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A Comprehensive Approach for Maintenance Performance Measurement


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Performance Indicators are needed in order to be able to control maintenance processes. Although diverse researches have been done attempting to develop maintenance performance measurement, most of the existing resources suggest Overall Equipment Effectiveness (OEE) for measuring maintenance performance. However, it does not provide the means for a complete performance analysis in maintenance, individually. This paper demonstrates different approaches for determining Maintenance Performance Indicators (MPI) and provides a comprehensive approach for evaluating performance in terms of economic and technical ratios, as well as OEE. Some examples are also presented for a better understanding of the demonstrated ratios.
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A Comprehensive Approach for Maintenance Performance
Arash Shahin
PhD in Quality Engineering, School of Mechanical and Systems Engineering
University of Newcastle, Newcastle upon Tyne, NE1 7RU, UK
Abstract Performance Indicators are needed in order to be able to control maintenance processes.
Although diverse researches have been done attempting to develop maintenance performance
measurement, most of the existing resources suggest Overall Equipment Effectiveness (OEE) for
measuring maintenance performance. However, it does not provide the means for a complete
performance analysis in maintenance, individually. This paper demonstrates different approaches for
determining Maintenance Performance Indicators (MPI) and provides a comprehensive approach for
evaluating performance in terms of economic and technical ratios, as well as OEE. Some examples
are also presented for a better understanding of the demonstrated ratios.
The costs of maintenance are estimated to be between 15% and 40% of production costs (Dunn, 1987;
Lofsten, 2000), and the trend toward automation has forced managers to pay more attention to
maintenance. Managers have figured out, that maintenance, with its high cost and low efficiency, is
one of the last cost saving frontiers in management (Sheu and Krajewski, 1994). Effective
maintenance is critical to many operations. It extends equipment life, improves equipment availability
and retains equipment in proper condition. Conversely, poorly maintained equipment may lead to
more frequent equipment failures, poor utilization of equipment and delayed production schedules.
Misaligned or malfunctioning equipment may result in scrap or products of questionable quality.
Finally, poor maintenance may mean more frequent equipment replacement because of shorter life
(Swanson, 2001).
Performance indicators are numeric values for an aspect of a (sub) process that isn’t influenced by
related processes and is representative as a measure for the effectiveness and/or efficiency of that
aspect of the (sub) processes. Management requires performance information to be able to control the
maintenance process. In practical situations, performance indicators are needed by the maintenance
manager, supervisors, foremen and engineers, as well as planners. The absolute value of such
indicators compared to a norm (to be chosen beforehand) or a trend in this value can be used to glean
performance levels (Arts et al., 1998).
Performance measurement has received much attention lately (see, for example, Neely et al., 1995
for a review). In the past, maintenance performance reporting in many companies has been limited to
a minimum budget reporting (Pintelon, 1990). This is only partly due to the fact that both academics
and practitioners have, for a long time, failed to recognize maintenance management as a full-grown
business function. Another reason is that maintenance performance reporting is difficult. The main
issue is of course, “how to measure maintenance performance?” or “what performance indicators do
we need in order to gain sufficient insight in the maintenance operations?” It is clear that due to the
complexity of the maintenance function and its dependence on the specificity of the situation on-hand
these questions are not easy to answer. Various researchers provide the following synthesis that most
organizations and their managers are aware that they need to measure productivity for (i) comparing
their own performance with that of their competitors, (ii) knowing the relative performance of their
individual departments, (iii) comparing relative benefits of various inputs, and (iv) collective
bargaining purposes while dealing with trade unions (Aggarwal, 1980).
Whatever form they take, productivity and efficiency calculations are made for two purposes; to
decide upon the allocation of resources, and to evaluate the performance of a business after resources
have been committed (planning and control). A partial maintenance productivity goal for instance is
that the firm should seek to maximize its maintenance productivity in economic terms, and should aim
at producing any level of output which is decided upon at minimum maintenance cost with respect to
the machine’s state (i.e. availability). Therefore, the aim of a maintenance quality assessment is to get
an idea about the performance of maintenance through the assessment of the existing problems, both
from the organizational and operational point of view, so as to be able to suggest measures of
improvement, determine the priorities for the recommended measures and set up a plan of action
(Groote, 1995).
