Opportunistic maintenance (OM) as a new advancement in maintenance approaches

Opportunistic maintenance (OM)as a new advancement inmaintenance approaches

A reviewHasnida Ab-Samat and Shahrul Kamaruddin

School of Mechanical Engineering, University Science Malaysia (USM),Seberang Perai Selatan, Malaysia

Abstract

Purpose – This paper reviews the literature on opportunistic maintenance (OM) as new advancemaintenance approach and policy. The purpose of this paper is to conceptually identify commonprinciple and thereby provide absolute definition, concept and characteristics of this policy.Design/methodology/approach – A conceptual analysis was conducted on various literaturesto clarify a number of principle and concepts as a method for understanding information on OM.The analysis involves the process of separating the compound terms used in the literatures into a fewparts, analyse them and then recombining them to have more clear understanding of the policy.Findings – The paper discussed the maintenance approach, genealogy, principle, concept andapplications of OM both in numerical analysis and real industry. OM policy is developed based oncombination of age replacement policy and block replacement policy and in practical; OM is appliedas the combination of corrective maintenance which is applied when any failure occurred, withpreventive maintenance (PM) – a planned and scheduled maintenance approach to prevent failure tohappen. Any machine shutdown or stoppages due to failure is the “opportunity” to conduct PM eventhough it is not as planned. The characterization of OM was provided in order to present its theoreticalnovelty for researchers and practical significance for industries.Practical implications – To date, there is no publication that reviews the OM in-depth and providesclear understanding on the topic. Therefore, this paper aims to show lineage of OM and the currenttrend in researches. This discussion will pave the way of new research areas on this optimalmaintenance policy. Clear definition and principle of OM provided in this paper will trigger interest inits practicality as well as aid industries to understand and conduct OM in operation plant.Originality/value – This paper discussed the available literature about OM in various perspectivesand scopes for further understanding of the topic by maintenance management professionals andresearchers. Therefore, OM can be widely studied and applied in real industry as it is an effective andoptimal maintenance policy.

Keywords Preventive maintenance, Corrective maintenance, Maintenance policy,Opportunistic maintenance, Optimal maintenance

Paper type Literature review

1. Maintenance systemThe role of maintenance in today’s manufacturing systems is becoming moreimportant as companies start to adopt the system as one of their profit generatingelements (Waeyenbergh and Pintelon, 2002; Sharma et al., 2011) and a supportingfunction (Samat et al., 2011). Maintenance is conducted to ensure that all the equipmentwithin the company is repaired, replaced, adjusted and modified according to

The current issue and full text archive of this journal is available atwww.emeraldinsight.com/1355-2511.htm

Received 18 April 2013Revised 27 November 2013Accepted 7 March 2014

Journal of Quality in MaintenanceEngineeringVol. 20 No. 2, 2014pp. 98-121r Emerald Group Publishing Limited1355-2511DOI 10.1108/JQME-04-2013-0018

The authors would like to convey their appreciations to Ministry of Higher Education (MOHE)and Universiti Sains Malaysia (USM) for the funding provided in conducting this research.

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production requirements. This way, the whole manufacturing processes are guaranteedto operate effectively and efficiently (Arts et al., 1998; Parida and Kumar, 2006).However, based on the research conducted by Mobley (1990), cited by Chan et al. (2005),15-40 per cent of total production costs are spent on maintenance activities. On theother hand, Bevilacqua and Braglia (2000) stated that maintenance costs can reach15-70 per cent of production costs, varying according to the type of industry.Consequently, further research by Wireman (2003) showed that up to 33 per cent of thismaintenance cost is actually wasted or spent unnecessarily. These percentage show alot of improvements could be carried out in order to achieve an effective and optimizedmaintenance system. Nowadays, the literatures on maintenance is centred on problemsin the optimization of maintenance policies. The aim is to ensure a system has theability to satisfy consumer demand with the minimal maintenance cost possible,without sacrificing component useful lifetime or reliability (Nourelfath and Ait-Kadi,2007; Zhou et al., 2012).

The focus of this paper is directed towards the various literatures that discuss thetypes of maintenance techniques applied in the industry. The aim is to investigate thelineage of opportunistic maintenance (OM) policy as a new maintenance concept whichalso widely relates to the optimal maintenance system. The origins and genealogy ofOM principles in various published literature are critically analysed using the criticalconceptual method. The first section of this paper reviews OM monikers, genealogy,principles and aims. Then, the categorization of literature based on system type,research classifications and optimal criteria adopted are presented in the secondsection. Subsequently, various applications of OM are presented in order to define itspracticality in the industry. The fourth section consists of a discussion of issues, meritsand also downsides of OM policy both in numerical analysis and real industryapplication. Lastly, conclusions are drawn in presenting the concept and application ofOM for future research.

2. Conceptual analysis of OM policyA tremendous number of publications can be found discussing maintenancetechniques since the maintenance technique transition from corrective maintenance(CM) in 1940 to various operation research models (Garg and Deshmukh, 2006). Eachone is developed based on a variety of principles and to suit different situations. ForZheng (1995), the major objectives of maintenance policies are to increase systemreliability and availability while at the same time to reduce system maintenance cost.Among the various maintenance policies, some are widely implemented as they havestrong fundamental elements such as CM, preventive maintenance (PM), predictivemaintenance, condition-based maintenance (CBM), total productive maintenance(TPM), reliability-centred maintenance (RCM) and computer maintenance managementsystem.

Aside from these policies, a maintenance policy termed as opportunisticreplacement and inspection was introduced by McCall and Radner and Jorgenson(1963). It is based on research conducted by the RAND Corporation, situated in SantaMonica, CA, USA with the aim to find an optimal maintenance policy. It was firstapplied in a case study of a rocket engine (McCall, 1963) and a manned aircraft andballistic missile system (Radner and Jorgenson, 1963). The concept of this“opportunistic” maintenance policy is the dependency of the components andequipment in a system. With this policy, maintenance is to be performed on a givenpart, at a given time, depending on the state of the rest of the system. The focus of

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implementation is to predict the relative frequencies of a variety of maintenance actionssuch as inspection and replacement of failed components in a system. This policy isintriguing as in current years researchers referred to this policy as an optimalmaintenance system.

However, advancement on this policy requires profound study to find its origin,principles and applications. Therefore, critical conceptual analysis was conducted inthis paper as an effort to fully understand the opportunistic replacement andinspection policy or referred to as OM throughout the paper. Furner (2004) describedthe analysis as a technique of precisely defining the meaning of a concept using theprocess of identifying and also specifiying the concept within a classification ofconditions or phenomena. For this paper, the technique is used by breaking down theissues to examine the major elements in OM policy. Since there is no one single book ormanual published on the matter, the conceptual analysis were mainly focused onjournal papers. The flow of conceptual analysis conducted on OM policy is shown inFigure 1.

