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APPLICATIONS AND CASE STUDIES
Maintenance management: literature
review and directions

Data in Journal of Quality in Maintenance Engineering · January 2006

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APPLICATIONS AND CASE STUDIES Maintenance
management
Maintenance management:
literature review and directions
205
Amik Garg and S.G. Deshmukh
Mechanical Engineering Department,
Indian Institute of Technology Delhi, New Delhi, India

Abstract
Purpose – The purpose of this paper is to review the literature on maintenance management and
suggest possible gaps from the point of view of researchers and practitioners.
Design/methodology/approach – The paper systematically categorizes the published literature
and then analyzes and reviews it methodically.
Findings – The paper finds that important issues in maintenance management range from various
optimization models, maintenance techniques, scheduling, and information systems etc. Within each
category, gaps have been identified. A new shift in maintenance paradigm is also highlighted.
Practical implications – Literature on classification of maintenance management has so far been
very limited. This paper reviews a large number of papers in this field and suggests a classification in
to various areas and sub areas. Subsequently, various emerging trends in the field of maintenance
management are identified to help researchers specifying gaps in the literature and direct research
efforts suitably.
Originality/value – The paper contains a comprehensive listing of publications on the field in
question and their classification according to various attributes. The paper will be useful to
researchers, maintenance professionals and others concerned with maintenance to understand the
importance of maintenance management
Keywords Maintenance, Optimization techniques, Production scheduling, Performance measures,
Information systems
Paper type Literature review

Introductions
Throughout the years, the importance of the maintenance function and therefore of
maintenance management has grown. The widespread mechanization and automation
has reduced the number of production personnel and increased the capital employed in
the production equipment and civil structures. As a result, the fraction of employees
working in the area of maintenance as well as the fraction of maintenance spending on
the total operational costs has grown over the years. In refineries, for instance, it is not
uncommon that the maintenance and operations departments are the largest, and each
comprises 30 percent of the total manpower. Furthermore, next to the energy costs,
maintenance costs can be the largest part of any operational budget. Yet, the main
question faced by the maintenance management, whether its output is produced more
effectively, in terms of contribution to company profits and efficiently, in terms of Journal of Quality in Maintenance
manpower and materials employed, is very difficult to answer. Engineering
Vol. 12 No. 3, 2006
A lot of literature is available from various resources in the field of maintenance pp. 205-238
management. Dekker and Scarf (1998) have presented various classifications of q Emerald Group Publishing Limited
1355-2511
maintenance optimization models by analyzing 112 papers. In the area of maintenance DOI 10.1108/13552510610685075

JQME performance measurement an overview of various performance measurement systems
12,3 (PMS), including indicators, reference numbers and surveys, has been discussed in
detail (Pintelon and Puyvelde, 1997). Various approaches for measuring maintenance
performance have also been reviewed (Tsang et al., 1999). In another invited review,
Wang (2002) has undertaken a survey of maintenance policies of deteriorating systems
and has finally summarized, classified and compared various existing maintenance
206 policies for both single- and multi-unit systems with emphasis on single unit systems.
The specific objectives of this paper are:
(1) To suggest a classification of available literature in the field of maintenance
management.
(2) To identify critical observations on each classification.
(3) To identify emerging trends in the field of maintenance management.
(4) Based on above, to suggest directions for future researchers in this field.
(5) As far as possible to consolidate all available literature on maintenance
management.

The organization of this paper is as follows:
(1) After a brief introduction, in the next section existing literature on maintenance
has been classified in to a number of areas and sub areas.
(2) Detailed discussion on these areas/sub areas along with critical observations on
each is undertaken in the next section.
(3) In the final section, changing trends in the area of maintenance management
have been identified along with recommendations for the future research work.

Outcome of the literature survey
A total of 142 papers were collected and analyzed. A broad classification of this
literature in to six areas giving year wise distribution is given at Table I. These areas
are:
(1) (A) maintenance optimization models;
(2) (B) maintenance techniques;
(3) (C) maintenance scheduling;
(4) (D) maintenance performance measurement;
(5) (E) maintenance information systems; and
(6) (F) maintenance policies.

Maintenance optimization models can be both qualitative and quantitative. The former
includes techniques like total productive maintenance (TPM), reliability centered
maintenance (RCM), etc. while the later incorporates various deterministic/ stochastic
models like Markov Decision, Bayesian models, etc. There has been a long journey of
maintenance techniques evolution from corrective maintenance (CM) in 1940 to various
operation research (OR) models for maintenance as on today. A number of latest
techniques have been analyzed under this broad area. Unlike scheduling in production,
the maintenance schedule becomes immediately out of place as soon as an emergency job
is received. Maintenance scheduling is therefore another challenging area, which merits

Area 2004-2006 2003 2002 2001 2000 1999 1998 1997 1996 1995 Others Total A Maintenance optimization models 3 5 6 2 4 2 3 1 – – 1 (1992) 27 B Maintenance techniques 6 8 9 15 5 6 4 3 2 – – 58 C Maintenance scheduling 1 1 2 2 2 1 – – – – – 9 D Maintenance performance measurement 3 4 1 3 1 3 3 2 1 2 – 23 E Maintenance information system 1 – 1 – – 3 – – 1 – – 6 F Maintenance policies 2 1 5 4 1 1 2 1 1 – 1 (1992) 19 Total 16 19 24 26 13 16 12 7 5 2 2 142 management Maintenance research areas Classifications of 207 Table I. .

etc. maintenance optimization models cover four aspects: (1) a description of a technical system. During the past few decades widespread mechanization and automation has reduced the number of production personnel while capital employed in production equipment has increased manifold (Dekker and Scarf. periodic repair policy. failure limit policy. They also brought out that equipment overhauls comprised the largest group of applications (about 30) followed by the area of vehicle replacements (about ten). 1998). block repair policy. or classified as in which model focuses on an application tool. A suggested sub classification of this area in to 12 sub areas is discussed as follows. (2) a modeling of the deterioration of the system in time and possible consequences for this system. (A) Maintenance optimization models The quantitative approach to maintenance optimization is covered in detail in the literature with a total of 24 papers in past few years. . each of which has different characteristics. Dekker and Scarf (1998) also present another classification of these models as age and block replacement models. advantages and disadvantages and requires extensive research. The later is further classified as stochastic models under risk or under warranty. In general. Further sub classification of these areas in to several sub areas has also been attempted after critical scrutiny. like a decision support system or expert system and which mentions applications of the tool. The first maintenance management information system (MMIS) appeared in the 1980s due to the full recognition of maintenance as an important business function.3 effective functioning of any organization as whatever gets measured has a higher probability of its completion. An effective performance measurement system is essential for 12. 208 Similarly. Analysis of research areas with observations Each of the above six areas and sub areas have been discussed in detail with critical observations in this section. Markov decision models and delay time models. They have further identified a total of 112 papers containing applications and classified them as case studies if models have been used with real data to provide advice on real problems to the management. The same has now become an essential component of any maintenance organization and thus merits investigation also under a separate area. or classified as in which a new model is put central and in which indications are given about applications of the model. The detailed classification of the same is represented in Figure 1 and year-wise distribution tabulated in Table II. The models have been classified according to the modeling of the deterioration as deterministic or stochastic models.JQME separate investigation. the various maintenance policies can be classified as age replacement policy. its function and importance. and (4) an objective function and an optimization technique which helps in finding the best balance. (3) a description of the available information about the system and actions open to management.

Maintenance management classification tree showing various sub areas . Maintenance management 209 Figure 1.

7 Simulation – – 2 – 1 – – – – – 1(1992) 4 A.9 SMM – – 1 – – – – – – – – 1 B. Sub-classification of Sub-area Area Sub area 2004-2006 2003 2002 2001 2000 1999 1998 1997 1996 1995 Others total Total A Maintenance optimization models 27 A.3 PM – – – 1 1 – – – – – – 2 (continued) . areas 12.3 MCDM – 1 – – – – – 1 – – – 2 A.2 CBM 1 – 2 1 – 2 – – – – – 6 B.10 RBM – 1 – – – – – – – – – 1 Sub total 8 8 9 15 5 6 4 3 2 – – 58 C Maintenance scheduling 9 C.8 ECM – – 1 – – – – – – – – 1 B.3 TPM 1 – – 7 2 1 – – – – – 11 B.1 Bayesian 1 1 – – – – – – – – – 2 A.6 Predictive maintenance – 1 – – – 1 – – – – 2 – B.10 Petri nets – – – – – 1 – – – – – 1 A.1 PM 3 4 4 5 2 1 1 1 – – – 21 B.2 Predictive – – 1 – – – – – – – – 1 C.2 MILP – 1 – – – – – – – – – 1 A.1.1 CBM 1 1 – – – – – – – – – 21 C.6 MAIC – – 1 – – – – – – – – 1 A.12 Miscellaneous(includes review paper) 1 – 1 – 1 – 2 – – – – 5 Sub total 3 5 6 2 4 2 3 1 – – 1 27 B Maintenance techniques 58 B.1 Techniques C.5 Galbraith – 1 – – – – – – – – – 1 A.7 Outsourcing 1 – – – – 1 – 1 – – – 3 B.5 RCM – 2 – 1 1 – 1 – – – – 5 B.1.11 Maintenance organization modeling 1 – – – 1 – – – – – – 2 A.9 AHP – – – – 1 – 1 – – – – 2 A.1.8 Markovian deterioration – 2 1 – 1 – – – – – 4 – A.4 Fuzzy linguistic – 1 – 1 – – – – – – – 2 A.4 CMMS – 1 – 1 – 1 1 1 2 – – 7 B.3 210 JQME Table II.

1.1 Various models – – – – – 1 – 1 – – – 2 D.5 Maintenance personnel – – – – – 1 – – – – – 1 Sub total 1 1 2 2 2 1 – – – – – 9 D Maintenance performance measurement – D.1.8 Maintenance productivity index – – – – 1 – – – – – – 1 D.3 Repair rate modifying activities – – – 1 – – – – – – – 1 C.6 TMM – – – – – – – – – 1 – 1 D.1.2 Computerized data based info system to reduce MTTR/MTBF – – – – – – – – 1 – – 1 E.1.4 Combining production and maintenance – – – – 1 – – – – – – 1 C.1.2 Overall equipment/craft effectiveness – 2 – 1 – – 1 – – – – 4 D.3 Development of DSS in maintenance planning – – – – – 1 – – – – – 1 E.1.4 Miscellaneous 1 – 1 – – 1 – – – – – 3 Sub total 1 – 1 – – 3 – – 1 – – 6 (continued) management Maintenance 211 Table II.5 Miscellaneous – 1 1 – – – – 1 1 – – 4 Sub total 3 4 1 3 1 3 3 2 1 2 – 23 E Maintenance information system 6 E.3 Relation with maintenance strategy 2 – – 1 – – – – – – – 3 D.3 BSC – 1 – – – – 1 – – – – 2 D.1 Opportunity created by IT – – – – – 1 – – – – – 1 E.1.1. Sub-area Area Sub area 2004-2006 2003 2002 2001 2000 1999 1998 1997 1996 1995 Others total Total C.2 Wear out components – – 1 – – – – – – – – 1 C.5 MIS – – – – – – 1 – – – – 1 D.2 VBM 1 – – – – – – – – – – 1 D.4 QFD – – – 1 – – – – – – – 1 D.7 System audit approach – – – – – 1 – – – 1 – 2 D. .1 Techniques 23 D.4 Effect of failure on effectiveness – – – – – 1 – – – – – 1 D.

