Journal of Quality in Maintenance Engineering
Opportunistic maintenance (OM) as a new advancement in maintenance approaches: A
review
Hasnida Ab-Samat Shahrul Kamaruddin
Downloaded by UNIVERSITY OF WESTERN SYDNEY At 00:21 08 February 2015 (PT)
Article information:
To cite this document:
Hasnida Ab-Samat Shahrul Kamaruddin , (2014),"Opportunistic maintenance (OM) as a new advancement
in maintenance approaches", Journal of Quality in Maintenance Engineering, Vol. 20 Iss 2 pp. 98 - 121
Permanent link to this document:
http://dx.doi.org/10.1108/JQME-04-2013-0018
Downloaded on: 08 February 2015, At: 00:21 (PT)
References: this document contains references to 91 other documents.
To copy this document: permissions@emeraldinsight.com
The fulltext of this document has been downloaded 315 times since 2014*
Users who downloaded this article also downloaded:
Amik Garg, S.G. Deshmukh, (2006),"Maintenance management: literature review and
directions", Journal of Quality in Maintenance Engineering, Vol. 12 Iss 3 pp. 205-238 http://
dx.doi.org/10.1108/13552510610685075
Professor Uday Kumar, Associate Professor Aditya Parida and Associate Professor Ramin Karim, Phillip
Tretten, Ramin Karim, (2014),"Enhancing the usability of maintenance data management systems",
Journal of Quality in Maintenance Engineering, Vol. 20 Iss 3 pp. 290-303 http://dx.doi.org/10.1108/
JQME-05-2014-0032
Eleonora Bottani, Gino Ferretti, Roberto Montanari, Giuseppe Vignali, (2014),"An empirical study on the
relationships between maintenance policies and approaches among Italian companies", Journal of Quality
in Maintenance Engineering, Vol. 20 Iss 2 pp. 135-162 http://dx.doi.org/10.1108/JQME-11-2012-0039
Access to this document was granted through an Emerald subscription provided by 448547 []
For Authors
If you would like to write for this, or any other Emerald publication, then please use our Emerald for
Authors service information about how to choose which publication to write for and submission guidelines
are available for all. Please visit www.emeraldinsight.com/authors for more information.
About Emerald www.emeraldinsight.com
Emerald is a global publisher linking research and practice to the benefit of society. The company
manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as
providing an extensive range of online products and additional customer resources and services.
Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee
on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive
preservation.
*Related content and download information correct at time of download.
The current issue and full text archive of this journal is available at
www.emeraldinsight.com/1355-2511.htm
JQME
20,2
Opportunistic maintenance (OM)
as a new advancement in
maintenance approaches
Downloaded by UNIVERSITY OF WESTERN SYDNEY At 00:21 08 February 2015 (PT)
98
Received 18 April 2013
Revised 27 November 2013
Accepted 7 March 2014
A review
Hasnida 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 advance
maintenance approach and policy. The purpose of this paper is to conceptually identify common
principle and thereby provide absolute definition, concept and characteristics of this policy.
Design/methodology/approach – A conceptual analysis was conducted on various literatures
to 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 few
parts, analyse them and then recombining them to have more clear understanding of the policy.
Findings – The paper discussed the maintenance approach, genealogy, principle, concept and
applications of OM both in numerical analysis and real industry. OM policy is developed based on
combination of age replacement policy and block replacement policy and in practical; OM is applied
as the combination of corrective maintenance which is applied when any failure occurred, with
preventive maintenance (PM) – a planned and scheduled maintenance approach to prevent failure to
happen. Any machine shutdown or stoppages due to failure is the “opportunity” to conduct PM even
though it is not as planned. The characterization of OM was provided in order to present its theoretical
novelty for researchers and practical significance for industries.
Practical implications – To date, there is no publication that reviews the OM in-depth and provides
clear understanding on the topic. Therefore, this paper aims to show lineage of OM and the current
trend in researches. This discussion will pave the way of new research areas on this optimal
maintenance policy. Clear definition and principle of OM provided in this paper will trigger interest in
its 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 perspectives
and scopes for further understanding of the topic by maintenance management professionals and
researchers. Therefore, OM can be widely studied and applied in real industry as it is an effective and
optimal maintenance policy.
Keywords Preventive maintenance, Corrective maintenance, Maintenance policy,
Opportunistic maintenance, Optimal maintenance
Paper type Literature review
Journal of Quality in Maintenance
Engineering
Vol. 20 No. 2, 2014
pp. 98-121
r Emerald Group Publishing Limited
1355-2511
DOI 10.1108/JQME-04-2013-0018
1. Maintenance system
The role of maintenance in today’s manufacturing systems is becoming more
important as companies start to adopt the system as one of their profit generating
elements (Waeyenbergh and Pintelon, 2002; Sharma et al., 2011) and a supporting
function (Samat et al., 2011). Maintenance is conducted to ensure that all the equipment
within the company is repaired, replaced, adjusted and modified according to
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.
Downloaded by UNIVERSITY OF WESTERN SYDNEY At 00:21 08 February 2015 (PT)
production requirements. This way, the whole manufacturing processes are guaranteed
to 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 the
other hand, Bevilacqua and Braglia (2000) stated that maintenance costs can reach
15-70 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 this
maintenance cost is actually wasted or spent unnecessarily. These percentage show a
lot of improvements could be carried out in order to achieve an effective and optimized
maintenance system. Nowadays, the literatures on maintenance is centred on problems
in the optimization of maintenance policies. The aim is to ensure a system has the
ability 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 the
types of maintenance techniques applied in the industry. The aim is to investigate the
lineage of opportunistic maintenance (OM) policy as a new maintenance concept which
also widely relates to the optimal maintenance system. The origins and genealogy of
OM principles in various published literature are critically analysed using the critical
conceptual 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 second
section. Subsequently, various applications of OM are presented in order to define its
practicality in the industry. The fourth section consists of a discussion of issues, merits
and also downsides of OM policy both in numerical analysis and real industry
application. Lastly, conclusions are drawn in presenting the concept and application of
OM for future research.
2. Conceptual analysis of OM policy
A tremendous number of publications can be found discussing maintenance
techniques since the maintenance technique transition from corrective maintenance
(CM) in 1940 to various operation research models (Garg and Deshmukh, 2006). Each
one is developed based on a variety of principles and to suit different situations. For
Zheng (1995), the major objectives of maintenance policies are to increase system
reliability and availability while at the same time to reduce system maintenance cost.
Among the various maintenance policies, some are widely implemented as they have
strong fundamental elements such as CM, preventive maintenance (PM), predictive
maintenance, condition-based maintenance (CBM), total productive maintenance
(TPM), reliability-centred maintenance (RCM) and computer maintenance management
system.
Aside from these policies, a maintenance policy termed as opportunistic
replacement and inspection was introduced by McCall and Radner and Jorgenson
(1963). It is based on research conducted by the RAND Corporation, situated in Santa
Monica, CA, USA with the aim to find an optimal maintenance policy. It was first
applied in a case study of a rocket engine (McCall, 1963) and a manned aircraft and
ballistic missile system (Radner and Jorgenson, 1963). The concept of this
“opportunistic” maintenance policy is the dependency of the components and
equipment in a system. With this policy, maintenance is to be performed on a given
part, at a given time, depending on the state of the rest of the system. The focus of
Opportunistic
maintenance
99
JQME
20,2
Downloaded by UNIVERSITY OF WESTERN SYDNEY At 00:21 08 February 2015 (PT)
100
Figure 1.
Four elements in
conceptual analysis
for opportunistic
maintenance (OM) policy
implementation is to predict the relative frequencies of a variety of maintenance actions
such as inspection and replacement of failed components in a system. This policy is
intriguing as in current years researchers referred to this policy as an optimal
maintenance system.
However, advancement on this policy requires profound study to find its origin,
principles and applications. Therefore, critical conceptual analysis was conducted in
this paper as an effort to fully understand the opportunistic replacement and
inspection policy or referred to as OM throughout the paper. Furner (2004) described
the analysis as a technique of precisely defining the meaning of a concept using the
process of identifying and also specifiying the concept within a classification of
conditions or phenomena. For this paper, the technique is used by breaking down the
issues to examine the major elements in OM policy. Since there is no one single book or
manual published on the matter, the conceptual analysis were mainly focused on
journal papers. The flow of conceptual analysis conducted on OM policy is shown in
Figure 1.
The analysis process starts with the literature published on opportunistic
and optimal maintenance, which were scrutinized in order to find the policy moniker
used to described OM. Even though OM was first described as opportunistic
replacement and inspection in 1963, the same theory used by Rander and Jorgenson
was applied to much research afterwards, yet named in many different terms.
The various monikers used to discussed OM policy over the years reflected the
applications as well as the principle used in the literature. The list of monikers used
are stated in Section 2.1. In connection with the moniker, conceptual analysis was
also conducted to study the origins and genealogy of OM. Since the advancement
of application on maintance system in 1940s, various maintenance policies were
developed from a basic principle of a “run, failed and repair” approach. This resulted
with a grouping of approaches and techniques to cater to many types of equipment
and system conditions in the industry.
Further on, the principle and concept of OM in various literatures were revised in
Section 2.3. Changes in technology and process due to high customer demand requires
maintenance to be conducted as precisely and as effectively as possible. Because of
that, optimization becomes the focus of much maintenance research, which then
brought OM into the limelight. For that very reason, the simple approach of using the
opportunity of a component failure to conduct maintenance tasks on other related
components was tried and altered to satisfy numerous system conditions. The analysis
on this paper brings forward some of the common OM principles and concepts used in
the publications. The conceptual analysis ends with a study of focuses and aims of OM
in the publised research. Relating to various principles and concepts introduced in the
literature, the objectives of conducting OM policy also varies. Therefore, the objectives
were gathered and discussed, as the process, to understand the advantages of OM
policy. All the information gathered via this conceptual analysis was used to define the
OM overall, as presented in Section 2.5 of this paper.