The analysis process starts with the literature published on opportunisticand optimal maintenance, which were scrutinized in order to find the policy monikerused to described OM. Even though OM was first described as opportunisticreplacement and inspection in 1963, the same theory used by Rander and Jorgensonwas applied to much research afterwards, yet named in many different terms.The various monikers used to discussed OM policy over the years reflected theapplications as well as the principle used in the literature. The list of monikers usedare stated in Section 2.1. In connection with the moniker, conceptual analysis wasalso conducted to study the origins and genealogy of OM. Since the advancementof application on maintance system in 1940s, various maintenance policies weredeveloped from a basic principle of a “run, failed and repair” approach. This resultedwith a grouping of approaches and techniques to cater to many types of equipmentand system conditions in the industry.

Further on, the principle and concept of OM in various literatures were revised inSection 2.3. Changes in technology and process due to high customer demand requiresmaintenance to be conducted as precisely and as effectively as possible. Because ofthat, optimization becomes the focus of much maintenance research, which thenbrought OM into the limelight. For that very reason, the simple approach of using theopportunity of a component failure to conduct maintenance tasks on other relatedcomponents was tried and altered to satisfy numerous system conditions. The analysison this paper brings forward some of the common OM principles and concepts used inthe publications. The conceptual analysis ends with a study of focuses and aims of OMin the publised research. Relating to various principles and concepts introduced in theliterature, the objectives of conducting OM policy also varies. Therefore, the objectiveswere gathered and discussed, as the process, to understand the advantages of OMpolicy. All the information gathered via this conceptual analysis was used to define theOM overall, as presented in Section 2.5 of this paper.

Moniker GenealogyPrincipleand Concept

Focus andAim

Figure 1.Four elements inconceptual analysisfor opportunisticmaintenance (OM) policy

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2.1 Publications and monikers of OM policyThe OM concept was first coined in 1963 as opportunistic replacement and inspectionpolicy and the trend continued for a few years afterwards, before it changed to becomeOM, as is widely used today. In the project by Radner and Jorgenson (1963), thecombination of PM action and CM were used to reduce the set-up costs of amaintenance action. The concept was then further reviewed as a new maintenancepolicy in a paper by McCall (1965). After the introduction of the OM concept, asidefrom a few publications, no other researchers further discussed the topic until early the1980s. Nevertheless, OM returned to become a topic of interest by the 1990s andthe trend shows the increase of publications on OM by the year 2000. The graph inFigure 2 shows the number of journal publications on OM from the years 1963 until2012. Over 70 publications are found to directly or indirectly delve into the discussionand application of the OM policy issues. Even though it can be considered as a smallnumber compared to other maintenance approaches and policies like RCM, TPM andCBM, it is a significant trend considering the short genealogy of OM. There is anencouraging and steady increment on the number of publication in recent years.

From the number of publications it can be seen that there are also some trends in thenaming of the OM policy. Conceptual analysis conducted on the publications showsthat over the years, OM policy is widely studied and implemented, but with variousmonikers in order to suit the concept used in the research. The study of monikers inthis paper is deemed important as the naming reflects the root of the principlesused. The names also indicate the overall idea of the policy used in the publications.As listed in Table I most publications on OM in the 1970s and 1980s used the term“opportunistic replacement and inspection policy”. This is due to the fact thatmaintenance was first viewed as tasks involving only repair and replacement ofcomponents with the aim of restoring broken machinery and equipment intooperational condition. It was only until after a few decades that the process of cleaning,lubricating, calibrating, etc., were considered as maintenance tasks. Then, the conceptwas also named as opportunistic-based maintenance and opportunity-based agereplacement. Age-based maintenance can be defined as activities done based on theage renewal of a machine, which is preventively maintained until it reaches a certain

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1. McCall (1963) Opportunistic replacement and inspection2. Radner and Jorgenson (1963) Opportunistic replacement and inspection3. McCall (1965) Opportunistic replacement and inspection4. Duncan and Scholnick (1973) Opportunistic replacement and interrupt strategies5. Sethi (1976) Optimal opportunistic replacement6. Vergin and Scriabin (1977) Opportunistic maintenance and preventive maintenance7. Day and George (1981) Opportunistic replacement, look-ahead-maintenance and

stationary policies8. L’Ecuyer and Haurie (1983) Preventive replacement9. Epstein and Wilamowsky (1985) Opportunistic replacement

10. Liang (1985) Piggyback preventive maintenance11. Thomas (1986) Opportunistic replacement12. Pullen and Thomas (1986) Opportunistic replacement13. Ozekici (1988) Optimal replacement14. Dekker and Smeltink (1991) Opportunity-based block replacement or one-opportunity-

look-ahead15. Zheng and Fard (1991) Opportunistic hazard rate replacement16. Fard and Zheng (1991) Opportunistic replacement17. Zheng and Fard (1992) Opportunistic replacement18. Dekker and Dijkstra (1992) Opportunity-based age replacement19. Zheng (1995) All opportunity-triggered replacement20. Mann et al. (1995) Condition-based preventive maintenance21. Savic et al. (1995a) Opportunity-based maintenance22. Savic et al. (1995b) Opportunity-based maintenance23. Dekker (1996) Optimal maintenance24. Tan and Kramer (1997) Opportunistic maintenance25. Mohamed-Salah et al. (1999) Opportunistic maintenance26. Jhang and Sheu (1999) Opportunity-based age replacement27. Sherwin (1999) Opportunistic maintenance28. Satow et al. (2000) Opportunity-based age replacement29. Rao and Bhadury (2000) Opportunistic maintenance30. Pham and Wang (2000) Opportunistic maintenance31. Bevilacqua and Braglia (2000) Opportunistic maintenance32. Crocker and Kumar (2000) Opportunistic or on-condition maintenance33. Cassady et al. (2001) Opportunistic maintenance34. Scarf and Deara (2003) Opportunistic maintenance35. Grall et al. (2002) Condition-based inspection/replacement36. Jiang and Ji (2002) Age replacement37. Wang (2002) Opportunistic maintenance38. Dekker and van Rijn (2003) Opportunistic maintenance39. Kaspi and Shabtay (2003) Opportunistic replacement40. Haque et al. (2003) Opportunistic replacement41. Degbotse and Nachlas (2003) Opportunistic preventive maintenance42. Satow and Osaki (2003) Opportunistic-based age replacement43. Das and Acharya (2004) Age replacement during delay time and opportunistic age

replacement during delay time44. Saranga (2004) Opportunistic maintenance45. Amari and McLaughlin (2004) Condition-based maintenance46. Castanier et al. (2005) Opportunistic replacement47. Cui and Li (2006) Opportunistic maintenance48. Zhou et al. (2006) Opportunistic condition-based preventive maintenance

(continued)

Table I.List of monikersused in publicationson opportunisticmaintenance from1963 till 2012

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number of time periods without a failure (Khazraei and Deuse, 2011). In the researchusing the name, the decisions regarding maintenance tasks conducted during failureare based on a calculation of the age or the remaining lifetime of a component.