2. MCDM: multiple criteria decision making. QFD: quality function deployment. SMM: strategic maintenance management. MTTR: mean time to repair.2. PM: preventive maintenance. CMMS: computerized maintenance management systems. TMM: total maintenance management.2 Simulation in maintenance 1 – 1 – – – – – – – – 2 F. EMQ: economic manufacturing quantity .4 Object-oriented maintenance management – 1 – – – – – – – – – 1 F. RCM: reliability centered maintenance.2.4 Miscellaneous (includes review papers) – – 2 – – – – 1 1 – (1992) 5 Sub total 2 1 5 4 1 1 2 1 1 – 1 19 Total 16 19 24 26 13 16 12 7 5 2 2 – 142 Notes: MILP: mixed integer linear programming. MAIC: Materially per Apparecchiature de Impiariti Chemiei.3 Customized maintenance concept – – 1 – – – – – – – – 1 F.3 212 JQME Table II. AHP: analytic hierarchy process. CBM: condition-based maintenance. TPM: total productive maintenance. ECM: electronic counter measures. RBM: risk-based maintenance.1 Maintenance integration – – 1 2 1 1 – – – – – 5 F. ECM: effectiveness-centered maintenance.2. MTBF: mean time between failure. 12.2 Emerging maintenance concepts F.1 EMQ determination in imperfect PM – – – 1 – – 1 – – – – 2 F. Sub-area Area Sub area 2004-2006 2003 2002 2001 2000 1999 1998 1997 1996 1995 Others total Total F Maintenance policies 19 F. DSS: decision support systems.3 New ideas 1 – – 1 – – 1 – – – – 3 1 F. MIS: maintenance information systems. BSC: balanced score card. VBM: vibration-based maintenance.

e. Goel et al. Al-Najjar and Alsyouf (2003) assess and select most informative (efficient) maintenance approach using fuzzy MCDM evaluation methodology. Markovian probabilistic models for optimizing maintenance policy have also been discussed by Bruns (2002). which focus mainly on deriving an effective maintenance policy at the operational stage. A reliability allocation model at the design stage is coupled with the existing optimization framework to identify the optimal size and initial reliability for each unit of equipment at the design stage.e. replacement and order lead times. 2003). minimizing total service cost) and for modeling of continuously monitored deteriorating systems. Petri nets and maintenance organization modeling. (2003) present a new mathematical formulation i. total quality management (TQM)/TPM/RCM model etc.7 and A. A fully Bayesian i. Eindhoven University of Technology model (EUT). Chiang and Yuan (2001) and Lam (1999) in great detail. (A. (1998).3 to A. Chen and Popova (2002) and Barata et al. the proposed integrated approach provides a designer with an opportunity to improve the operational availability at the design stage itself. Swanson (2003) has applied Galbraith’s information processing model to study how the maintenance function applies different strategies to cope with the environmental complexity. A simulation model (Sarker and Haque. Triantaphyllou et al.e. Bevilacqua and Braglia (2000) describe an application of AHP for selecting the best maintenance strategy for an oil refinery. Mechefske and Wang (2003. and suggests maintenance can be a contributor to profits by use of information technology (IT) and showed that integrated IT permits co-planning of production with .11) Analytic hierarchy process (AHP). MILP for the integrated design. Pieri et al.6) Maintenance approach using fuzzy multiple criteria decision making (MCDM) and linguistic approaches. (2002) use Monte Carlo simulation to determine optimum maintenance policy (i. In contrast to earlier approaches. 2001) have used a fuzzy linguistic approach to achieve subjective assessments of maintenance strategies and practices in an objective manner.g. Rochdi et al.2) Mixed integer linear programming (MILP) formulation.8) Simulation and Markovian probabilistic models. (A. (2002) have presented a knowledge-based decision support system. This approach is in contrast with the classical probabilistic approach (for example: Ho and Silva (2006) have used unbiased estimators for MTTF) that assumes the existence of true probabilities and probability distributions. failure and block) for an automated production line in a steel rolling mill. (1997) earlier reported similar approach. selected out of opportunistic. Similar attempts were made earlier by Labib et al. (A. Balakrishnan (1992) demonstrates application of simulation models to evaluate maintenance policies (i.9 to A. Sherwin (2000) reviews maintenance organization models. Marquez and Heguedas (2002).(A. production and maintenance planning for a multi-process plant. 2000) has also been developed to reduce maintenance and inventory costs for a manufacturing system with stochastic item failure. (1999) utilize Petri nets for maintenance modeling of industrial systems.e. subjective approach towards Maintenance straightforward means of presenting uncertainty related to future events to decision management makers in the context of an inspection maintenance decision problem has also been optimally discussed (Apeland and Scarf.e. advanced terotechnological model (ATM). Materially per Apparecchiature de Impiariti Chemiei (MAIC) for maintenance of a chemical plant.1) Bayesian approach. 213 (A.

these models may be useful to maintenance engineers if they are capable of incorporating information about the repair and maintenance strategy. the methods of failure detection. A total of 20 papers have been published under this .. . However. data problems. Bevilacqua et al. Thus applications of these models are very limited in industry. . Presently. (2005) have used an artificial neural network (ANN) 12. that justify reasonableness of assumptions. Sander and Wang. There are few problems in applying quantitative optimization models like: DSSs are needed to optimize maintenance. Anderson et al. Recently. lighting maintenance. A series of tasks performed at a frequency dictated by the passage of time. (B. like RCM. which presently focuses on the design of systems and not on maintenance. .JQME maintenance. Dhillon and Liu 214 (2006) have reviewed extensively on human-related errors in maintenance.12) Miscellaneous. Engineers need to be taught economics of maintenance and principles of optimization. limited work is directed towards developing an operational decision support system. (B) Maintenance techniques Another classification of the work done on maintenance is on various techniques. etc. These techniques have been further sub classified in to ten areas. inspection in nuclear industry. A total of 54 papers are identified under this broad area. scheduling of overhauls of electric power stations. The applications of the same have been very limited so far as virtually no case studies have been published. each of which has been discussed in detail as follows. the engineering management policies. . These models have flourished only as a mathematical discipline with in operations research (OR). 1998) were distributed in the areas of road maintenance. More application will be seen if models are developed in conjunction with the problem owner. However. machine condition that either extend the life of an asset or detect that an asset had critical wear and is going to fail or break down constitute PM. Limited application is also attributed to inadequate definition of a problem by its owners and lack of training in education of engineers. . and the applicability of model in a given system environment that can give greater confidence in estimates based on small numbers of production data. The rest of the papers (Hsieh and Chiu. manpower requirement planning.3 framework for failure rate prediction. failure mechanisms.1) Preventive maintenance (PM). etc. It can be said that its impact on decision making within a maintenance organization has so far been limited. 2002. 2000. This sudden rise is due to the fact that IT (both soft and hardware) is now becoming available at low cost and is rapidly developing. many researchers are pursuing the development of various mathematical maintenance models to estimate the reliability measures and determine the optimum maintenance policies. Some observations . etc. the amount of production. No attempts have been made to integrate these quantitative approaches with qualitative ones. (A. and the gap between theory and practice. It seems that a number of papers have been published in the last five years in this area.

(2002) demonstrate development of a model for minimizing the cost per unit time of inspection and PM through selection of a unique interval. S) which are further classified as good. (2001) consider a Bayesian theoretic approach to determine an optimal adaptive PM policy with minimal repair. Bris et al. PM policy has also been suggested for a degradation system with an acceptable reliability level.category in the last seven years. In first. Bloch-Mercier (2002) also proposes a PM policy with sequential checking procedure for a Markov deteriorating system. PM due and down). without accounting for the lower unplanned downtime. (2003) demonstrate usefulness of simulation tool (simulator MELISSA Cþ þ ) for optimizing PM cost in a semiconductor-manufacturing environment. An age reduction model models degraded behavior of components and genetic algorithm is used for deciding the optimal activity combination at each PM. – – – – – -. (2005) discuss about optimal preventive policies for a shock model with pre-specified damage levels. It is shown that increased PM activity can lower total expected work-in-process (WIP) inventory on its own i. Lai et al. Gupta et al. Breakdowns occurring in combined corrective/PM context have been modeled and also both direct and indirect 215 maintenance costs estimated. (2001) present the periodic PM of a system with deteriorated components. Badı́a et al. (2001) derive optimal PM strategies under intermittently used environment. (2003) propose a state and time dependent PM policy for a multistage Markovian deteriorating system. The recent ones include a paper by Chelbi and Maintenance Ait-Kadi (2004) that presents a mathematical model for joint strategy of buffer stock management production and PM for a randomly failing production unit operating in an environment where repair and PM durations are random. Qian et al. Motta et al. PM intervals are defined in such a way that hazard rate is same for all. Hsu (1999) addresses the joints effects of PM and replacement policies on a queue-like . Zhao (2003) has introduced degradation ratio to represent the imperfect effect assuming that the system after PM action starts a new failure process. (2003) have shown the efficiency of an optimization method to minimize the PM cost of series parallel systems based on the time dependent Birnbaum importance factor and using Monte Carlo simulation (applied with programming tool APLAB) and genetic algorithm. In second. (2001a. Dohi et al. Charles et al. Chen et al. doubtful. In a more recent work Juang and Anderson (2004) consider a Bayesian theoretic approach to determine an optimal adaptive PM policy with minimum repair. (2002) present a statistical approach of analysis and decision that uses reliability techniques to define the best periodicity for PM of power system protective relays.e. Sheu et al. Tsai et al. b) present an easy-to-implement state dependent PM policy consistent with the production environment. Salameh and Ghattas (2001) determine the just-in-time (JIT) buffer level by trading off the holding cost per unit time and the shortage cost per unit time such that their sum is minimum. This is demonstrated for a production unit subjected to regular PM. In the literature reported for the period 2001-1997. Gürler and Kaya (2002) suggest a control policy where the system is replaced when a component enters a PM due or down state (life time of each component is described by various stages (O. Ben-Daya and Alghamdi (2000) present two sequential PM models. (2000) has discussed application of the sequel method to determine the optimal policy as when to carry out PM action for an engine/replace the engine. age reduction of the system is assumed to depend on the level of PM activities.