Moniker
Genealogy
Principle
and Concept
Focus and
Aim
Opportunistic
maintenance
101
Publications on Opportunistic Maintenance (1963-2012)
12
10
8
6
4
2
2011
2009
2007
2005
2003
2001
1999
1997
1993
1995
1989
Year
1991
1987
1985
1983
1981
1979
1977
1975
1973
1971
1969
1967
1965
0
1963
No. of Publication
Downloaded by UNIVERSITY OF WESTERN SYDNEY At 00:21 08 February 2015 (PT)
2.1 Publications and monikers of OM policy
The OM concept was first coined in 1963 as opportunistic replacement and inspection
policy and the trend continued for a few years afterwards, before it changed to become
OM, as is widely used today. In the project by Radner and Jorgenson (1963), the
combination of PM action and CM were used to reduce the set-up costs of a
maintenance action. The concept was then further reviewed as a new maintenance
policy in a paper by McCall (1965). After the introduction of the OM concept, aside
from a few publications, no other researchers further discussed the topic until early the
1980s. Nevertheless, OM returned to become a topic of interest by the 1990s and
the trend shows the increase of publications on OM by the year 2000. The graph in
Figure 2 shows the number of journal publications on OM from the years 1963 until
2012. Over 70 publications are found to directly or indirectly delve into the discussion
and application of the OM policy issues. Even though it can be considered as a small
number compared to other maintenance approaches and policies like RCM, TPM and
CBM, it is a significant trend considering the short genealogy of OM. There is an
encouraging 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 the
naming of the OM policy. Conceptual analysis conducted on the publications shows
that over the years, OM policy is widely studied and implemented, but with various
monikers in order to suit the concept used in the research. The study of monikers in
this paper is deemed important as the naming reflects the root of the principles
used. 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 that
maintenance was first viewed as tasks involving only repair and replacement of
components with the aim of restoring broken machinery and equipment into
operational condition. It was only until after a few decades that the process of cleaning,
lubricating, calibrating, etc., were considered as maintenance tasks. Then, the concept
was also named as opportunistic-based maintenance and opportunity-based age
replacement. Age-based maintenance can be defined as activities done based on the
age renewal of a machine, which is preventively maintained until it reaches a certain
Figure 2.
Number of publications
on opportunistic
maintenance
from 1963 to 2012
JQME
20,2
Downloaded by UNIVERSITY OF WESTERN SYDNEY At 00:21 08 February 2015 (PT)
102
Table I.
List of monikers
used in publications
on opportunistic
maintenance from
1963 till 2012
No
1.
2.
3.
4.
5.
6.
7.
Reference
Maintenance policy
McCall (1963)
Radner and Jorgenson (1963)
McCall (1965)
Duncan and Scholnick (1973)
Sethi (1976)
Vergin and Scriabin (1977)
Day and George (1981)
Opportunistic replacement and inspection
Opportunistic replacement and inspection
Opportunistic replacement and inspection
Opportunistic replacement and interrupt strategies
Optimal opportunistic replacement
Opportunistic maintenance and preventive maintenance
Opportunistic replacement, look-ahead-maintenance and
stationary policies
Preventive replacement
Opportunistic replacement
Piggyback preventive maintenance
Opportunistic replacement
Opportunistic replacement
Optimal replacement
Opportunity-based block replacement or one-opportunitylook-ahead
Opportunistic hazard rate replacement
Opportunistic replacement
Opportunistic replacement
Opportunity-based age replacement
All opportunity-triggered replacement
Condition-based preventive maintenance
Opportunity-based maintenance
Opportunity-based maintenance
Optimal maintenance
Opportunistic maintenance
Opportunistic maintenance
Opportunity-based age replacement
Opportunistic maintenance
Opportunity-based age replacement
Opportunistic maintenance
Opportunistic maintenance
Opportunistic maintenance
Opportunistic or on-condition maintenance
Opportunistic maintenance
Opportunistic maintenance
Condition-based inspection/replacement
Age replacement
Opportunistic maintenance
Opportunistic maintenance
Opportunistic replacement
Opportunistic replacement
Opportunistic preventive maintenance
Opportunistic-based age replacement
Age replacement during delay time and opportunistic age
replacement during delay time
Opportunistic maintenance
Condition-based maintenance
Opportunistic replacement
Opportunistic maintenance
Opportunistic condition-based preventive maintenance
8.
9.
10.
11.
12.
13.
14.
L’Ecuyer and Haurie (1983)
Epstein and Wilamowsky (1985)
Liang (1985)
Thomas (1986)
Pullen and Thomas (1986)
Ozekici (1988)
Dekker and Smeltink (1991)
15.
16.
17.
18.
19.
20.
21.
22.
23.
24.
25.
26.
27.
28.
29.
30.
31.
32.
33.
34.
35.
36.
37.
38.
39.
40.
41.
42.
43.
Zheng and Fard (1991)
Fard and Zheng (1991)
Zheng and Fard (1992)
Dekker and Dijkstra (1992)
Zheng (1995)
Mann et al. (1995)
Savic et al. (1995a)
Savic et al. (1995b)
Dekker (1996)
Tan and Kramer (1997)
Mohamed-Salah et al. (1999)
Jhang and Sheu (1999)
Sherwin (1999)
Satow et al. (2000)
Rao and Bhadury (2000)
Pham and Wang (2000)
Bevilacqua and Braglia (2000)
Crocker and Kumar (2000)
Cassady et al. (2001)
Scarf and Deara (2003)
Grall et al. (2002)
Jiang and Ji (2002)
Wang (2002)
Dekker and van Rijn (2003)
Kaspi and Shabtay (2003)
Haque et al. (2003)
Degbotse and Nachlas (2003)
Satow and Osaki (2003)
Das and Acharya (2004)
44.
45.
46.
47.
48.
Saranga (2004)
Amari and McLaughlin (2004)
Castanier et al. (2005)
Cui and Li (2006)
Zhou et al. (2006)
(continued)
Downloaded by UNIVERSITY OF WESTERN SYDNEY At 00:21 08 February 2015 (PT)
No
Reference
Maintenance policy
49.
50.
51.
52.
53.
54.
55.
56.
57.
58.
59.
60.
61.
62.
63.
64.
65.
66.
67.
68.
69.
70.
71.
72.
73.
74.
75.
76.
77.
Lai and Chen (2006)
Iung et al. (2007)
Wang et al. (2008)
Nicolai and Dekker (2008)
Zequeira et al. (2008)
Almgren et al. (2008)
Levrat et al. (2008)
Derigent et al. (2009)
Besnard et al. (2009)
Chien (2009)
Nilsson et al. (2009)
Laggoune et al. (2009)
Zhou et al. (2009)
Samhouri (2009)
Laggoune et al. (2010)
Bedford et al. (2011)
Khazraei and Deuse (2011)
Sharma et al. (2011)
Xiang et al. (2012)
Ding and Tian (2012)
Cheng et al. (2012)
Xu et al. (2012)
Hu et al. (2012)
Taghipour and Banjevic (2012a)
Taghipour and Banjevic (2012b)
Koochaki et al. (2012)
Vu et al. (2012)
Almgren et al. (2012)
Zhou et al. (2012)
Periodic replacement
Opportunistic preventive maintenance
Condition-based order-replacement
Opportunistic maintenance
Opportunistic maintenance
Opportunistic replacement
Opportunistic preventive maintenance
Opportunistic maintenance
Opportunistic maintenance
Preventive maintenance replacement
Opportunistic maintenance
Opportunistic replacement
Opportunistic preventive maintenance
Opportunistic maintenance
Opportunistic replacement
Opportunistic maintenance
Opportunistic maintenance
Opportunistic maintenance
Condition-based and age-based preventive maintenance
Opportunistic maintenance
Opportunistic maintenance
Group preventive maintenance
Opportunistic predictive maintenance
Opportunistic replacement
Opportunistic replacement
Opportunistic maintenance
Opportunistic maintenance
Opportunistic maintenance
Opportunistic replacement
number of time periods without a failure (Khazraei and Deuse, 2011). In the research
using the name, the decisions regarding maintenance tasks conducted during failure
are 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-ahead
maintenance. It is to portray the process of replacement of parts or components if they
reach their limited lifetime, and right before they experience failure. The concept is to
look into the planning of maintenance for each component in a system and replace
components that will reach their life limits. For Liang (1985), the term “piggyback
preventive maintenance” was used for the OM concept. All parts in a system have their
own PM intervals, yet the PM is not carried out until an unscheduled maintenance for
another part occurs, thus the term piggyback. The research focuses on the planning of
bringing forward any scheduled maintenance on non-failed components when a failure
occurs on a component in the same system.
Another fascinating term used for OM is “optimal replacement or optimal
maintenance” (Ozekici, 1988; Fard and Zheng, 1991; Zheng and Fard, 1991, 1992;
Zheng, 1995; Dekker, 1996). The terms used actually show the researchers’ agreement
that OM policy has the potential to be used as an optimal maintenance system. In this
Opportunistic
maintenance
103
Table I.
JQME
20,2
Downloaded by UNIVERSITY OF WESTERN SYDNEY At 00:21 08 February 2015 (PT)
104
research, OM is portrayed as an optimum or ideal maintenance policy for the reason
that the concept will provide the best or most favourable maintenance schedule for the
systems studied. Ozekici (1988)for example focuses on a complex system such as a jet
engine or an electronic computer, which calls for maintenance to avoid any failures
occurring during operation. The approach in this research is to use the stochastic
dependency of components to one another in order to plan maintenance according to
the component interactions. Any shutdown of the system will be used as the
opportunity to conduct multiple maintenance tasks on multiple components based on
their reliability.
Aside from these, OM – which is the policy where PM activities are conducted
during 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 at
the same time. In the research, analysis is focused on the planning and combining of
CM and PM tasks especially from an industry point of view. Most research uses the
moniker 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 attributed
to the fact that there is no literature that specifically discusses the policy.