Interestingly, some researchers (Day and George, 1981; Dekker and Smeltink, 1991;Dekker and Dijkstra, 1992) also called this policy one-opportunity-look-aheadmaintenance. It is to portray the process of replacement of parts or components if theyreach their limited lifetime, and right before they experience failure. The concept is tolook into the planning of maintenance for each component in a system and replacecomponents that will reach their life limits. For Liang (1985), the term “piggybackpreventive maintenance” was used for the OM concept. All parts in a system have theirown PM intervals, yet the PM is not carried out until an unscheduled maintenance foranother part occurs, thus the term piggyback. The research focuses on the planning ofbringing forward any scheduled maintenance on non-failed components when a failureoccurs on a component in the same system.

Another fascinating term used for OM is “optimal replacement or optimalmaintenance” (Ozekici, 1988; Fard and Zheng, 1991; Zheng and Fard, 1991, 1992;Zheng, 1995; Dekker, 1996). The terms used actually show the researchers’ agreementthat OM policy has the potential to be used as an optimal maintenance system. In this

No Reference Maintenance policy

49. Lai and Chen (2006) Periodic replacement50. Iung et al. (2007) Opportunistic preventive maintenance51. Wang et al. (2008) Condition-based order-replacement52. Nicolai and Dekker (2008) Opportunistic maintenance53. Zequeira et al. (2008) Opportunistic maintenance54. Almgren et al. (2008) Opportunistic replacement55. Levrat et al. (2008) Opportunistic preventive maintenance56. Derigent et al. (2009) Opportunistic maintenance57. Besnard et al. (2009) Opportunistic maintenance58. Chien (2009) Preventive maintenance replacement59. Nilsson et al. (2009) Opportunistic maintenance60. Laggoune et al. (2009) Opportunistic replacement61. Zhou et al. (2009) Opportunistic preventive maintenance62. Samhouri (2009) Opportunistic maintenance63. Laggoune et al. (2010) Opportunistic replacement64. Bedford et al. (2011) Opportunistic maintenance65. Khazraei and Deuse (2011) Opportunistic maintenance66. Sharma et al. (2011) Opportunistic maintenance67. Xiang et al. (2012) Condition-based and age-based preventive maintenance68. Ding and Tian (2012) Opportunistic maintenance69. Cheng et al. (2012) Opportunistic maintenance70. Xu et al. (2012) Group preventive maintenance71. Hu et al. (2012) Opportunistic predictive maintenance72. Taghipour and Banjevic (2012a) Opportunistic replacement73. Taghipour and Banjevic (2012b) Opportunistic replacement74. Koochaki et al. (2012) Opportunistic maintenance75. Vu et al. (2012) Opportunistic maintenance76. Almgren et al. (2012) Opportunistic maintenance77. Zhou et al. (2012) Opportunistic replacement Table I.

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research, OM is portrayed as an optimum or ideal maintenance policy for the reasonthat the concept will provide the best or most favourable maintenance schedule for thesystems studied. Ozekici (1988)for example focuses on a complex system such as a jetengine or an electronic computer, which calls for maintenance to avoid any failuresoccurring during operation. The approach in this research is to use the stochasticdependency of components to one another in order to plan maintenance according tothe component interactions. Any shutdown of the system will be used as theopportunity to conduct multiple maintenance tasks on multiple components based ontheir reliability.

Aside from these, OM – which is the policy where PM activities are conductedduring CM stoppages – is also called “CBM/PM” (Mann et al., 1995; Grall et al., 2002;Amari and McLaughlin, 2004; Castanier et al., 2005; Zhou et al., 2006; Wang et al., 2008;Xiang et al., 2012). This is to suit a scenario where CM and PM tasks are conducted atthe same time. In the research, analysis is focused on the planning and combining ofCM and PM tasks especially from an industry point of view. Most research uses themoniker to highlight the application as well as the practicality of OM policy.Nevertheless, gradually the terms changed to become OM as is widely used today.It can be observed that the existence of these many monikers for OM can be attributedto the fact that there is no literature that specifically discusses the policy.

2.2 Genealogy of OM policyFrom the monikers listed in Table I and analysed in previous section, it can beobserved that the origins of OM policy can be attributed to age replacement policy(ARP) and block replacement policy (BRP).

2.2.1 ARP. Jiang and Ji (2002) defined ARP as preventive replacement activitiesperformed after a given continuous operation time (noted as T) without experiencing anyfailure. A failure replacement is conducted if the system fails before the optimal time,T (Liang, 1985). The principle of ARP is that a component is replaced when it hasachieved its lifetime. The advantage of implementing this policy is that it ensuresmaximum usage/lifetime of the component. Zheng and Fard (1991) stated that ARP is atraditional approach for a single-unit system or for equipment with a small number ofcomponents. This is because the replacement of components based on their age becomesa very complex maintenance activity if implemented in a multi-component system.When aiming for a cost-effective maintenance system, ARP becomes a tedious taskof keeping track of all the components’ lifetimes and useful life. ARP is also notcost-effective when viewed from a maintenance perspective because various componentsin a system means myriad occasions of maintenance activities needing to be conducted,and the process will disturb production. Another issue is that components will possiblyfail before their lifetime, caused by environmental and external conditions, as well as thefailure of other integrated components. Multi-component systems also require complexmaintenance planning and scheduling.

2.2.2 BRP. Another maintenance policy that initiates the growth of the OM conceptis BRP. It is traditionally applied in multi-component systems where maintenanceactivities are done on more than one component at the same time (Zheng, 1995). Basedon the fact that, most of the time, components in a system are dependent on oneanother, BRP suggested that maintenance or replacement activities are conducted on ablock or a group of components. According to Wang (2002), it is a periodic maintenancepolicy where a component is preventively maintained at fixed time intervals andthe activities are independent of the previous failures of the component. BRP is

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implemented with the aim of having the minimum maintenance activities possible asan effort to maximize the production rate. Savic et al. (1995a) discussed BRP as a groupreplacement policy where it was assumed that when any component is replaced, theother components that belong to the same group will be replaced as well. However, themain concern with BRP is high possibility that newly replaced components will need tobe replaced again. This will definitely increase maintenance costs and also create wastein the form of component lifetime.