3) TPM. measurement going beyond a predetermined limit. originating from Japan centers on solving maintenance problems using quality circles method. The PM service is based on some reading.e. Cooke (2000) presents case studies in implementing TPM in four manufacturing companies. These are ineffective maintenance resulting in breakdowns. which uses an approach to determine the appropriate time for checking the performance of implementing TPM. In a little older work. Ireland and Dale (2001) discuss study of TPM in three companies. Wang and Lee (2001) discuss an application of TPM. systematic PM and conditional PM. (B. poor raw material/processing defects and quality losses during machine starting. operation at reduced speed. Some of the advantages of implementing TPM in an organization are better understanding of the equipment performance.2) Condition-based maintenance. (B. “ seven simple tools of TQM”. If a machine cannot hold a 216 tolerance. Barbera et al. These companies had followed Nakajima’s seven steps of autonomous maintenance. Grall et al. (2001) investigate the relationship between TPM and manufacturing performance (MP) through structural equation modeling (SEM). (2002) focus on the mathematical modeling of a condition based inspection/replacement policy for a stochastically and continuously deteriorating single unit system. Luce (1999) selects the best maintenance method using Weibull Law by comparing CM. Das (2001) presents a case study where TPM is implemented in a step-by-step manner and also develops some parameters for measuring the effectiveness of TPM. Finally. Chen and Trivedi (2002) explain condition-based maintenance in detail and also derive a closed form expression of system availability when device undergoes both deterioration as well as Poisson type failures. three strong tools i. (2002) consider a continuously monitored multi-component system and use a generic algorithm for determining the optimal degradation level beyond which PM is to be performed. idling and minor stoppages of equipment. Marseguerra et al. improved teamwork. Gupta et al. less adversarial approach between production and maintenance. (1999) discuss a condition-based maintenance model with exponential failures and fixed inspection intervals for a two-unit system in series.3 adapting JIT manufacturing systems to PM interruptions. In the still older . Gupta and Al-Turki (1998) discuss 12. It is shown that there is a significant and positive indirect relationship between TPM and MP through JIT practices. McKone et al. (2001a. In the recent works. (1997) present an approach to generate an adaptive PM schedule. set up and adjustment time loss.JQME production system with minimum repair at failures. Finlow-Bates et al. which maximizes the net savings from PM subject to workforce constraints. The methodology uses Markov models for reliability prediction. a condition-based maintenance is initiated. Saranga and Knezevic (2001) use reliability condition predictor (RCP) for reliability prediction of condition-based maintenance systems. A total of six papers have been published in the last five years. It is shown that maximization of OEE requires reduction of six big losses. four thinking models of “Kepner-Tregoe” and “Root cause analysis” are to be navigated as all three are complementary to each other. Jamali et al. TPM benefits were modeled using AHP by Kodali and Chandra (2001) and Kodali (2001). Gopalakrishnan et al. within this level condition-based maintenance policy can be implemented. (2000) show that in order to implement TPM successfully. TPM. b) address basics of TPM and its key issue overall equipment effectiveness (OEE). etc. (2005) evolve joint optimal periodic and conditional maintenance strategy.

It is now only in the past ten years or so that this concept has started coming to the industry. This interval enables an organization to implement a comprehensive RCM program effectively. A total of five papers have been published in the last few years. Wickers (1996) presents a method called “Front end maintenance analysis” which has widely been applied in industry and proved to be an effective method of identifying parameters for condition monitoring and clearly shows the link between these parameters and a maintenance management system. Wessels (2003) proposes a cost optimized scheduled maintenance interval that uses costs as the constraint and overcomes quantitative complexity by use of computer/software technology. This framework is also tested for 97 plants.5) RCM. employee management involvement (EI) and environmental/organizational factors such as country. An alternative approach for some specified uncertainties are also discussed. Older works include a paper by Singer (1999) that discusses a seven-step plan for using all the features of the CMMS package. Gabbar et al. CMMS provide 217 capabilities to store retrieve and analyze information. Swanson (1997) provides information regarding the characteristics and use of CMMS. (B. Eisinger and Rakowsky (2001) discuss a probabilistic approach in the modeling of uncertainties in RCM. industry/company characteristics. It directs maintenance efforts at those parts and units where reliability is critical. leading to non-optimum maintenance strategies. (B. (1999) propose a theoretical framework for understanding the use Maintenance of TPM and how it depends on managerial factors such as JIT. This policy emphasizes the fact that the best policy is the one. A total of two papers were found on . Hipkin and Cock (2000) discuss implementation of RCM and TPM with respect to TQM and business process re-engineering (BPR) and show as to how maintenance implementation follows the path of other interventions. Predictive maintenance consists in deciding whether or not to maintain a system according to its state. They conclude by saying that these uncertainties in the decision making of RCM might be unacceptable in many practical applications.works McKone et al. Two important papers in the area of CMMS include a paper by Labib (1998) that uses a formalized decision analysis approach based on multiple criteria and rule-based system for finding the worst machines. Shamsuddin et al. (2003) present an improved RCM (automated environment) process as integrated with CMMS. TQM. A total of seven papers have been published in the last eight years.4) Computerized maintenance management systems (CMMS).6) Predictive maintenance. etc. Jones and Collis (1996) present findings of a questionnaire survey examining use of computers in maintenance management and concludes that computers are still not optimally utilized and there is considerable potential for future development. which improves the life cycle profit. (B. Finally. The paper also includes literature review of CMMS. Fernandez et al. The major components of the enhanced RCM process are identified and a prototype as integrated with the various modules of the adopted CMMS is implemented. (2005) argue that TPM can go much beyond much maintenance and may encompass a host of business functions within an organization. Rausand (1998) present a structured approach to RCM and discuss its various steps at length. Reliability centered approach was founded in the 1960s and initially oriented towards aircraft maintenance. (2003) propose a maintenance maturity grid to support the CMMS implementation. Leger and Movel (2001) deal with computer-aided integration of maintenance in an enterprise.

(2002) review in detail various basic concepts of maintenance and discuss need of ECM approach. They recommended that practitioners should not abandon the traditional maintenance methods but follow given guidelines for utilizing periodic maintenance with the new technologies. (2005) discuss the prioritized warranty repairs for an outsourcing context. Murthy and Asgharizadeh (1999) deal with game theoretic formulation model to determine outsourcing agent’s optimal strategy with regards to the price structure. and has several features that are practical to enhance the performance of maintenance practices and encompasses core concepts of quality management. This approach overcomes some of the deficiencies of RCM and TPM approaches as these do not deal with issues like operating load on the equipment and its effect on the degradation process. (B. It is also shown that various scientific disciplines such as management accounting. Khan and Haddara (2003) outline this approach in detail and contrast it with the current approaches. (1998) a general predictive replacement model based on dynamic programming is presented. The . Martin (1997) discusses in detail the various facets of maintenance outsourcing. In the strategic maintenance management (SMM) approach. Paper also discusses various research issues from an industrial engineering point of view. and maintenance strategy development and performance measurement. The other goals are to reduce the number of full-time equivalents (FTEs) and concentrate organization’s talent. Effectiveness centered maintenance (ECM) stresses “doing the right things” instead of “doing things right”. the number of customers to service and the number of service channels. operations management. computer science and socio technology all need to be involved at different stages as the issues of selecting suitable maintenance contracts. This approach focuses on system functions and customer service. Risk-based maintenance ensures a sound maintenance strategy meeting the dual objectives of minimization of hazards caused by unexpected failure of equipment and a cost effective strategy. involving the use of mathematical models that integrate technical. contract portfolio management and the design of a contractor’s maintenance function are all very much related to one another. The SMM approach in contrast. safer and lower costs.10) Risk-based maintenance. (B. McKone and Weiss (2002) have presented detailed guidelines 12. Murthy et al. This refers to transferring workload to outsiders with the goals of getting higher quality maintenance at faster. energy and resources in to areas called core competencies. determination of contractor capabilities.7) Maintenance outsourcing. etc. Pun et al. It is composed of people participation. Determination of ECM performance indices has also been attempted in this paper. commercial and operational aspects from business viewpoint.JQME this type of maintenance. 218 (B. In addition. (2002) in a recent work outline this approach in detail with presentation of few case studies. (B.9) Strategic maintenance management. long-term strategic issues and outsourcing of maintenance. Two papers are published in this area.8) Effectiveness centered maintenance. The ECM approach is more comprehensive as compared with TPM and RCM. TPM and RCM. these approaches to a large extent are qualitative or at the most semi-quantitative.3 for implementing predictive maintenance in manufacturing industry. As a result SMM views maintenance from a perspective that is broader than that of RCM or TPM. quality improvement. is more quantitative. Buczkowski et al. maintenance is viewed as a multi disciplinary activity. In an older work by Chu et al.

more work on its integration with CMMS can be attempted. etc. Various simulation tools and mathematical models are attempted in recent past for minimizing the PM cost. Increased use of analysis and coordination capabilities and greater use of CMMS by personnel outside maintenance function have the potential to improve maintenance responsiveness and equipment condition. risk estimation module. At the same time the Japanese gave birth to TPM in the context of manufacturing. maintenance cost was considered as an unavoidable cost and the only maintenance carried out was CM. etc. A total of eight papers are published in the last few years. Similarly.e. not many applications have been published. (2003) discuss development of a new probabilistic method based on the semi . . Since the 1950s. The area of maintenance outsourcing has tremendous potential for future work as more and more clients are becoming interested in contracting out maintenance and contractors take great interest in solving current problem areas. Preventive and condition-based maintenance continue to be the areas where the maximum number of papers have been published. The uses so far appear to be only a storehouse for equipment information. risk evaluation module and maintenance planning management module. .e. RCM. . as is evident from the emergence of the new approaches like ECM. a more integrated approach to maintenance involving a close linkage to reliability (R) and maintainability (M) was recognized. materials/parts. as a PM tool and a maintenance work-planning tool. availability of unit to be serviced. A case study using this methodology is also presented. information needed to complete the job and the necessary permissions. various models for optimum PM policy determination have been attempted using selection of unique inspection interval. CMMS appears to be used less often as a device for analysis and coordination.proposed methodology is comprehensive and quantitative. introduction of degradation ratio. It comprises of three main Maintenance modules i. the mechanic(s). (C) Maintenance scheduling Scheduling means bringing together in the precise timing the six elements of a successful maintenance job i. . However. RCM has the potential to be utilized in an automated environment i. The evolution of OR and its 219 applications during the Second World War led to the widespread use of PM. . As a future scope. Dieulle et al. Maintenance is increasingly viewed as a multi disciplinary activity involving integration of existing techniques like TPM. While it might be expected that maintenance personnel would be the primary users of CMMS. SMM and RBM. . Some observations . The term “R & M” became very popular. an opportunity exists for a more broad use of CMMS by production personnel. more work needs to be done to link CMMS design and use with actual maintenance performance. In the 1970s. Up to about 1940. OR models for maintenance have appeared at an ever-increasing rate. .e. tools. which gave birth to RCM.