2.2 Genealogy of OM policy
From the monikers listed in Table I and analysed in previous section, it can be
observed 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 activities
performed after a given continuous operation time (noted as T) without experiencing any
failure. 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 has
achieved its lifetime. The advantage of implementing this policy is that it ensures
maximum usage/lifetime of the component. Zheng and Fard (1991) stated that ARP is a
traditional approach for a single-unit system or for equipment with a small number of
components. This is because the replacement of components based on their age becomes
a very complex maintenance activity if implemented in a multi-component system.
When aiming for a cost-effective maintenance system, ARP becomes a tedious task
of keeping track of all the components’ lifetimes and useful life. ARP is also not
cost-effective when viewed from a maintenance perspective because various components
in a system means myriad occasions of maintenance activities needing to be conducted,
and the process will disturb production. Another issue is that components will possibly
fail before their lifetime, caused by environmental and external conditions, as well as the
failure of other integrated components. Multi-component systems also require complex
maintenance planning and scheduling.
2.2.2 BRP. Another maintenance policy that initiates the growth of the OM concept
is BRP. It is traditionally applied in multi-component systems where maintenance
activities are done on more than one component at the same time (Zheng, 1995). Based
on the fact that, most of the time, components in a system are dependent on one
another, BRP suggested that maintenance or replacement activities are conducted on a
block or a group of components. According to Wang (2002), it is a periodic maintenance
policy where a component is preventively maintained at fixed time intervals and
the activities are independent of the previous failures of the component. BRP is
Downloaded by UNIVERSITY OF WESTERN SYDNEY At 00:21 08 February 2015 (PT)
implemented with the aim of having the minimum maintenance activities possible as
an effort to maximize the production rate. Savic et al. (1995a) discussed BRP as a group
replacement policy where it was assumed that when any component is replaced, the
other components that belong to the same group will be replaced as well. However, the
main concern with BRP is high possibility that newly replaced components will need to
be replaced again. This will definitely increase maintenance costs and also create waste
in the form of component lifetime.
2.3 Principles and concepts of opportunistic maintenance
In the early literature, the general sources’ idea for OM is that component and
equipment are assumed to fail stochastically, and that the failures are independent
according to known probability distribution (Radner and Jorgenson, 1963). Similarly,
the fundamental basis of OM by McCall (1963) divided the maintenance activities into
inspection and replacement with components having a stochastic dependency on one
another. According to Nicolai and Dekker (2008), the principle behind OM is stochastic
dependency that implies that the state of components can influence the state of other
components. The principle can also be referred to as failure interaction or probabilistic
dependence of one component on another in a system.
For the concept of OM, the abstract idea is about opportunities that may arise in a
maintenance system. Dekker and van Rijn (2003) defined the word “opportunity” in
OM as “any event at which a unit can be maintained preventively without incurring
cost penalties for the shutdown of the unit”. The characteristic of the opportunity is
that it occurs randomly and has a limited duration due to the fact that longer machine
downtime will increase maintenance costs and disrupt the production flow. If this is
happening, then the practicality and advantage of OM is void. On the other hand, as
stated by Kaspi and Shabtay (2003), the opportunity to replace a component needs to
be utilized as in any case of machine failure as the stoppage of production line has
already occurred. The opportunity may arise during shutdown periods for particular
equipment 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 replace
components which are found to be defective or need replacement in the immediate
future, during the maintenance of a sub-system or module (Saranga, 2004). This way
the cost of future maintenance or replacement activities can be avoided (Pullen and
Thomas, 1986).
2.4 Focus and aims of opportunistic maintenance
The ultimate objective of maintenance activity is to maintain the system functionality
to the maximum lifetime possible, and with optimum trade-offs between machine
downtime and maintenance costs, while at the same time avoiding any hazardous
failures (Saranga, 2004). This is also the focus of OM policy. Overall, OM aims to reduce
the amount of planned downtime for machines while at the same time maximizing the
lifetime or reliability of components. All these are to ensure the best possible lifetime
for the components in avoidance of costly and risky failures during operation.
OM applications in literature lean towards an optimal maintenance system because
OM use trade-off approaches between the reliability of a component with maintenance
costs. Koochaki et al. (2012) pointed-out that the aim of OM is to group maintenance
activities of two or more components in order to reduce maintenance costs. For
Mohamed-Salah et al. (1999) the main objective of OM application is to reduce the total
number of maintenance activities in the production line and consequently reduce the
Opportunistic
maintenance
105
JQME
20,2
Downloaded by UNIVERSITY OF WESTERN SYDNEY At 00:21 08 February 2015 (PT)
106
total maintenance cost. One or more components can be repaired or replaced through
the application of one or more maintenance approaches in the system (Zheng and
Fard, 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 be
reduced. This is coupled with the assumption that replacing more than one component
at the same time is cheaper than replacing the components separately (Zheng and Fard,
1992).
2.5 The OM concept defined
Since the earliest publications in 1963, there is not a single paper that specifically
states the original definition of OM policy. Radner and Jorgenson (1963) used the
concept of optimal replacement policy on a unit of component during failure and
maintenance of other components if the unit reached a certain age limit. The age limit
is decided to be as close as possible to the end of the component’s lifetime. The concept
tested 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 a
component can be replaced preventively at maintenance opportunities that appear
randomly. Rao and Bhadury (2000) stated that in OM, PM of a component is conducted
when opportunities arise due to failures of other components. It is similar with
Mann et al. (1995) who stated that the intervals between PM activities on a component
are 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 where
the flexibility of maintenance activities is optimized considering the stochastic
and uncertain nature of equipment failures, quality rejects, batch times, batch sizes
and also production targets. These factors are important for a production and
manufacturing company.
Samhouri (2009) described OM as a systematic method of collecting, investing,
preplanning and publishing a set of proposed maintenance activities and then acting
based on the plan whenever an opportunity arises caused by an unscheduled failure
or repair. As maintenance is no longer traditional work of replacing and inspecting the
condition of components, Samhouri captures the concept of maintenance planning
and 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 an
opportunistic strategy which combines corrective and PM activities performed on
different processors of a line. OM should be conducted when technical and economical
conditions are satisfied in the effort to achieve optimal maintenance. The principle
behind this policy is the dependency of a component on another component especially
in multi-component system. The dependency trait is used to conduct PM on one
component while conducting CM on the other. That way, it can be concluded that
the fundamental concept is that machine downtime to repair a component is an
“opportunity” to maintain other components in the system. Therefore, any production
stoppages due to a component failure can be taken advantage of by conducting
maintenance activities on other related components.
From these various descriptions and definitions, the key points that could be
extracted are that OM is the planning and scheduling of maintenance activities and
opportunities in achieving an optimal maintenance system with a balanced trade-off
between maintenance cost and component/system reliability. Taking the consideration
of the dependency principle and the opportunity concept as discussed in previous
sections of this paper, OM is best described as the planning and scheduling of
Downloaded by UNIVERSITY OF WESTERN SYDNEY At 00:21 08 February 2015 (PT)
maintenance activities to repair a component, whilst at the same time opportunistically
repair/replace other components in the system, with the aim to avoid future failures
and reduce the amount of machine downtime.
3. Classification of literature on OM
Once the genealogy, names, concepts and aims of OM are successfully analysed, the
next step is to analyse the application of OM in various publications. As previously
discussed, OM originates from ARP and BRP, where ARP is mostly implemented for
single component systems while BRP is mostly used for multi-component systems.
Therefore, as OM shares the threads from both policies, it was also implemented
in both types of system. Additional discussion on the topic is prepared in the
next 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 can
be identified.
3.1 Classification from the type of system perspective
Application of a concept is always the focal point in research. In doing so, applications
are done in a set of dependent or independent elements or components that work
together and form a system. In the early literature, McCall (1963) and Radner and
Jorgenson (1963) used opportunistic replacement and an inspection policy on a system
with only one part being monitored while several others were ignored. Opportunistic
action 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 for
them, while the lone part is only repaired or replaced when the opportunity arises
caused by the failure of other parts. This will save maintenance time and cost. The part
is assumed to have a stochastic failure rate. This similar approach was also used by
Liang (1985) and Degbotse and Nachlas (2003).
However, some researchers used a single-unit system or multiple unit systems with
two components as the example of OM application. The principle behind OM is that
all 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 components
are affected and need to be maintained. A failure of one component will become an
opportunity to fix the other component. While Rao and Bhadury (2000) and Levrat
et al. (2008) opted for more versatile applications discussing the OM application for
systems with multi-equipment and multi-components connected to the equipment in
series. When components are connected in series they will be dependent on one
another. The failure of one component will certainly affect the whole process of the
equipment. Nevertheless, the research did not apply OM in a real case study system
which makes it hard to comprehend the implementation processes and gauge its
practicality. OM concept is mostly useful and easily practiced in continuously run
systems that have high cost rate of downtime or failure (Sherwin, 1999). It is prudent to
say that OM is effective for this type of system because the failure of a component
provides an opportunity to replace the other components (Satow and Osaki, 2003).
Regardless, application of OM in a multi-component context will required a complex
system of maintenance planning and scheduling.
3.2 Classification from the type of research approach perspective
To show the trend of research approaches conducted in OM, publications are
categorized into four different types of approach, namely as numerical analysis, case
Opportunistic
maintenance
107
JQME
20,2
Downloaded by UNIVERSITY OF WESTERN SYDNEY At 00:21 08 February 2015 (PT)
108
study, simulation and review paper. The classification is based on the type of research
conducted and discussed in the paper. A paper or manuscript is classified under
numerical analysis if it provides an algorithm, theorem and mathematical model in
OM research. As OM is a new maintenance policy, most publications in the numerical
analysis approach linger around the mathematical analysis of maintenance
opportunities and also optimization based on component lifetime, reliability and
maintenance cost. For a case study, papers classified in this group are those that use
data from a real environment or industry to solve OM issues from a practical point
of view. Some papers which further tested and simulated the OM model or algorithm
using computer software are grouped under the simulation type. The last type of paper
is a review type where a survey of literature on maintenance was discussed. Figure 3
shows the percentage of papers based on their classification.
