathered for
148 global companies considered to be in the
top quartile of their industry according to earn-
ings and/or market share (Humphries, 1998).
The industries represented in this study
included mining and metals, refining and petro-
June/July 2005 CIM Bulletin P. F. KNIGHTS and P. OYANADER
June/July 2005 5
3For each company, percentage planned maintenance is calcu-
lated as the average of the percentage planned maintenance for
each equipment fleet weighted by fleet size. Mine B does not
consider the blasthole drill and auxiliary equipment fleets. Mine
C does not include the haul truck fleet.
Fig. 3. Annual maintenance cost per equipment vs percentage planned maintenance. 3
Table 2. Comparison of results with global industry benchmark results (Humphries, 1998)
Performance Index Quadrant
Fourth Third Second First
Client (Effectiveness) Indices
Availability per fleet (%)
Blasthole drills <78% 78 to 84 (78%) 85 to 91 >91%
Shovels <78% 78 to 84 (84% 85 to 91 >91%
Wheel loaders <78% 78 to 84 (78%) 85 to 91 >91%
Haul trucks <78% 78 to 84 (83%) 85 to 91 >91%
Auxiliary equipment <78% 78 to 84 (79%) 85 to 91 >91%
Accident frequency
Severity index
Financial Indices
Maintenance cost per ton extracted (US$/ton)
Maintenance cost as per cent of mining cost
Maintenance cost per equipment (KUS$/equipment)
Labour cost as per cent of direct cost of maintenance
Spares and repairables as per cent of direct cost of maintenance
Contractors as per cent direct cost of maintenance <8 8 to 19 20 to 40 >40 (50%)
Overhead as per cent direct cost of maintenance
Internal Process Indices
Planned maintenance per fleet (%)
Blasthole drills <65% (35%) 66 to 78 79 to 94 95
Shovels <65% (56%) 66 to 78 79 to 94 95
Wheel loaders <65% (40%) 66 to 78 79 to 94 95
Haul trucks <65% (44%) 66 to 78 79 to 94 95
Auxiliary equipment <65% (42%) 66 to 78 79 to 94 95
Scheduled maintenance compliance <15 15 to 35 36 to 70 (67%) >70
Maintenance man hours as per cent production man hours
Contractor man hours as per cent production man hours
Overtime (%)
Ratio of supervisors to technicians (span of control) <1/9 (1/8) 1/9 to 1/17 1/18 to 1/40 >1/40
Ratio of maintenance planners to technicians <1/25 (1/14) 1/25 to 1/59 1/60 to 1/80 >1/80
Stores turnover <0.5 0.5 to 0.7 0.7 to 1.2 >1.2 (1.8)
Stores service level <93 (92%) 93 to 96 97 to 99 >99
Learning and Growth Indices
Training cost as per cent total maintenance cost
Annual training hours per maintenance employee <40 40 to 69 (55) 70 to 80 >80
Employees in training as per cent total employees
Training investment per maintenance employee (US$/person)
chemical, paper, automotive, textiles, food pro-
cessing, rubber products, power, electronics,
consumer products, and chemicals and phar-
maceuticals. Of these companies, 75% were
North American, 16% Asian, and 9% Euro-
pean. Table 2 presents a comparison of
selected mine maintenance benchmark results
(the figures in parenthesis are the average
benchmark results) with the results published
by Humphries.
It can be seen that, in 2000, fleet avail-
abilities for Chilean open pit operations were
low by world standards (third quartile)4.In
addition, planned maintenance percentages
and annual training hours per maintenance
employee are in the lowest quartile. This rein-
forces the point made in the previous discus-
sion; in Chile,fleet availability and maintenance
costs could be considerably improved by
improving and/or developing maintenance
planning standards, and training personnel in
planned maintenance practices.
Conclusions and Recommendations
For the six open pit mines that partici-
pated in the study and collectively represented
58% of the copper production of Chile in 2000,
maintenance costs averaged 44% of mining
costs. This highlights the importance of the
direct cost of maintenance to the financial per-
formance of mines.
