Machine-Learning-for-industrial-applications-A-comprehensive-literature-reviewExpert-Systems-with-Applications

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Expert Systems With Applications 175 (2021) 114820
Available online 4 March 2021
0957-4174/© 2021 Elsevier Ltd. All rights reserved.
Review 
Machine Learning for industrial applications: A comprehensive 
literature review 
Massimo Bertolini a, Davide Mezzogori b,*, Mattia Neroni b, Francesco Zammori b 
a Enzo Ferrari Engineering Department, University of Modena and Reggio Emilia Via P. Vivarelli, 10, 41125 Modena, Italy 
b Department of Engineering and Architecture, University of Parma, Parco Aree delle Scienze, 181/A, 43124 Parma, Italy 
A R T I C L E I N F O 
Keywords: 
Literature review 
Industrial applications 
Deep Learning 
Machine Learning 
Operation management 
A B S T R A C T 
Machine Learning (ML) is a branch of artificial intelligence that studies algorithms able to learn autonomously, 
directly from the input data. Over the last decade, ML techniques have made a huge leap forward, as demon-
strated by Deep Learning (DL) algorithms implemented by autonomous driving cars, or by electronic strategy 
games. Hence, researchers have started to consider ML also for applications within the industrial field, and many 
works indicate ML as one the main enablers to evolve a traditional manufacturing system up to the Industry 4.0 
level. Nonetheless, industrial applications are still few and limited to a small cluster of international companies. 
This paper deals with these topics, intending to clarify the real potentialities, as well as potential flaws, of ML 
algorithms applied to operation management. A comprehensive review is presented and organized in a way that 
should facilitate the orientation of practitioners in this field. To this aim, papers from 2000 to date are cate-
gorized in terms of the applied algorithm and application domain, and a keyword analysis is also performed, to 
details the most promising topics in the field. What emerges is a consistent upward trend in the number of 
publications, with a spike of interest for unsupervised and especially deep learning techniques, which recorded a 
very high number of publications in the last five years. Concerning trends, along with consolidated research 
areas, recent topics that are growing in popularity were also discovered. Among these, the main ones are pro-
duction planning and control and defect analysis, thus suggesting that in the years to come ML will become 
pervasive in many fields of operation management. 
1. Introduction 
In the new global economy, competition fosters complexity, which 
directly affects manufacturing processes, products, companies, and 
supply chain dynamics. Now that we are entering into the Industry 4.0 
era (Lu, 2017), the new managerial paradigm is shifting from the need 
for low variability, through products’ commonalities and processes’ 
repeatability, as advocated in the lean thinking theory (Liker, 2004), 
toward the so-called mass-customization where, conversely, wide- 
markets goods should be rapidly modified and re-manufactured, at 
low cost, to satisfy a specific customer’s need (Coronado et al. 2004). In 
this scenario, resilience, reconfigurability, and flexibility are key issues 
of competitiveness, as clearly expressed by the ‘smart manufacturing’ 
concept, indicating a company that has the potential to fundamentally 
change how products are designed, manufactured, supplied, used, 
remanufactured, and eventually retired (Kusiak, 2018). Information 
technology, sensor networks, computerized controls, production man-
agement software, and, more in general, the Industrial Internet of Things 
(IIoT) are basic prerequisites for a company to be smart. Yet, these de-
vices alone are not enough, and a manufacturing system cannot be 
considered smart, unless its overall functioning is regulated by intelli-
gent control technologies, for a quick, accurate, and reliable response to 
internal and external events (Mittal et al., 2016). Furthermore, as noted 
by Kusiak (2017), smart manufacturing must embrace big data and, to 
this aim, information system and production management software 
must be coupled and/or enriched with deep analytical skills (Waller and 
Fawcett, 2013) and with learning ability (Monostori, 2003), to ensure 
competitiveness and effectiveness. 
Shreds of evidence also suggest that data are one of the most valuable 
assets of a firm and, especially for innovative companies, big data 
management is a key issue of competitiveness (Harding et al, 2006). Not 
only a proper data management may help in differentiating from 
* Corresponding author at: Department of Engineering and Architecture, University of Parma, Parco Aree delle Scienze, 181/A, 43124 Parma, Italy. 
E-mail addresses: massimo.bertolini@unimore.it (M. Bertolini), davide.mezzogori@unipr.it (D. Mezzogori), mattia.neroni@unipr.it (M. Neroni), francesco. 
zammori@unipr.it (F. Zammori). 