Observations of practice led researchers to develop the following prescriptions for practice that
had been independently developed in several of the firms (Schroeder et al., 1986): (i) measurements
should be understandable by all organization members, (ii) measurements should be accepted by the
individual involved, (iii) rewards and measurements should be compatible and (iv) measures should be
result oriented. It is important to note that the calculation of so-called overall equipment effectiveness
(OEE) in Total Productive Maintenance (TPM) as the product of availability, quality performance and
speed, is not really a complete analysis. It does not take account of costs and profits, and so is not a
complete measure by which competitive machines or systems should be compared, or the deterioration
of systems over time should be monitored (Sherwin, 2000).
In this paper, an overview based on literature study and industrial practice, of commonly-used
performance measurement systems is taken with some of their examples. Next, the overviewed
concepts are classified in order to provide a comprehensive approach for measuring maintenance
performance. Finally, conclusions and recommendations concerning the proposed approach will be
Maintenance Performance Indicators (MPI) in detail
Maintenance performance is generally hard to measure, as one should not only consider quantifiable
parameters but also the quality of the performed maintenance and its organization. As maintenance is
a logistic function integrated into a production process, its efficiency is hard to appreciate in absolute
value. Consequently, performance parameters cannot be chosen among operational figures. They
must be defined in relative values, i.e. through ratios (Groote, 1995). Maintenance is a service
function for production. Both the merits and shortcomings of the service rendered are not immediately
apparent. Because of the time-lag effect it is difficult to specify the amount and intensity of the
service (and the corresponding required amount of money) needed for ensuring proper plant
performance. Another aspect which makes it difficult to measure maintenance output is the fact that
maintenance activity is closely related to production activity and organization, which in turn is
affected by still other functions (e.g. sales). Maintenance managers often have access to many data,
but seldom receive the information they need: data often have to be collected from many different
sources, and are often not structured and not aggregated. This means that processing the data to obtain
useful management information, e.g. a few typical performance indicators, is a time-consuming
business. Data accuracy and report timeliness are other frequent problems encountered in
maintenance performance reporting. Furthermore, the maintenance manager often lacks the tools (e.g.
query facilities for the computer systems) or time to draw up the required reports. Fortunately the ever
improving MMIS (maintenance management information systems) assist in solving these problems
(Pintelon, 1997). In the following, MPI is more demonstrated through the means of productivity
Productivity is the value output(s) divided by the value input(s). In the case of maintenance workers,
the output can be considered as the number of tasks completed, while the input is the time taken to
complete them using the same scale of measurements. Visser (1998) models maintenance as a
transformation process encapsulated in an enterprise system. In the input-output model, the resources
deployed to maintenance include labour, materials, spares, tools, information and money. The way
maintenance is performed will influence the availability of production facilities, the volume, quality
and cost of production, as well as safety of the operation. These, in turn will determine the
profitability of the enterprise. Since the use of external service providers has always been an option in
maintenance decisions, the inputs to the maintenance process should also include these external
resources (Figure 1).
Enterprise System
Production System
Maintenance System
Figure 1. Input-output model for the enterprise system (Visser, 1998; Tsang, 2002)
Good productivity when planning maintenance is vital. Good productivity is reached when the total
maintenance costs and down-time costs are reduced to a minimum with a given minimum level for the
state of the production system, i.e. the minimum production rate is fulfilled (Lofsten, 2000). Some
difficulties, such as finding all inputs and outputs, organizations use other measures than total
productivity (Dunn, 1987). Productivity measurement attempts to answer the basic question of how
much input is required to achieve a particular output. This question may be posed for a single
machine, manufacturing cell or an entire economy. A useful measure of productivity is partial
productivity. This measures the total output divided by one kind of input.
Partial factor productivity = inputSingle
outputTotal (1)
However, there are two categories of ratios under which the performance indicators can be presented:
(1) Economic ratios, which allow the follow-up of the evolution of internal results and certain
comparisons between maintenance services of similar plants.
(2) Technical ratios, which give the maintenance manager the means of following the technical
performance of the installations.
