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Review
A Review of Data-Driven Decision-Making Methods for
Industry 4.0 Maintenance Applications
Alexandros Bousdekis , Katerina Lepenioti, Dimitris Apostolou * and Gregoris Mentzas
Citation: Bousdekis, A.; Lepenioti, K.;
Apostolou, D.; Mentzas, G. A Review
of Data-Driven Decision-Making
Methods for Industry 4.0 Maintenance
Applications. Electronics 2021,10, 828.
https://doi.org/10.3390/electronics
10070828
Academic Editor: Carlos A. Iglesias
Received: 9 March 2021
Accepted: 25 March 2021
Published: 31 March 2021
Publisher’s Note: MDPI stays neutral
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Copyright: © 2021 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
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Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
Information Management Unit (IMU), Institute of Communication and Computer Systems (ICCS),
National Technical University of Athens (NTUA), 157-80 Athens, Greece; albous@mail.ntua.gr (A.B.);
klepenioti@mail.ntua.gr (K.L.); gmentzas@mail.ntua.gr (G.M.)
*Correspondence: dapost@unipi.gr
Abstract:
Decision-making for manufacturing and maintenance operations is benefiting from the
advanced sensor infrastructure of Industry 4.0, enabling the use of algorithms that analyze data,
predict emerging situations, and recommend mitigating actions. The current paper reviews the
literature on data-driven decision-making in maintenance and outlines directions for future re-
search towards data-driven decision-making for Industry 4.0 maintenance applications. The main
research directions include the coupling of decision-making with augmented reality for seamless
interfacing that combines the real and virtual worlds of manufacturing operators; methods and
techniques for addressing uncertainty of data, in lieu of emerging Internet of Things (IoT) devices;
integration of maintenance decision-making with other operations such as scheduling and planning;
utilization of the cloud continuum for optimal deployment of decision-making services; capability of
decision-making methods to cope with big data; incorporation of advanced security mechanisms;
and coupling decision-making with simulation software, autonomous robots, and other additive
manufacturing initiatives.
Keywords:
Internet of Things; intelligent decision-making; data analytics; big data; predictive main-
tenance
1. Introduction
The current trend of automation and data exchange in manufacturing is enabled by
emerging technological advancements including the Internet of Things (IoT), cloud comput-
ing, and cyber-physical systems. This trend is often cited as “Industry 4.0”, “smart manu-
facturing”, and “digital factory” [
1
]. The large volume of data generated by manufacturing
automation and sophisticated machines and sensors have been described in various reviews
of industrial communication and data management systems, e.g., [
2
,
3
]. Predictive main-
tenance in particular is gaining a crucial role in cost reduction and business performance
improvement [
4
]. Predictive maintenance utilizes heterogeneous data sources for detecting
abnormal behaviors of equipment (diagnosis), predicting future failure modes (prognosis),
and supporting decisions ahead of time (proactive decision-making) [5].
The need to support data-driven decision-making in Industry 4.0 has leveraged the
development of new methods and algorithms aiming to support engineers in making
optimal decisions about maintenance and operational actions [
6
,
7
]. To the best of our
knowledge, this is the first literature review about data-driven decision-making algorithms
for manufacturing maintenance operations. This paper starts by surveying prominent
decision-making approaches for manufacturing maintenance operations, a well-studied
application area, and analyses decision-making algorithms that are triggered by real-time,
data-driven analytics. The analysis of the state of the art leads to a synthesis of research
challenges and the definition of a research agenda for data-driven decision-making for
Industry 4.0 maintenance applications.
Electronics 2021,10, 828. https://doi.org/10.3390/electronics10070828 https://www.mdpi.com/journal/electronics
Electronics 2021,10, 828 2 of 20
2. Scope of the Literature Review
In this section, we describe the scope of our literature review. First, we briefly dis-
cuss the role of decision-making in maintenance according to the Potential Failure and
Functional Failure (P-F) interval and the recent technological advancements (
Section 2.1
).
We also present other literature reviews related to decision-making algorithms in main-
tenance along with their contributions and limitations and we identify the need for the
current literature review (Section 2.2). Finally, we present our methodology for the literature
review (Section 2.3).
2.1. The Role of Decision-Making in Manufacturing Maintenance
Methods used for predictive maintenance can be classified into three categories [
8
]:
(i) model-based, relying on the physical models of the equipment operation and the
manufacturing process; (ii) knowledge-based, relying on expert knowledge and being
addressed by knowledge management systems; and (iii) data-driven, relying on data
analytics and machine learning algorithms. In this work, we focus on the data-driven
methods for maintenance decision-making.
Condition monitoring, i.e., the process of monitoring the condition in order to identify
a significant change that is indicative of a developing fault [
9
], is a major component of
predictive maintenance [
10
]. During the last years, due to the emergence of Industry 4.0,
condition monitoring techniques have evolved from visual inspections and manual analysis
of datasets to high-frequency sensors generating real-time big data on several parameters
such as vibration, temperature, and thermography. On the basis of these data, one can apply
advanced data analytics techniques in order to handle the uncertainty due to the stochastic
degradation process as well as the uncertainty in prognostic output and to support decision-
making under time constraints. Decision-making in predictive maintenance indicates the
phase that is triggered by data-driven, (near) real-time predictions (e.g., about future failure
modes) in order to generate proactive recommendations about maintenance actions and
plans that eliminate or mitigate the impact of the predicted failure.
An important and well-established principle of maintenance is the P-F curve, which is
shown in Figure 1. The P-F curve indicates how a part of equipment starts being degraded
to the point at which the forthcoming failure can be predicted (the potential failure point
“P”). Thereafter, if it is not predicted and no suitable action is taken, it continues to
deteriorate—usually at an accelerating rate—until it reaches the point of functional failure
(Point “F”)—this is known as the P-F interval [
11
]. The P-F interval allows for actions to be
taken so as to avoid the forthcoming failure or provide the necessary remedies [8].
Electronics 2021, 10, x FOR PEER REVIEW 2 of 20
research challenges and the definition of a research agenda for data-driven decision-mak-
ing for Industry 4.0 maintenance applications.