It can be observed that close to 62 per cent of the publications focus on the
numerical analysis aspect of OM. The numerical analyses make use of various
theorems and mathematical methods to develop the OM schedule, and to evaluate the
effectiveness 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 numerical
methods like Monte-Carlo simulation (Tan and Kramer, 1997; Crocker and Kumar,
2000; Laggoune et al., 2010), genetic algorithm (GA) (Savic et al., 1995a, b; Haque
et 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’s
age and reliability. While Fuzzy logic (Haque et al., 2003; Derigent et al., 2009) and
Markov decision theory (Sethi, 1976; Ozekici, 1988; Amari and McLaughlin, 2004;
Xiang et al., 2012) were used in deliberations on optimal maintenance activities and
in 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 discussing
maintenance policies. It shows that there is little awareness of the OM concept. Aside
from papers by McCall (1963) and Wang (2002), OM is only briefly mentioned in other
reviews of maintenance policies. Reviews done by Thomas (1986) and Nicolai and
Dekker (2008) do include a short discussion on the concept of OM, yet the authors did
not 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
Research Approaches in Opportunistic Maintenance
Review
10%
Case Study
21%
Figure 3.
Percentage of publications
on research approaches
in OM research
Simulation
7%
Numerical Analysis
62%
Downloaded by UNIVERSITY OF WESTERN SYDNEY At 00:21 08 February 2015 (PT)
another for deteriorating items in multi-components systems. The aim of the reviews is
to find the optimal maintenance for the system.
Similar to review papers, case studies and simulation papers discussing OM
application are hard to find. Despite an abundance of papers conducting numerical
analysis of OM, few or almost no real world applications have been done following
the models or computational-based approaches presented. The main problem can
be attributed to the complex equations and assumptions used in the studies.
As hypotheses, assumptions and limitations are commonly applied in computation
analysis; it still cannot be implemented in real industry without focusing only on the
most promising possibilities or by using partial (incomplete) data for performance
measures. More often, the publications presenting case studies in real industry are
too specific for the company’s system and lack the flexibility for further improvement
or implementation in another company or industry. Therefore, further analyses on the
applications of these methods are very much required in order to provide a proper
framework of OM policy.
Opportunistic
maintenance
109
3.3 Classification from the performances measures perspective
The core issue in OM research concerns the technical and economical conditions of the
components for conducting replacement or repair. Another factor analysed in this
paper is type of performance measure or criteria used by researchers in order to
calculate and analyse the practicality as well as the effectiveness of OM. From the
conceptual analysis conducted on the publications of OM, age and cost are the basic
standard or principle by which were used to measure the performance of the OM
system. Figure 4 shows the pie chart of optimal criteria used in literature to measure
the effectiveness of OM activities. Because money is always the definite performance
measure agreed by both researchers and practitioners, almost half of research concerns
the performance on maintenance cost. The critical point is whether the OM conducted
is cost-effective or not. This issue was extensively discussed by Mohamed-Salah et al.
Optimal Criteria in Opportunistic Maintenance
Others
6%
Failure rate
4%
Failure rate and Cost
9%
Age
16%
Age and Cost
16%
Cost
49%
Figure 4.
Percentage of
publications on various
optimal criteria’s
in OM research
JQME
20,2
Downloaded by UNIVERSITY OF WESTERN SYDNEY At 00:21 08 February 2015 (PT)
110
(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 to
investigate how the component failure rate can be reduced by conducting OM on a
two-component system. The failures are assumed to happen stochastically throughout
the production process and are considered as “opportunities”. As the wisest step is to
take advantage of the situation and replace other parts as well, the researchers focused
on the planning, scheduling and decision making of OM activities to save maintenance
time and improve machine availability. Further on, cost becomes the main concern for
most researchers. Savic et al. (1995b) stated that the choice depends on the probability
distribution of components’ residual lives and also on the cost of maintenance if it
was carried out on a not-yet-failed component. Maintenance costs included cost for
repair 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 unit
time due to machine failure and production stoppages. Aside from that, Bevilacqua
and Braglia (2000) included manpower and spare parts costs when calculating the
maintenance cost when conducting OM. Laggoune et al. (2009, 2010) conducted
research on OM cost structure and suggested that deterioration-based decisions can be
included to solve the cost issue. The solution is found by analysing the cost or benefit
balance of the component that can be preventively replaced during CM activities.
Another common optimal criterion used in performance measurement is the age or
reliability of component. To avoid waste in the form of good-age or lifetime of
components, 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) and
Cassady et al. (2001) used Weibull wear out distribution while Satow et al. (2000) and
Cheng 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 and
failure. The intention when using these tools is to ensure components replaced during
OM 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 economical
conditions in the maintenance system are satisfied. Therefore, aside from cost and age,
some research focused on finding the balance between low maintenance cost and high
component 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 multiple
optimal criteria in decision making and planning of OM activities. Their aim is finding
the optimal maintenance system when implementing the OM concept.
4. Application of OM in industries
Despite numerous publications reported in relation to OM, its application in real
industry remains limited. Nevertheless, this section of the paper will discuss the
publications that apply and analyse the OM concept as an effort to show where OM is
used and how practical the concept is. From the perspective of real industry, the
concept of “opportunity” is twofold. First, it is applied when a shutdown of a system
for CM activities takes place. Second, an opportunity is taken when the system is down
for PM, in other words the PM is meant for the scheduled replacement of a component
where OM is applied on the other maintenance-significant components which have the
potential 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
Downloaded by UNIVERSITY OF WESTERN SYDNEY At 00:21 08 February 2015 (PT)
when performing CM on a failed component. OM can be conducted either by choice
or based on the physical condition of the equipment (Cui and Li, 2006). OM can be
considered as a modification of run-to-fail maintenance management philosophy
(Samhouri, 2009). Generally, as a result from this opportunity, total equipment
downtime and maintenance costs will be reduced.
Tan and Kramer (1997) applied OM in a chemical processing plant for PM
optimization. Similar to a nuclear power plant, the chemical industry requires timely
and effective maintenance because of the sensitivity and criticality of the equipment
and machines. The number of scheduled breakdowns for maintenance need to be
reduced to the minimum because the researchers found that lost production costs (net
income lost) in a chemical plant can range from $500 up to $100,000 per hour. Even
worse, a typical chemical refinery was estimated to lose $20,000 to $30,000 per hour out
of their production costs due to equipment failures. This estimation is based on ten
days 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 was
used to find the cost-effectiveness of OM in the chemical plant.
For research carried out by Samhouri (2009), GAs were employed to decide whether
the OM activity is cost-effective or not. The research focused on OM strategy that
involves several non-linear variables which affect the total cost of maintenance
that 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 a
huge space of potential solutions available. Other than that, Scarf and Deara (2003)
considered OM policies in a simulation study of block replacement and modified
BRPs for a two-component system. This simulation focused on system reliability and
cost-effectiveness or economic dependence. It was discussed that policy IVc1
(opportunistic independent modified block replacement) and policy IVc2 (opportunistic
group modified block replacement) are among the near-optimal policies found in the
research.
Nilsson et al. (2009) applied OM in a nuclear power plant by reconstructing
replacement schedules of shaft seals in a feed-water pump system. The approach used
is calculating the total cost of maintenance and then minimizing the cost according to
some 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 to
the discount rate. The conclusion drawn is that the OM optimization model is a
deterministic model and applicable in practice. Amari and McLaughlin (2004) also
focused on an optimal maintenance model. The research involved Markov analysis to
provide a closed-form analytical solution for their model. The researchers stressed the
importance of minimum overall maintenance costs or maximum system performance
measures. Both issues are at the core of OM principle.
One of the concerns in the publications is how OM is considered as an interruption
to production operation. Iung et al. (2007) emphasises the integration of maintenance
and production strategy planning in developing OM tasks that keep conjoint the
product-production-equipment performance. This is done by developing an OM
task by synchronizing it with production and keeping the production and the
equipment performances simultaneously to preserve the product conditions. It means
moving from a conventional maintenance approach towards new condition-based
and predictive ones performed only when a certain level of equipment deterioration
(impacting product conditions) occurs rather than after a specified period of time.
Opportunistic
maintenance
111
JQME
20,2
Downloaded by UNIVERSITY OF WESTERN SYDNEY At 00:21 08 February 2015 (PT)
112
Odds algorithm was used as a decision-making tool relevant to find the right place to
stop, among the production stops, for performing optimal “just-in-time” maintenance
action.
In a different research area, Bonarini and Sassaroli (1997) applied the opportunistic
concept and present an Opportunistic Model-Based Diagnosis System (OMISSYS) – a
system to diagnose faults or failures in plants whose components are described by
different types of models. As models and data are prone to be affected by uncertainty
and imprecision, OMISSYS applies opportunistically different reasoning mechanisms
on available models to find a set of diagnoses for a given system. The results
show that the system is able to speed up the reasoning process by focusing on the most
promising aspect of the plant. This research is a good example of how an opportunistic
concept can save time and produce an optimal system. This way, a company can reduce
the amount of equipment shut down for maintenance and have a more productive
operation.
Current research on OM revolves around the optimization of the maintenance
system. Instead of using the common OM principle as discussed in Section 2, Levrat
et al. (2008) studied the age of components for repair and failure with an odd-based
decision-making tool. The aim is to have effective and synchronized activities between
maintenance and production. Another informative research is by Derigent et al. (2009)
who used Fuzzy modelling to assess the proximity of components when conducting
OM. Numerical analysis was conducted within the decision-making process in order
to achieve optimal maintenance. The situation is that when maintenance is conducted
on a component, the technician or operator will locate another component close to
the previous component so that additional maintenance actions can be undertaken.
The other components also need to be controlled and maintained within the time
allocated for maintenance. Similar to Levrat et al. (2008), this research strove to make
OM practical and help the decision maker to optimize maintenance activities according
to resources, material and time available in the company.