(2006) use data management techniques for optimization in CBM. Lee and Lin (2001) discuss scheduling problems involving repair and maintenance rate modifying activities for a problem commonly found in the surface mounted technology of electronic assembly lines. The interval between maintenance for the components is optimized by minimizing the total cost. Artana and Ishida (2002) address a method for determining the optimum maintenance schedule for components in wear out phase. 12. Gopalakrishnan et al. (2002) discuss a predictive maintenance scheduling structure for a gradually deteriorating single unit system. . Most papers in the maintenance field are based on the assumption that machines are always available at constant speed. etc. which is based on regenerative. e. It is this stochastic nature that makes maintenance scheduling a challenging problem and distinguishes with production scheduling. This model is considered . reference numbers and surveys. in applications it is very common for a machine to be in a subnormal condition after running for a certain period of time. Another reason is that maintenance performance reporting is difficult. Grall et al. and semi-regenerative process theory. In this section various maintenance performance measurement techniques have been identified from the literature. (2001) 220 present a tabu search heuristic for PM scheduling while Greenwood and Gupta (2000) present workforce constrained PM scheduling using evolution strategies. Maintenance managers often have access to many data. a few typical performance indicators is a time-consuming business.3 Inspection random time is chosen with the help of a maintenance scheduling function and a gamma process models deterioration. (D) Maintenance performance measurement In the past. Elaborate models like Hibi. production on equipment availability and support responsiveness. two works have been reported. but seldom receive the information they need. engineers will focus on techniques. top management is interested in budget performance. In the area of PM scheduling.1. Duffuaa and Al-Sultan (1999) deal with scheduling maintenance personnel using a stochastic programming model that is a stochastic version of the Robert and Escudero model for scheduling maintenance personnel. This means that processing the data to obtain useful management information. Motivated by this. maintenance performance reporting was limited to minimum budget reporting. Pintelon and Puyvelde (1997) present an overview of various PMS including indicators. Some observations . Luck and the maintenance management tool (MMT) have been presented along with case studies on MMT. However. Tsang et al. accountants will think of maintenance in terms of costs.e. (D.g. Integration of various scheduling models in to MMISs may be investigated as a future research area to ensure effective planning and scheduling of maintenance jobs. Sloan and Shanthikumar (2000) deal with combined production and maintenance scheduling for a multiple product single machine production system.1) Review of various models.JQME sequential property in a condition-based maintenance environment for scheduling. (D. These have been sub-classified as follows: . The maintenance performance perceived also depends on the perspective applied i.1) Measurement techniques.

(D.1. e. Tsang et al. A number of performance indices have been listed for maintenance performance measurement. Value-based performance measure attempts 221 to assess the impact of maintenance activities on the future value of the associated assets. QFD uses a type of three-stage matrix diagram to present data and information and is also referred to as “a house of quality”. . Similarly. The paper also highlights that although the framework might be complicated and may demand extra resources for small and medium-sized companies. Applying BSC for managing maintenance performance is potential research area that may be explored. The paper emphasizes on the value rather than the cost of operations and maintenance in the emerging business environment. to be more proactive. (D. .5) PMS using maintenance information systems.1. In a recent work Liyanage and Kumar (2003) have applied BSC to develop operations and maintenance performance (O&M) management process in the oil and gas industry.4) PMS using quality function deployment (QFD) technique.1. The reason is that the matrix in QFD fits together to form a house-shaped diagram.6 and D. . than other scientific approaches for maintenance Maintenance performance reporting. The balance scorecard (BSC) provides an alternative and holistic approach to measurement that is developed on the notion that no single measure is sufficient to indicate the total performance of a system. . and stresses that there is a need to move from a plant-based policy to a more or less long-term business oriented approach. the application showed that it could easily be adapted for the needs of the company. (1998) describe that MMIS is required to measure performance. (2001) look at the role of PMS in maintenance with particular reference to developing a new PMS using QFD technique.3) Performance measures based on VBM model.1. Various approaches to measuring maintenance performance have been reviewed. (D. A model is developed by Al-Najjar and Alsyouf (2004) for economic impact of vibration-based maintenance (VBM (part of condition-based maintenance)) and further utilized to develop relevant maintenance performance measures. The model has utilized life cycle costs (LCC) as monitoring parameters to provide the required information for decision making. in past one month and a network of detailed reports (DR). Raouf and Ben-Daya (1995) propose a systematic . Tsang (1998) also discusses the concept of BSC as a SMM tool for performance measurement in industry.7) PMS using total maintenance management (TMM) and systems audit approach. Systems audit and data envelopment analysis (DEA) are the other two approaches covered in detail. (D. (1999) discuss pitfalls relating to indiscriminate use of common maintenance performance measures. . to ensure cost effective actions and enhance continual improvement efforts cost effectively. Kutucuoglu et al.g.2) Balanced scorecard.1. (D. an extensive list of indices for the maintenance manager with benchmarks for these indices has also been published in a paper of National Petroleum Refiners Association cited in the above work. MMT consists of a control board (CB) which allows a management quick evaluation of maintenance performance during a fixed duration. Arts et al.1.

In a recent work. Presently. Bamber et al.3 presentation of a methodology to measure the effectiveness of the current status of maintenance management. (2003) explore the purpose of the OEE concept in modern operations. the output prices of the produced products and input prices (maintenance costs) are shown to change over time. lack of labor. including downtime and other production losses that reduce output/machine hour or capacity utilization and does not include factors that reduce capacity utilization.g. etc. Jeong and Phillips (2001) highlight that accurate estimation of equipment utilization is very essential. They also present the methodology for designing the necessary data collection system that can serve as a template for any industry. it is more likely that the responsibility ad authority to carry out improvements is gained from management. (D. OCE deals with the productivity of labor resources (OEE ¼ Availability £ Performance £ Quality. In the discussed partial productivity model. Additionally. cross-functional team (CFT) is necessary. OEE can be said to be a measure of progress of TPM in an organization. They present a new loss classification scheme for computing OEE for a capital-intensive industry and provide justification for this scheme. OCE ¼ Craft utilization £ Craft efficiency £ Craft service quality). and should aim at producing any level of output which is decided on at minimum maintenance cost with respect to the production system’s state (Löfsten. . e. Ljungberg (1998) argues that it should be beneficial to change focus and use a comprehensive model for losses and proposes a TPM model with eight equipment losses. Another work on 222 systems approach by Dwight (1999) argues that an absolute definition of maintenance performance in terms of changes in value presents difficult practical problems. lack of material input. A partial maintenance productivity goal is that the firm should seek to maximize its maintenance productivity in economic terms. Swanson (2001) reports results of a study of the relationship between maintenance strategies and performance. This paper has discussed that in order to effectively address all six big losses and hence improve OEE. through the use of CFT. In another work. definition of OEE includes six big losses. The paper further suggests that the systems audit approach for performance measurement can potentially overcome some of these problems.1.3) Performance measurement relationship with maintenance strategy. Expected changes in the prices of outputs and of current inputs are built into the model. Peters (2003) has introduced overall craft effectiveness (OCE) like the concept of OEE. CFT accordingly has the combined necessary skills and knowledge of entire system of manufacture to identify correctly the practices and activities that relate to the six big losses. planned downtime. The analysis has demonstrated a strong positive relationship between proactive (preventive and predictive) and aggressive (TPM) maintenance strategies .2) Overall equipment/craft effectiveness. Results of a study of performance ratios from three industries are also presented. The author also says that the data collection problem has not been sufficiently treated in the literature and has suggested a method for collecting disturbance data where computerized systems are combined with manual recording. In the area of systems audit approach.JQME approach to TMM and discuss various issues pertaining to TMM along with 12. 2000).8) Maintenance productivity index. Groote (1995) presents a maintenance evaluation approach based on a quality audit and quantifiable maintenance performance indicators. (D. (D.

Problems and factors associated with implementation of PMS using QFD technique are also important and require further research. . (D. repair and operating supplies (MRO) stores utilizing integrated systems technology resulting in an . sound maintenance management requires a lot of interaction with other business functions as the era of enterprise-wide information management and business planning becomes a norm rather than an exception. Crocker (1999) pays attention to three areas – inspection effectiveness. The maintenance manager also lacks the tools. 1997). (2006) discuss various maintenance strategies for equipment under lease.and performance. reference numbers. (2006) have evaluated the effectiveness of various maintenance strategies. (E) Maintenance information systems The impact of IT on maintenance management is still relatively young in the business arena.g. Performance measures can also be classified as value-based performance measure. systems audit approach and data envelopment analysis. Popular PIs are indicators. The rest of the papers deal with various areas such as machine tools (Aronson. Pongpech et al. He has indicated how IT is actively used in a number of normal working situations and has pointed to new uses. software maintenance (Bandi et al. the remaining three approaches are useful for measuring the maintenance performance of an organization. A DEA approach will be appropriate for quantitative comparison of operational efficiencies of multiple maintenance organizations. Data accuracy and report timeliness are other problems in maintenance performance reporting.g. query or time to draw required reports. surveys and various models (Hibi.4) Effect of maintenance induced failures on operational effectiveness. Three possible applications suggested in the paper are BPR through workgroup computing technology for better performance. Some observations . maintenance performance reporting was limited to minimum budget reporting.5) Miscellaneous. . 2002). repair effectiveness and 223 maintenance-induced failures. 2003). work has only been directed in the design of such a system. Luck and MMT) out of which MMT has gained lots of popularity in the recent past. . decentralized maintenance. . Feasibility of applying BSC model for managing performance is still an unexplored area that needs further research. . mill performance (Lamb. e. So far. These areas are usually ignored during the design of the system and also during its operations. managers may be more management comfortable in making these investments in maintenance. (1999) address some important opportunities created by the IT (r)evolution for maintenance management. organizational transformations. Examining case studies on MMT can be a potential research area. 1996) and space (Cooke and Paulsen. Pintelon et al. (D. Integration of PMS with an effective MMIS is presently lacking.. Pintelon et al. In the past. balance scorecard. It is therefore important to understand how they affect operational effectiveness of the system and what steps can be taken to avoid their effects. In addition. e. The paper concludes by saying that through demonstration of Maintenance impact these strategies can have on plant performance.