2.3 Principles and concepts of opportunistic maintenanceIn the early literature, the general sources’ idea for OM is that component andequipment are assumed to fail stochastically, and that the failures are independentaccording to known probability distribution (Radner and Jorgenson, 1963). Similarly,the fundamental basis of OM by McCall (1963) divided the maintenance activities intoinspection and replacement with components having a stochastic dependency on oneanother. According to Nicolai and Dekker (2008), the principle behind OM is stochasticdependency that implies that the state of components can influence the state of othercomponents. The principle can also be referred to as failure interaction or probabilisticdependence of one component on another in a system.

For the concept of OM, the abstract idea is about opportunities that may arise in amaintenance system. Dekker and van Rijn (2003) defined the word “opportunity” inOM as “any event at which a unit can be maintained preventively without incurringcost penalties for the shutdown of the unit”. The characteristic of the opportunity isthat it occurs randomly and has a limited duration due to the fact that longer machinedowntime will increase maintenance costs and disrupt the production flow. If this ishappening, then the practicality and advantage of OM is void. On the other hand, asstated by Kaspi and Shabtay (2003), the opportunity to replace a component needs tobe utilized as in any case of machine failure as the stoppage of production line hasalready occurred. The opportunity may arise during shutdown periods for particularequipment and/or due to failures of other components (Rao and Bhadury, 2000).It means that OM provides the maintenance worker an opportunity to repair or replacecomponents which are found to be defective or need replacement in the immediatefuture, during the maintenance of a sub-system or module (Saranga, 2004). This waythe cost of future maintenance or replacement activities can be avoided (Pullen andThomas, 1986).

2.4 Focus and aims of opportunistic maintenanceThe ultimate objective of maintenance activity is to maintain the system functionalityto the maximum lifetime possible, and with optimum trade-offs between machinedowntime and maintenance costs, while at the same time avoiding any hazardousfailures (Saranga, 2004). This is also the focus of OM policy. Overall, OM aims to reducethe amount of planned downtime for machines while at the same time maximizing thelifetime or reliability of components. All these are to ensure the best possible lifetimefor the components in avoidance of costly and risky failures during operation.OM applications in literature lean towards an optimal maintenance system becauseOM use trade-off approaches between the reliability of a component with maintenancecosts. Koochaki et al. (2012) pointed-out that the aim of OM is to group maintenanceactivities of two or more components in order to reduce maintenance costs. ForMohamed-Salah et al. (1999) the main objective of OM application is to reduce the totalnumber of maintenance activities in the production line and consequently reduce the

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total maintenance cost. One or more components can be repaired or replaced throughthe application of one or more maintenance approaches in the system (Zheng andFard, 1991). By conducting CM and PM simultaneously, more failures can be avoided,and the number of times equipment needs to be shut down for maintenance can bereduced. This is coupled with the assumption that replacing more than one componentat the same time is cheaper than replacing the components separately (Zheng and Fard,1992).

2.5 The OM concept definedSince the earliest publications in 1963, there is not a single paper that specificallystates the original definition of OM policy. Radner and Jorgenson (1963) used theconcept of optimal replacement policy on a unit of component during failure andmaintenance of other components if the unit reached a certain age limit. The age limitis decided to be as close as possible to the end of the component’s lifetime. The concepttested is practical for a two-component system since it is easy to monitor and plan.Dekker and Smeltink (1991) described OM as a block replacement model in which acomponent can be replaced preventively at maintenance opportunities that appearrandomly. Rao and Bhadury (2000) stated that in OM, PM of a component is conductedwhen opportunities arise due to failures of other components. It is similar withMann et al. (1995) who stated that the intervals between PM activities on a componentare no longer fixed, but are only performed “when needed”. For Tan and Kramer (1997),OM is part of general planning and scheduling in a manufacturing system wherethe flexibility of maintenance activities is optimized considering the stochasticand uncertain nature of equipment failures, quality rejects, batch times, batch sizesand also production targets. These factors are important for a production andmanufacturing company.

Samhouri (2009) described OM as a systematic method of collecting, investing,preplanning and publishing a set of proposed maintenance activities and then actingbased on the plan whenever an opportunity arises caused by an unscheduled failureor repair. As maintenance is no longer traditional work of replacing and inspecting thecondition of components, Samhouri captures the concept of maintenance planningand scheduling as well as stating the situation of opportunity arising for maintenance.From a practice point of view, Mohamed-Salah et al. (1999) defined OM as anopportunistic strategy which combines corrective and PM activities performed ondifferent processors of a line. OM should be conducted when technical and economicalconditions are satisfied in the effort to achieve optimal maintenance. The principlebehind this policy is the dependency of a component on another component especiallyin multi-component system. The dependency trait is used to conduct PM on onecomponent while conducting CM on the other. That way, it can be concluded thatthe fundamental concept is that machine downtime to repair a component is an“opportunity” to maintain other components in the system. Therefore, any productionstoppages due to a component failure can be taken advantage of by conductingmaintenance activities on other related components.

From these various descriptions and definitions, the key points that could beextracted are that OM is the planning and scheduling of maintenance activities andopportunities in achieving an optimal maintenance system with a balanced trade-offbetween maintenance cost and component/system reliability. Taking the considerationof the dependency principle and the opportunity concept as discussed in previoussections of this paper, OM is best described as the planning and scheduling of

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maintenance activities to repair a component, whilst at the same time opportunisticallyrepair/replace other components in the system, with the aim to avoid future failuresand reduce the amount of machine downtime.

3. Classification of literature on OMOnce the genealogy, names, concepts and aims of OM are successfully analysed, thenext step is to analyse the application of OM in various publications. As previouslydiscussed, OM originates from ARP and BRP, where ARP is mostly implemented forsingle component systems while BRP is mostly used for multi-component systems.Therefore, as OM shares the threads from both policies, it was also implementedin both types of system. Additional discussion on the topic is prepared in thenext subsection. Classification of the literature was also conducted in this paper.The aim is to show the research trend and interest in order that future research canbe identified.