Percentage planned maintenance of
equipment fleets are low by world standards,
averaging 35%, 56%, and 44%, respectively,
for blasthole drill, shovel, and haul truck fleets.
Fleet availabilities were found to be sig-
nificantly influenced by the percentage of
planned maintenance achieved (preventive,
predictive, and programmed component
replacement). For example, haul truck fleet
availabilities were found to increase on average
by 1% for every 4% increase in planned main-
tenance over the range examined.
Maintenance cost per equipment was
found to decrease non-linearly with increases in
percentage planned maintenance. An empirical
relationship was determined that can be used to
estimate potential cost savings as a result of
improved planned maintenance practices.
The ratio of maintenance planning staff to
maintenance technicians varied between 1/10
and 1/24, indicating improvement opportuni-
ties for some of the participating companies.
Investment in technical training (including
planned maintenance practice) was found to
be low by global industry standards.
It is concluded that fleet availability and
maintenance costs in Chilean open pit opera-
tions could be considerably improved by
improving and/or developing maintenance
planning standards, and investing in training
personnel in planned maintenance techniques.
Acknowledgments
The authors would like to acknowledge
the assistance of the Mining Council of Chile
for supporting this study, as well as the main-
tenance and business improvement personnel
of the eight participating companies who gave
so generously of their time and knowledge.
References
ARTS, R.H.P.M, KNAPP, G., and MANN, L., 1998.
Some aspects of measuring maintenance per-
formance in the process industry. Journal of
Quality in Maintenance Engineering, 4, p. 6-11.
CAMP, R., 1994. Business Process Benchmarking.
American Society for Quality Control
(ASQC)/Quality Press, Milwaukee, 464 p.
CAMPBELL, J., 1995. Uptime: Strategies for Excel-
lence in Maintenance Management. Productivity
Press, Portland, Oregon, 192 p.
DE GROOTE, P., 1995. Maintenance performance
analysis: A practical approach. Journal of Quality
in Maintenance Engineering, 1, p. 4-24.
DUFFUAA, S., RAOUF, A., and CAMPBELL, J., 1999.
Planning and Control of Maintenance Systems:
Modeling and Analysis. John Wiley & Sons Inc.,
New York, 371 p.
DWIGHT, R., 1999. Searching for real maintenance
performance measures. Journal of Quality in
Maintenance Engineering, 5, p. 258-275.
HUMPHRIES, J., 1998. Best-in-class maintenance
benchmarks. AISE Steel Technology Magazine,
75.
KAPLAN, R. and NORTON, D., 1992. The balance
scorecard measures that drive performance. Har-
vard Business Review,January-February,p. 71-79.
TSANG, A., 1998. A strategic approach to managing
maintenance performance. Journal of Quality in
Maintenance Engineering, 4, p. 87-94.
WATSON, G., 1993. Strategic Benchmarking: How to
Rate Your Company’s Performance against the
World Best. John Wiley & Sons Inc., New York,
288 p.
WIREMAN, T., 1998. Developing Performance Indica-
tors for Managing Maintenance. Industrial Press
Inc., New York, 195 p.
P. F. KNIGHTS and P. OYANADER CIM Bulletin June/July 2005
6CIM Bulletin Vol. 98, N° 1088
4The availability figures in Table 2 correspond to those listed for
discrete processes by Humphries (1998).
... Maintenance costs in industries can reach 40% of production costs [14] or more [15]. Furthermore, with the recent advances in the industry, the increase in the level of automation and the complexity of the production systems, the maintenance costs can increase significantly [16]. ...