Westerkamp (2002) measures information engineered performance standards (EPS). e.g. There are very promising developments in IT which can help to improve maintenance practice and create better competitiveness.g. Maintenance software is commercially available either as a standalone package or as a module in integrated systems. SAP. equipment data. Wang (2002) has undertaken a survey of maintenance policies of deteriorating systems. The paper. . (F) Maintenance policies Two invited reviews are worth discussing under this sub section. it was found that the users found the system easy to operate because of the object-oriented style of using the icons for operating the system. Buying sophisticated hardware or software is not the complete answer. work order planning etc.JQME integrated business function approach and redefining external relationships. The whole system was developed for a powder coating factory. is not specific to maintenance management information. The initial MMIS.g. Rimes. Some observations . The system design and analysis and the decision support system design and development are all developed in an object-oriented environment. Problems relating to application and development of an object-oriented decision support system for maintenance management is another potential field for future. ERP) and plant-floor (e.g.g. more specific maintenance software containing modules. . supporting mainly mainframe applications. (1999) report findings from a study of systems development and maintenance issues conducted in the UK. became available. these became even more user friendly with the use of graphical user interface (GUI) and multimedia applications. management execution systems). however. Maximo. 12. Progressively. etc. Evolution in middleware software is a recent trend that is much broader than MMIS. Satyanarayana and Prasad (1996) have attempted to introduce maintenance information system for the maintenance department of a Fishing Fleet Company in order to reduce MTTR and increase MTBF. . For spare parts management GUI-MMIS offers inventory administration in addition to inventory control models. This software should manage the communications between corporate (e. The system is menu-driven and user 224 friendly and with little modifications can be used for any industry. He . In the implementation stages. Recently. . e. Nagarur and Kaewplang (1999) present a computerized decision support system to assist in maintenance planning.3 outsourcing through inter-enterprise technology resulting to extended business partnerships. (2005) deliberate on the fundamental issues related to maintaining or not to maintain equipment with reference to the information needs of a decision maker. Some of the packages are R/5. . Bardey et al. Fitzgerald et al. Paper relates to information system maintenance in general rather being on maintenance information systems. e. In the first. were mostly administratively oriented. In the early 1980s the first MMIS appeared due to full recognition of maintenance as an important business function.

. A such integrated system has been shown to be developed and implemented in this paper using methods and principles of maintenance performance auditing. Another important review was by Pham and Wang (1996) in which imperfect maintenance has been discussed in detail. the system after maintenance will not be as good as new. The same was demonstrated for a make-to-stock production system consisting of a single deteriorating machine. (2001) present a case study of an electronic manufacturing company that was lacking a sound maintenance management system. each kind of policy has different characteristics. The performance of maintenance and production system integrated using a Markov decision process and evaluated with help of approximate methods was found to work extremely well. RCM planning and control. Research on TQM. Thousands of management maintenance and replacement models have been created which can fall in to some categories of maintenance policies like age replacement policy. cost recording and tracing. periodic PM policy. Iravani and Duenyas (2002) indicate that the common practice of making maintenance and production decisions separately can be rather costly and that there are significant benefits for making these decisions in an integrated fashion.has said that in the past decades. etc. maintenance and replacement problems of Maintenance deteriorating systems have been extensively studied in literature. many researchers believe and argue . Author has finally summarized. Jonsson (1999) re-emphasizes that integration of maintenance produces better results. condition monitoring and on-line feedback control and integrated planning and control. Strategy for integration of these two computerization areas in to logistics management is essential to take advantage of both modern maintenance strategy and production planning strategy. random age replacement policy. which produces a single item. Imperfect maintenance study has indicated a significant breakthrough in reliability and maintenance theory. (F. block replacement policy. quality improvement and manufacturing capabilities. classified and compared various 225 existing maintenance policies for both single-unit and multi-unit systems with emphasis on single unit systems. Ip et al. The authors have said that the maintenance of deteriorating systems is often imperfect i.e. (2000) suggest designing an enhanced MRPII system that incorporates systematic integration of maintenance management with the use of the integration definition method (IDEF) model. In order to illustrate the methodology. Data gathered and analyzed from 293 Swedish maintenance managers in manufacturing firms showed that integration and long-term planning of maintenance both affect prevention. Tu et al. Research activities in maintenance engineering have been conducted over the past 30 years and more than 40 mathematical imperfect maintenance models have been proposed for estimating the reliability measures and determining the optimum maintenance policies. advantages and disadvantages. However. Many companies using MRPII very often appear to be isolated from maintenance management activities. failure limit policy.1) Maintenance integration. paper has also describes a lamp manufacturing company with highly sophisticated equipment that depends on modern maintenance strategies as well as MRPII type production and planning. The authors have summarized various treatment methods and optimal policies on the imperfect maintenance in the paper along with a few important results. but younger. JIT and TPM generally investigates the implementation and impact of these programs in isolation.

A case in which maintenance action is imperfect and may lead a production system to a worse condition has been considered. Polimac and Polimac (2001) highlight the above aspects and state that maintenance methods applied at present should be combined in a comprehensive neural management maintenance system. trend prediction. Cua et al.2) Simulation in maintenance.2) Emerging maintenance concepts. efficiency and fast responses. (F. El Hayek et al. ANNs embody computational networks based on biological metaphor to simulate the brain action. (2001) investigate the practices of the three programs simultaneously. It is an interconnection of computational elements known as neurons. Waeyenbergh and Pintelon (2002) intimate that more and more companies are searching for a customized maintenance concept.2. (2005) discuss performance of maintenance based on life cycle costs of complex machinery using simulation as a technique. A maintenance concept can be defined as the set of various maintenance interventions (corrective. A case has been examined for two commonly used maintenance strategies and it is shown that for both optimal maintenance policies can be obtained. They opined that the available models in the literature could be enhanced by focusing more on model validation and design of the experiment for the simulation study.JQME conceptually the value of understanding the joint implementation of these 12.3 manufacturing programs. .2. Andijani and Duffuaa (2002) review the literature on the use of simulation in maintenance.2. (F. (1998) investigate imperfect maintenance policies for deteriorating production systems such that the EMQ can be obtained. (F. etc. (F. A variety of future research studies are also possible in this area.) and the general structure in which these interventions are foreseen. matching and completion. which would permanently monitor the system and suggest the most appropriate actions and strategies. It is shown that there is evidence showing the compatibility of the practices in the three programs. Application of ANN in maintenance management would certainly utilize all the above knowledge and methods. The framework described in this paper offers some guidelines to develop such a concept and borrows some ideas from maintenance concepts in the literature. ranging from experience of maintenance workers to data captured by modern information and communication technology (ICT) means. Emerging maintenance concepts are: neural management maintenance. proper design of experiments and sound ways of output analysis.3) Customized maintenance concept. A number of maintenance concepts have 226 emerged in recent past. An important feature of the framework is that it allows incorporating all information available in the company. This should add quality in the decision-making process and consequently reduce the overall maintenance costs. capability to generalize and reliability. The main capabilities of ANN include superb pattern classification. . condition-based. Tseng et al. .1) Economic manufacturing quantity (EMQ) determination in an imperfect PM. preventive. These are as follows: . The reviewed literature has been evaluated with respect to the elements of sound simulation study and it is observed that many of the simulation studies on maintenance systems ignored model validation.

. However. The author has recommended including a simple question about the mean remaining life length on the work-order forms. (2003) highlight proper design and integration of maintenance management in to ERP systems enable enterprises to effectively manage their production. Extensive literature on change over and maintenance as a separate subjects has been published. only few of these 227 systems developed or installed have actually considered maintenance strategies. Cheung et al. (F. Integration of maintenance management in to MRPII using appropriate system design is invaluable. 1997). maintenance policy decision step module. The rest of the papers not directly relevant are on maintenance strategy assessment (Jonsson.4) Miscellaneous. During the last decade. (2005) have used a novel concept of expert system for aircraft maintenance that is viewed as service industry.4) Object-oriented maintenance management. performance measurement module and the continuous improvement module. identification of most important systems (MISs) and most management critical components (MCCs) module.2. 2002) and nuclear environment maintenance (Chockie and Bjorkelo. expert judgment) to get credible input data. (F. In practice it is difficult to estimate the “naked” failure rate. where appropriate. Nikolopoulos et al. many companies have made large investments in the development and implementation of enterprise resource planning (ERP) systems. Oien (1998) stresses the importance of utilizing the knowledge of maintenance engineer (i.3) New ideas. schedule and control production and maintenance. planning and scheduling. The proposed framework can be seen as built out of five modules. McIntosh et al. because overhauls and other PM actions tend to corrupt the recorded life lengths. This work presents the design of an object oriented maintenance management model and its integration in to an ERP system. . yet relatively little consideration has been given to the impact that each discipline can have on the other. well-documented improvement techniques that are used to enhance change over performance do not seem to be widely applied. (F. By this approach the knowledge of maintenance engineers may be incorporated in a simple and cost-effective way. which are Maintenance start-up module.e. Limited research has been attempted to consider maintenance strategy in design of MRPII. Despite the manifest similarities in some changeover and maintenance tasks. . Some observations . value of change in maintenance (Call. (2001) highlight these aspects and assert that the maintenance community might give greater attention to what has occurred in the improvement of change over performance. Further research can be done using this approach to enhance MRPII system and improve logistics management and physical distribution functions of any organization. Maintenance management must be integrated with other functional departments like production and quality control. computer aided integrated maintenance management (CIMM) is needed by manufacturing companies to integrate. Most maintenance optimization models need an estimate of the so-called “naked” failure rate function as input. 1992). to the discipline of maintenance.

During the 1950s only CM was undertaken i. . e. Future studies in the joint implementation of TQM. 12. Concept of reliability came during 1980s in the form of RCM. data management. Similarly. This modular approach should allow selection of modules for which a decision support module is foreseen. JIT and TPM are possible. which revolved around solving maintenance problems using quality circles method. Further work in this direction is a potential research area. very interested trends have been noticed. which directed maintenance efforts at those parts and units where reliability is critical followed by TPM.3 Studies could help examining the close linkage among functions and also pinpoint exact nature of interactions among them. This was gradually translated to PM and its various forms like predictive maintenance. . more modules. etc. Consolidated summary A consolidated summary highlighting emerging trends of various attributes is given in Table III. The correlation between the two will be an interesting area for research. A glaring change in attitude towards maintenance from a necessary evil to “external and internal partnerships” is noticed. which are capable of incorporating information about the repair and maintenance . “fix it when it breaks”. The same will assist future researchers in the field of maintenance management to identify literature gaps and direct research efforts suitably.e. standardization etc. Directions for future work are as follows: (1) Modeling and optimization oriented: . There appears to be similarities between changeover and maintenance tasks. Design of this module has a future scope of research. Well-documented improvement techniques that are used to enhance changeover performance do not seem to be widely applied. A new technique neural management maintenance is yet to be developed which will combine the already applied methods in maintenance and which should reduce 228 involvement of analysts/ engineers in processing the data and thus add quality in decision-making process. maintenance policy. material selection. condition-based maintenance. which will give a full understanding of relationship between TQM. Knowledge of maintenance engineers i. . . . Developing optimization models for maintenance engineers. Problems related to implementation of an object-oriented maintenance management model and its integration to ERP may be taken as a future work. More and more organizations are resorting to this form of vertical contracting to further build up their core competency levels. Limited work is directed towards developing an operational decision support system utilizing a suitable maintenance optimization model. In future development of the customized maintenance concept framework.JQME . should be added and the content of each module be carefully elaborated on. Emerging tend is towards integration of these various approaches in the form of ECM or SMM. there have been developments in the area of maintenance outsourcing. In the areas of performance measurement and information systems also.g. . the expert judgment may be useful in the design of customized maintenance concept.e. JIT and TPM.

g. surveys. 229 maintenance . quality data envelopment analysis (DEA) function deployment (QFD) PMS integration with information system Poor Developing Data collection in measurement of OEE Not sufficiently treated Designing of data collection system Evolution of MMIS Mainframe applications and mostly Evolution in middleware software (e. administration oriented communication between corporate and plant floor). neural management maintenance. models Maintenance management tool (MMT) model – Hibi and Luck Performance measurement approaches Value based approach. e. integrated system technology. Balance score card (BSC) approach. Maintenance optimization models (quantitative) Mathematical discipline with limited applications Application oriented. SMM. integration with qualitative approaches Maintenance outsourcing Not existed Part of vertical contracting Maintenance scheduling Models in isolation Integration with MMIS Performance indicators Reference numbers.Attribute Earlier Emerging Attitude towards maintenance Necessary evil Technical matter – profit contributor – partnership Maintenance strategy CM-PM-RCM-TPM Integration of various approaches. correlation between changeover performance and maintenance tasks management Maintenance management Emerging trends in Table III.g. object-oriented maintenance management model and its integration to ERP. customized maintenance concept framework. knowledge of maintenance engineers utilization. Joint implementation of TQM. work group computing. systems audit approach. ECM. inter-enterprise technology Spare parts management Inventory administration Graphical user interface (GUI) – maintenance management information systems (MMIS) New maintenance policies Techniques in isolation Maintenance strategy in MRP system. JIT and TPM. etc. indicators.