Contents lists available at ScienceDirect 
Expert Systems With Applications 
journal homepage: www.elsevier.com/locate/eswa 
https://doi.org/10.1016/j.eswa.2021.114820 
Received 15 March 2020; Received in revised form 29 December 2020; Accepted 28 February 2021 
mailto:massimo.bertolini@unimore.it
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mailto:francesco.zammori@unipr.it
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Expert Systems With Applications 175 (2021) 114820
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competitors and gaining a competitive advantage, but companies that 
use data-driven decision-making approaches have proven to easily 
outperform their competitors, being, on average, 5% more productive 
and 6% more profitable (McAfee et al., 2012). Unfortunately, while in 
many cases companies perceive the utility of their data, often they do 
not have the knowledge needed to exploit their data-silos and lack a 
clear understanding of what is important to be measured. As a result, the 
informative content of the data is missed, and real and valuable 
knowledge gets lost (Harding et al, 2006). If so, the well-known mana-
gerial expression that ‘quality trumps quantity’ becomes true because, if 
managers do not know how to select truly meaningful data easily and 
rapidly, a large and detailed data warehouse can be as harmful as a total 
lack of relevant information. Hence, optimizing data collection, usage 
and sharing have become vital for many companies (Kusiak, 2017) and 
Machine Learning (ML), a branch of Artificial Intelligence (AI) dealing 
with algorithms that learn directly from the input data, is expected to 
play a key role in the fulfillment of these needs. Not surprisingly, many 
works (Lu, 2017; Xu et al., 2018), indicate ML as one of the main en-
ablers to evolve a traditional manufacturing system up to the Industry 
4.0 level. It is worth noting that, a spike of academic interest followed 
the report by Pham and Afify (2005), one of the first to have shown 
potential applications of ML to operation management. From that 
moment, researchers started to consider ML applications also within 
industrial fields, especially for pattern and image recognition, natural 
language processing, operations optimization, data mining, and 
knowledge discovery (Wuest et al., 2016). Since then, as it will be 
described in later sections, the number of papers published in this field 
has ever increased, and the trend has been recently fueled by many 
government initiatives, like Industry 4.0 (Germany), Smart Factory 
(South Korea), and Smart Manufacturing (USA), calling for a radical 
change in the manufacturing paradigm, based on processes’ augmen-
tation and enhancements due to Information Technologies (IT). 
Especially in the last decade, the state of the art of ML techniques has 
made a huge leap forward, as demonstrated by the algorithms used byautonomous driving cars or by electronic strategy games. Both tasks 
were considered many years away from a practical solution (Martínez- 
Díaz and Soriguera, 2018; Müller, 2002) yet autonomous driving cars 
are already being tested in urban environments, and AlphaGo has 
overwhelmed the world champion of the Go game (Silver, 2016). 
Similarly, DeepMind recently reported the development of an AI that 
successfully learned to play better than humans in many other strategy 
games (Silver, et al., 2017). Furthermore, enabling technologies (i.e., 
sensors, open-source software, public datasets, computational power, 
cloud services, etc.) are now mature and available at low cost and 
government initiatives offer interest-free (or even non-refundable) loans 
and/or fiscal incentives to support investments in IT projects. 
Owing to these favorable issues, the time seems to be right to 
implement ML in the industry, and indeed, according to the Gartner 
Hype Cycle for Emerging Technologies (Burton & Barnes, 2017), Arti-
ficial Intelligence and especially Machine and Deep Learning have 
reached the peak of inflated expectation. Nonetheless, industrial appli-
cations of these technologies are still rare and generally confined within 
a small cluster of big international companies. Should this trend 
continue, a ‘disillusion phase’ may follow soon and the ‘plateau of pro-
ductivity’ may never be reached. Presumably, a detrimental element of 
acceptance can be found in the widespread concern that AI could 
jeopardize many jobs, increasing the unemployment phenomenon as 
noted by Korinek and Stiglitz (2017) and as clearly indicated in the 
McKinsey report by Manyika et al. (2017). In our opinion this is a 
misconception: if on the one hand, it is true that automation will replace 
human labor, on the other one hand replacement will concern redundant 
and repetitive tasks. Having the ability to learn representations auton-
omously, ML and especially DL models can extract knowledge directly 
from raw data, freeing researchers from the expensive and time- 
consuming step of feature extraction and feature engineering (LeCun 
et al, 2015). Thus, it is not daredevil to assume that the most successful 
implementations will be those augmenting, and assisting human deci-
sion making, freeing people from low value-added tasks. 