Economic ratios
Ratios linked to maintenance costs
Among the economic ratios that exist, those which seem to be the most representative have been
chosen hereafter. These types of ratios must obviously be completed by a ‘customizing’ for each
company. Here are some examples of this type of ratio:
production of valueAdded
emaintenanc ofcost Direct
The direct cost of maintenance comprises: cost of manpower, cost of materials (spare parts, lubricants,
miscellaneous), cost of subcontracted work and overheads. The added value of production constitutes
the total cost of production less the cost of raw materials. This ratio fixes the importance of
maintenance in a plant. Using the added value and not the total cost of production eliminates the
important fluctuations in the plant itself as well as between enterprises due to the fluctuation in the
price of raw materials.
up-start since hours operating ofNumber
up-start sinceunit production a of emaintenanc of costs Cumulative
This ratio links the total direct costs of maintenance to a time unit. Another important ratio is:
cost emaintenancdirect Total
costmanpower emaintenanc Total
Ratios in relation to spare parts
Some examples of this type of ratio are:
equipment production of t valueReplacemen
estock valu Average
This ratio takes into account the components of maintenance costs in relation to exterior ones.
Likewise, it has a comparative value for similar plants or for a developing enterprise.
periodmonth -12 aover estock valu Average
periodmonth -12 aover spares issued of valueCumulated
This ratio gives the stock rotation. This means the number of times the value of the stock is issued per
partssafety without estock valu Average
months 12over partssafety of issues of valueCumulated months 12over issues of valueCumulated
This ratio eliminates the safety-parts issues in the ratio of stock rotation. These parts are generally
supplied together with the production equipment. From the accountancy point of view, they are often
considered together with the fixed assets. A substantial reduction in the stock value then arises
without decreasing the value of the issues. Here too the stock rotation would not reflect the real
situation. Even if it is sometimes difficult to define and classify the safety parts with precision, the last
ratio will be more precise than the previous one. When considering two types of spare parts
classifications, it can be seen that the last ratio is hardly influenced by variations, whereas the previous
ratio is very sensitive. In other words, any error in classification of safety parts will have a limited
impact on the last ratio and a strong impact on the previous one. Also, ratios in relation to spare parts,
such as consumption of store items are important:
cost emaintenancdirect Total
purchasesdirect and issues store Total
Ratios in relation to manpower
Some examples of this type of ratio are:
emaintenanc ofcost Direct
(manpower) tingsubcontrac ofCost
This ratio follows the evolution of the policy adopted for subcontracting. Subcontracting is defined as
the total amount of maintenance operations which are given to outside companies. It should be
mentioned that ‘subcontracting’ often means manpower and material. In order to have a good idea
about the impact of each component on the maintenance cost, it would be good to consider them apart.
emaintenanc ofcost Direct
personnel emaintenanc ofCost
This ratio gives an idea of the impact of fixed or temporary personnel.
Technical ratios
The technical ratios, far more numerous than the economic ratios, are also much more varied. This is
why only those considered to be fundamental and applicable to all companies are described hereafter.
Contrary to the economic ratios, which are often in relation to the whole plant maintenance, the
technical ratios often concern single lines, machines or apparatus.
Basic MPI: Effectiveness
Effectiveness is a function of utilization (U), performance (P) and method level (M). Utilization (U) is
a measure of how much of the available time is spent working (as distinct from waiting or idling). It is
influenced by late starts, early finishes and extended breaks:
Utilization = hoursdelay hours work Total
hours work Total
Utilisation is the ratio of actual output to designed capacity. The designed capacity of the machine is
the maximum output that could, ideally, be achieved in a week. This ignores the time needed for
maintenance and setups. The effective capacity is the maximum output that could reasonably be
expected. This takes into account the time needed for maintenance and setups (Hartmann, 1987;
Meeks, 1984). The aim of capacity planning is to match available capacity to forecast demand, but is
not focused in this paper. Al-Muhaisen (2002) also suggested an adjusted factor to be multiplied by
the above function.
Performance (P) is a measure of the speed at which people work. It is determined by the
conditions surrounding their work, by the level of their innate and acquired skills, and by the effort
which they put into the task.
Performance = hours Actual
hours Standard
Method level (M) is the method used compared to good maintenance practice. Availability and
quality of standard practices is determined by the types of tools, equipment and work sequences used.