TPM. (3) Information system oriented: . . Examining case studies on MMT with a view to allow quick evaluation of maintenance performance during a fixed duration. Designing of an effective data collection system for computing OEE in an organization and problems with its implementation.g. JIT and TPM to examine their close linkage and pinpointing exact nature of interactions. RCM has potential to be utilized in an automated environment i. . . . etc. ERP) and plant-floor information systems.e. . Integration of various scheduling models in to MMISs may be investigated to ensure effective planning and scheduling of maintenance jobs. Feasibility of applying BSC model for managing maintenance performance and applications of BSC in military applications.g. Problems related to implementation of object oriented maintenance management model and its integration to ERP. . (2) Performance measurement oriented: 230 . more work on its integration with CMMS can be attempted. the engineering management policies. communications between corporate (i. (5) Maintenance concept oriented: . decentralized MRO stores) and recasting of external relationships. Integration of quantitative optimization models with qualitative ones like RCM. Evolution in middleware software. . Future scope includes BPR through workgroup computing technology. Integration of PMS with an effective MMIS. .3 detection. e. outsourcing through inter-enterprise technology. . Integration of maintenance management in to MRPII using appropriate system design that will improve logistics management and physical distribution functions of the organization. organizational transformations (e. (4) Maintenance management integration oriented: . More work needs to be done to link CMMS design and use with actual maintenance performance. Use of CMMS by personnel outside maintenance function has the potential to improve maintenance responsiveness and equipment condition. .e. e.g. Future studies in the joint implementation of TQM.JQME strategy. Problems relating to application and development of an object oriented decision support system for maintenance management. the methods of failure 12. . . etc. Problems and factors associated with implementation of PMS using QFD technique require further research.

References Al-Najjar. Vol. Y. “Cross functional team working for overall equipment effectiveness (OEE)”. (2003). Andijani. Vol. International Journal of Production Economics. Proceedings Annual Reliability and Maintainability Symposium IEEE. e. pp. P. 6-11. Knowledge of maintenance engineers i. S. 4. . M. Bamber. Artana. Development of the customized maintenance concept framework to include more modules. T. 1. pp. Reliability Engineering and System Safety. (2002). Vol. 452.. maintenance policy. T. M. Vol. pp. Arts. Aronson. material selection. (2004). pp. 3.H.M. “A fully subjective approach to modeling inspection maintenance”. “Spreadsheet modeling of optimal maintenance schedule for components in wear-out phase”. “Enhancing a company’s profitability and competitiveness using integrated vibration-based maintenance: a case study”. 2. Sharp. 1st IEE/MechE International Conference on Power Station Maintenance: Profitability through Reliability. (2002). 109-18. 4. 13 No. 231 . 84 No. (2003). pp. 1. 223-38.g. 4 No. K. Al-Najjar. 410-25. A. and Clemente.. 45-63. 157-63. (1998). Production Planning and Control. A.D. G.e. 9 No. and Rasmussen. . “A simulation model for maintenance planning”. M. “Decision support in long term and short term maintenance planning using condition monitoring information”. Anderson. “Critical evaluation of simulation studies in maintenance”.T. 1.C. Vol. data management. Thorstensen. (6) Human behavior oriented. pp. 643-57. 157 No. “Selecting the most efficient maintenance approach using fuzzy multiple criteria decision making”.B.J. and Ishida. I. J. to allow selection of modules for which a decision support module is foreseen.A.J. 85-100. European Journal of Operational Research. B. pp. 71-6. R. “Some aspects of measuring maintenance performance in the process industry”. Journal of Quality in Maintenance Engineering. Balakrishnan. and Motara. C. Reliability Engineering and System Safety. I. F. Manufacturing Engineering. (2003). A new technique i.R. K. Knapp. Badı́a. “What to measure for proper maintenance”. N. pp. Conference Publication No. B.M. The area of maintenance outsourcing has tremendous potential for investigation as more and more clients are becoming interested in contracting out maintenance. . and Scarf.. 148 No. Vol. pp. 129 No. (2002). the expert judgment may be useful in the design of customized maintenance concept. (1998). etc.A. Vol. Berrade. pp. 336-41.e. Apeland. P.M. 3. Vol. Journal of Quality in Maintenance Engineering. and Duffuaa. 77 No. Vol. pp. and Alsyouf. 81-91. 2. (1992). S. European Journal of Operational Research.M. neural management maintenance is yet to be developed Maintenance to reduce involvement of analysts/engineers in data processing and thus management adding quality in decision-making process.G. 78 No.. Castka. and Alsyouf. (2002).P.. B. “Optimal inspection and preventive maintenance of units with revealed and unrevealed failures”. standardization. and Lawrence.

(2004).. M. Pibouleau. V. “Closed-form analytical results for condition-based maintenance”. Vaishnavi. and Yalaoui. “On an imperfect preventive maintenance model”. European Journal of Operational Research. 77 No. Vol. Marseguerra. P. Vol. 2. 82 No. 165-72. Vol. “Simulation modelling of repairable multi-component deteriorating systems for ‘on condition’ maintenance optimization”. 6. 4. 7. 2. Buczkowski. R. 12. 255-64... 29 No. “Optimal maintenance strategies for systems with partial repair options and without assuming bounded costs”. and Turk. (2005). Journal of Quality in Maintenance Engineering.. 76 No. “A preventive maintenance policy with sequential checking procedure for a Markov deteriorating system”. 11 No. 3. and Trivedi. Chen. Vol. B. Reliability Engineering and System Safety. Vol. Soares. S. Vol. 146-65. pp. “On a dynamic preventive maintenance policy for a system under inspection”. D. 1. Ben-Daya. Plant Engineering. D. Vol. A.3 Vol. 699-714. R. W. European Journal of Operational Research. M. 281-90. Ip. L. Vol. pp. 76 No. I. IEEE Transactions on Software Engineering. 71 No. Reliability Engineering and Systems Safety. H. M. pp. Call. (1999). (2000). 11 No. 3. and Alghamdi. and Domenech. L. 247-55.. Vol. (2003). J. (2002). 1. 449-67.. “The analytic hierarchy process applied to maintenance strategy selection”.. 3. pp. Charles. 17 No. 1. M. European Journal of Operational Research. . “A condition based maintenance model for a two-unit series system”. Hartmann. International Journal of Quality & Reliability Management. D. Reliability Engineering and System Safety. (2002). 712-8. D. 279-94. (2005). pp.. Bardey. and Montanari. M. (2005).K. (2005). Artiba. pp. K. A. (2002). Vol. Vol. and Watson. C. (2002). “Optimization of preventive maintenance strategies in a multipurpose batch plant: application to semiconductor manufacturing”. Chiang. Vol. Vol. 116 No. A. pp. 61-9. E. (2003).. and Popova. 11. Reliability Engineering and System Safety. E. J.S. Chelbi.G. pp. and Kulkarni.H. pp. Vol. and Zio. “Expert system for aircraft maintenance services industry”. C. 1. pp. 348-58.. 115-20.E. Chen. Journal of Quality in Maintenance Engineering. Y. E. (2003). Vol. 31-2. European Journal of Operational Research. pp. and Yuan. pp. Azzaro-Pantel. (2002).H. Vol. (2001). Bruns. S. 3. International Journal of Quality & Reliability Management. “Value of change in maintenance: altering routines is a worthwhile investment”. P.S. Vol. C. pp. E. M. M. pp. V. and Lu... pp. F.. pp. Bris. 232 Barbera. Riane. 2. 43-51. 4. 71-83.K. A. 3. Cheung. A. “Outsourcing prioritized warranty repairs”. 139 No. 22 No. Floru. “Predicting maintenance performance using object-oriented design oriented metrics”.E. J. “Optimal maintenance policy for a Markovian system under periodic inspection”. Bevilacqua. Computers & Chemical Engineering. J. 661-70. Barata. 70 No. 56 No. and Ait-Kadi. Braglia. (2000). Schneider. R. 142 No. and Yuan. T. “New method to minimize the preventive maintenance cost of series parallel systems”. 41-7. 1. Chen. 548-76. pp. pp. 27 No. “Maintenance policies with two-dimensional warranty”. Bloch-Mercier. Vol.G. Reliability Engineering and System Safety. pp. 77-87. Journal of Quality in Maintenance Engineering. and Eeckhoudt. Chen. Bevilacqua. Frosolini.S. F. F. M. Reliability Engineering and System Safety..JQME Bandi. (2002). Chatelet. “To maintain or not to maintain? What should a risk-averse decision maker do?”.. D. 1. and Braglia. (2003). Reliability Engineering and System Safety. “Failure rate prediction with artificial neural networks”. 156 No. 11 No. 80 No. “Analysis of a production/inventory system with randomly failing production unit submitted to regular preventive maintenance”.