Article
Full-text available
In capital-intensive industries, physical assets and maintenance activities play a relevant and strategic role in terms of providing operational continuity and business sustainability. As a result, maintenance support structures are highly complex and sophisticated. Therefore, maintenance capacity planning must be addressed using reliable techniques to assure the adequate service levels and availabilities of critical assets at the minimum opportunity cost. There has been relatively limited research on how to determine and optimize the maintenance support structure (human resources) in such organizations. This paper proposes a novel technique for dimensioning and optimizing maintenance capacity that combines Time-Driven Activity-Based Costing and Life-Cycle Costing with the Weibull function-based reliability model. Following the main principles of the Design Science Research we propose a sophisticated but simple artifact. Through this model, it is possible to compute maintenance costs and assess both used and idle capacities, considering the behavior over time of the failure rates and the reliability of critical assets within a plant. To demonstrate how the proposed methodology addresses the problem, the model was applied in a real medium-sized Chilean comminution plant and a sensitivity analysis was performed, particularly, to evaluate the relevance of appropriate maintenance workforce planning.
... Formulas (17) (17)(18)(19) for the various management levels of maintenance, diagnostic and repair reproduces the Markov process occurring at the STAT and allows calculating machine state probability with time as well as without reference to time [4]. The open-pit trucks operation is consistently studied. ...
Article
Full-text available
Open-pit truck technical operation determines mined bulk transportation effectiveness increasing equipment’s commercial operation duration based on a robust strategy of vehicle’s maintenance, early and high-quality diagnostics, upkeep and recovery. Developed mathematical model of BelAZ open-pit truck operation for various levels of organization of servicing replicates a Markov process in the system of technological automotive transport considering possibilities of vehicle states with time and steady-state condition. The performance of open-pit trucks was consistently evaluated and the model of the technological automotive transport system was synthesized. The validation analysis of the obtained model showed its suitability for optimization of open-pit truck performance. The calculated optimal controlling actions on the open-pit truck allow developing an algorithm and technique for dynamic adjustment of parameters of maintenance, diagnostics and repair of BELAZ open-pit trucks that will become the basis for a sustainable future of industrial transport technical operation.
... They are, in parts, differently organized and use different names for the time components. This shows that despite current efforts made by the Global Mining Standards and Guidelines Group [5,6] the mining industry has not yet developed a common standard of deriving or stating equipment performance [7,8]. Most large mining companies have their internal "standard" nomenclature and time usage model. ...
... Several authors have developed studies that aim to quantify spare parts-related costs. For instance, Ref. [11] carried out a maintenance benchmarking study in a series of Chilean open pit copper mines; in such study, maintenance was found, on average, to be responsible for 44% of productions costs. The same authors reported that spares and repairable parts constitute, on average, one-third of the maintenance costs. ...
Article
Full-text available
The concept of life cycle sustainability assessment provides an interdisciplinary space to discuss the main challenges in addressing sustainability from a long-term perspective. However, uncertainty related to cost parameters constitutes one of the main challenges in LCC. This uncertainty prevents managers from precisely anticipating the exact values of important variables. These variables include the costs of spare parts inventories, which in some cases can constitute high percentages of the total cost of a product or service. We propose an activity-based costing model that handles uncertainty using triangular numbers, along with a matrix representation, and Weibull functions to incorporate the projected equipment reliability into the model. A case study based on a spare parts distribution center, which serves the mining industry in Northern Chile, is presented. The results obtained are compared with a traditional costing method. The advantage of using the proposed approach over the traditional one is evident. Future research is directed towards the implementation of a larger number of inventory policies, along with experimentation with other types of fuzzy numbers.
... Maintenance and repairs costs are a large component of production costs in the mining industry. In 2001, a benchmarking study of six open pit Chilean copper mines, collectively responsible for 58 per cent of Chilean copper production, found that, on average, maintenance accounted for 44 per cent of mining costs (Knights and Oyanader, 2005) and between 15 and 25 per cent of milling costs (depending on whether primary crushers, water supply systems and tailings dams are included in mill maintenance costs) (Knights and Oyanader, 2004). ...