2. Scope of the Literature Review
In this section, we describe the scope of our literature review. First, we briefly discuss
the role of decision-making in maintenance according to the Potential Failure and Func-
tional Failure (P-F) interval and the recent technological advancements (Section 2.1). We
also present other literature reviews related to decision-making algorithms in mainte-
nance along with their contributions and limitations and we identify the need for the cur-
rent literature review (Section 2.2). Finally, we present our methodology for the literature
review (Section 2.3).
2.1. The Role of Decision-Making in Manufacturing Maintenance
Methods used for predictive maintenance can be classified into three categories [8]:
(i) model-based, relying on the physical models of the equipment operation and the man-
ufacturing process; (ii) knowledge-based, relying on expert knowledge and being ad-
dressed by knowledge management systems; and (iii) data-driven, relying on data ana-
lytics and machine learning algorithms. In this work, we focus on the data-driven meth-
ods for maintenance decision-making.
Condition monitoring, i.e., the process of monitoring the condition in order to iden-
tify a significant change that is indicative of a developing fault [9], is a major component
of predictive maintenance [10]. During the last years, due to the emergence of Industry
4.0, condition monitoring techniques have evolved from visual inspections and manual
analysis of datasets to high-frequency sensors generating real-time big data on several
parameters such as vibration, temperature, and thermography. On the basis of these data,
one can apply advanced data analytics techniques in order to handle the uncertainty due
to the stochastic degradation process as well as the uncertainty in prognostic output and
to support decision-making under time constraints. Decision-making in predictive
maintenance indicates the phase that is triggered by data-driven, (near) real-time predic-
tions (e.g., about future failure modes) in order to generate proactive recommendations
about maintenance actions and plans that eliminate or mitigate the impact of the predicted
failure.
An important and well-established principle of maintenance is the P-F curve, which
is shown in Figure 1. The P-F curve indicates how a part of equipment starts being de-
graded to the point at which the forthcoming failure can be predicted (the potential failure
point “P”). Thereafter, if it is not predicted and no suitable action is taken, it continues to
deteriorate—usually at an accelerating rate—until it reaches the point of functional failure
(Point “F”)—this is known as the P-F interval [11]. The P-F interval allows for actions to
be taken so as to avoid the forthcoming failure or provide the necessary remedies [8].
Figure 1. Potential Failure and Functional Failure (P-F) curve.
Figure 1. Potential Failure and Functional Failure (P-F) curve.
2.2. Related Literature Reviews
Table 1shows related literature reviews published between 2011 and 2018 [
6
,
12
–
15
].
Each review has a specific contribution and focal point as outlined in the same table.
Still, existing reviews have the following limitations: (i) they do not distinguish between
Electronics 2021,10, 828 3 of 20
static and dynamic models, e.g., offline and real-time models; (ii) decision-making is not
necessarily triggered by data-driven analytics such as predictions; (iii) they focus on specific
categories of decision methods, such as optimization, and/or maintenance aspects, such
as maintenance policy; (iv) they are not matched to the Industry 4.0 pillars. Contrarily,
our twork focuses on the review and analysis of data-driven decision-making methods
that rely largely on data analytics and can enable emerging Industry 4.0 applications such
as predictive maintenance.
2.3. Methodology of the Literature Review
The review protocol followed is based on the one presented by Tranfield et al. [
16
]
and is shown in Table 2. We searched for research published after 2013, the year when the
Working Group Industry 4.0 published their first report. Note that out review does not cover
papers describing data-driven maintenance decision-making outside of the manufacturing
or production environment, such as for end products or components, e.g., [
17
,
18
], or urban
facilities equipment, e.g., [19].
Table 1. Related reviews.
Reference Title Contributions Limitations
[12]A literature review and future
perspectives on
maintenance optimization
•It reviews the literature on
methods and techniques for
maintenance optimization
models and associated
case studies.
•It reveals emerging trends
(e.g., towards the use of
simulation for
maintenance optimization).
•It includes only
optimization models.
•It deals with corrective,
preventive, and
predictive maintenance.
•It does not distinguish
between static and
dynamic models.
•
Optimization is not necessarily
executed on the basis
of predictions.
[13]Maintenance optimization
models: a review and analysis
•
It presents a review of existing
maintenance
optimization models.
•It outlines the effectiveness of
preventive and risk-based
maintenance with respect to
corrective maintenance.
•It concludes that most of the
literature addresses
optimization solutions in static
environments. Thus, it
outlines the importance of
dynamic models.
•It includes only
optimization models.
•It deals with corrective,
preventive, and
predictive maintenance.
•
Optimization is not necessarily
executed on the basis
of predictions.
[14]Joint maintenance and inventory
optimization systems: a review
•It reviews the literature on
joint maintenance and
inventory optimization
models for
non-repairable parts.
•It classifies literature on the
basis of certain criteria.
•It identifies several
research gaps.
•It includes only joint
maintenance and inventory
optimization models.
•It deals with various
maintenance strategies.
•It does not distinguish
between static and
dynamic models.
Electronics 2021,10, 828 4 of 20
Table 1. Cont.
Reference Title Contributions Limitations
[15]Maintenance policy
optimization—literature review
and directions
•It reviews research on optimal
maintenance policy selection
issues associated with
methods used as well as
the applications.
•Works are systematically
classified in terms of certainty,
uncertainty, and risk, as well
as in terms of approaches for
optimal maintenance policy.
•It identifies a large gap
between academic research
and industrial application.
•It deals with various
maintenance strategies.
•It does not distinguish
between static and
dynamic models.
•It includes limited
real-time models.
•Policy optimization is not
necessarily executed on the
basis of predictions.
[6]Industrial maintenance
decision-making:
a systematic literaturereview
•It identifies in literature the
application areas of industrial
maintenance decision-making,
the relationships between
these areas, and the ways in
which authors integrate tools
and methods.
•
It proposes a framework based
on information from the
literature, which summarizes
the origin and flow of
information used in the
development of models.
•It identifies trends towards
joint production and
maintenance optimization and
the utilization autonomous
equipment predictions.
•It deals with various
maintenance strategies.
•Decision-making is not
necessarily executed on the
basis of predictions.
•It does not distinguish
between static and
dynamic models.
Table 2. Review protocol.