Apart from that, one of the main barriers to pervasive industrial 
adoption of ML is the lack of a clear understanding of these methodol-
ogies and the lack of awareness of what ML can and cannot do (LaValle 
et al., 2011). As posed by the notorious ‘No Free Lunch Theorem’ 
formulated by Wolpert and Macready (1997), ML cannot solve all in-
dustrial problems and its practical adoption, as an alternative to more 
mature technologies, must be carefully evaluated and pondered. Clearly, 
it is important to make an informed decision, without being influenced 
by the trend and the fashion of the moment. The ability to choose an 
algorithm (or a subset of algorithms), suitable for a specific task or 
problem, is a core competence for data analysts and/or practitioners 
who want to apply ML in industrial settings, as this choice can make the 
difference between failure and success. Yet, in absence of experience 
and/or on previous studies of similar nature, envisioning a way to 
deploy ML at the industrial level to improve business’ performances is 
challenging, especially considering the vast number of algorithms (and 
possible variations differentiating in terms of operating characteristics 
and of complexity) that have been proposed in technical literature. Such 
variety can be disorienting and misleading, and the problem is further 
complicated by the lack of a repository of best use-cases, for each in-
dustry and organization. So, we believe that a systematic literature re-
view focused on the historical developments of ML for industrial 
applications, may be extremely useful to highlight present and future 
trends and, above all, to orient industrial practitioners in the selection 
and in a more conscious use of ML techniques. 
Specifically, to clarify the real potentialities, as well as potential 
flaws, of ML algorithms applied in the field of operation management, 
papers from 2000 to date will be reviewed and categorized in terms of 
applied algorithm and application field. Insights, concerning trends and 
evolutions in the subject matter will be provided, and possible future 
developments will be investigated as well. 
The remainder of the paper is organized as follows. Section 2 gives a 
brief introduction and defines the technical lexicon that will be used in 
the paper. Section 3 describes the searching methodology that led to the 
identification of the set of papers that will be analyzed, in a general and 
more detailed way, in Section 4. Lastly, conclusions and general remarks 
will be drawn in Section 5. 
2. A brief introduction of Machine Learning theory 
A single definition of ML cannot be properly formulated, as this term 
encompasses a multitude of different approaches taken from the field of 
computer science and of multivariate statistics. Nonetheless, a good 
definition can be found in Murphy (2012), who defines ML as the «set of 
methods that can automatically detect patterns in data, and then use the 
uncovered patterns to predict future data, or to perform other kinds of de-
cision making under uncertainty». Although very clear, this definition 
gives too much emphasis on pattern recognition and decision-making 
that, as important as they may be, do not cover the whole spectrum of 
ML approaches and methodologies. So, more in general, we could define 
ML as a set of methodologies and algorithms capable of extracting 
knowledge from data, and continuously improve their capabilities, by 
learning from experience (i.e., from data accumulating over time). 
Please note that learning, as defined by Simon (1983), denotes a change 
that makes a system more and more adaptive, enabling it to perform the 
same task (or tasks drawn from the same population) more effectively 
the next time. 
It is also worth noting that, in many ways, ML overlaps with the so- 
called Statistical Learning (SL), an important field of statistics aimed to 
model and to understand complex datasets (Gareth et al., 2013). Both 
ML and SL models are characterized by the ability to self-adapt (at least 
to some extent), to changes in the data and/or in the environment, and 
to readjust their output accordingly. This pivotal element explains the 
recent increasing interest in these disciplines, as they perfectly match 
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Expert Systems With Applications 175 (2021) 114820
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the need to process and to analyze Big Data generated by the widespread 
use of electronic devices, web searches, social media, and social media 
marketing. 
2.1. Machine Learning areas 
ML is commonly divided into three broad areas, namely Supervised 
Learning (SL), Unsupervised Learning (UL), and Reinforcement Learning 
(RL) (Murphy, 2012), as detailed below. 
2.1.1. Supervised Learning (SL) 
Supervised Learning, also called predictive learning, includes many 
algorithms, of which the most commons are: Neural Networks, Support 
Vector Machines, Decision Trees (and their extensions, such as Random 
Forests and XGBoost), Logistic Regression, and Naïve Bayes Classifiers. 