and Wolff. Dohi. Vol. and Finlow-Bates. D. (2000). 965-79. Cooke. El Hayek. (1999). L. Vol. Maintenance Proceedings Annual Reliability and Maintainability Symposium IEEE.L. C. Vol.. (2001). 33 No. R. (1999). Robotics and Computer-integrated Manufacturing. management Chu. and Kelly. U. “Human error in maintenance: a review”. Vol. 3.. International Journal of Production Economics. 10. pp. 19 No. 5. C. and Liu.. Cooke. 385-97. S. 4. Y. 1037-46. D. JIT.S.G. “A stochastic programming model for scheduling maintenance personnel”. and Paulsen. pp. pp. R.W. and TPM and manufacturing performance”. O. European Journal of Operational Research. 54 No. “A decision support maintenance management system: development and implementation”. J. Vol. 11 No. and Petty.K. The TQM Magazine. Duffuaa. J. D. “Computer-aided RCM-based plant maintenance management system”. Applied Mathematical Modelling. J. (2003).. “Optimal periodic maintenance strategy under an intermittently used environment”. B. pp. 111-9.J. pp. 2. Kaio. Vol. Vol. 258-75. Journal of Quality in Maintenance Engineering. Finlow-Bates. 17 No. International Journal of Quality & Reliability Management. T.E. 319-28. Cua. 4.O. Y. and Probert. R. “Relationships between implementation of TQM. and Bjorkelo. and Scarf. H. (1998). K. H. K. (2003). (2001). pp.. and Roussignol. 135-41. 9. 1. S. van Voorthuysen. Berenguer. 12. T. 6. Reliability Engineering and System Safety. 55 No. 2. Reliability Engineering and System Safety. Suzuki. Fitzgerald. Reliability Engineering and System Safety. (2001). Yamashita. Crocker. pp. pp. 4. 5 No. 166-77. 19 No. R. “Total predictive maintenance: a comprehensive tool for achieving excellence in operational systems”. Journal of Quality in Maintenance Engineering. S. Philippides. pp. P. McKone. Proth. 285-95. pp. 2. Labib. (2005). Visser. “Effective maintenance practices to manage system ageing”. pp. pp. 5. International Journal of Information Management.. 5 No. 333-47. 159-64. 449-58. TQM and RCA to TPM”.A. Grall. “On the impact of optimization models in maintenance decision making: the state of the art”. Vol. Gabbar. and Al-Sultan. 284-9. Journal of Quality in Maintenance Engineering. F. S. and Shimada. E. 30 No. 12 No. (2000). “Information systems development: maintenance and enhancement: findings from a UK study”. Dwight. 1003-16. A. Vol. Vol. pp. Vol. and Osaki. Industrial Engineering Journal.. A. pp. (2003). 12 No. “Searching for real maintenance performance measures”. 15-23. A.. (2001). M. IIE Transactions. “An integrated approach to problem solving: linking K-T. Vol.Chockie. 150 No. 23 No. “Predictive maintenance: the one-unit replacement model”. Vol. 71 No. Dieulle. “Effectiveness of maintenance”. and Schroeder. “Implementing TPM in plant maintenance: some organizational barriers”. Vol. . “Modeling of uncertainties in reliability centered maintenance – a probabilistic approach”. 2. (1998). G. A.W. pp. 19 No. pp. M. B.O. 4. 675-94. Das. (1992). pp. Eisinger. P.. K. Vol. “Sequential condition-based maintenance scheduling for a deteriorating system”. N. Vol. and Rakowsky. International Journal of Quality & Reliability Management. K. 451-61.S. pp. Walmisley. Dekker. (1999). pp. 60 No. Vol. Vol. “Optimizing life cycle cost of complex machinery with rotable modules using simulation”. C. Dhillon. K. Fernandez. 21-36. 3.A. R.. 307-13. Journal of Quality in Maintenance Engineering. “Concepts for measuring maintenance performance and methods 233 for analyzing competing failure modes”... 20 No. Journal of Operations Management. (1999). 8. (2006). (1997).

(2000). and Artiba. K. pp. 3. 11. pp. production. C. 76 No. 1. 23 No. H. . and Srinivasan. 132 No. J. 1404-16. “Integrated optimal reliable design. C. (1998). Computers and Industrial Engineering. 3. (1995). 141-50.G. Ü. “Workforce constrained preventive maintenance scheduling using evolution strategies”. 51 No.Y. 21 No. Vol. 61-72. K. Prestige Journal of Management and Research. (2000). Management Science. M. 6 No. IIE Transactions. “A tabu search heuristic for preventive maintenance scheduling”. 177-91. and Silva. “Simultaneous determination of preventive maintenance and replacement policies in a queue-like production system with minimal repair”. “A maintenance policy for a system with multi-state components: an approximate solution”.C. Vol. “Operational efficiency and effectiveness measurement”. 1543-55. 833-59. Journal of Quality in Maintenance Engineering. D. 2. 2. Vol. Z. and Dale. A. P. R. Omega. pp. M.A. M. S. Journal of Quality in Maintenance Engineering. W. (2003). Jamali. 107-14. pp. Vol. L. A.L. 5 No. pp. Vol. (2006). and Duenyas. I. 277-92. Journal of Quality in Maintenance Engineering. Dieulle. 28 No.M. pp. B.T. Vol. Iravani. 3. C. (1999). 423-35.B. 2. (2000). 827-40. Production Planning and Control. J. D.. 2. Groote. Cléroux. and Fung. (1997). Günalay. Vol. 40 No.P. 3. Vol. Reliability Engineering and System Safety. (2005). 117-27.C.. 4-24. 323-39.S. M. 161-7. Mohan. Journal of Quality in Maintenance Engineering. Hipkin. 349-59. Y.. and Phillips. Grall. Berenguer.A. Gupta. F. M. Hsu. Y. Vol. Gupta. 3. 2. International Journal of Operations & Production Management. A.. (2001a). S. 1. L. 183-91. 7 No. “The relationship between preventive maintenance and manufacturing system performance”. “Continuous time predictive maintenance scheduling for a deteriorating system”. (2002). pp.. (2001). and Miller. 11 No.. 27 No.F. pp. pp. pp. Ip. 63 No. 31 No.. pp.. and Chitale.M. Reliability Engineering and System Safety. (2001). Vol. Decision Sciences. Gürler. International Journal of Quality & Reliability Management. “Integrated maintenance and production control of a deteriorating production system”. 149-60. European Journal of Operational Research. A. 5. and Gupta. 146-62. A. M. Vol. C. 4. (2002). 141 No. and He. Ahire. and Chiu.3 Chemical Engineering. Vol. pp. pp.M. pp. 1 No. “A study of total productive maintenance implementation”. I. IEEE Transactions on Reliability. Vol. 1.D. L. and Roussignol. 234 Gopalakrishnan. Ait-Kadi. G. and Turki. L. 689-98. (2001). Computers & 12. Vol. Gupta. 43 No. (2002).D. Vol.K. “Joint optimal periodic and conditional maintenance strategy”. Sonwalker. R. “TQM and BPR: lessons for maintenance management”.K. M. “Adapting just-in-time manufacturing systems to preventive maintenance interruptions”. “Optimal maintenance policy in a multistate deteriorating standby system”. Kwong. Ireland. 6. Vol. “Unbiased estimators for mean time to failure and percentiles in a Weibull regression model”. 11. R. Grievink. pp.D.R.C. pp. “Design of maintenance system in MRPII”. European Journal of Operational Research. A. (2002). “Maintenance performance analysis: a practical approach”. and Cock.C. (2001b). 4. S. Jeong. D. pp. Ho. Vol. and Kaya. Vol. Vol. Gopalakrishnan. pp..H.JQME Goel.D. 9 No. “Maximizing the effectiveness of a preventive maintenance system: an adaptive modeling approach”. Greenwood. Hsieh. pp. and maintenance planning for multipurpose process plants”. “Overall equipment effectiveness through total productive maintenance”.. 34 No. and Weijnen.

155-64. “Single-machine scheduling with maintenance and repair rate-modifying activities”. 695-705. Vol. Integrated Manufacturing Systems. and Anderson. 33-7. pp. Production Planning and Control. 1. P. pp. R. 294-306. Luce. 70 No. (1999). 4. A. Labib. 2. pp.Y.. 495-507. Z. “Practices of preventive maintenance and replacement for engines: a case study”. (2004). 60-61. Y. 124 No. J. (2001). 3 No. G. . (2001). Pulp and Paper. O’Connor. “Analytic hierarchy process of justification of total productive maintenance”. G. Mechanical Systems and Signal Processing. pp.M. P. Kodali. “The status of maintenance management in Swedish manufacturing firms”. “An optimal maintenance model for a combination of secondhand-new or outdated-updated system”. (1998). (1996).W. (1999). 1. 9 No. 3. and Haddara.Y. Labib. (2000). 93-8. pp. 18 No. Journal of Loss Prevention in the Process Industries. 2. Vol. B.Jones.I. Vol.M. and Movel. Vol. 12 No. pp. “Choice criteria in conditional preventive maintenance”. Vol. 3. K. Jonsson. Vol. and Wang. 1. 176-87. 739-52. pp. 66-75. 173-94. Vol. (2001). M. S. Vol. pp.. G. 4 No. (1997). P. Vol. 163-8. 493-513. and Sharp. Lam.. 6. Journal of Quality in Maintenance Engineering. Vol. (1996). Lee. R. Vol.. Vol. F. Property Maintenance Management. 16 No. Productivity. “Towards a value based view on operations and maintenance performance management”.B. 21 No. (1998). pp. “Computerized maintenance management systems”. Production Planning and Control. Leger. O. Journal of Quality in Maintenance Engineering. Liyanage. “Measuring maintenance performance – in search for a maintenance productivity index”. Leung. (2001). 333-50. Vol. pp. 7. A.G. Khan. 12 No. 2. International Journal of Production Economics. K. Journal of Quality in Maintenance Engineering. Vol. 13 No.W. International Journal of Operations & Production Management. and Lin. “Qualification of TPM benefits through AHP model”. International Journal of Production Economics. 5. European Journal of Operational Research. “Determining true cost of maintenance performance can generate new profits”. Tao. pp. 453-73. pp..K. pp. Kodali.J. R. R. Vol. “A framework for managing maintenance using performance measurement systems”. S. Vol. “Measurement of overall equipment effectiveness as a basis for TPM activities”.Y. European Journal of Operational Research. (2003). Ljungberg. (2003). pp. C. 4. “Company-wide integration of strategic maintenance: an empirical analysis”. European Journal of Operational Research.F. management Jonsson. “World-class maintenance using a computerized maintenance management system”. Juang. 87-98. C. K. 119 No. European Journal of Operational Research. and Collis. H. Kutucuoglu. pp. S. 14 No. pp. International Journal of Operations & Production Management. Irani. F. 135 No. S. pp. pp. “Risk based maintenance: a quantitative approach for maintenance/inspection scheduling and planning”.K. 47-58. 9 No. 265-73. (2001). J. and Williams. M. U. 10. pp. Lamb. and Chandra.N. “A Bayesian method on adaptive preventive maintenance 235 problem”. 563-73. Vol. 42 No. 155 No. “An effective maintenance system using the analytic hierarchy process”. (2000). Löfsten. Vol. 63 No. 2. “Integration of maintenance in the enterprise: towards an enterprise modeling based framework compliant with proactive maintenance strategy”. (1998). 233-58. (1999). 2. J. Hamali. 1. 4. and Kumar.B. Vol. Lai.