Article
Full-text available
Many maintenance managers find it difficult to justify investments in maintenance improvement initiatives. In part, this is due to a tendency by mine managers to regard maintenance purely as a cost centre, and not as a process able to influence productive capacity and profit. It is also hindered by a lack of alignment between commonly used maintenance performance measures and key business drivers, and the lack of formal business training amongst maintenance professionals. With this in mind, a model to assist maintenance managers in evaluating the benefits of maintenance improvement projects was recently formulated. The model considers four cost saving dimensions. These are: 1. reduction in the cost of unplanned repairs and maintenance, 2. increased or accelerated production and/or sales, 3. spares inventory reduction, and 4. reduction in over-investment in physical assets and operating costs. This paper discusses the application of this model and a number of numerical examples are given to justify investments in maintenance improvement projects having varying objectives.
Chapter
The costs of spare parts represent an important share of the operation and maintenance costs of capital goods. Spare parts may remain in depots and warehouses for long periods of time, and, many of them require logistics activities. There are several life-cycle cost estimation models for capital-intensive production systems however, commonly, they do not consider logistics costs adequately. Thus, to incorporate all these aspects into the computation of the cost of ownership of spare parts, a time-driven activity-based costing model is proposed in this paper. Furthermore, since the dynamics of spare parts management are based on the reliability of the components, the Weibull’s function is integrated into the model. An application is presented considering a logistics distribution center. The presented approach allowed the costs estimation besides the idle capacity estimation, two important information inputs in logistics management. The results show the usefulness of the proposed model. The opportunities for further research and applications are also discussed.
Chapter
Full-text available
Different factors drive transition from Linear Economy (LE) to Circular Economy (CE). In this context, this article aims to perform a systematic literature review about the factors that drive the transition to circular economy using a bibliometric analysis. The results of this bibliographic study lie in the identification of main publications and journals, author citation analysis and taxonomy of the theory. The publications trend was between 2011 2018, and in the last two years, the amount of articles in this subject was significantly increasing.
Chapter
Full-text available
The continuous progress of the industry manufacturing globalization and competition between manufacture companies is becoming stronger. Currently, the simple production of a product does not fully meet the market demands. That's why manufacturing companies were forced to create new strategies for survival and development. As a result, many manufacturers began to seek benefits by adding services to end-products, which means moving from selling products to selling products-services systems (PSS). PSS enables innovations for products and business processes that result in the growth of business activities with new and existing customers. In addition, modularized PSS can resolve conflicts between customization and low costs. Therefore, this article analyze the techniques used for PSS modularization and the advantages it brings to companies and customers. It also allow to observe that currently techniques and / or methods for the PSS modularization are: house of quality, fuzzy logic, clustering method, correlation analysis, mathematical optimization models and other qualitative-empirical models.
Conference Paper
Full-text available
This paper discusses possibilities of technology and knowledge transfer from the Nuclear industry to Mining Operations in the field of Reliability and Maintenance. Both industries face important challenges with regard to safety, productivity and environment. The Nuclear Industry has achieved significant levels of productivity while remaining safe through a systematic approach using accumulated knowledge and developing industry tailored Reliability and Maintenance Processes. Some examples of their potential application in mining have been given. The paper presents also some indications and suggestions concerning further research work in this field.
Article
In this paper an indicator is used that reflects the “state” of a system as a function of the ages of the (groups of) subsystems of which it consists. The contribution of the ages of the subsystems to the state of the system is defined by their weights. The indicator can be interpreted as the virtual age of the system, and can therefore be used to define age-reduction factors of different types of repair in a virtual age or age-reduction process. The state indicator is used as the time scale in a proportional intensity model. In this way, the joint impact of different repair strategies and covariates on the system failure intensity can be evaluated. This relationship is then used to address the question of which subsystems to replace whenever a system comes in for repair and when to set the preventive inspection/repair interval, in order to minimize the expected costs per unit time until the next inspection and/or repair. A numerical and a practical example are given.