Its values range from 90 per cent for poor to 100 per cent for a good maintenance system. Thus:
Effectiveness = P × M × U
The most common method used to calculate the two indices (U and P) is a statistical sampling of the
current work.
Advanced MPI: Overall Equipment Effectiveness (OEE)
Technical ratios can be placed under two categories:
(1) Those which interest the users of the equipment and are a measure of the efficiency of
(2) Those which more directly interest the maintenance manager in measuring the efficiency of
maintenance policy.
Both are covered by an overall equipment effectiveness indicator (OEE), which is a company or
production sector performance indicator (Nakajima, 1989). However, to eliminate waste, Toyota
became one of the first companies to implement TPM (Nakajima, 1988). Toyota measures six
categories of equipment losses throughout its production system. These include: (a) equipment
failures, (b) setup and adjustment, (c) idling and minor stoppages, (d) reduced speed, (e) defects in the
process, and (f ) reduced yield (Nakajima, 1986; Ben-Daya and Duffuaa, 1995). OEE takes into
account all the six losses has been defined as (Nakajima, 1986; Groote, 1995; Blanchard, 1997; Ollila
and Malmipuro, 1999; Park and Han, 2001):
OEE = Availability × Speed or amount of production × Quality rate
Availability = timeproductionPlanned
downtime Unplanned- timeProduction Planned
Speed = productionofamountPlanned
Quality rate = amountActual
Availability is the degree to which the operation is ready to work. An operation or machine is not
available if it has either failed or is being repaired. There are several different ways of measuring
availability depending on how many of the reasons for not operating are included. Lack of availability
because of planned maintenance or changeovers for example. Availability is one dimension of the
multidimensional measure state and we have to transform the state to availability. In the maintenance
case, there are only nonlinear functions between preventive maintenance and state, state and
availability and so on (Lofsten, 2000). Also, when production systems are designed so that they run in
a trouble-free manner, and can be easily rectified when necessary, they are said to have a high
maintainability. This can be quantified by the mean time to repair. The measure of reliability, the
mean time before failure, can also be combined with the mean time to repair to give the overall
measure of an operation’s availability.
The total company or production sector performance is then given by OEE × P with P = Planning
Referring to Nakaiima’s (1988), an OEE of 85 per cent is considered as being world class and a
benchmark to be established for a typical manufacturing capability; nevertheless, the inference from
Kotze (1993) article is that an OEE of less than 50 per cent is more realistic.
Some examples of the technical ratios
A number of other technical indicators are interesting to follow up. Hereafter some examples are
given. They often consider partial components of the OEE:
unplanned) and (planned emaintenancfor HourstimeproducionlTheoritica
By “hours theoretically available in a period of time” is meant the hours during which, if the machine
is technically in working condition, it can really be used. For a 30-day month in a factory running at
full capacity, this corresponds to 720 hours. The hours for maintenance are considered as downtime
due to breakdown, preventive maintenance, repairs, inspection, lost time awaiting spares and waiting
time for maintenance personnel. Detailed analysis of causes of downtime will highlight whether the
reasons are of a maintenance or production origin. The above ratio indicates the time during which the
equipment should normally be in production. It is one of the principal performance ratios of
unplanned) and (planned emaintenancfor Downtime hours operating gross ofNumber
hours operating gross ofNumber
It is the ratio of operational availability, influenced by maintenance. The number of operating hours is
defined by the theoretical production time less planned and unplanned downtime (for maintenance and
other reasons). Downtime for maintenance includes: repairs, preventive and corrective maintenance,
overhauls and troubleshooting due to micro failures.
hours operating gross ofNumber
emaintenanc unplannedfor downtime of hours ofNumber
The numerator is calculated based on total downtime for maintenance reasons, less the hours for
planned maintenance. This ratio represents the lost production hours due to unplanned downtime
(breakdown) for maintenance reasons.
hours operating gross ofNumber
stops production ofNumber
This ratio characterizes the number of failures in the system per unit of time and is a measure of the
failure or breakdown rate. It is generally preferred to the previous one wherever production of wastes
(speed losses) at the time of shutdown or start-up is important and expensive.
hours operating gross ofNumber
unplanned) and (planned hours emaintenanc ofNumber
This ratio measures the evolution of the state of material. It can provide a forecast, by material group,
of the maintenance workload for the personnel.
emaintenanc plannedfor hoursman ofNumber
eshootingfor troubl hoursman ofNumber
This ratio measures the efficiency of the maintenance policy. By ‘troubleshooting (unplanned
maintenance)’ is meant the urgent interventions carried out because of the risk of serious accident or
stoppage of production as well as those necessary to restart an apparatus under satisfactory conditions.