Item Description
Keywords (data-driven OR real-time OR Internet of Things OR sensor) AND decision
making AND maintenance AND (Industry 4.0 OR smart factory OR ∅)
Inclusion criteria
Papers with decision-making algorithms for maintenance in Industry 4.0
Exclusion criteria Papers with conceptual approaches
Scientific databases ACM; ArXiv; Emerald; IEEE; ScienceDirect; SpringerLink
Time period January 2013 to March 2021
3. Analysis and Synthesis
In this section, we present the analysis and synthesis of the reviewed papers. The pro-
cess that we followed included the following steps:
1.
We structured the literature on data-driven decision-making algorithms for main-
tenance applications in three areas of contribution, as shown in Table 3. We also
assigned each reviewed paper to the respective area.
2.
We categorized the methods used in the reviewed papers and we matched them
with the areas of contribution as well as with the applications that are presented,
as shown in Figures 2and 3, respectively. It should be noted that some research works
incorporate combinations of decision methods, and therefore their references may
Electronics 2021,10, 828 5 of 20
belong to more than one category of methods. Similarly, in some cases, the proposed
methods are evaluated in more than one application. Since most of the papers also
include a prediction algorithm for deriving prognostic information to be fed into
the decision-making algorithm, in this research, we isolated and analyzed only the
decision-making algorithms.
3.
For each area of contribution, we further specified the categories of methods and
the applications of evaluation, while we also performed a thorough discussion and
synthesis of the respective papers (Sections 3.1–3.3).
Electronics 2021, 10, x FOR PEER REVIEW 5 of 20
The overview of the results of this section with respect to the research gaps in the
literature of data-driven decision-making algorithms is presented in Table 4. For each area
of contribution, we present the main gaps that have not been sufficiently fulfilled by the
literature.
Table 3. Areas of decision-making algorithms in manufacturing maintenance.
Area of Contribution Number of References References
Cost estimation and maintenance planning 21 [5,20–39]
Joint scheduling and planning 14 [40–54]
Multi-state and multi-component systems
optimization 12 [55–66]
Figure 2. The categories of methods for each area of contribution.
Figure 3. The applications for each area of contribution.
Figure 2. The categories of methods for each area of contribution.
Table 3. Areas of decision-making algorithms in manufacturing maintenance.
Area of Contribution Number of References References
Cost estimation and maintenance planning 21 [5,20–39]
Joint scheduling and planning 14 [40–54]
Multi-state and multi-component systems optimization 12 [55–66]
Electronics 2021, 10, x FOR PEER REVIEW 5 of 20
The overview of the results of this section with respect to the research gaps in the
literature of data-driven decision-making algorithms is presented in Table 4. For each area
of contribution, we present the main gaps that have not been sufficiently fulfilled by the
literature.
Table 3. Areas of decision-making algorithms in manufacturing maintenance.
Area of Contribution Number of References References
Cost estimation and maintenance planning 21 [5,20–39]
Joint scheduling and planning 14 [40–54]
Multi-state and multi-component systems
optimization 12 [55–66]
Figure 2. The categories of methods for each area of contribution.
Figure 3. The applications for each area of contribution.
Figure 3. The applications for each area of contribution.
Electronics 2021,10, 828 6 of 20
The overview of the results of this section with respect to the research gaps in the
literature of data-driven decision-making algorithms is presented in Table 4. For each
area of contribution, we present the main gaps that have not been sufficiently fulfilled by
the literature.
Table 4. The research gaps per area of contribution.
Area of Contribution Research Gaps
Cost estimation and maintenance planning
(1)
Take into account the current level of degradation instead
of predictions.
(2)
Process batches of data instead of real-time data.
(3)
Not utilize data-driven methods leading to
problem-specific algorithms.
(4)
Rely on the assumption of perfect maintenance or replacement,
without considering imperfect maintenance actions with
various degrees.
(5)
Not take into consideration other factors that affect overall
business performance.
Joint scheduling and planning
(1)
Process batches of data instead of real-time data.
(2)
Not utilize data-driven methods leading to
problem-specific algorithms.
(3)
Rely on the assumption of perfect maintenance or replacement,
without considering imperfect maintenance actions with
various degrees.
(4)
Include only (usually two) pre-defined objectives.
(5)
Assume single component manufacturing systems.
Multi-state and multi-component systems optimization
(1)
Process batches of data instead of real-time data.
(2)
Not utilize data-driven methods leading to
problem-specific algorithms.
(3)
Challenges in their implementation in a data-driven
manufacturing environment due to their complexity.
(4)
Not investigate structural dependence among components.
3.1. Cost Estimation and Maintenance Planning
The area of “cost estimation and maintenance planning” includes algorithms that are
able to recommend the most appropriate maintenance actions according to the company’s
policies and the estimations regarding the potential impacts and risks of the candidate
actions. In this way, the algorithms aim at minimizing long-term costs, thus enabling the
scheduling and planning of mitigating maintenance actions.
Table 5presents the methods that are used in the papers belonging to this area of
contribution, while Table 6presents the applications in the context of which the proposed
algorithms and methods are evaluated. As shown in Table 5, mathematical program-
ming/optimization methods are widely used, while there is also a considerable amount of
research on rule-based systems and heuristics as well as on Markov models. As shown in
Table 6, applications on rotating machinery and on energy have gathered research interest
the most.
The stochastic nature of the degradation process as well as the uncertainty existing
in prognostic algorithms leads to high uncertainty also in the decision-making process.
For this reason, many papers have tackled these challenges. Hong et al. [
20
] investigate the
influence of stochastic degradation on optimal maintenance decisions. Tang et al. [
21
] pro-
posed a method for an optimal maintenance policy on the basis of residual life estimation
for a slowly degrading system subject to soft failure and condition monitoring. The opti-
mization problem is formulated and solved in a semi-Markov decision process framework
in order to minimize the long-run expected average cost. A similar method is proposed for
tackling the presence of competing risks (soft and hard failure) in a degrading system [
22
].