5. Findings and suggestion of future work
Swanson (2001) categorized various types of maintenance policy into three main
groups, named as reactive, proactive and aggressive maintenance approaches. Each
approach has a designated technique regarding how the policies were implemented in a
system. In the case of reactive maintenance, a fire-fighting approach was used where
equipment in a system is run until it experiences a failure before any replacement or
repair tasks are conducted. This approach is commonly used in the early history of
maintenance systems and an example of the policy is CM. The approach of the
proactive maintenance system contradicts the first group. Applying a preventive
principle, the approach is to monitor the condition of each piece of equipment and then
conduct maintenance tasks before the equipment fails. This is done in order to avoid
equipment breaking down when it is operating. Policies like PM and RCM are best to
describe the approach. The third group, aggressive maintenance, used an improvement
approach. The principle is to conduct analysis and plan for improvement of the overall
equipment operation. This type of approach not only focuses on the equipment
itself 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 of
these groups. Instead, OM is best grouped under a new approach introduced by
Khazraei and Deuse (2011). In the review on the taxonomy of maintenance policies, the
Downloaded by UNIVERSITY OF WESTERN SYDNEY At 00:21 08 February 2015 (PT)
new approach is called prospective maintenance. The character of this approach is that
PM tasks are conducted when a machine breaks down due to failure. According to
Sherwin (1999), OM have a rule of conducting preventive tasks that are due or overdue
on a system which was forced to stop because of a component’s failure. The stoppage is
treated as an opportunity and aims to reduce the cost and time to conduct maintenance
on the system. Based on the literature studied, a few more findings can be discussed
regarding the merits, demerits and future of OM research.
5.1 Merits in implementing OM
According to Tan and Kramer (1997), performing OM on components in a system will
improve its overall reliability; reduce future or potential downtime and, at the same
time, increase the production rate and a company’s net income. The advantage of OM is
that CM combined with PM can be used to save set-up costs (Cui and Li, 2006) and to
guarantee the expected performance of the system (Levrat et al., 2008). Companies can
also save the set-up time for equipment by reducing the total amount of downtime due
to failures. Cassady et al. (2001) put the situation as doing more at less cost. For Dekker
and Dijkstra (1992), opportunities in OM policy means conducting cost-effective
preventive replacement activities on a system when it is not required for service or
when it cannot operate.
According to Pham and Wang (2000), the process of conducting PM on non-failed
but degraded components at the time of CM activities for another failed component
may reduce unexpected CM at a fairly low cost. This is because PM together with CM
can be conducted without substantial additional expenses. The reduction of
maintenance costs was proven in case studies in a wind turbine system (Besnard
et al., 2009) and in oil-refining facilities (Laggoune et al., 2009). OM also helps to
optimize maintenance activities and decision making. Indirectly, OM can improve
production 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 and
extend its lifetime (Zhou et al., 2009). Based on the benefits of OM stated in the
literature, it can be concluded that the main benefits of implementing OM can be
divided into three groups. The first group is the reduction of failure, the second is the
reduction of cost and the final group is increment in equipment/system age.
5.2 Demerits and issues of OM
The OM principle is that whenever maintenance is conducted on a failed component,
other maintenance-significant components which have the potential to fail in
the near future will also be repaired or replaced. The challenge is to find the
maintenance-significant component. The researchers contemplate the possibility that a
good component will be replaced during OM which will lead to other problem such as
higher maintenance and spare parts costs. Then OM can no longer be considered
as a cost-effective approach. The direct costs due to component failure and replacement
are usually very high, and the impact of the different replacement intervals on the
overall 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 economic
and effective when a system has many components as well as experiencing high failure
rates. So much so that it can be concluded that OM is not cost-effective for single unit
systems. However, multi-components systems would require complex planning and
Opportunistic
maintenance
113
JQME
20,2
Downloaded by UNIVERSITY OF WESTERN SYDNEY At 00:21 08 February 2015 (PT)
114
scheduling of maintenance activities. Other than that, Besnard et al. (2009) argue that
OM 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 its
operation, thus the OM with combination of CM and PM will take more time to be
conducted. This will create a loss of production rate and product sales. Then the
company 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 a
failure occurs.
Aside from that, the big issue with OM is whether maintenance activities conducted
under the policy are “underdone” or “overdone” (Wang et al., 2008). The calculations in
OM always evolved around the two issues, one is finding the optimal maintenance with
the minimum overall cost and two is finding an optimal system that maximizes the
system’s performance measure (Amari and McLaughlin, 2004). Researches need to
solve these issues in order to allow OM to achieve an optimal system. The optimal
maintenance system is the one that strives to ensure no excessive maintenance is
conducted as it will result in high maintenance costs, while an inadequate amount of
maintenance 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 maintenance
activities. Cui and Li (2006) stated that by combining CM and PM, one may not know
in advance which maintenance action should be taken. Thus, there is the likelihood of
sacrificing the plan-able feature of PM, so it is not possible to conduct work preparation
in advance (Levrat et al., 2008). An unexpected production demand or a spare parts
shortage can also cause problems in OM activities (Zequeira et al., 2008). Nevertheless,
this drawback can be remedied by having effective maintenance planning and
scheduling. Carefully planned activities will avoid redundancy of tasks, while good
maintenance scheduling will ensure companies are prepared in terms of human
resources and spare parts for the activities.
5.3 Future work
The 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 though
OM is found to have originated from ARP and BRP concepts, no publication directly
addresses the issue and studies the evolution both in theory and in real industry
application. As discussed in Sections 2.2 and 2.3, ARP focuses on the age of
components were they to be maintained or replaced separately, but BRP focuses on
maintenance on a group of components. Both policies contradict one another yet were
claimed as the backbone of OM policy. Therefore, it would be interesting to see the
connections between ARP, BRP and OM from a principle point of view. More research
is needed on the issue so that OM can be more practical and easy to implement. These
connections are also crucial for the development of OM assumptions, rules and
limitations for industry application.
From a maintenance planning point of view, the issues awaiting exploration
in OM research can be divided into short-term, middle-term and long-term challenges.
The short-term challenge is the maintenance planning and scheduling of
multiple-component systems in both parallel and series systems. Research is
required in this area especially for manufacturing plants with various processes
Downloaded by UNIVERSITY OF WESTERN SYDNEY At 00:21 08 February 2015 (PT)
and multiple machines for each process. There should be some way to properly
plan OM on the multiple machines to achieve an optimal maintenance system.
The middle-term issue is concerned with production rate. OM application will affect the
number of products manufactured in the maintained systems. Therefore, researchers
should focus on the challenge of ensuring maintenance conducted not only does not
interrupt the manufacturing process, but also that the tasks should be planned to
accommodate the production plan so the machine availability is as high as possible.
The ideal situation would be that the machine operated whenever required and did
not experience failure during operation. Finally, the long-term challenge is how
the application of OM policy can benefit a company in the quest to be a leader in the
industry as well as achieving world class operation and quality. This concern about
the effectiveness and optimality of OM can lead a company into becoming a successful
organization. This way, maintenance will be an important value-added system in the
company.
Aside from that, future work should explore the OM concept from different
optimal criteria in a maintenance system. For the purpose of achieving an optimal
situation, it is not the case of achieving a perfect balance between all parameters in a
system because that is an impossible feat. The process required a complex system
with some criteria need to be weighted according to their importance. The best way
to achieve the situation is by having a specific aim or objective of performance
measure to be fulfilled. Future research can be directed into development of a
standard decision support system for an optimal maintenance system. The model
can consist of a few key performance measures like failure rate, maintenance cost
and reliability of equipment as factors which then can be chosen according to the
company’s aim and objective for improvement. Artificial intelligent methods such
genetic algorithm, Fuzzy logic and Poisson distribution can be applied to achieve an
optimized OM system.
Another branch of research that can be explored in OM policy is regarding the
practical rules or framework for implementation in the industry. The numerical
analysis and theories in OM research need to be translated into practical theories for
practitioners. As mathematical models developed for OM are always custom-made to
fit certain situations selected by the researcher, it cannot be immediately implemented
in the real system. Some adjustment with assumptions, limitations and rules should
be developed for successful implementation of OM in the industry. That way, OM can
be proved to be a catalyst for an optimal maintenance system. Aside from that, more
extensive analysis should be done based on real data from companies to test the
practicality of OM concepts in the industry. Studies and simulations of OM activities
are also very much needed in finding the optimal trade-off especially between cost and
reliability. A simulation of a maintenance system is required to reduce the gap between
theories and practical implementation. As there is lots of numerical analysis published
and models introduced in OM research, it should be simulated to find loopholes and
test its practicality.
6. Conclusions
Effective and optimized maintenance systems are highly acquired in today’s
manufacturing 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 in
maintenance systems for decades. The number of publications has gradually increased
Opportunistic
maintenance
115
JQME
20,2
Downloaded by UNIVERSITY OF WESTERN SYDNEY At 00:21 08 February 2015 (PT)
116
over the years, showing an encouraging trend and growing interest on this
maintenance policy. OM is commonly applied in research on optimal maintenance
systems as well as in effective maintenance policy. The OM concept has been widely
and numerically studied for maintenance scheduling for multiple components, single
component and two components as well as multi-equipment and multi-component
systems. This policy evolves from age replacement and BRPs which used the
concept of components’ relation to one another in a system to conduct maintenance
simultaneously.
Most publications provide numerical analysis and models on the optimal
scheduling with a trade-off between the age of a component and maintenance cost
when conducting OM. OM is the planning and scheduling of an optimal maintenance
system that prospectively conducted PM activities on dependent/related components
when a component failed, with consideration for the lowest maintenance cost possible
and without sacrificing its reliability. OM can be put into practice to reduce the number
of machine breakdowns and machine stoppages especially for continuous systems.
From the various literature reviewed in this paper, it can be concluded that discussion
on 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 expand
its concept and improve its principle.
References
AlDurgam, M.M. and Duffuaa, S.O. (2013), “Optimal joint maintenance and operation policies to
maximize overall systems effectiveness”, International Journal of Production Research,
Vol. 51 No. 5, pp. 1319-1330.
Almgren, T., Andreasson, N., Anevski, D., Patriksson, M., Stromberg, A.B. and Svensson, J.