Apart from implementation and operational differences, all SL methods 
aim to learn a good approximation ̂f of the true mapping f from the input 
vector x→ to the outputs vector y→, using information contained in a 
dataset of training examples, generated either performing experiments 
or through the direct observation of the phenomenon under analysis. 
More precisely, the data set is built by registering, for each observedexample, the true value of the response variable y, together with the 
known values of the input vector x→. The data set of examples is then 
split into a ‘training’ and ‘test’ set; the first one is used to reconstruct ̂f by 
iteratively minimizing a predefined cost (or loss) function, whereas the 
second one is used to assess the prediction accuracy of the model, on 
data that were ‘not seen’ during the training phase. 
Output variables may be either categorical or continuous. In the first 
case, the problem is known as a classification task, and a classic example 
could be that to generate a model to detect process failures or to predict 
the quality level (expressed on a categorical scale) of new production 
batches, starting from a dataset containing the physical properties x→ and 
the quality level y of completed production batches. Conversely, if vari-
ables are continuous, the problem is known as a regression task, and an 
industrial example could be that to predict a certain physical property, 
such as the thickness or the surface roughness of items processed by a 
numerical control machine. In this case, the task could be traced back to 
an image recognition problem, as different pictures of the manufactured 
items, taken before and after the machining process, could be converted 
into a vector of features, to generate the predictive variables x→. 
2.1.2. Unsupervised Learning (UL) 
Unsupervised Learning is concerned with unlabelled datasets, where 
no ground truth is available (i.e., the output vector y→ is missing). Hence, 
the goal is not to make a prediction, but rather to detect and to extract 
patterns in the data, whose nature or even whose existence could be 
partially or completely unknown. For these reasons, UL is sometimes 
referred to as descriptive learning and it is associated with knowledge 
discovery techniques. 
Broadly speaking, UL could be divided into three sub-areas (Murphy, 
2012): clustering, density estimation, and dimensionality reduction. 
Clustering is the task of grouping a set of objects in such a way that 
objects in the same group are more similar to each other than to those in 
other groups. A common example is a marketing-driven need to find 
groups of customers similar in terms of purchase behavior. If informa-
tion about class membership is not known, notorious algorithms, such as 
Hierarchical Clustering or K-Means, can be effectively used to this scope. 
Density estimation is a wide set of techniques that can be used to 
discover useful properties (e.g. skewness or multimodality) or even to 
generate an estimate of an unobservable underlying probability density 
function, of a dataset of observed data. Rescaled histograms are the most 
basic approach for density estimation, but more complex techniques can 
be also be used such, as Parzen Windows and vector quantization. 
Dimensionality reduction is frequently needed, especially in the case 
of Big-Data analysis, as a way to compress data, without altering and/or 
distorting their original informative content. Principal Component 
Analysis is the classical way to perform this task, but many neural 
network topologies (such as Autoencoders) can be employed too, to 
learn the best-compressed representation of the original data. In a 
broader sense, all Deep Learning (DL) models can be considered as a way 
to capture both the hidden representation of the data and the most 
relevant relationships among them. Accordingly, DL is also referred to as 
Representational Learning (Bengio et al., 2013). 
2.1.3. Reinforcement Learning (RL) 
Reinforcement Learning differentiates from the other ML ap-
proaches, as it implements a computational approach to learn from in-
teractions with an environment (Sutton and Barto, 1998). Rather than 
generating a mapping from the input to the output space, RL generates a 
mapping from situations (environment state) to actions. Akin the 
learning process of a person, RL does not require a pre-existing dataset 
but, with the goal to learn autonomously how to make decisions, it ex-
ploits a set of agents that learn by doing, following a rewarded trial and 
error approach. More precisely, the agent is free to interact with the 
environment, by performing a predefined set of actions, according to a 
predefined policy. Each action modifies the system’s state, and such 
modification is quantified through a specific reward signal, which is sent 
back to the agent. Since the objective of the agent is to maximize its total 
reward, it will learn, by doing, the best reaction to each possible external 
scenario, or system’s state. It is worth noting that Q-learning (Watkins, 
1989) is one of the most popular reinforcement learning algorithms, in 
which the agent learns actions’ values, which define the agent policy, 
without the need to have an explicit model of the environment. 