Electronics 2021,10, 828 7 of 20
Xu et al. [
23
] proposed a method for optimized replacement decisions on the basis of remain-
ing useful life (RUL) estimation. Wan et al. [
24
,
25
] propose a collaborative maintenance
planning system that manages information and knowledge to support decision-making in
maintenance process planning. Chen et al. [
26
] propose a fuzzy logic system that allows
operators to optimize real-time operation and maintenance scheduling. Yildirim et al. [
26
]
present a maintenance framework that integrates the sensor-driven predictive maintenance
technologies with optimal maintenance scheduling models. Fouladirad et al. [
27
] propose
an approach for global optimization of costs for a gradually deteriorating system subject to
change in the deterioration rate. Si et al. [
28
] propose a method that considers not only the
expectation of the maintenance cost but also its variability. Lepenioti et al. [
29
] exploited
the recent advancements of machine learning for performing prescriptive analytics on the
basis of enterprise and operational data. To do this, the authors applied multi-objective re-
inforcement learning for providing decisions based upon the predictions of a deep learning
algorithm. Hoong et al. [
30
] propose an algorithm that self-learns an optimal maintenance
policy and provides actionable recommendation for each equipment with the use of deep
reinforcement learning.
Table 5. Methods for cost estimation and maintenance planning.
Category of Methods References
Mathematical programming/optimization [5,20,23,26,27,33,35–37]
Rule-based system and heuristics [5,20,24,31,37]
Markov and probabilistic models [21–33]
Simulation [23,31]
Fuzzy logic and inference [25]
Machine learning [29,30,32,34]
Table 6. Evaluation of cost estimation and maintenance planning.
Applications References
Laser equipment [21,22]
Rotating machinery [23,26,29–31]
Semiconductor manufacturing [32]
Oil drilling [33]
Energy [20,25,34,37]
Automotive [33]
Numerical example [5,27,35,36]
Several papers take into account the current level of degradation that is derived from
the analysis of the indicators measured by sensors instead of predictions about future
failure modes, while they usually process batches of data at pre-defined sampling times.
However, such approaches are not optimal when decisions are taken under time constraints
increasing the maintenance costs of the manufacturing company.
The majority of research works regarding cost estimation and maintenance plan-
ning do not adopt a data-driven approach for decision-making, and thus they are lim-
ited to specific problems, domains, and industries. That is why mathematical program-
ming/optimization and rule-based systems are the most common categories of methods.
Consequently, they cannot be transferred to a different production process with similar
challenges in a straightforward way. The most generic approaches aiming at tackling
the challenges of the smart factory are presented in [
31
–
34
]. Terkaj et al. [
31
] propose the
use of an ontology-based virtual factory in order to enable in situ simulation for assess-
ing the future impact of maintenance planning decisions. Susto et al. [
32
] proposed an
approach with dynamical decision rules in the context of a multiple classifier machine
learning methodology aiming at minimizing the expected cost. Bousdekis et al. [
33
] uti-
lized proactive event-driven computing in maintenance decision-making and represent the
Electronics 2021,10, 828 8 of 20
decision-making process instead of the physical process. Roccheta and Bellani [
34
] devel-
oped a reinforcement learning framework for the prognostic-based optimal management
of the maintenance of power grids. The authors of [
35
] investigated the use of the particle
swarm optimization algorithm to quantify the effect of RUL uncertainty on predictive
maintenance planning by integrating it with a random sampling-based strategy to select a
sequence that performs better for different values of RUL associated with different jobs.
The majority of the algorithms rely on the assumption of perfect maintenance or
replacement, without considering imperfect maintenance actions with various degrees.
Imperfect maintenance actions for deteriorating systems were considered only in three
papers. Do et al. [
5
] investigated the impacts of imperfect maintenance actions to propose
an adaptive maintenance policy that can help to select optimal (perfect or imperfect)
maintenance actions at each inspection time according to the remaining useful life (RUL)
estimation. Wu et al. [
36
] propose an optimization model in order to minimize the total
cost of imperfect degradation-based maintenance by determining an optimal interval of
condition monitoring and the degradation level after imperfect repairs. Bumblauskas
et al. [
37
] propose proactive event-driven decision methods based on Markov Decision
Process (MDP) and optimization models for recommending both perfect and imperfect
maintenance actions at optimal times with respect to costs.
3.2. Joint Scheduling and Planning
As presented in Section 3.1, most cost estimation and maintenance planning ap-
proaches do not address factors such as production costs, equipment availability, spare parts
inventory, and transportation costs, to name a few. This limitation in prior research paved
the way for newer joint scheduling and planning approaches aiming at optimizing jointly
manufacturing processes, including maintenance. For example, maintenance planning
should be coordinated with spare parts ordering and possible delays, as well as inventory
cost management. Table 7presents the methods that are used in the papers belonging in
this area of contribution, while Table 8presents the applications in the context of which
the proposed algorithms and methods are evaluated. As shown in Table 7, mathematical
programming/optimization methods are widely used (due to the fact that most of the
algorithms are limited to specific problems, domains, and industries), while there is also
a considerable amount of research on Markov models. As shown in Table 8, most of the
papers in this area evaluate their results in numerical examples, without dealing with case
studies. The evaluation of joint scheduling and planning methods in various application
domains and real-life case studies is still at its dawn.
Table 7. Methods for joint scheduling and planning.
Category of Methods References
Mathematical programming/optimization [38,41–44,46,49,50,52–54]
Rule-based system and heuristics [47–50]
Markov and probabilistic models [39,45,51,53]
Simulation [50]
Machine learning [46]
Table 8. Evaluation of joint scheduling and planning.
Applications References
Furniture industry [46]
Rotating machinery [47,48,52,54]
Oil drilling [39]
Hydraulic pumps [49]
Automotive [43]
Railway [41]
Numerical example [38,42,45,50,51,53]
Electronics 2021,10, 828 9 of 20
Similar to the previous area, most of the joint scheduling and planning algorithms
rely on the assumption of perfect maintenance or replacement. From the reviewed papers,
only [
38
,
39
] deal with decision methods capable of recommending both perfect and imper-
fect maintenance actions. Moreover, apart from [40], the rest of the reviewed papers were
involved with single-component systems.
Joint scheduling and planning deal with approaches that optimize predictive mainte-
nance decisions jointly with
•Spare parts inventory ([41–44])
Jiang et al. [
41
] investigated the impact of inventory deterioration on predictive
maintenance decision-making. Van Horenbeek and Pintelon [
42
] quantified the added
value of predictive information (RUL) in dynamic joint maintenance and inventory decision-
making. Bousdekis et al. [
43
] propose a proactive event-driven decision model for joint
predictive maintenance and spare parts inventory optimization in the frame of the e-
maintenance concept. Bousdekis et al. [
44
] propose a proactive event-driven model for joint
maintenance and logistics optimization for recommending optimal (perfect or imperfect)
maintenance actions and associated spare parts orders along with optimal timing.