Troubleshooting always causes an immediate disfunctioning in the production programme and
maintenance personnel. ‘Planned maintenance’ includes all maintenance work except that which
involves major overhauling work which can shut down the material for a long period.
personnel emaintenancby spent hoursman Total
workpreparedon spent hoursMan
This ratio measures the level of work preparation. It can be a sign of the efficiency of the maintenance
jobs for these orkedactually w timeof totalSum
workemaintenancfor allocated timeof totalSum
This ratio gives an indication concerning the performance of interventions. From the foregoing
discussion two aspects are apparent and must be considered:
One is the interdependence of the ratios in general. A ratio on its own rarely signifies anything
specific. It must always be backed up or confirmed by examining others in relation to the same topic.
Another is the need for a precise terminology which is used in the numerators and denominators.
plannerby planned hoursCraft
This ratio will determine whether the field supervision is using the craftsmen on those work orders that
were planned (Arts et al., 1998).
hourslabor Actual
hourslabor Standard
This will determine whether the planners are capable and whether the productivity of the craftsmen
has changed.
scheduled orders work Total
scheduled as executed ordersWork
will determine whether the field supervision are following the approved schedule.
hours emaintenanc Total
hours emaintenanc Preventive
monitors the relative amount of preventive maintenance (PM) done by the unit. The profile of the
plant will dictate what is an appropriate amount of PM.
orderson work performed hours actual Total
hours work plannedon performed hours actual Total
The indicator gauges the effectiveness of maintenance management. An inadequate low score
indicates a possible need for increasing productivity of planners or their number. Another explanation
is that the fraction of corrective maintenance is too high, which would lead to revision of the
maintenance concepts. The measurement of this fraction using the actual time worked is considered
superior to calculating the fraction of the total number of work orders prepared, since, generally, there
is a great variability in the duration of the work orders.
placein isconcept emaintenanc preventive which
forequipment on orders work ofnumber Total
placein isconcept emaintenanc preventive
for whichequipment of failure todue
orders work emaintenanc corrective ofNumber
orderWork = basis seight weekon hours available Total
basis seight weekon planned hours ofNumber
A possible need for additional labor (contractors) can be identified using the work order backlog. A
value between 50 and 70 percent is thought to indicate a good performance, since corrective
maintenance can likely be executed without endangering preventive maintenance tasks.
New methodology: A comprehensive model for maintenance performance measurement
Some properties cannot be represented by a single measure; two or more are required. Such a
requirement can be revealed by careful analysis of the meaning of the concept. The ‘state’ of objects
or production systems is such a property, i.e., a ‘multidimensional property’ (capacity, availability,
etc.). On the other hand, we must realize that in the traditional approaches, MPI has been considered
only a partial productivity index, and the firm may in fact have multiple objectives. However, in this
paper it is assumed that the ratios, both economic and technical permit maintenance managers to
follow the evolution of maintenance performance and to knowingly make any decision necessary for
improved management. Therefore, considering all types of MPI demonstrated, a comprehensive
approach is summarized and suggested for measuring maintenance performance (Figure 2). As it is
shown, it is also assumed that the elements in the basic approach are compatible with the elements in
the new approach; hence the broken lines indicate this relationship. The new approach enables
managers to balance their resources and activities with their planned objectives. In other words,
considering the proposed approach, a maintenance manager could be able to achieve more objectives,
technically and economically; and could optimize the maintenance activities in order to access to
maximum possible values for both types of ratios, simultaneously, as well as gaining the individual
benefits of indicators, which allow (Groote, 1995):
the taking of any immediate necessary action to face emergencies;
the request for analysis reports and detailed studies on certain topics;
the correction of deviations, by specific actions, or verifying the effects of any previous links;
the preparation, in detail and with justification, of budgets for operation and investment;
the informing of management and other departments of the technical and economic progress of
maintenance in the plant;
the justification of reorganization or restructuring and follow-up of the results of these modifications
by using existing ratios or by new ratios created for this purpose.