3.1 Classification from the type of system perspectiveApplication of a concept is always the focal point in research. In doing so, applicationsare done in a set of dependent or independent elements or components that worktogether and form a system. In the early literature, McCall (1963) and Radner andJorgenson (1963) used opportunistic replacement and an inspection policy on a systemwith only one part being monitored while several others were ignored. Opportunisticaction taken on the non-monitored part depends on the state of other parts. The term“monitored” reflects the situation where other parts follow PM activities planned forthem, while the lone part is only repaired or replaced when the opportunity arisescaused by the failure of other parts. This will save maintenance time and cost. The partis assumed to have a stochastic failure rate. This similar approach was also used byLiang (1985) and Degbotse and Nachlas (2003).

However, some researchers used a single-unit system or multiple unit systems withtwo components as the example of OM application. The principle behind OM is thatall components in the equipment have the tendency to be dependent on one another.When one component fails, there is a high possibility one or more other componentsare affected and need to be maintained. A failure of one component will become anopportunity to fix the other component. While Rao and Bhadury (2000) and Levratet al. (2008) opted for more versatile applications discussing the OM application forsystems with multi-equipment and multi-components connected to the equipment inseries. When components are connected in series they will be dependent on oneanother. The failure of one component will certainly affect the whole process of theequipment. Nevertheless, the research did not apply OM in a real case study systemwhich makes it hard to comprehend the implementation processes and gauge itspracticality. OM concept is mostly useful and easily practiced in continuously runsystems that have high cost rate of downtime or failure (Sherwin, 1999). It is prudent tosay that OM is effective for this type of system because the failure of a componentprovides an opportunity to replace the other components (Satow and Osaki, 2003).Regardless, application of OM in a multi-component context will required a complexsystem of maintenance planning and scheduling.

3.2 Classification from the type of research approach perspectiveTo show the trend of research approaches conducted in OM, publications arecategorized into four different types of approach, namely as numerical analysis, case

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study, simulation and review paper. The classification is based on the type of researchconducted and discussed in the paper. A paper or manuscript is classified undernumerical analysis if it provides an algorithm, theorem and mathematical model inOM research. As OM is a new maintenance policy, most publications in the numericalanalysis approach linger around the mathematical analysis of maintenanceopportunities and also optimization based on component lifetime, reliability andmaintenance cost. For a case study, papers classified in this group are those that usedata from a real environment or industry to solve OM issues from a practical pointof view. Some papers which further tested and simulated the OM model or algorithmusing computer software are grouped under the simulation type. The last type of paperis a review type where a survey of literature on maintenance was discussed. Figure 3shows the percentage of papers based on their classification.

It can be observed that close to 62 per cent of the publications focus on thenumerical analysis aspect of OM. The numerical analyses make use of varioustheorems and mathematical methods to develop the OM schedule, and to evaluate theeffectiveness of OM based on the age of the component/equipment replaced or repaired,and also the maintenance cost involved. Aside from that, some theorems and numericalmethods like Monte-Carlo simulation (Tan and Kramer, 1997; Crocker and Kumar,2000; Laggoune et al., 2010), genetic algorithm (GA) (Savic et al., 1995a, b; Haqueet al., 2003; Saranga, 2004; Samhouri, 2009), Weibull distribution (Mann et al., 1995;Cassady et al., 2001; Laggoune et al., 2010; Xiang et al., 2012) and Poisson distribution(Satow et al., 2000) are utilized for formulating assumptions regarding the equipment’sage and reliability. While Fuzzy logic (Haque et al., 2003; Derigent et al., 2009) andMarkov decision theory (Sethi, 1976; Ozekici, 1988; Amari and McLaughlin, 2004;Xiang et al., 2012) were used in deliberations on optimal maintenance activities andin choosing the best trade-off between age and cost during OM implementation.

OM is rarely included in a review paper particularly when the review is discussingmaintenance policies. It shows that there is little awareness of the OM concept. Asidefrom papers by McCall (1963) and Wang (2002), OM is only briefly mentioned in otherreviews of maintenance policies. Reviews done by Thomas (1986) and Nicolai andDekker (2008) do include a short discussion on the concept of OM, yet the authors didnot directly use the term OM to define the policy being introduced in their papers.Both literatures discussed and used the concept of component dependency to one

Numerical Analysis62%

Case Study21%

Review10%

Simulation7%

Research Approaches in Opportunistic Maintenance

Figure 3.Percentage of publicationson research approachesin OM research

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another for deteriorating items in multi-components systems. The aim of the reviews isto find the optimal maintenance for the system.

Similar to review papers, case studies and simulation papers discussing OMapplication are hard to find. Despite an abundance of papers conducting numericalanalysis of OM, few or almost no real world applications have been done followingthe models or computational-based approaches presented. The main problem canbe attributed to the complex equations and assumptions used in the studies.As hypotheses, assumptions and limitations are commonly applied in computationanalysis; it still cannot be implemented in real industry without focusing only on themost promising possibilities or by using partial (incomplete) data for performancemeasures. More often, the publications presenting case studies in real industry aretoo specific for the company’s system and lack the flexibility for further improvementor implementation in another company or industry. Therefore, further analyses on theapplications of these methods are very much required in order to provide a properframework of OM policy.

3.3 Classification from the performances measures perspectiveThe core issue in OM research concerns the technical and economical conditions of thecomponents for conducting replacement or repair. Another factor analysed in thispaper is type of performance measure or criteria used by researchers in order tocalculate and analyse the practicality as well as the effectiveness of OM. From theconceptual analysis conducted on the publications of OM, age and cost are the basicstandard or principle by which were used to measure the performance of the OMsystem. Figure 4 shows the pie chart of optimal criteria used in literature to measurethe effectiveness of OM activities. Because money is always the definite performancemeasure agreed by both researchers and practitioners, almost half of research concernsthe performance on maintenance cost. The critical point is whether the OM conductedis cost-effective or not. This issue was extensively discussed by Mohamed-Salah et al.

Failure rate4%

Age16%

Cost49%

Age and Cost16%

Failure rate and Cost9%

Others6%

Optimal Criteria in Opportunistic Maintenance

Figure 4.Percentage of

publications on variousoptimal criteria’s

in OM research

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(1999), Pham and Wang (2000), Rao and Bhadury (2000), Saranga (2004), Zhou et al.(2006), Besnard et al. (2009) and Laggoune et al. (2009).