•Production planning ([38,45–52])
Kouedeu et al. [
38
] examined the joint analysis of the optimal production and mainte-
nance planning policies for a manufacturing system subject to random failures and repairs.
Jafari and Makis [
45
] considered the joint optimization of economic manufacturing quantity
and maintenance policy. The problem was formulated and solved in the semi-Markov deci-
sion process framework in order to minimize the long-run expected average cost per unit
time. Cinus et al. [
46
] propose a decision support system that processes sensor data and Key
Performance Indicators (KPIs) using an artificial neural network (ANN)-based knowledge
system and integrates the maintenance actions within the weekly production schedule.
Mourtzis et al. [
47
,
48
] propose an augmented reality mobile application, interfaced with a
shop-floor scheduling tool, in order to enable the operator to decide on immediately calling
AR remote maintenance or scheduling maintenance tasks for later along with production
tasks. Liu et al. [
49
] present an integrated decision model that coordinates predictive main-
tenance decisions on the basis of prognostics information with single-machine scheduling
decisions so that the total expected cost is minimized.
Zhai et al. [50]
propose a decision
model for predictive maintenance and job shop scheduling for machine deterioration under
time-varying operational conditions. Nguyen et al. [
51
] present a dynamic model for pre-
dictive maintenance policy formulated on the basis of partially observable markov decision
processes. Mi et al. [
52
] proposed an integrated decision-making approach supported by
digital twin-driven cooperative awareness and interconnection framework.
•Product quality ([53,54])
Lee and Ni [
53
] present an approach for determining maintenance and product dis-
patching policies and the relationship between machine degradation and product quality.
They used a Markov decision process for long-term decision-making and integer pro-
gramming for short-term decision-making. Gu et al. [
54
] present an algorithm tackling
the co-effect between manufacturing system component reliability and product quality.
The algorithm results in the optimal maintenance strategy, obtained by optimizing the
quality cost, maintenance cost, and interruption cost simultaneously.
•Supplier selection ([44])
Bousdekis et al. [
44
] propose an approach for real-time, event-driven proactive sup-
plier selection with the use of Markowitz portfolio optimization theory, on the basis of the
optimal times for replacement and ordering the spare parts. The algorithm is triggered by
real-time, sensor-driven predictions about future failures and future spare parts prices.
Electronics 2021,10, 828 10 of 20
3.3. Multi-State and Multi-Component Systems Optimization
In many realistic problems, there are complex multi-component systems with un-
certainties in the system reliability structure [
55
], while the decision-making algorithms
for single-component systems are not usually suitable for multi-component systems [
6
].
To this end, “multi-state and multi-component systems optimization” includes algorithms
that allow for the identification of intermediate stages of their health state and that take
into account inter-component relations. These relations may be of a stochastic nature, im-
plying that the degradation of one component may probably impact the condition of others.
Relations may also be of economic nature, e.g., a positive economic relation implies that it
can be more economical if two components are jointly maintained than if done separately.
Table 9presents the methods that are used in the papers belonging in this area of
contribution, while Table 10 presents the applications in the context of which the proposed
algorithms and methods are evaluated. As shown in Table 9, mathematical program-
ming/optimization methods are widely used, while there is also a considerable amount
of research on rule-based systems and heuristics as well as on Markov and probabilistic
models. As shown in Table 10, most of the papers in this area evaluate their results in
numerical examples, without dealing with case studies.
Table 9. Methods for multi-state and multi-component systems optimization.
Category of Methods References
Mathematical programming/optimization [42,56–65]
Rule-based system and heuristics [51,57,58,61,62]
Markov and probabilistic models [55,56,58,60]
Statistics [61]
Simulation [60]
Table 10. Evaluation of multi-state and multi-component systems optimization.
Applications References
Energy [60]
Crane systems [64]
Inertial navigation system [57]
Suspension systems [55]
Unmanned aerial vehicles [65]
Numerical example [42,56,58,59,61–63]
The complexity of multi-state and multi-component systems optimization poses
challenges in their scalable and efficient implementation in the context of a data-driven
manufacturing environment. Due to this complexity, the reviewed papers usually use
algorithms taking advantage of more than one method [56–62], as shown in Table 9.
The dependencies in multi-state and multi-component systems deal with economic
dependence, stochastic dependence, or a combination of both. Structural dependence
among components was not investigated in the reviewed papers. Below, we describe the
reviewed research work for each category of dependence.
•Economic dependence
Le and Tan [
63
] proposed a multi-state strategy that combines both inspection and
continuous monitoring to reduce unnecessary thorough inspection and to improve the sys-
tem’s reliability. An optimal maintenance strategy was derived on the basis of an iterative
algorithm to minimize the mean long-run cost-rate. They assumed that the maintenance is
imperfect and the degradation is a continuous-time Markov process.
Zhou et al. [56]
pro-
posed a maintenance optimization method for a multi-state series-parallel system consider-
ing economic dependence and state-dependent inspection intervals. The objective function
is the average revenue per unit time calculated on the basis of the semi-regenerative theory
and the universal generating function (UGF).
Electronics 2021,10, 828 11 of 20
Xia et al. [
64
] proposed a method of multi-level scheduling in order to predict main-
tenance requirements according to machine degradation and maintenance opportunity.
A global-objective model is used to make the machine-level decision for availability-
effective and cost-effective maintenance intervals. Nguyen et al. [
59
] presented an approach
for multi-level decision-making for multi-component system with a complex structure.
They utilized decision rules for optimally identifying a group of several components as
well as a cost-based group improvement factor, taking into account the predictive reliability
of the components, the economic dependencies, and the location of the components in
the system. Keizer et al. [
62
] proposed an algorithm for clustering predictive maintenance
tasks for systems with both economic dependencies and redundancy with the use of a
dynamic programming model.
•Stochastic dependence
Jiang et al. [
57
] proposed an approach for providing a predictive maintenance pol-
icy for a complex structure by considering not only components’ RUL, but also the
timing of when system reliability falls below a set threshold. Their approach balances
three factors: components importance to the system, risk degree, and detection difficulty.