Efficiency OEE
Method level
Spare parts Manpower
Figure 2. A comprehensive model for MPI
In this paper different types of maintenance performance indicators (MPI) was demonstrated in terms
of productivity ratios. Some examples were presented for each types of the indicators, both
technically and economically. Also, a comprehensive model was designed and presented for MPI. It
seems that the use of the proposed model could make it possible for the maintenance managers to
achieve more overall objectives, as well as individual objectives, in the traditional approaches.
However, the important point is that how each of those different indicators, i.e. technical ratios and
economic ratios could affect each other as well as the final goals and strategies of organizations. This
is something, which could provide a considerable opportunity for future studies.
Although the comprehensive approach is expected to provide great advantages to the
organizations, one of the major problems is that a production process does not maintain a constant
output per unit of input. Inputs and outputs vary, and this variation must be taken into account.
Differences in productivity arising from specific local factors are therefore of less interest to
management. In order to measure the degree to which the output has been maximized it is necessary
to select a period with respect to which the measure is to be made. It is clear that in most cases one
cannot consider only the short range effects of a decision but must also consider the ‘long-range
consequences’. On the other hand, Great care must obviously be taken when examining published
ratios in international literature without further explanation. Care should also be taken when
comparing ratios of maintenance departments from different enterprises.
Maintenance becomes a part of total performance approach together with other major topics such
as quality, global cost, safety, and environment. Assessing maintenance performance is a matter of
both an in-depth practical field experience as well as structured methodology for auditing. The future
will increasingly focus on global company effectiveness which has already resulted in fundamental
changes in management patterns. Executives, therefore, need efficient decision rules. Part of them
will, without doubt, deal with maintenance due to its key role in this process. Assessing maintenance
performance gains an important dimension in this framework and new approaches, such as
‘comprehensive maintenance measurement’ are expected to enhance existing approaches.
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The prime goal of maintenance tasks is to protect items functions. These tasks come at high costs. Evaluating and comparing different maintenance tasks is strategic in making smart decisions to select the best competitive solutions that will bring the needed economic benefits to the plant. To achieve these objectives, a WLC model is vital as it predicts how the economic life of an asset can impact maintenance performance and future cash flows. Given this, FSØ facility is an oil and gas producing plant that treats effluents to meet her environmental limits. In achieving these limits, the effluent is skimmed, and the recovered oil is fed back into the processing chain. In a recent development at the facility, the plant tripped because of gas release during the skimming operation. The outcome of the CTA revealed three mutually exclusive maintenance tasks to be selected to avoid future recurrence. However, the criteria for the alternative to be selected requires that it must be economical and has the best competitive solutions. Meeting this objective requires both financial and non-financial approach to enhance decision making. Hence, a WLC was considered, and evaluation was performed on the three competing options using different WLC models to compare which options meet the criteria. The results revealed that option A, B, and C has an NPV of $(98,366.66), $(128,353.88), and $(103,397.99), respectively. Similarly, the IRR for each of the options were 37.44%, 28.60%, and 24.26%, respectively. Also, option A has a profitability index of-1.2, while B and C have a PI of-2.9 and-22.4, respectively. The EAC for each Option A, B, and C were $(11,554.11), $(15,076.40), and $(12,145.09), respectively. Alternatively, a CEA performed for the options considered revealed that A, B, and C has an incremental BCR of 0.002104, 0.001363, and 0.001944, respectively. However, the future is unknown; hence the Sensitivity and Risk analysis conducted revealed that option C is more sensitive to variations in the cost and the probability of success is 91.05% compared to A and B whose certainty was 83.24% and 86.00%, respectively. Therefore, these results indicate that option A has greater potentials to significantly improve value in the long term if proactively implemented, hence should be prioritised.