(2008), “Optimization of opportunistic replacement activities: a case study in the aircraft
industry”, University of Gothenburg, Gothenburg, available at: www.math.chalmers.se/
Math/Research/Preprints/2008/45.pdf (accessed 21 July 2011).
Almgren, T., Andréasson, N., Patriksson, M., Strömberg, A.B., Wojciechowski, A. and Önnheim,
M. (2012), “The opportunistic replacement problem: theoretical analyses and numerical
tests”, Mathematical Methods of Operations Research, Vol. 76 No. 3, pp 289-319.
Amari, S.V. and McLaughlin, L. (2004), “Optimal design of a condition-based maintenance
model”, Reliability and Maintainability, Annual Symposium-RAMS, pp. 528-533.
Arts, R.H.P.M, Knapp, G.M. and Man, L. Jr. (1998), “Some aspects of measuring maintenance
performance in the process industry”, Journal of Quality in Maintenance Engineering,
Vol. 4 No. 1, pp 6-11.
Bedford, T., Dewan, I., Meilijson, I. and Zitrou, A. (2011), “The signal model: a model for
competing risks of opportunistic maintenance”, European Journal of Operational Research,
Vol. 214 No. 3, pp. 665-673.
Besnard, F., Patriksson, M., Stromberg, A.-B., Wojciechowski, A. and Bertling, L. (2009),
“An optimization framework for opportunistic maintenance of offshore wind power
system”, IEEE Bucharest Power Tech Conference, pp. 1-7.
Bevilacqua, M. and Braglia, M. (2000), “The analytic hierarchy process applied to maintenance
strategy selection”, Reliability Engineering & System Safety, Vol. 70 No. 1, pp. 71-83.
Bonarini, A. and Sassaroli, P. (1997), “Opportunistic multimodel diagnosis with imperfect
models”, Information Sciences, Vol. 103 No. 1-4, pp. 161-185.
Cassady, C., Murdock, W.P. Jr and Pohl, E.A. (2001), “Selective maintenance for support
equipment involving multiple maintenance actions”, European Journal of Operational
Research, Vol. 129 No. 2, pp. 252-258.
Downloaded by UNIVERSITY OF WESTERN SYDNEY At 00:21 08 February 2015 (PT)
Castanier, B., Grall, A. and Bérenguer, C. (2005), “A condition-based maintenance policy with
non-periodic inspections for a two-unit series system”, Reliability Engineering & System
Safety, Vol. 87 No. 1, pp. 109-120.
Chan, F.T.S., Lau, H.C.W., Ip, R.W.L., Chan, H.K. and Konga, S. (2005), “Implementation of total
productive maintenance – a case study”, International Journal Production Economics,
Vol. 95 No. 1, pp. 71-94.
Cheng, Z., Yang, Z. and Guo, B. (2012), “Opportunistic maintenance optimization of a two-unit
system with different unit failure patterns”, Quality, Reliability, Risk, Maintenance, and
Safety Engineering (ICQR2MSE), International Conference, IEEE, June, pp. 409-413.
Chien, Y.-H. (2009), “A number-dependent replacement policy for a system with continuous
preventive maintenance and random lead times”, Applied Mathematical Modelling, Vol. 33
No. 3, pp. 1708-1718.
Crocker, J. and Kumar, U.D. (2000), “Age-related maintenance versus reliability centred
maintenance: a case study on aero-engines”, Reliability Engineering & System Safety,
Vol. 67 No. 2, pp. 113-118.
Cui, L. and Li, H. (2006), “Opportunistic maintenance for multi-component shock models”,
Mathematical Methodology of Operational Research, Vol. 63, pp. 493-511.
Das, A.N. and Acharya, D. (2004), “Age replacement of components during IFR delay time”, IEEE
Transactions on Reliability, Vol. 53 No. 3, pp. 306-312.
Day, J.A. and George, L.L. (1981), Opportunistic Replacement of Fusion Power System Parts
(No. UCRL-85894; CONF-820108-1), Lawrence Livermore National Lab, Livermore, CA.
Degbotse, A.T. and Nachlas, J.A. (2003), “Use of nested renewals to model availability under
opportunistic maintenance policies”, Reliability and Maintainability Symposium, Annual,
pp. 344-350.
Dekker, R. (1996), “Applications of maintenance optimization models: a review and analysis”,
Reliability Engineering & System Safety, Vol. 51 No. 3, pp. 229-240.
Dekker, R. and Dijkstra, M.C. (1992), “Opportunity-based age replacement: exponentially
distributed times between opportunities”, Naval Research Logistics, Vol. 39 No. 2, pp. 175-190.
Dekker, R. and Smeltink, E. (1991), “Opportunity-based block replacement”, European Journal of
Operational Research, Vol. 53 No. 1, pp. 46-63.
Dekker, R. and van Rijn, C. (2003), “PROMPT, a decision support system for opportunity-based
preventive maintenance”, available at: http://people.few.eur.nl/rdekker/pdf_files/
paper_PROMPT.pdf (accessed 27 November 2011).
Derigent, W., Thomas, E., Levrat, E. and Iung, B. (2009), “Opportunistic maintenance based
on fuzzy modelling of component proximity”, CIRP Annals – Manufacturing Technology,
Vol. 58 No. 1, pp. 29-32.
Ding, F. and Tian, Z. (2012), “Opportunistic maintenance for wind farms considering multi-level
imperfect maintenance thresholds”, Renewable Energy, Vol. 45 No. 1, pp. 175-182.
Duncan, J. and Scholnick, L.S. (1973), “Interrupt and opportunistic replacement strategies
for systems of deteriorating components”, Operational Research Quarterly (1970-1977),
Vol. 24 No. 2, pp. 271-283.
Epstein, S. and Wilamowsky, Y. (1985), “Opportunistic replacement in a deterministic
environment”, Computers & Operations Research, Vol. 12 No. 3, pp. 311-322.
Fard, N. and Zheng, X. (1991), “An approximate method for non-repairable systems based on
opportunistic replacement policy”, Reliability Engineering & System Safety, Vol. 33 No. 2,
pp. 277-288.
Furner, J. (2004), “Conceptual analysis: a method for understanding information as evidence, and
evidence as information”, Archival Science, Vol. 4 Nos 3-4, pp. 233-265.
Opportunistic
maintenance
117
JQME
20,2
Downloaded by UNIVERSITY OF WESTERN SYDNEY At 00:21 08 February 2015 (PT)
118
Garg, A. and Deshmukh, S.G. (2006), “Maintenance management – literature review and
directions”, Journal of Quality in Maintenance Engineering, Vol. 12 No. 3, pp. 205-238.
Grall, A., Berenguer, C. and Dieulle, L. (2002), “A condition-based maintenance policy for
stochastically deteriorating systems”, Reliability Engineering & System Safety, Vol. 76
No. 2, pp. 167-180.
Haque, S.A., Kabir, A.B.M.Z. and Sarker, R.A. (2003), “Optimization model for opportunistic
replacement policy using genetic algorithm with fuzzy logic controller”, Evolutionary
Computation, CEC , The 2003 Congress, Vol.4, pp. 2837-2843.
Hu, J., Zhang, L. and Liang, W. (2012), “Opportunistic predictive maintenance for complex
multi-component systems based on DBN-HAZOP model”, Process Safety and
Environmental Protection, Vol. 90 No. 5, pp. 376-388.
Iung, B., Levrat, E. and Thomas, E. (2007), “‘Odds algorithm’-based opportunistic maintenance
task execution for preserving product conditions”, CIRP Annals – Manufacturing
Technology, Vol. 56 No. 1, pp. 13-16.
Jhang, J.P. and Sheu, S.H. (1999), “Opportunity-based age replacement policy with minimal
repair”, Reliability Engineering & System Safety, Vol. 64 No. 3, pp. 339-344.
Jiang, R. and Ji, P. (2002), “Age replacement policy: a multi-attribute value model”, Reliability
Engineering & System Safety, Vol. 76 No. 3, pp. 311-318.
Kaspi, M. and Shabtay, D. (2003), “Optimization of the machining economics problem for a
multistage transfer machine under failure, opportunistic and integrated replacement
strategies”, International Journal of Production Research, Vol. 41 No. 10, pp. 2229-2247.
Khazraei, K. and Deuse, J. (2011), “A strategic standpoint on maintenance taxonomy”, Journal of
Facilities Management, Vol. 9 No. 2, pp. 96-113.
Koochaki, J., Bokhorst, J.A., Wortmann, H. and Klingenberg, W. (2012), “Condition based
maintenance in the context of opportunistic maintenance”, International Journal of
Production Research, Vol. 50 No. 23, pp. 6918-6929.
Laggoune, R., Chateauneuf, A. and Aissani, D. (2009), “Opportunistic policy for optimal
preventive maintenance of a multi-component system in continuous operating units”,
Computers & Chemical Engineering, Vol. 33 No. 9, pp. 1499-1510.
Laggoune, R., Chateauneuf, A. and Aissani, D. (2010), “Impact of few failure data on the
opportunistic replacement policy for multi-component systems”, Reliability Engineering &
System Safety, Vol. 95 No. 2, pp. 108-119.
Lai, M.T. and Chen, Y.C. (2006), “Optimal periodic replacement policy for a two-unit system with
failure rate interaction”, International Journal of Advance Manufacturing Technology,
Vol. 29 Nos 3-4, pp. 367-371.
L’Ecuyer, P. and Haurie, A. (1983), “Preventive replacement for multicomponent systems: an
opportunistic discrete-time dynamic programming model”, IEEE Transactions on
Reliability, Vol. R-32 No. 1, pp. 117-118.
Levrat, E., Thomas, E. and Iung, B. (2008), “Odds-based decision-making tool for opportunistic
production-maintenance synchronization”, International Journal of Production Research,
Vol. 46 No. 19, pp. 5263-5287.
Liang, T.Y. (1985), “Optimum piggyback preventive maintenance policies”, IEEE Transactions
on Reliability, Vol. R-34 No. 5, pp. 529-538.
McCall, J.J. (1963), “Operating characteristics of opportunistic replacement and inspection
policies”, Management Science, Vol. 10 No. 1, pp. 85-97.