Book
Analyzing maintenance as an integrated system with objectives, strategies and processes that need to be planned, designed, engineered, and controlled using statistical and optimization techniques, the theme of this book is the strategic holistic system approach for maintenance. This approach enables maintenance decision makers to view maintenance as a provider of a competitive edge not a necessary evil. Encompassing maintenance systems; maintenance strategic and capacity planning, planned and preventive maintenance, work measurements and standards, material (spares) control, maintenance operations and control, planning and scheduling, maintenance quality, training, and others, this book gives readers an understanding of the relevant methodology and how to apply it to real-world problems in industry. Each chapter includes a number exercises and is suitable as a textbook or a reference for a professionals and practitioners whilst being of interest to industrial engineering, mechanical engineering, electrical engineering, and industrial management students. It can also be used as a textbook for short courses on maintenance in industry. This text is the second edition of the book, which has four new chapters added and three chapters are revised substantially to reflect development in maintenance since the publication of the first edition. The new chapters cover reliability centered maintenance, total productive maintenance, e-maintenance and maintenance performance, productivity and continuous improvement © John Wiley and Sons 1999. And Springer International Publishing Switzerland 2015.
Article
Best-in-class benchmarks provide a mechanism to help evaluate improvement opportunities in the plant maintenance function. Fluor Daniel gathered these benchmarks from 148 global companies that are considered top quartile in their industry in terms of earnings and market share. For maximum effectiveness, Fluor Daniel uses these benchmarks with a set of best practices listings which include strategies, policies, methods, and procedures that have been correlated with top quartile performance against the benchmarks. When actual performance against the benchmarks indicates opportunity for significant improvement, a gap analysis against the best practices associated with the benchmark can reveal how to achieve this improvement.
Article
Performance indicators of operational maintenance can help maintenance staff improve its operations, so that the direct and indirect costs of failure processes can be reduced. Many papers have been written on performance indicators for operational maintenance. However, no consensus on which indicators to use in a particular industry has been reached so far. The authors take an industrial engineering approach to this problem by describing the information system needed to be able to make any inferences on operational maintenance performance in the process industry. The indicators suggested focus on finding the most costly equipment from a maintenance perspective, the cost of the current maintenance concept and the major components of maintenance costs. It is emphasized that standards and procedures need to be developed and that adherence to them has to be ensured.
Article
Performance measurement remains a complex issue. This is particularly so if some absolute measure of performance is sought. A definition of performance in terms of value is restated and further developed. Reacting to this strict definition, the systems audit approach to measuring performance is developed and its use illustrated. Concludes that the absolute definition of maintenance performance, in terms of changes in value, presents difficult practical problems. Notes that a systems audit approach to performance measurement can potentially overcome some of these problems while preserving the focus on both business outcomes.
Article
Performance of the maintenance function is typically measured for operational control purposes. Since maintenance also has a strategic dimension, its performance measurement system should be linked to the espoused strategy of the function in order to get the maximum impact. This paper presents a structured approach to managing maintenance performance developed from this premise. The measurement system features a balanced scorecard (BSC) composed of key performance indicators (KPIs). The BSC is used to inform employees of the strategy pursued by the maintenance function, and to track the effectiveness of action plans in meeting the strategic objectives. The various steps involved in the process are also discussed.
Article
Describes a practical approach to carrying out maintenance performance analysis through a quality audit, using quantifiable performance indicators. The principles of a practical approach to performing a quality audit of a maintenance department are presented. The audit comprises: a survey and collection of relevant information about the organization and the operation of the maintenance department; an in-depth analysis of the information from a performance point of view; formulation of recommendations, setting priorities and plan of proposed actions; and performing cost benefit analysis to justify the proposed actions. A selection of quantifiable maintenance performance indicators used in the evaluation process is given and the values of ten performance indicators in three industrial sectors in Europe are given to illustrate the proposed approach.
A strategic approach to managing maintenance performance
TSANG, A., 1998. A strategic approach to managing maintenance performance. Journal of Quality in Maintenance Engineering, 4, p. 87-94.