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Proper management of maintenance offers many companies significant potential for improving productivity and profitability. Traditional management thinking regards maintenance costs as accidental, rather than planned and controllable. Additionally, research in maintenance management has focused on preventive maintenance and has ignored corrective maintenance even though the latter is also considered to be a critical activity in industry. This study proposes a decision model that could assist in a comparative evaluation of alternative corrective maintenance policies. This decision model consists of a simulation model and economic analysis. The simulation model predicts inventory costs and delivery performance of a corrective maintenance policy in various production systems. Based on simulation results, an economic analysis, consisting of a net present value model and breakeven models, determines the economic value of alternative maintenance policies. A detailed example is offered to evaluate two particular correciive maintenance policies (machine redundancy and worker flexibility) although the decision model can be applied to other options. The results of the example demonstrate the decision model's capability to assist managers in selecting the best corrective maintenance policy.
Specific attention to systematic maintenance improvement in Japan started in the early 1950's with the introduction of preventive maintenance. The succeeding developments, culminating in what is known today as TPM (Total Productive Maintenance) are described. The approach was promoted in industry nationwide by the Japan Institute of Plant Engineers (JIPE), and later by its successor, Japan Institute of Plant Maintenance (JIPM). The twelve steps in the introduction of the TPM program are explained, together with the presentation of some considerations about the typically Japanese way of communication by means of small groups, comparable to the quality control circles.
An existing effective system of productivity measurement is desirable if productivity improvement initiatives are to take place. Yet amongst white collar workers, productivity measurement tends to be ill-defined and often non-existent. This article outlines a framework within which managers can measure white collar productivity.
This paper is based on a critical analysis of 27 published case-studies on productivity improvement efforts. First, the paper emphasizes why productivity is important for individual companies, for organizations as well as for nations, and outlines the most commonly used definitions and measures of productivity. Next, the paper attempts to identify all the productivity-related factors mentioned in the case-studies, and puts them under various groups of factors in such a way that each group falls under the general responsibility of one of the traditional functional managers. Further, the paper gives a critical review of commonly used, specialized productivity measures as they exist in the various U.S. industry groups, such as manufacturing, government, service industry, and others. An additional two sections are devoted to the critical assessment of the major contributors to productivity, and to the uncontrollable situations (statesof nature) that can damage productivity efforts. These two sections are followed by a focus on common problems in measuring productivity and on the misdirected efforts of certain companies or organizations. Next, the paper proposed an all-encompassing composite measure of productivity for the benefit of managers, and also outlines a sequence of essential steps that must be accomplished to obtain excellent productivity results from investment in improvement efforts. Finally, the paper underlines those precautions and safeguards without which most productivity efforts can end in futility. This section also outlines a philosophy for productivity personnel, which is—“Productivity improvement is an organic, effort-needing, time-consuming, and ongoing process.”
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.
The contemporary business environment has raised the strategic importance of the maintenance function in organizations which have significant investment in physical assets. Four strategic dimensions of maintenance management are identified, namely service-delivery options, organization and work structuring, maintenance methodology and support systems. The alternatives available are reviewed: the guidelines for selection of these alternatives, the key decision areas in each of the four dimensions, as well as the critical success factors for the transformation process are discussed. The two factors that permeate in these strategic dimensions are human factors and information flow; the latter can be made more efficient by embracing the e-maintenance model.
Maintenance is not usually noticed in the context of quality. Some quality philosophies recognize that production equipment must be maintained in order to manufacture quality products. So-called quality gurus have not emphasized that maintenance could have a significant role in quality. The studies on maintenance have concentrated on the cost effects. There is no idea what could be the magnitude of maintenance on quality deficiencies in different industries. The study carried out in five Finnish industries revealed that, in the process industries, maintenance is usually among the three most important reasons why there are quality problems. In the heavy industries utilizing continuous processes maintenance seems to have the highest impact on quality.
An enhanced approach for implementing total productive maintenance (TPM) in the manufacturing environment is discussed. The failure mode, effects, and critical analysis (FMECA) is an excellent tool that can be used in identifying system failures, failure modes and frequencies. The reliability centered maintenance (RCM) which is based primarily on the FMECA, can be used in developing a life-cycle oriented preventive maintenance programme. Finally, managers, engineers and technicians must be familiar with what is available and must utilize these tools.