The early researchers (McCall, 1963; Radner and Jorgenson, 1963) started toinvestigate how the component failure rate can be reduced by conducting OM on atwo-component system. The failures are assumed to happen stochastically throughoutthe production process and are considered as “opportunities”. As the wisest step is totake advantage of the situation and replace other parts as well, the researchers focusedon the planning, scheduling and decision making of OM activities to save maintenancetime and improve machine availability. Further on, cost becomes the main concern formost researchers. Savic et al. (1995b) stated that the choice depends on the probabilitydistribution of components’ residual lives and also on the cost of maintenance if itwas carried out on a not-yet-failed component. Maintenance costs included cost forrepair as well as the cost of the machine downtime (Taghipour and Banjevic, 2012a).For Tan and Kramer (1997), cost is calculated based on production lost per unittime due to machine failure and production stoppages. Aside from that, Bevilacquaand Braglia (2000) included manpower and spare parts costs when calculating themaintenance cost when conducting OM. Laggoune et al. (2009, 2010) conductedresearch on OM cost structure and suggested that deterioration-based decisions can beincluded to solve the cost issue. The solution is found by analysing the cost or benefitbalance of the component that can be preventively replaced during CM activities.

Another common optimal criterion used in performance measurement is the age orreliability of component. To avoid waste in the form of good-age or lifetime ofcomponents, age is considered as deterministic factor during analysis. Day and George(1981) used a Bathtub curve to predict a component’s life limits. Mann et al. (1995) andCassady et al. (2001) used Weibull wear out distribution while Satow et al. (2000) andCheng et al. (2012) used the Poisson process to identify opportunities for maintenance.In recent years, researchers like Chien (2009), Laggoune et al. (2010) and Xiang et al.(2012) actively applied Weibull distributions to forecast a component’s lifetime andfailure. The intention when using these tools is to ensure components replaced duringOM are near their useful lifetime and to reduce spare parts cost. Mohamed-Salah et al.(1999) stressed out that OM can be applied only if certain technical and economicalconditions in the maintenance system are satisfied. Therefore, aside from cost and age,some research focused on finding the balance between low maintenance cost and highcomponent reliability. There are some researchers like Bevilacqua and Braglia (2000),Scarf and Deara (2003), Jiang and Ji (2002) and Derigent et al. (2009) who used multipleoptimal criteria in decision making and planning of OM activities. Their aim is findingthe optimal maintenance system when implementing the OM concept.

4. Application of OM in industriesDespite numerous publications reported in relation to OM, its application in realindustry remains limited. Nevertheless, this section of the paper will discuss thepublications that apply and analyse the OM concept as an effort to show where OM isused and how practical the concept is. From the perspective of real industry, theconcept of “opportunity” is twofold. First, it is applied when a shutdown of a systemfor CM activities takes place. Second, an opportunity is taken when the system is downfor PM, in other words the PM is meant for the scheduled replacement of a componentwhere OM is applied on the other maintenance-significant components which have thepotential to fail in the near future (Savic et al., 1995b). Combining the two policies,OM was developed with the practice of conducting PM on failure-prone components

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when performing CM on a failed component. OM can be conducted either by choiceor based on the physical condition of the equipment (Cui and Li, 2006). OM can beconsidered as a modification of run-to-fail maintenance management philosophy(Samhouri, 2009). Generally, as a result from this opportunity, total equipmentdowntime and maintenance costs will be reduced.

Tan and Kramer (1997) applied OM in a chemical processing plant for PMoptimization. Similar to a nuclear power plant, the chemical industry requires timelyand effective maintenance because of the sensitivity and criticality of the equipmentand machines. The number of scheduled breakdowns for maintenance need to bereduced to the minimum because the researchers found that lost production costs (netincome lost) in a chemical plant can range from $500 up to $100,000 per hour. Evenworse, a typical chemical refinery was estimated to lose $20,000 to $30,000 per hour outof their production costs due to equipment failures. This estimation is based on tendays of lost production per year excluding scheduled outages. Tan and Kramer (1997)studied the idea of optimally utilizing system downtime opportunities (CM activities)to perform PM at a lower overall cost. Monte-Carlo simulation with a GA approach wasused to find the cost-effectiveness of OM in the chemical plant.

For research carried out by Samhouri (2009), GAs were employed to decide whetherthe OM activity is cost-effective or not. The research focused on OM strategy thatinvolves several non-linear variables which affect the total cost of maintenancethat should be optimized to achieve cost-effective decisions on maintenance activities.GAs were found to be well-suited to solve maintenance problems where there is ahuge space of potential solutions available. Other than that, Scarf and Deara (2003)considered OM policies in a simulation study of block replacement and modifiedBRPs for a two-component system. This simulation focused on system reliability andcost-effectiveness or economic dependence. It was discussed that policy IVc1(opportunistic independent modified block replacement) and policy IVc2 (opportunisticgroup modified block replacement) are among the near-optimal policies found in theresearch.

Nilsson et al. (2009) applied OM in a nuclear power plant by reconstructingreplacement schedules of shaft seals in a feed-water pump system. The approach usedis calculating the total cost of maintenance and then minimizing the cost according tosome constraints, and discounting to model the value of money in time. After that,a sensitivity analysis was done where the different parameters vary in relation tothe discount rate. The conclusion drawn is that the OM optimization model is adeterministic model and applicable in practice. Amari and McLaughlin (2004) alsofocused on an optimal maintenance model. The research involved Markov analysis toprovide a closed-form analytical solution for their model. The researchers stressed theimportance of minimum overall maintenance costs or maximum system performancemeasures. Both issues are at the core of OM principle.

One of the concerns in the publications is how OM is considered as an interruptionto production operation. Iung et al. (2007) emphasises the integration of maintenanceand production strategy planning in developing OM tasks that keep conjoint theproduct-production-equipment performance. This is done by developing an OMtask by synchronizing it with production and keeping the production and theequipment performances simultaneously to preserve the product conditions. It meansmoving from a conventional maintenance approach towards new condition-basedand predictive ones performed only when a certain level of equipment deterioration(impacting product conditions) occurs rather than after a specified period of time.

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Odds algorithm was used as a decision-making tool relevant to find the right place tostop, among the production stops, for performing optimal “just-in-time” maintenanceaction.

In a different research area, Bonarini and Sassaroli (1997) applied the opportunisticconcept and present an Opportunistic Model-Based Diagnosis System (OMISSYS) – asystem to diagnose faults or failures in plants whose components are described bydifferent types of models. As models and data are prone to be affected by uncertaintyand imprecision, OMISSYS applies opportunistically different reasoning mechanismson available models to find a set of diagnoses for a given system. The resultsshow that the system is able to speed up the reasoning process by focusing on the mostpromising aspect of the plant. This research is a good example of how an opportunisticconcept can save time and produce an optimal system. This way, a company can reducethe amount of equipment shut down for maintenance and have a more productiveoperation.