Huynh et al. [58]
introduced a multi-level decision-making approach that puts forward
an n-component deteriorating system with a k-out-of-n structure. On the basis of the
degradation and failure model of the considered k-out-of- n system, the authors proposed
two opportunistic predictive maintenance strategies with different types of maintenance
decision-making. Lee and Pan [
55
] presented a predictive maintenance scheme for com-
plex systems by employing discrete time Markov chain models for modelling multiple
degradation processes of components and a Bayesian network (BN) model for predicting
system reliability.
•Combination of economic and stochastic dependence
Van Horenbeek and Pintelon [
42
] presented a dynamic maintenance policy for multi-
component systems that minimizes the long-term mean maintenance cost per unit time.
The ability of the maintenance policy to react to changing component deterioration and
dependencies within a multi-component system was quantified, and the results showed
significant cost savings. Azadeh et al. [
60
] proposed a model to evaluate the effectiveness
of maintenance in multi-component systems using two system performance indicators:
reliability and cost. To estimate the reliability and costs of the system, the authors developed
the proposed Markovian discrete-event simulation model.
Li et al. [
61
] focused on the stochastic dependence between components due to the
common environment modelled by Lévy copulas. Wang et al. [
65
] proposed an approach
for group maintenance of multi-level systems, in which the reliability of a system is assessed
using a Bayesian network (BN) of causes and effect as well as multi-objective programming
that is used to optimize plans for joint maintenance of units and components.
4. Research Agenda for Data-Driven Decision-Making for Industry 4.0
Maintenance Applications
The specification and documentation of platform Industry 4.0 provide a common view
upon which many advancements in industrial technology are based [
66
]. In this section,
we investigate how the nine pillars of Industry 4.0 [
67
], shown in Figure 4, affect and
converge with data-driven decision-making for maintenance. The following subsections
describe our propositions for future research in the context of the Industry 4.0 pillars.
Table 11 shows the research agenda of data-driven decision-making algorithms in the
context of the Industry 4.0 pillars. For each pillar, we summarize the main propositions for
future research in the context of Industry 4.0.
Electronics 2021,10, 828 12 of 20
Electronics 2021, 10, x FOR PEER REVIEW 12 of 20
Figure 4. The nine pillars of Industry 4.0.
Table 11 shows the research agenda of data-driven decision-making algorithms in
the context of the Industry 4.0 pillars. For each pillar, we summarize the main propositions
for future research in the context of Industry 4.0.
Table 11. Research agenda of decision-making for Industry 4.0 maintenance applications.
Industry 4.0 Pillars Future Research Directions
Augmented reality
1. Interface with decision-making algorithms for maintenance appli-
cations.
2. Application to the shop-floor during the actual manufacturing op-
erations.
Internet of Things
1. Supporting the autonomy of networked manufacturing systems
and machines.
2. Eliminating the uncertainty in order to avoid implementing inap-
propriate autonomous maintenance actions.
3. Fast learning from the shop-floor, exploiting the large availability of
data sources.
System integration
1. Horizontal and vertical integration according to Industry 4.0 princi-
ples.
2. Effective interoperability taking into account RAMI 4.0, CPS archi-
tectures, and communication protocols.
3. Decision-making algorithms that take into account the whole con-
text of the manufacturing enterprise.
4. Human as an integral part of system integration.
Cloud computing
1. Alignment with the concept of cloud manufacturing.
2. Seamless and modular communication through cloud-based plat-
forms.
3. Communication protocols and standards for guiding the develop-
ment of future algorithms.
4. Addressing the challenges of reliability, availability, adaptability,
and safety.
Big data analytics
1. Maintenance decision-making can benefit from prescriptive analyt-
ics for big data.
2. Automated data-driven model building in order to represent the
decision-making process instead of the physical process.
3. Scalable and efficient algorithms for processing streams of failure
predictions and providing meaningful insights.
Figure 4. The nine pillars of Industry 4.0.
Table 11. Research agenda of decision-making for Industry 4.0 maintenance applications.
Industry 4.0 Pillars Future Research Directions
Augmented reality 1. Interface with decision-making algorithms for maintenance applications.
2. Application to the shop-floor during the actual manufacturing operations.
Internet of Things
1. Supporting the autonomy of networked manufacturing systems and machines.
2. Eliminating the uncertainty in order to avoid implementing inappropriate autonomous
maintenance actions.
3. Fast learning from the shop-floor, exploiting the large availability of data sources.
System integration
1. Horizontal and vertical integration according to Industry 4.0 principles.
2. Effective interoperability taking into account RAMI 4.0, CPS architectures, and
communication protocols.
3. Decision-making algorithms that take into account the whole context of the
manufacturing enterprise.
4. Human as an integral part of system integration.
Cloud computing
1. Alignment with the concept of cloud manufacturing.
2. Seamless and modular communication through cloud-based platforms.
3. Communication protocols and standards for guiding the development of future algorithms.
4. Addressing the challenges of reliability, availability, adaptability, and safety.
Big data analytics
1. Maintenance decision-making can benefit from prescriptive analytics for big data.
2. Automated data-driven model building in order to represent the decision-making process
instead of the physical process.
3. Scalable and efficient algorithms for processing streams of failure predictions and providing
meaningful insights.
4. Human feedback mechanisms aiming at improving the decision-making algorithms.
Cyber security 1. Encryption techniques, risk assessment methodologies, and cyber-attack detection and
response methods.
2. Modular integration of decision-making software services in a secure and reliable way.
Electronics 2021,10, 828 13 of 20
Table 11. Cont.
Industry 4.0 Pillars Future Research Directions
Additive manufacturing 1. Decision-making algorithms for manufacturing processes in the context of
additive manufacturing.
Autonomous robots
1. Consider robots as productions resources in decision-making algorithms.
2. Assign appropriate maintenance tasks to robots.
3. Decide which tasks will be assigned to robots and which ones to humans.
4. Uncertainty of decision-making is a significant challenge in automatic action implementation
through autonomous robots.
Simulation
1. Approaches for digital twins in predictive maintenance.
2. Decision-making algorithms for enhancing the capabilities of digital twins, e.g., by evaluating
different scenarios.
3. Information fusion methods incorporated in decision-making algorithms for fully exploiting
the available data and knowledge.