McCall, J.J. (1965), “Maintenance policies for stochastically failing equipment: a survey”,
Management Science, Vol. 11 No. 5, pp. 493-524.
Mann, L. Jr, Saxena, A. and Knapp, G.M. (1995), “Statistical-based or condition-based preventive
maintenance?”, Journal of Quality in Maintenance Engineering, Vol. 1 No. 1, pp. 46-59.
Downloaded by UNIVERSITY OF WESTERN SYDNEY At 00:21 08 February 2015 (PT)
Mohamed-Salah, O., Daoud, A.K. and Ali, G. (1999), “A simulation model for opportunistic
maintenance strategies”, Emerging Technologies and Factory Automation, Proceedings,
ETFA’99, 7th IEEE International Conference, Vol. 1, pp. 703-708.
Nicolai, R.P. and Dekker, R. (2008), Maintenance of Multi-Component Systems: A Review. Complex
System Maintenance Handbook, Springer, London, pp. 263-286.
Nilsson, J., Wojciechowski, A., Strömberg, A.B., Patriksson, M. and Bertling, L. (2009),
“An opportunistic maintenance optimization model for shaft seals in feed-water pump
systems in nuclear power plants”, Proceeding of IEEE Bucharest Power Tech Conference,
pp. 1-7.
Nourelfath, M. and Ait-Kadi, D. (2007), “Optimization of series-parallel multi-state systems
under maintenance policies”, Reliability Engineering & System Safety, Vol. 92 No. 12,
pp. 1620-1626.
Ozekici, S. (1988), “Optimal periodic replacement of multicomponent reliability system”,
Operations Research, Vol. 36 No. 4, pp. 542-552.
Parida, A. and Kumar, U. (2006), “Maintenance performance measurement (MPM): issues and
challenges”, Journal of Quality in Maintenance Engineering, Vol. 12 No. 3, pp. 239-251.
Pham, H. and Wang, H. (2000), “Optimal (t,T) opportunistic maintenance of a k-out-of-n:
G system with imperfect PM and partial failure”, Naval Research Logistics, Vol. 47 No. 1,
pp. 223-239.
Pullen, K.W. and Thomas, M.U. (1986), “Evaluation of an opportunistic replacement policy for
a 2-unit system”, IEEE Transactions on Reliability, Vol. R-35 No. 3, pp. 320-324.
Radner, R. and Jorgenson, D.W. (1963), “Opportunistic replacement of a single part in presence of
several monitored parts”, Management Science, Vol. 10 No. 1, pp. 70-83.
Rao, A.N. and Bhadury, B. (2000), “Opportunistic maintenance of multi-equipment
system: a case study”, Quality and Reliability Engineering International, Vol. 16
No. 6, pp. 487-500.
Samat, H.A, Kamaruddin, S. and Azid, I.A. (2011), “Maintenance performance measure:
a review”, Pertanika Journal of Science and Technology, Vol. 19 No. 2, pp. 199-211.
Samhouri, M.S. (2009), “An intelligent opportunistic maintenance (OM) system: a genetic
algorithm approach”, Science and Technology for Humanity (TIC-STH), IEEE Toronto
International Conference, IEEE, pp. 60-65.
Saranga, H. (2004), “Opportunistic maintenance using genetic algorithms”, Journal of Quality
in Maintenance Engineering, Vol. 10 No. 1, pp. 66-74.
Satow, T. and Osaki, S. (2003), “Opportunity-based age replacement with different intensity
rates”, Mathematical and Computer Modelling, Vol. 38 Nos 11-13, pp. 419-1426.
Satow, T., Teramoto, K. and Nakagawa, T. (2000), “Optimal replacement policy for a cumulative
damage model with time deterioration”, Mathematical and Computer Modelling, Vol. 31
Nos 10-12, pp. 313-319.
Savic, D.A., Walters, G.A. and Knezenic, J. (1995a), “Optimal opportunistic maintenance policy
using genetic algorithms, 1: formulation”, Journal of Quality in Maintenance Engineering,
Vol. 1 No. 2, pp. 34-49.
Savic, D.A., Walters, G.A. and Knezenic, J. (1995b), “Optimal, opportunistic maintenance policy
using genetic algorithms, 2: analysis”, Journal of Quality in Maintenance Engineering,
Vol. 1 No. 3, pp. 25-34.
Scarf, P.A. and Deara, M. (2003), “Block replacement policies for a two-component system with
failure dependence”, Naval Research Logistics, Vol. 50 No. 1, pp. 70-87.
Sethi, D.P.S. (1976), “Opportunistic replacement policies for maintained systems”, Operation
Research Center Report No. 76-26, University of California, Berkeley, CA.
Opportunistic
maintenance
119
JQME
20,2
Downloaded by UNIVERSITY OF WESTERN SYDNEY At 00:21 08 February 2015 (PT)
120
Sharma, A., Yadava, G.S. and Deshmukh, S.G. (2011), “A literature review and future
perspectives on maintenance optimization”, Journal of Quality in Maintenance
Engineering, Vol. 17 No. 1, pp. 5-25.
Sherwin, D.J. (1999), “Age-based opportunistic maintenance”, Journal of Quality in Maintenance
Engineering, Vol. 5 No. 3, pp. 221-235.
Swanson, L. (2001), “Linking maintenance strategies to performance”, International Journal of
Production Economics, Vol. 70 No. 3, pp. 237-244.
Taghipour, S. and Banjevic, D. (2012a), “Optimal inspection of a complex system subject to
periodic and opportunistic inspections and preventive replacements”, European Journal of
Operational Research, Vol. 220 No. 3, pp. 649-660.
Taghipour, S. and Banjevic, D. (2012b), “Optimum inspection interval for a system under periodic
and opportunistic maintenance”, IEEE Transactions, Vol. 44 No. 11, pp. 932-948.
Tan, J.S. and Kramer, M.A. (1997), “A general framework for preventive maintenance
optimization in chemical process operations”, Computers & Chemical Engineering, Vol. 21
No. 12, pp. 1451-1469.
Thomas, L.C. (1986), “A survey of maintenance and replacement models for maintainability and
reliability of multi-item systems”, Reliability Engineering, Vol. 16 No. 4, pp. 297-309.
Vergin, R.C. and Scriabin, M. (1977), “Maintenance scheduling for multicomponent equipment”,
AIIE Transaction, Vol. 9 No. 3, pp. 297-305.
Vu, H.C., Do Van, P., Barros, A. and Bérenguer, C. (2012), “Maintenance activities planning and
grouping for complex structure systems”, Annual Conference of the European Safety
and Reliability Association, PSAM11 & ESREL, Helsinki, June.
Waeyenbergh, G. and Pintelon, L. (2002), “A framework for maintenance concept development”,
International Journal of Production Economics, Vol. 77 No. 3, pp. 299-313.
Wang, H. (2002), “A survey of maintenance policies of deteriorating systems”, European Journal
of Operational Research, Vol. 139 No. 3, pp. 469-489.
Wang, L., Chu, J. and Mao, W. (2008), “A condition-based order-replacement policy for a
single-unit system”, Applied Mathematical Modelling, Vol. 32 No. 11, pp. 2274-2289.
Wireman, T. (2003), Benchmarking Best Practices in Maintenance Management, Industrial Press,
New York, NY.
Xiang, Y., Cassady, C.R. and Pohl, E.A. (2012), “Optimal maintenance policies for systems subject
to a Markovian operating environment”, Computers & Industrial Engineering, Vol. 62
No. 1, pp. 190-197.
Xu, B., Han, X.S., Li, M. and Xiao, D.L. (2012), “System maintenance scheduling: review and
prospect”, Innovative Smart Grid Technologies-Asia (ISGT Asia), IEEE, May, pp. 1-6.
Zequeira, R.I., Valdes, J.E. and Berenguer, C. (2008), “Optimal buffer inventory and opportunistic
preventive maintenance under random production capacity availability”, International
Journal of Production Economics, Vol. 111 No. 2, pp. 686-696.
Zheng, X. (1995), “All opportunity-triggered replacement policy for multiple-unit systems”, IEEE
Transactions on Reliability, Vol. 44 No. 4, pp. 648-652.
Zheng, X. and Fard, N. (1991), “A maintenance policy for repairable systems based on
opportunistic failure-rate tolerance”, IEEE Transactions on Reliability, Vol. 40 No. 2,
pp. 237-244.
Zheng, X. and Fard, N. (1992), “Hazard-rate tolerance method for an opportunistic-replacement
policy”, IEEE Transactions on Reliability, Vol. 41 No. 1, pp. 13-20.
Zhou, X., Xi, L. and Lee, J. (2006), “A dynamic opportunistic maintenance policy for continuously
monitored systems”, Journal of Quality in Maintenance Engineering, Vol. 12 No. 3,
pp. 294-305.
Downloaded by UNIVERSITY OF WESTERN SYDNEY At 00:21 08 February 2015 (PT)
Zhou, X., Xi, L. and Lee, J. (2009), “Opportunistic preventive maintenance scheduling for
a multi-unit series system based on dynamic programming”, International Journal of
Production Economics, Vol. 118 No. 2, pp. 361-366.
Zhou, Y., Zhang, Z. and Ma, L. (2012), “Maintenance optimisation of a series-parallel system
with multi-state components considering economic dependence”, Quality, Reliability, Risk,
Maintenance, and Safety Engineering (ICQR2MSE), International Conference on IEEE,
pp. 427-431.
