A proactive decision making framework for condition-based maintenance
| DOI | https://doi.org/10.1108/IMDS-03-2015-0071 |
| Published date | 10 August 2015 |
| Date | 10 August 2015 |
| Pages | 1225-1250 |
| Author | Alexandros Bousdekis,Babis Magoutas,Dimitris Apostolou,Gregoris Mentzas |
| Subject Matter | Information & knowledge management,Information systems,Data management systems |
A proactive decision making
framework for condition-based
maintenance
Alexandros Bousdekis and Babis Magoutas
Information Management Unit, Institute of Communications and
Computer Systems (ICCS), National Technical University of Athens,
Athens, Greece
Dimitris Apostolou
Department of Informatics, University of Piraeus, Piraeus, Greece, and
Gregoris Mentzas
Information Management Unit, Institute of Communications and
Computer Systems, National Technical University of Athens, Athens, Greece
Abstract
Purpose –The purpose of this paper is to perform an extensive literature review in the area of
decision making for condition-based maintenance (CBM) and identify possibilities for proactive online
recommendations by considering real-time sensor data. Based on these, the paper aims at proposing a
framework for proactive decision making in the context of CBM.
Design/methodology/approach –Starting with the manufacturing challenges and the main
principles of maintenance, the paper reviews the main frameworks and concepts regarding CBM that
have been proposed in the literature. Moreover, the terms of e-maintenance, proactivity and decision
making are analysed and their potential relevance to CBM is identified. Then, an extensive literature
review of methods and techniques for the various steps of CBM is provided, especially for prognosis
and decision support. Based on these, limitations and gaps are identified and a framework for proactive
decision making in the context of CBM is proposed.
Findings –In the proposedframeworkfor proactive decisionmaking, the CBM conceptis enriched inthe
sense that it is structured into two components: the information space and the decision space. Moreover, it
is extended in a way that decision space is further analyzed according to the types of recommendations
that can be provided. Moreover, possible inputs and outputs of each step areidentified.
Practical implications –The paper provides a framework for CBM representing the steps that need
to be followed for proactive recommendations as well as the types of recommendations that can
be given. The framework can be used by maintenance management of a company in order to conduct
CBM by utilizing real-time sensor data depending on the type of decision required.
Originality/value –The results of the work presented in this paper form the basis for the development
and implementation of proactive Decision Support System (DSS) in thecontext of maintenance.
Keywords Decision making, Condition-based maintenance, E-maintenance, Proactivity,
Real-time data, Recommendations
Paper type Research paper
1. Introduction
In manufacturing, equipment maintenance is a significant contributor to the total
company’s cost, so having an optimal maintenance policy in terms of cost, equipment
downtime and quality is an important efficiency enabler (Waeyenbergh and Pintelon, Industrial Management & Data
Systems
Vol. 115 No. 7, 2015
pp. 1225-1250
©Emerald Group Publis hing Limited
0263-5577
DOI 10.1108/IMDS-03-2015-0071
Received9March2015
Revised 19 May 2015
Accepted 3 June 2015
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/0263-5577.htm
This work is partly funded by the European Commission project FP7 STREP ProaSense “The
Proactive Sensing Enterprise”(612329).
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Proactive
decision
making
framework
2004; Garg and Deshmukh, 2006). Maintenance is related to all the processes of
a manufacturing firm and focusses not only on avoiding the equipment breakdown but
also on improving business performance, for example, in terms of productivity,
elimination of malfunctions, etc. Various maintenance policies have been examined in
both the academic and industrial realms and a multitude of maintenance strategies
have been recommended in an effort to develop a holistic approach for maintenance
management, which supports both reactive and proactive support maintenance actions
(Waeyenbergh and Pintelon, 2004).
“Proactivity”in the context of information systems refers to the ability to avoid or
eliminate undesired future events or exploit future opportunities by implementing
prediction and automated decision making technologies (Engel and Etzion, 2011).
Proactivity is leveraged with novel information technologies that enable decision
making and support human actions before a predicted critical event occurs.
Application domains that can take advantage of such technologies include transport,
fraud management and maintenance (Artikis et al., 2014; Magoutas et al., 2014).
In manufacturing, sensors have the capability of measuring a multitude of parameters
frequently and collecting plenty of data. Analysis of Big Data, both historical and real-
time, can facilitate predictions on the basis of which proactive maintenance decision
making can be performed.
E-maintenance is related to the notion of proactivity because it supports the
transmission of the enterprise from “fail and fix”to “predict and prevent”concept while,
at the same time, maintenance is addressed as an enterprise process, integrated with
both internal and external business processes (Macchi et al., 2014), for improving
business performance (Lee et al., 2006; Iung et al., 2009). E-maintenance assumes that
data should be available to all enterprise components and actors with the aid of ICT
at the right time and place in order to make optimal maintenance decisions based on
underlying predictions (Iung et al., 2009).
Generally, the need for a business turning from reactive to proactive is increasing.
Proactive enterprise leads to increased situation awareness capabilities even ahead of
time. This will lead to a new class of enterprise systems, proactive and resilient
enterprises, that will be continuously aware of that what “might happen”in the relevant
business context and optimize their behavior to achieve what “should be the best
action”even during stress and balancing on demanding margins. Proactive enterprise
systems will be able to suggest early on to the decision makers the most appropriate
process adjustments to avoid singular system behavior and optimize its perform ance
(Magoutas et al., 2014).
Although during the last years there have been some efforts toward increasing the
level of proactivity in maintenance decision making, existing approaches are still under
development and suffer from some limitations. The degree of proactivity is usually
low and decisions are narrowed to recommendations about the maintenance schedule,
i.e., the sequence of maintenance actions, the maintenance strategy or, more rarely, the
optimal time of applying a predefined action. In other words, optimization is done for
one criterion at a time, while recommendations involve a general decision. Moreover,
contributions are not presented as part of a wider framework that can support their
integration in manufacturing processes. In addition, the vast majority of prognostic
models are validated within a laboratory environment by doing experiments and not in
industrial settings. This paper aims to review existing works in maintenance decision
making methods and synthesize a generic framework that can support
the development of proactive decision support systems (DSS) that include
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