Current research on OM revolves around the optimization of the maintenancesystem. Instead of using the common OM principle as discussed in Section 2, Levratet al. (2008) studied the age of components for repair and failure with an odd-baseddecision-making tool. The aim is to have effective and synchronized activities betweenmaintenance and production. Another informative research is by Derigent et al. (2009)who used Fuzzy modelling to assess the proximity of components when conductingOM. Numerical analysis was conducted within the decision-making process in orderto achieve optimal maintenance. The situation is that when maintenance is conductedon a component, the technician or operator will locate another component close tothe previous component so that additional maintenance actions can be undertaken.The other components also need to be controlled and maintained within the timeallocated for maintenance. Similar to Levrat et al. (2008), this research strove to makeOM practical and help the decision maker to optimize maintenance activities accordingto resources, material and time available in the company.

5. Findings and suggestion of future workSwanson (2001) categorized various types of maintenance policy into three maingroups, named as reactive, proactive and aggressive maintenance approaches. Eachapproach has a designated technique regarding how the policies were implemented in asystem. In the case of reactive maintenance, a fire-fighting approach was used whereequipment in a system is run until it experiences a failure before any replacement orrepair tasks are conducted. This approach is commonly used in the early history ofmaintenance systems and an example of the policy is CM. The approach of theproactive maintenance system contradicts the first group. Applying a preventiveprinciple, the approach is to monitor the condition of each piece of equipment and thenconduct maintenance tasks before the equipment fails. This is done in order to avoidequipment breaking down when it is operating. Policies like PM and RCM are best todescribe the approach. The third group, aggressive maintenance, used an improvementapproach. The principle is to conduct analysis and plan for improvement of the overallequipment operation. This type of approach not only focuses on the equipmentitself but also on maintenance management, resource allocation and production rate.TPM falls under the aggressive approach.

Looking at these three types of approach, the principle of OM does not fit in any ofthese groups. Instead, OM is best grouped under a new approach introduced byKhazraei and Deuse (2011). In the review on the taxonomy of maintenance policies, the

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new approach is called prospective maintenance. The character of this approach is thatPM tasks are conducted when a machine breaks down due to failure. According toSherwin (1999), OM have a rule of conducting preventive tasks that are due or overdueon a system which was forced to stop because of a component’s failure. The stoppage istreated as an opportunity and aims to reduce the cost and time to conduct maintenanceon the system. Based on the literature studied, a few more findings can be discussedregarding the merits, demerits and future of OM research.

5.1 Merits in implementing OMAccording to Tan and Kramer (1997), performing OM on components in a system willimprove its overall reliability; reduce future or potential downtime and, at the sametime, increase the production rate and a company’s net income. The advantage of OM isthat CM combined with PM can be used to save set-up costs (Cui and Li, 2006) and toguarantee the expected performance of the system (Levrat et al., 2008). Companies canalso save the set-up time for equipment by reducing the total amount of downtime dueto failures. Cassady et al. (2001) put the situation as doing more at less cost. For Dekkerand Dijkstra (1992), opportunities in OM policy means conducting cost-effectivepreventive replacement activities on a system when it is not required for service orwhen it cannot operate.

According to Pham and Wang (2000), the process of conducting PM on non-failedbut degraded components at the time of CM activities for another failed componentmay reduce unexpected CM at a fairly low cost. This is because PM together with CMcan be conducted without substantial additional expenses. The reduction ofmaintenance costs was proven in case studies in a wind turbine system (Besnardet al., 2009) and in oil-refining facilities (Laggoune et al., 2009). OM also helps tooptimize maintenance activities and decision making. Indirectly, OM can improveproduction quality and yield. Without unplanned downtime caused by failures,availability of machines will be high and their effectiveness will be improved. Also,repairing equipment before breakdowns will improve the equipment reliability andextend its lifetime (Zhou et al., 2009). Based on the benefits of OM stated in theliterature, it can be concluded that the main benefits of implementing OM can bedivided into three groups. The first group is the reduction of failure, the second is thereduction of cost and the final group is increment in equipment/system age.

5.2 Demerits and issues of OMThe OM principle is that whenever maintenance is conducted on a failed component,other maintenance-significant components which have the potential to fail inthe near future will also be repaired or replaced. The challenge is to find themaintenance-significant component. The researchers contemplate the possibility that agood component will be replaced during OM which will lead to other problem such ashigher maintenance and spare parts costs. Then OM can no longer be consideredas a cost-effective approach. The direct costs due to component failure and replacementare usually very high, and the impact of the different replacement intervals on theoverall maintenance cost is often sensitive and significant (Laggoune et al., 2009).

The findings from research by Liang (1985) highlighted that the “opportunity”concept actually performed worse than standard PM policy. It only becomes economicand effective when a system has many components as well as experiencing high failurerates. So much so that it can be concluded that OM is not cost-effective for single unitsystems. However, multi-components systems would require complex planning and

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scheduling of maintenance activities. Other than that, Besnard et al. (2009) argue thatOM has a problem with time constraints on the production line. As is already known,any maintenance activities will interrupt or cause the production line to stop itsoperation, thus the OM with combination of CM and PM will take more time to beconducted. This will create a loss of production rate and product sales. Then thecompany may face an increase in production costs due to product back-ordering(Zequeira et al., 2008). Hence, in order to have an optimized maintenance system,researchers need to find the best combination of CM and PM activities each time afailure occurs.

Aside from that, the big issue with OM is whether maintenance activities conductedunder the policy are “underdone” or “overdone” (Wang et al., 2008). The calculations inOM always evolved around the two issues, one is finding the optimal maintenance withthe minimum overall cost and two is finding an optimal system that maximizes thesystem’s performance measure (Amari and McLaughlin, 2004). Researches need tosolve these issues in order to allow OM to achieve an optimal system. The optimalmaintenance system is the one that strives to ensure no excessive maintenance isconducted as it will result in high maintenance costs, while an inadequate amount ofmaintenance will cause a system to experience failures or drift into an undesirable state(AlDurgam and Duffuaa, 2013).

Another OM drawback is regarding the planning and scheduling of maintenanceactivities. Cui and Li (2006) stated that by combining CM and PM, one may not knowin advance which maintenance action should be taken. Thus, there is the likelihood ofsacrificing the plan-able feature of PM, so it is not possible to conduct work preparationin advance (Levrat et al., 2008). An unexpected production demand or a spare partsshortage can also cause problems in OM activities (Zequeira et al., 2008). Nevertheless,this drawback can be remedied by having effective maintenance planning andscheduling. Carefully planned activities will avoid redundancy of tasks, while goodmaintenance scheduling will ensure companies are prepared in terms of humanresources and spare parts for the activities.