Further reading
Bonarini, A. and Sassaroli, P. (1993), “Opportunistic multimodel-based diagnosis: framing all the
knowledge 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 on
a 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 with
Management from the Universiti Sains Malaysia in 2007, and completed her MSc in the School
of Mechanical Engineering at the University Science Malaysia (USM) in 2010. She is currently
pursuing a PhD at USM. Her research interests include industrial engineering, manufacturing
systems 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, and
a PhD from the University of Birmingham in 2003. He is currently an Associate Professor in
the School Mechanical Engineering (under the manufacturing engineering with management
programme), Universiti Sains Malaysia. He has various past experiences with manufacturing
industries from heavy to electronics industries especially in the field of industrial engineering,
manufacturing processes and product design. He has more than 60 publications in reputed
international and national journals/conferences. His current research interests include simulation and
modelling of manufacturing systems, production planning and control, maintenance management
and application of artificial intelligence techniques in manufacturing. Dr Shahrul Kamaruddin is the
corresponding author and can be contacted at: meshah@eng.usm.my
To purchase reprints of this article please e-mail: reprints@emeraldinsight.com
Or visit our web site for further details: www.emeraldinsight.com/reprints
Opportunistic
maintenance
121
This article has been cited by:
Downloaded by UNIVERSITY OF WESTERN SYDNEY At 00:21 08 February 2015 (PT)
1. Binghai Zhou, Jiadi Yu, Jianyi Shao, Damien Trentesaux, Salih Duffuaa, M Bendaya. 2015. Bottleneckbased opportunistic maintenance model for series production systems. Journal of Quality in Maintenance
Engineering 21:1. . [Abstract] [PDF]
Journal of Quality in Maintenance Engineering
Improvement of industrial performance with TPM implementation
Teonas Bartz Julio Cezar Mairesse Siluk Ana Paula Barth Bartz
Downloaded by UNIVERSITY OF WESTERN SYDNEY At 00:18 08 February 2015 (PT)
Article information:
To cite this document:
Teonas Bartz Julio Cezar Mairesse Siluk Ana Paula Barth Bartz , (2014),"Improvement of industrial
performance with TPM implementation", Journal of Quality in Maintenance Engineering, Vol. 20 Iss 1 pp. 2 19
Permanent link to this document:
http://dx.doi.org/10.1108/JQME-07-2012-0025
Downloaded on: 08 February 2015, At: 00:18 (PT)
References: this document contains references to 36 other documents.
To copy this document: permissions@emeraldinsight.com
The fulltext of this document has been downloaded 420 times since 2014*
Users who downloaded this article also downloaded:
Elaine Aspinwall, Maged Elgharib, (2013),"TPM implementation in large and medium size
organisations", Journal of Manufacturing Technology Management, Vol. 24 Iss 5 pp. 688-710 http://
dx.doi.org/10.1108/17410381311327972
Abhishek Jain, Rajbir Bhatti, Harwinder Singh, (2014),"Total productive maintenance (TPM) implementation
practice: A literature review and directions", International Journal of Lean Six Sigma, Vol. 5 Iss 3 pp.
293-323 http://dx.doi.org/10.1108/IJLSS-06-2013-0032
Jitendra Kumar, Vimlesh Kumar Soni, Geeta Agnihotri, (2014),"Impact of TPM implementation on Indian
manufacturing industry", International Journal of Productivity and Performance Management, Vol. 63 Iss 1
pp. 44-56 http://dx.doi.org/10.1108/IJPPM-06-2012-0051
Access to this document was granted through an Emerald subscription provided by 448547 []
For Authors
If you would like to write for this, or any other Emerald publication, then please use our Emerald for
Authors service information about how to choose which publication to write for and submission guidelines
are available for all. Please visit www.emeraldinsight.com/authors for more information.
About Emerald www.emeraldinsight.com
Emerald is a global publisher linking research and practice to the benefit of society. The company
manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as
providing an extensive range of online products and additional customer resources and services.
Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee
on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive
preservation.
*Related content and download information correct at time of download.
The current issue and full text archive of this journal is available at
www.emeraldinsight.com/1355-2511.htm
JQME
20,1
Improvement of industrial
performance with TPM
implementation
Downloaded by UNIVERSITY OF WESTERN SYDNEY At 00:18 08 February 2015 (PT)
2
Received 19 July 2012
Revised 21 July 2013
Accepted 12 October 2013
Teonas Bartz, Julio Cezar Mairesse Siluk and
Ana Paula Barth Bartz
Federal University of Santa Maria – UFSM, Santa Maria, Brazil
Abstract
Purpose – The purpose of this paper is to show the implementation of a maintenance management
model based on total productive maintenance (TPM) in a production line of a metallurgical company,
with high-precision equipment requiring effective maintenance to maintain the quality of the
production process.
Design/methodology/approach – Has been proposed a model for conducting the activities,
emphasizing the training activities of the teams involved in the implementation, collection and
analysis of industrial performance indicators from a year before the implementation of TPM.
The development followed the timetable of activities and the results of these performance indicators
were collected again after the application of the model.
Findings – It observed that after the implementation of TPM, and the results of these performance
indicators were collected again after the application of the model. Thus, it is concluded that the TPM
assists in improving industrial performance and competitiveness of the production line studied.
Originality/value – The angle, from which the paper approaches the TPM problem, is original for
the studied company and shows positives results. It allows the company to apply the model in their
others production lines and factories to achieve an improvement in industrial performance and
competitiveness.
Keywords Competitiveness, Total productive maintenance, Maintenance management,
Performance indicators, Performance evaluation
Paper type Case study
Journal of Quality in Maintenance
Engineering
Vol. 20 No. 1, 2014
pp. 2-19
r Emerald Group Publishing Limited
1355-2511
DOI 10.1108/JQME-07-2012-0025
1. Introduction
Competition between organizations is ever-increasing. According to Phusavat and
Kanchana (2008), some factors considered as priorities to achieve competitiveness are:
quality, reliability, flexibility, ability to meet demand and delivery. All of these factors
depend on how each organization manages its processes. Furthermore, they all involve
production planning and control, personnel management and acquisition of materials
in addition to maintenance management.
Lollar et al. (2010) emphasize that competitiveness can be achieved by two distinct
methods: if the organization has a competitive advantage in cost or by differentiation,
whether it is of product, service or both. Also, they point out that in addition to
evaluating organizational performance, efficiency and the effectiveness of its activities,
each organization must also work to increase the value of the activities involved in
the process.
Singh et al. (2008) suggest that organizational performance is directly related to the
development of research and improvements in methods and procedures, acquisition of
assets and in the application of a policy of continuous improvement, innovation and
organizational change. Searching this continuous improvement, Yang et al. (2010) show
that models for achieving them, such as total quality management and just-in-time,
Downloaded by UNIVERSITY OF WESTERN SYDNEY At 00:18 08 February 2015 (PT)
coupled with the collaboration of suppliers may favor the development of these
improvements.
The maintenance sector is undergoing a change of concept for entrepreneurs.
Previously seen as a support sector, it is now treated as strategic since to reach the
expected results the machinery and equipment must meet the expectations and plans
made. Maintenance, with a strategic vision for a competitive environment, requires to
look beyond the recovery of damages, the quality and productivity; interfering with
organizational outcomes (Otani and Machado, 2008). These components are related to a
modern management system and an efficient, economical and profitable production
system (Khan and Darrab, 2010).
For a production system to act without waste and be profitable, the maintenance
system should operate effectively. This becomes necessary because the large investments
made in organizations should generate profits, and the best way to maintain the
operation of equipment, is by managing its maintenance. Regarding TPM as a strategic
management model of maintenance, all areas of the company will work following this
model. TPM integrates actions, transforming the traditional models of management
through the continuous search for: eliminating waste, improving people, improvement
of production processes, quality and service. With this transformation will come an
evolution of the company in search of higher competitiveness.
Therefore, using a data-driven approach and presented by Verron et al. (2010), the
importance of maintenance management in achieving organizational goals was shown.
Peres and Lima (2008) point out that it is also necessary to take a systemic view of the
company so that decision making is quick and correct.
Having this in mind techniques and maintenance strategies are used with focus
on the prevention of equipment failures, and increasing equipment availability for
operation and reliability when in use. Among these strategies and techniques,
preventive maintenance, predictive maintenance, total productive maintenance (TPM)
and more recently, the reliability-centered maintenance stand out (Khalil et al., 2009).
Traditional methods of maintenance planning only partially meet businesses’
needs. Corrective maintenance should be employed in cases where preventive and
predictive maintenance becomes costly, but with it comes the risk of downtime
and unexpected drop in production. For Karim et al. (2009) this risk made it necessary
to develop different methods of maintenance in recent years in order to manage
the complexity generated by the technological evolution of equipment. Currently one of
the most accepted method is reliability-centered maintenance, a means used to ensure
confidence in equipment (Khanlari et al., 2008).
In that sense, the adoption of a model of strategic maintenance management such as
TPM has been increasingly accepted in industrial organizations. On the order hand, it
is clear that many companies do not deploy this model in the right way because the
correct application of a maintenance management model (and to observe and obtain
the results) takes an estimate of five years. What is observed is that companies try to
apply TPM and abandon the program because they do not see immediate results.
The aim of this paper is to present the results of the implementation of a TPM-based
maintenance management model in a production line in order to improve the
performance and competitiveness of a metalworking company.
2. Maintenance
Maintenance of industrial equipment has been a growing topic of discussion in the
business environment, as the purchase of new equipment is becoming increasingly
TPM
implementation
3
JQME
20,1
Downloaded by UNIVERSITY OF WESTERN SYDNEY At 00:18 08 February 2015 (PT)
4
unviable, mainly due to the high cost of new technologies. Therefore, equipment care
has become very important. In principle, maintenance simply consists of keeping
equipment in working condition. Currently, maintenance consists in involving the
person who works with the equipment in order to predict when it might suffer a
breakdown and make the necessary improvements to ensure the best course of action.
Moubray (2000) presents a maintenance evolution in four phases. The first generation,
from 1940 to 1950 includes corrective maintenance. The second generation, from 1950 to
1980 basically works with preventive maintenance. The third generation, from 1980 to
2000, contains the TPM, the reliability-centered maintenance and predictive maintenance.
For this author, we are the fourth generation since 2000, working with proactive
maintenance, which seeks to adapt to the strategic goals of the company.
Maintenance can be seen as a necessary evil, as a partially integral part of strategic
planning for business productivity, and also as fundamental to equipment availability.
From simple production service to the part which is essential in ensuring customer
service by improving the reliability of equipment and processes. These are ...
Purchase answer to see full
attachment