4.1. Augmented Reality
Augmented reality (AR) provides a seamless interface combining the real and virtual
world aiming at enhancing the collaboration between humans and smart environments [
47
].
With the advancement of portable devices’ processing and visualization capabilities, AR is
evolving into an intuitive user interface for displaying information and interacting with
machines and services in smart factories [48,68,69].
Although the potential of AR in maintenance operations has been outlined in the liter-
ature [
70
–
72
], its use as an advanced user interface with decision-making algorithms (e.g.,
recommending maintenance personnel which mitigating actions to implement and guiding
them in how to implement them, aiming at eliminating the impact of a predicted failure)
has not been investigated. We found only one paper [
47
] making use of AR for supporting
decisions. Moreover, AR has been used for training purposes; however, its application
on the shop-floor during the actual manufacturing and maintenance operations remains
a challenge.
4.2. Internet of Things
Internet of Things provides a dynamic global network infrastructure with self-configuring
capabilities based on standard and interoperable communication protocols where physical
and virtual “things” are interconnected and integrated into the information network [
73
].
In the manufacturing context, the value chain should be intelligent, agile, and networked
by integrating physical objects, human factors, intelligent machines, smart sensors, the pro-
duction process, and production lines together [
67
]. Since more and more physical objects
are connected to the manufacturing network and high-speed transactional data and infor-
mation is generated [74], scalability is a major challenge in Industry 4.0.
The evolutionary process will lead to networked manufacturing systems with a
high degree of autonomy as well as self-optimization capabilities [
75
]. They will be
organized in a decentralized manner, increasing robustness and adaptability [
75
]. Therefore,
the increasing availability of sensors and actuators will result in the need for decision-
making algorithms capable of supporting the autonomy of networked manufacturing
systems. On the other hand, the uncertainty existing in decision-making algorithms for
maintenance increases the risk of implementing inappropriate autonomous maintenance
actions. To this end, methods and techniques for eliminating the uncertainty and for fast
learning from the shop-floor are of utmost importance.
4.3. System Integration
System integration increases the value to a system by creating new functionalities
through the combination of sub-systems and software applications. The paradigm of Indus-
Electronics 2021,10, 828 14 of 20
try 4.0 is essentially outlined by three dimensions of integration: (a) horizontal integration
across the entire value creation network, (b) vertical integration and networked manufac-
turing systems, and (c) end-to-end engineering across the entire product life cycle [
67
].
The full digital integration and automation of manufacturing processes in the vertical and
horizontal dimension also imply automation of communication and cooperation, especially
along standardized processes.
The reviewed papers of the “joint scheduling and planning” area aim at integrating
operational with maintenance decision-making in manufacturing processes. However,
system integration aspects of decision-making algorithms in terms of interoperability,
service communication, modularity, scalability, and flexibility are not discussed. On the
other hand, architectures that include decision-making functionalities for maintenance
have started to emerge [
39
,
76
–
80
]. Following the trend of diagnostic and prognostic
algorithms, decision-making algorithms need to be integrated horizontally and vertically
according to the Industry 4.0 principles. In this way, the algorithms will be able to take
into account the whole context of the environment (e.g., production plan, supply chain,
inventory) by communicating effectively and interchanging data and information with
other manufacturing operations. RAMI 4.0 and CPS architectures are significant enablers
towards this direction. Finally, integration deals also with the human as an integral part of
the manufacturing environment in the sense that there is a cognitive interaction between
the human and the system. To this end, the human cyber physical system (H-CPS) concept
has arisen [
81
], which paves the way for the use of emerging technologies implementing
human–machine symbiosis.
4.4. Cloud Computing
The use of cloud-based architectures and technologies is strongly related to effective
systems integration, e.g., with the use of RESTful Application Programming Interfaces
(APIs) for accessing services provided by cloud computing vendors. Cloud consumers use
APIs as software interfaces to connect and consume resources in various ways, although the
optimal or contemporary route is to use a RESTful protocol-based API. To this end, the ser-
vices implementing data-driven decision-making functionalities should allow seamless
and modular communication through cloud-based platforms. This direction needs to be
developed in alignment with the concept of cloud manufacturing.
Cloud manufacturing is a smart networked manufacturing model that incorporates
cloud computing, aiming at meeting the growing demands for broader global cooperation,
knowledge-intensive innovation, and increased market-response agility [
82
]. Apart from
the technological perspective, this will lead to decision-making algorithms facilitating their
implementation in a cloud-based computational environment, but also to domain-specific
communication protocols and standards for guiding the development of future algorithms
requiring high computational power.
On the other hand, this research direction requires addressing the challenges of
reliability, availability, adaptability, and safety on machines and processes across spatial
boundaries and disparate data sources [
83
]. In addition, it needs to tackle the privacy and
security aspects on the cloud. Therefore, there is the need for robust algorithms that can
accurately support decision-making in the presence of uncertainty as well as methods to
quantify their confidence in a real-time and computationally demanding environment.
4.5. Big Data Analytics
The collection and processing of data from many different sources have significantly
enabled the information that is available to engineers and operators in manufacturing
facilities. Data management and distribution in the big data environment is critical for
achieving self-aware and self-learning machines and for supporting manufacturing deci-
sions. Data analytics is categorized into three main stages characterized by different levels
of difficulty, value, and intelligence [
84
]: (i) descriptive analytics, answering the questions
“What has happened?”, “Why did it happen?”, but also “What is happening now?”; (ii) pre-
Electronics 2021,10, 828 15 of 20
dictive analytics, answering the questions “What will happen?” and “Why will it happen?”
in the future; and (iii) prescriptive analytics, answering the questions “What should I do?”
and “Why should I do it?”.
Although big data analytics has been extensively used for real-time diagnosis and
prognosis in the context of predictive maintenance, their utilization in decision-making
algorithms is still at its early stages. Since the research interest has been gathered to descrip-
tive and predictive analytics [
85
], the immaturity of prescriptive analytics has inevitably
affected the predictive maintenance decision-making algorithms as well. As we presented
in our literature review, the vast majority of the existing predictive maintenance decision-
making algorithms rely on traditional mathematical programming methods. On the other
hand, prescriptive analytics has been realized mainly with domain-specific expert systems
or optimization models [86].