5.3 Future workThe increase of publications in relation to years shows the potential of OM policy. Still,more publications on the matter are required because its theory is not fully developed.There are also a few issues that need to be scrutinized and improved. Even thoughOM is found to have originated from ARP and BRP concepts, no publication directlyaddresses the issue and studies the evolution both in theory and in real industryapplication. As discussed in Sections 2.2 and 2.3, ARP focuses on the age ofcomponents were they to be maintained or replaced separately, but BRP focuses onmaintenance on a group of components. Both policies contradict one another yet wereclaimed as the backbone of OM policy. Therefore, it would be interesting to see theconnections between ARP, BRP and OM from a principle point of view. More researchis needed on the issue so that OM can be more practical and easy to implement. Theseconnections are also crucial for the development of OM assumptions, rules andlimitations for industry application.

From a maintenance planning point of view, the issues awaiting explorationin OM research can be divided into short-term, middle-term and long-term challenges.The short-term challenge is the maintenance planning and scheduling ofmultiple-component systems in both parallel and series systems. Research isrequired in this area especially for manufacturing plants with various processes

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and multiple machines for each process. There should be some way to properlyplan OM on the multiple machines to achieve an optimal maintenance system.The middle-term issue is concerned with production rate. OM application will affect thenumber of products manufactured in the maintained systems. Therefore, researchersshould focus on the challenge of ensuring maintenance conducted not only does notinterrupt the manufacturing process, but also that the tasks should be planned toaccommodate the production plan so the machine availability is as high as possible.The ideal situation would be that the machine operated whenever required and didnot experience failure during operation. Finally, the long-term challenge is howthe application of OM policy can benefit a company in the quest to be a leader in theindustry as well as achieving world class operation and quality. This concern aboutthe effectiveness and optimality of OM can lead a company into becoming a successfulorganization. This way, maintenance will be an important value-added system in thecompany.

Aside from that, future work should explore the OM concept from differentoptimal criteria in a maintenance system. For the purpose of achieving an optimalsituation, it is not the case of achieving a perfect balance between all parameters in asystem because that is an impossible feat. The process required a complex systemwith some criteria need to be weighted according to their importance. The best wayto achieve the situation is by having a specific aim or objective of performancemeasure to be fulfilled. Future research can be directed into development of astandard decision support system for an optimal maintenance system. The modelcan consist of a few key performance measures like failure rate, maintenance costand reliability of equipment as factors which then can be chosen according to thecompany’s aim and objective for improvement. Artificial intelligent methods suchgenetic algorithm, Fuzzy logic and Poisson distribution can be applied to achieve anoptimized OM system.

Another branch of research that can be explored in OM policy is regarding thepractical rules or framework for implementation in the industry. The numericalanalysis and theories in OM research need to be translated into practical theories forpractitioners. As mathematical models developed for OM are always custom-made tofit certain situations selected by the researcher, it cannot be immediately implementedin the real system. Some adjustment with assumptions, limitations and rules shouldbe developed for successful implementation of OM in the industry. That way, OM canbe proved to be a catalyst for an optimal maintenance system. Aside from that, moreextensive analysis should be done based on real data from companies to test thepracticality of OM concepts in the industry. Studies and simulations of OM activitiesare also very much needed in finding the optimal trade-off especially between cost andreliability. A simulation of a maintenance system is required to reduce the gap betweentheories and practical implementation. As there is lots of numerical analysis publishedand models introduced in OM research, it should be simulated to find loopholes andtest its practicality.

6. ConclusionsEffective and optimized maintenance systems are highly acquired in today’smanufacturing system. First coined in the 1963, OM is known as a simple“opportunistic replacement policy” to unique names like “piggyback maintenance” and“all opportunity-triggered replacement”, and the OM concept has been applied inmaintenance systems for decades. The number of publications has gradually increased

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over the years, showing an encouraging trend and growing interest on thismaintenance policy. OM is commonly applied in research on optimal maintenancesystems as well as in effective maintenance policy. The OM concept has been widelyand numerically studied for maintenance scheduling for multiple components, singlecomponent and two components as well as multi-equipment and multi-componentsystems. This policy evolves from age replacement and BRPs which used theconcept of components’ relation to one another in a system to conduct maintenancesimultaneously.

Most publications provide numerical analysis and models on the optimalscheduling with a trade-off between the age of a component and maintenance costwhen conducting OM. OM is the planning and scheduling of an optimal maintenancesystem that prospectively conducted PM activities on dependent/related componentswhen a component failed, with consideration for the lowest maintenance cost possibleand without sacrificing its reliability. OM can be put into practice to reduce the numberof machine breakdowns and machine stoppages especially for continuous systems.From the various literature reviewed in this paper, it can be concluded that discussionon OM practicality is still in the early stages but full of potential. For future work,simulation of OM implementation and case studies in industry are needed to expandits concept and improve its principle.

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Further reading

Bonarini, A. and Sassaroli, P. (1993), “Opportunistic multimodel-based diagnosis: framing all theknowledge we have to diagnose complex artifacts”, Artificial Intelligence for Applications,Proceedings, Ninth Conference, pp. 429-436.

Wang, L., Chu, J. and Wu, J. (2007), “Selection of optimum maintenance strategies based ona fuzzy analytical hierarchy process”, International Journal of Production Economics,Vol. 107 No. 1, pp. 151-163.

About the authors

Hasnida Ab-Samat received a BEng (Hons) degree in Manufacturing Engineering withManagement from the Universiti Sains Malaysia in 2007, and completed her MSc in the Schoolof Mechanical Engineering at the University Science Malaysia (USM) in 2010. She is currentlypursuing a PhD at USM. Her research interests include industrial engineering, manufacturingsystems and maintenance management.

Dr Shahrul Kamaruddin received a BEng (Hons) degree from the University of Strathclyde,Glasgow, Scotland in 1996, a MSc degree from the University of Birmingham, UK, in 1998, anda PhD from the University of Birmingham in 2003. He is currently an Associate Professor inthe School Mechanical Engineering (under the manufacturing engineering with managementprogramme), Universiti Sains Malaysia. He has various past experiences with manufacturingindustries from heavy to electronics industries especially in the field of industrial engineering,manufacturing processes and product design. He has more than 60 publications in reputedinternational and national journals/conferences. His current research interests include simulation andmodelling of manufacturing systems, production planning and control, maintenance managementand application of artificial intelligence techniques in manufacturing. Dr Shahrul Kamaruddin is thecorresponding author and can be contacted at: [email protected]

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Opportunistic maintenance (OM) as a new advancement in maintenance approaches