E. N. and Kaewplang.. 2. V.P. R.. (2001). and Eccleston. and Colosimo.H. pp.G. (1997). E. Vol. Murthy. 17 No. “The impact of total productive maintenance practices on manufacturing performance”. 8 No.N. pp.. Vol. G. “Using fuzzy linguistics to select optimum maintenance and condition monitoring strategies”.J. Vol. Nagarur. 2. Production and Operations Management.W. pp. A. “Guidelines for implementing predictive maintenance”. “Using fuzzy linguistics to select optimum maintenance and condition monitoring strategies”. K. (1999). 79 No.O. Pieri. “Optimal decision making in a maintenance service operation”.E. 2. McKone. 77 No. E. pp. Atrens. International Journal of Production Economics. 57 No. and Asgharizadeh. 81-90. Reliability Engineering and System Safety. pp. Metaxiotis. 76 No. 2. Reliability Engineering and System Safety. 6. (2002). C. K.K. K. “Invited review on imperfect maintenance”. 3. (2003). 1129-40. Mechefske. pp. Schroeder. “Strategic maintenance management”. M. 305-16. 75 No.E.S. G. A.. M. Murthy. Brandão. 15 No. Klein. “Changeover improvement: a maintenance perspective”. (2002). “Condition based maintenance optimization by means of genetic algorithms and Monte Carlo simulation”. Vol. Marquez. and Assimakopoulos. K.I. Pham. pp. Vol. and Owen. and Milanese. K. 39-58. and Podofillini. Lekatis. M. European Journal of Operational Research. Mileham. 367-77. 19 No. Vol. 53-156. “Total productive maintenance: a contextual 236 view”. Mechanical Systems and Signal Processing. 17 No. (2002).JQME McIntosh. Z. 4. 93-101. pp. (2003).R. and Cua.W.N. pp. and Wang. C. Martin. pp. and Wang. McKone. Vol. “Models for maintenance optimization: a study for repairable systems and finite time periods”. pp. Vol. Vol. Nikolopoulos. 248-57. pp. 259-73. Plant Engineering Barrington.C. S. 10. pp. R. (2001). 39-41. A. (2002). Vol. pp. (2002).K. pp. 3. Oien. D. 1. 123-44. 149-54. Zio. Vol.E. European Journal of Operational Research.. 1. 425-38. Industrial Management and Data Systems. J. Journal of Operations Management. Vol. Journal of Quality in Maintenance Engineering. (1999). S. K. 184-91. Culley. D. McKone.R. Vol. Vol. A. 73 No. 2. International Journal of Production Economics.A. D. 1. Schroeder.O. H. and Cua.. (2002). 116 No. 5 No. (1996). (2003). R.N. (2001). Mechanical Systems and Signal Processing. (1999). 287-305. Vol. “Contracting out maintenance and a plan for future research”. Vol. Reliability Engineering and System Safety. H. (1998). “Improved quality of input data for maintenance optimization using expert judgment”. “Measuring overall craft effectiveness are you a takeover target for contract maintenance”. Z.. N. Mechefske. Journal of Quality in Maintenance Engineering. . pp. K. Marseguerra. 94 No. 2. L. 60 No. “Integrating industrial maintenance strategy into ERP”. Vol. “Determination of preventive maintenance periodicities of standby devices”. 3 No. 12. 103 No. H. R. 109-24. Journal of Quality in Maintenance Engineering.. 11 No. “A data and knowledge-based system for supporting the maintenance of chemical plant”. pp. 2. Peters. 2. and Wang. Reliability Engineering and System Safety.A. 2.. 151-65. 3. and Weiss.3 pp. and Heguedas. J. Vol. Motta.. E.E.G. K. Journal of Operations Management. “An object oriented decision support system for maintenance management”. 143-59.

Satyanarayana. R. 3. (1997). 379-99. Journal of Quality in Maintenance Engineering. Journal of Quality in Maintenance Engineering. Industrial Engineering Journal. K. Chow. “Total maintenance management: a systematic approach”. Pintelon. 12 No. Vol. and Polimac. (2002). Vol. Vol. 891-4. 1. and Vereecke. D. F. Vol. 24 No. “Maintenance strategies for used equipment under lease”. Journal of Quality in Maintenance Engineering. Pun. 65 No. 6 No. Rochdi. pp. Journal of Quality in Maintenance Engineering. Applied Mathematical Modeling. and Shanthikumar. N. pp. Yeh. Y. R. Vol. “Reliability centered maintenance”.. and Wang. T. “Optimal just-in-time buffer inventory for regular preventive maintenance”. 7-20. Kumar. “TPM can go beyond maintenance: excerpt from a case implementation”... I. Driss. “A Bayesian approach to an adaptive preventive maintenance model”. 3. J. M. A. Vol. T. 1 No.W. Sarker. 1. 237 Polimac. T. pp. 157-61. Proceedings Annual Reliability and Maintainability Symposium IEEE. and Prasad. “Menu driven maintenance information system”. International Journal of Production Economics.C.H. 12 No. (2001). H. 1.S. Raouf. “A review of overall models for maintenance management”. K. D. 5 No.P. 4. V. Production and Operations Management. 71 No. “Are you using all the features of your CMMS? Following this 7-step plan can help uncover new benefits”. 60 No. 19-42. Plant Engineering. F. (2005). Vol.H. Vol. A.W. pp. R. 4. M. pp. Shamsuddin. (2001). P. and Lau. International Journal of Production Economics. Vol. B. (2000). Vol. pp. Vol. L. W. 2. 138-64. and Zahari. and Puyvelde. pp. 1.K. pp. Vol. L. Vol.. Rausand. pp. 1. 10.. J. (2001). 3 No. Vol. A. 6 No. 8 No. pp..K. 9-24. 9. “Optimal preventive maintenance policies for a shock model with given damage level”. Vol. Journal of Quality in Maintenance Engineering. and Boondiskulchock. (1999). 11 No. R. 2. Z. and Juang. pp. 219-24. (2001). G. 1. 216-27. and Ghattas. pp.V. J. 6-14..V. pp. 2. 8-11. “An effectiveness-centered approach to maintenance management: a case study”. 53 No. Journal of Quality in Maintenance Engineering. 32-4. (1995). P. (2000). and Ben-Daya. Kodo. (1998). (2000). 1. (2006). pp. Murthy. (2000). 11 No. Sheu. 25 No. M.. . 67 No. and Knezevic. M. Qian. (1999).Pintelon. Reliability Engineering and System Safety. “Information technology: opportunities for maintenance management”. “Reliability prediction for condition-based maintained systems”. T. pp. 119-24. 1. 74 No. “Combined production and maintenance scheduling for a multiple product single machine production system”. Reliability Engineering and System Safety. M. Saranga. Sloan. 1. Hassan. Salameh. (2005). 751-60. “Maintenance performance reporting systems: some Maintenance experiences”. Sander. 346-68. N. Singer. “Optimization of maintenance and spare provisioning policy using simulation”. 1. Chin. Preez.E. “Maintenance and reliability”. and Toshio. “Assessment of present maintenance practices and future trends”. Sherwin.D. B. and Mohamed. 52-67.. Journal of Quality in Maintenance Engineering. and Puyvelde. pp. 1-2. H. Journal of Quality in Maintenance Engineering. “Evaluating the effectiveness of maintenance strategies”. management Pintelon. and Haque. Reliability Engineering and System Safety. pp. 121-32. 33-44. 4-15. (2006). M. Reliability Engineering and System Safety. Vol. pp. pp. (1999). Pongpech. Lin. 71 No.. Vol. Journal of Quality in Maintenance Engineering. “Industrial systems maintenance modeling using Petri nets”. (1996). J.N. L. S. Vol. Vol. pp. A. C.

Proceedings Annual Reliability and Maintainability Symposium IEEE. 12 No. “Measuring information”. Wickers. “A strategic approach to managing maintenance performance”. “An information processing model of maintenance management”. and Knapp. H. 1. Journal of Quality in Maintenance Engineering. 89-97. (2002). Y. (1999). (2001). pp. L. 74 No. International Journal of Advanced Manufacturing Technology. “On preventive maintenance policy of a critical reliability level for system subject to degradation”.in To purchase reprints of this article please e-mail: reprints@emeraldinsight. Wessels. 79 No. W.C.. A. Vol. Reliability Engineering and System Safety.K.O. Tsang.JQME Swanson. H.K. W. European Journal of Operational Research. Vol. Vol. (2003). Production and Inventory Management Journal. USA. and Lee.ac. Vol. Vol. pp. and Teng.. pp. pp. 3. 9. 17 No. Vol. G. 691-715. (2002). pp. 70 No. 12. Swanson. and Ho. Y. B.L. A. 1. Wang.. 55 No. “Cost optimized scheduled maintenance interval for reliability centered maintenance”. Tsang. (1997). Journal of Quality in Maintenance Engineering. (2001).H. 45-64. 39-43. 4 No. E. 469-89. International Journal of Production Economics. pp. Tu.V. 692-703.C. 16-28. 412-6. 6. Tsang. International Journal of Production Economics. (1998). IEEE African Conference (24-27 September). 19 No. pp. 38 No. (2001). Vol. R. 139 No. H. “Computerized maintenance management systems: a study of system design and use”. Vol. (1997).C. K. 299-313. pp. “An integrated maintenance management system for an advanced manufacturing company”. pp. “A framework for maintenance concept development”. “Determining the most important criteria in maintenance decision making”. L.K. J. Mann. Deshmukh can be contacted at: deshmukh@mech. International Journal of Operations & Production Management.J.S. A. 1. Yam. “Data management for CBM optimization”. (1998). B. 77 No. 2. Kovalerchuk. pp. Jardine.P. Waeyenbergh.Y. (1996). pp. 87-94. 3 No. L. pp..H.K.T. (2006)..K. S.3 Swanson. Wang. A. Vol. L. Westerkamp. Jardine. P. Zhao. pp. Vol. and Leung. pp. “Optimizing maintenance functions by ensuring effective management of your computerized maintenance management systems”. Wang. Yeung. G. (2003). pp. 83 No. (2002). pp. “Linking maintenance strategies to performance”. A.X.T. Omega.emeraldinsight.H. T.iitd.A.. “Imperfect maintenance policies for deteriorating production systems”. 3. Tse. and Kolodny.G.W. 11.M. Corresponding author S. 2. 301-8. “Optimizing preventive maintenance for mechanical components using genetic algorithms”. 11-15. 788-95. 237-44.. Industrial Engineer.com Or visit our web site for further details: www. Reliability Engineering and System Safety. 37-51. International Journal of Production Economics. 3. W. L. 34 No. and Pintelon. A. Journal of Quality in Maintenance Engineering. 1. Vol. 29 No. “Measuring maintenance performance: a holistic approach”. Vol. H.. (2001). 191-201. 2. Stellenbusch. 7.S. Tseng. “Learning curve analysis in total productive maintenance”. 491-9. Vol.. pp. P. Vol. F. Ruey.com/reprints View publication stats .H. “A survey of maintenance policies of deteriorating systems”. 3. and Sun. 238 Triantaphyllou. (2003). Tsai. Vol. International Journal of Production Economics. R.