However, there is the need for data-driven generic decision-making algorithms rep-
resenting the decision-making process instead of the physical process. Building physical
models of industrial assets and processes is a complex and laborious task that does not
necessary exploit the knowledge that can be discovered from data, such as failure patterns
or patterns of causes and effects. Moreover, knowledge-based decision-making methods
are rather static, not capable of self-adaptation on the basis of emerging data. However,
dynamic shop-floor conditions require real-time decision-making, which requires both
advanced data infrastructures, e.g., distributed cloud computing for processing and storing
data, as well as new functionalities, e.g., computationally efficient, probabilistic algorithms
tailored for streaming data and capable of handling uncertainty. Finally, feedback mech-
anisms should be employed in order to adapt decision-making on the basis of changing
conditions such as new operating constraints. Although feedback mechanisms for diag-
nostic and prognostic algorithms have been well perceived, mechanisms for tracking the
recommended actions are still few in number, e.g., [87].
4.6. Cyber Security
With the increased connectivity and use of standard communications protocols that
come with Industry 4.0, the need to protect critical industrial systems and manufacturing
lines from cyber security threats increases significantly. As a result, secure, reliable com-
munications as well as sophisticated identity and access management of machines and
users on the basis of a cloud infrastructure are essential [
67
]. Existing technologies may not
be sufficient for industrial applications that have their own safety and security rules and
requirements [
74
]. With respect to decision-making algorithms for maintenance, these cy-
bersecurity techniques and algorithms will enable the efficient and modular integration of
decision-making software services with existing diagnostic and prognostic information
systems in a secure and reliable way.
4.7. Additive Manufacturing
Companies have just begun to adopt additive manufacturing, such as 3-D printing,
which they use mostly to prototype and produce individual components. With Industry
4.0, additive manufacturing methods will be widely used to produce faster and in a more
cost-effective way small batches of customized products that offer construction advantages,
such as complex, lightweight designs [
88
]. To this end, a maintenance solution should be
to deal with the specific requirements of such products and their respective manufactur-
ing processes. Decision-making algorithms in this context will deal with laser repairing
processes, taking into account the contextual background of additive manufacturing.
4.8. Autonomous Robots
Autonomous robots are beneficial for manufacturing processes since they can facilitate
production tasks that are difficult for humans to perform (e.g., lift heavy tools), need high
accuracy and flexibility, constitute boring routines, or even involve the need to access
Electronics 2021,10, 828 16 of 20
dangerous areas [
67
,
89
]. However, the high automation of production processes poses new
challenges in the execution of production tasks.
Autonomous robots constitute additional resources that need to be managed along
with the manufacturing equipment and the personnel. Therefore, maintenance decision-
making algorithms need to take into account the fact that certain maintenance actions
may be implemented by autonomous robots, something that affects the scheduling of
the maintenance tasks to be performed. These algorithms will assign the appropriate
maintenance tasks to humans or robots according to the nature of the task, the knowledge
of humans and robots, the trade-off between efficiency of humans and the efficiency of
robots for this particular task, the trade-off between the cost of executing the task by
a robot or a human, etc. Another important factor is the uncertainty of the prognostic
information and thus the suitability of the recommended predictive maintenance actions.
Recommendations about maintenance actions with high uncertainty of suitability may
be difficult to be addressed automatically by robots that do not have the intelligence to
understand the whole context and potential consequences of applying an inappropriate
action. On the other hand, the human can ignore the recommendations or react during
their execution if they realize that the actions are not suitable.
4.9. Simulation
Simulation is a well-established approach in manufacturing environments for avoid-
ing high costs and issues of reliability and safety by the actual implementation of new
approaches on the shop-floor. For example, decision-making algorithms for maintenance
have utilized such methods for evaluating different scenarios of potential failures and
mitigating or corrective actions. However, simulation obtains a completely new meaning in
the context of Industry 4.0, which is characterized by the strong connection of the physical
and the digital world in which natural and human made systems (physical space) are
tightly integrated with computation, communication, and control systems (cyberspace).
This concept is named cyber physical systems (CPS).
In the context of Industry 4.0, simulation reaches the next frontier: digital twin.
A digital twin is a virtual representation of a physical object generated with digital data.
A digital twin can be used to run simulations throughout the design, build, and operation
lifecycle [
90
]. However, its aim is not just digitally representing the physical counterpart.
Digital twins that address maintenance operations will need decision-making algorithms
capable of generating various scenarios along with their expected impact. To do this,
they will need to incorporate information fusion methods in order to automatically or
semi-automatically transform information from different sources and different points
in time into a representation that provides effective support for human or automated
decision-making. Such sources can be sensors, legacy systems, physical models, and expert
knowledge. In this way, they will be able to exploit all the available information sources
for constructing scenarios and for resulting in the most appropriate ones according to the
business objectives and constraints. Up until now, generic approaches for incorporating
decision-making algorithms in digital twins addressing predictive maintenance aspects are
an unexplored area.
5. Conclusions
The availability of data generated by manufacturing automation systems; sophisti-
cated, IoT-enabled machines; and a plethora of sensors challenges existing methods for
decision-making in the context of Industry 4.0 maintenance applications. In this paper,
we performed a literature review on data-driven decision-making methods for manufac-
turing applications with a focus on maintenance operations. Our analysis of the literature
highlights the emergence of an increasing number of data-driven decision-making methods
developed specifically to exploit the plethora of sensor-generated data in the context of
Industry 4.0. Coupled with the emergence of cyber-physical systems as well as cloud
technologies for processing and storing data, next-generation decision-making for main-
Electronics 2021,10, 828 17 of 20
tenance will be increasingly more responsive and capable of facilitating accurate and
proactive decisions.
Author Contributions:
Conceptualization K.L.; methodology, K.L., A.B.; analysis and synthesis K.L.,
A.B., D.A, G.M.; supervision G.M.; project administration G.M. and D.A.; funding acquisition, A.B.,
D.A., G.M. All authors have read and agreed to the published version of the manuscript.
Funding:
Research reported in this article was funded by the European Union’s Horizon 2020
Research and Innovation program, grant agreements UPTIME “Unified Predictive Maintenance
System” No. 768634.
Data Availability Statement:
No significant datasets were analyzed in this study or created to
support it.
Conflicts of Interest:
The authors declare no conflict of interest. The funders had no role in the design
of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript,
or in the decision to publish the results.
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