In addition to the reward signal, the learning process can also be 
supported by a superset of supervised and/or unsupervised algorithms, 
which should optimize the exploration and the exploitation of the action 
space of the agent. When all, or at least a part, of the implemented superset 
of algorithms are neural networks, the approach is known as Deep Rein-
forcement Learning (Li, 2017). In this regard, double Q-Learning is one of 
the examples of the application of Deep Learning models to improve the 
classic Q-learning algorithm (Van Hasselt et al., 2015). 
Anyhow, regardless of the implementation details, the final goal of 
an RL algorithm is to produce an artificial agent (or multiple agents 
interacting with each other) capable to make good decisions, based on 
the current state of the environment and its experience. For instance, 
from an industrial perspective, RL agents could be used to automate 
ordering strategies in multi-tier supply chain networks, or to update 
production parameters to maximize yield keeping operating costs at a 
minimum level. 
3. Searching methodology 
In line with the objectives of the present work, and owing to identify 
trends, potentialities, and criticalities concerning the use of ML for 
operation management, the review focuses on the following Research 
questions (Rq): 
- Rq. 1 – Which are the main application domains (i.e., industrial pro-
cesses) where ML has been successfully adopted? 
- Rq. 2 – Is the trend stable or has it modified through time, starting from 
2000? 
- Rq. 3 – Which are the most popular ML methodologies for operation 
management? 
- Rq. 4 – Is it possible to identify interesting development patterns? 
- Rq. 5 – Are there any criticalities in the use of ML algorithms for In-
dustrial Applications? 
- Rq. 6 – Which are the least studied domains and algorithms, which could 
benefit from renewed approaches? 
To answer the above-mentioned questions, the whole publications’ 
domain was investigated following a specific search-protocol, based on 
M. Bertolini et al. 
Expert Systems With Applications 175 (2021) 114820
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four main steps, as detailed below. 
3.1. Initial query-based search 
To collect as many publications as possible, in January 2020, a 
keywords-based search was made on three trustable and comprehensive 
scientific databases: Scopus, Web of Science, and Google Scholar. Aim-
ing to restrain the search to the papers dealing with Machine and 
Reinforcement Learning for operation management, possibly with a 
focus on Industry 4.0, data were filtered using the following query, 
where the asterisk (*) is the ‘all’ operator. 
KEY ({manufact*} OR {supply chain} OR {industry 4*}) 
AND ({machine learning} OR {reinforcement learning} OR {deep learning}) 
AND PUBYEAR ≥ 2000 
AND DOCTYPE (Article) 
AND (LIMIT-TO (LANGUAGE, English)) 
The query is reported here with a syntax similar to the one requiredby 
Scopus; yet, with minor adjustments, it was used to collect papers from 
Web of Science too. Conversely, papers retrieved from Google Scholar 
were manually filtered, due to a binding restriction, set by the search 
engine, that allows searching and filtering by title only. 
Anyhow, the filter (either applied manually or automatically) 
returned papers with at least a keyword belonging to the Set A =
{manufacturing, supply chain, industry 4.0} and a keyword to Set B =
{machine learning, reinforcement learning, deep learning}, provided 
that all the following inclusion criteria were met: 
- C1 – Studies must be either conference of journals peer-reviewed 
publications (i.e., other kinds of scientific works, such as books, 
patents, and Ph.D. thesis were not considered); 
- C2 – Language must be English; 
- C3 – Only recent studies, published starting from 2000, are 
considered. 
Such an extensive search returned a total of 678 publications, 370 
from Scopus, 218 from Web of Science, and 90 from Google Scholar. 
3.2. Search enlargement 
Next, despite the high number of collected papers, to avoid possible 
omissions of other relevant works, the search was enlarged using cross- 
reference and citation graph analysis, as detailed next. 
3.2.1. Cross-reference analysis 
To enlarge the search, we considered the list of all citations found in 
the original set of 678 papers. Such a list was automatically generated 
leveraging on the Scopus APIs (https://dev.elsevier.com/sc_apis.html), 
which allows us to retrieve all citations and their related metadata (i.e., 
keywords, abstract, authors, etc.). 
Also, to exclude papers unrelated to the stream of the research herein 
considered, the inclusion criteria C1, C2, and C3 were re-used to filter 
the obtained citations’ list. However, the constraints imposed on the 
keywords were partially relaxed, as we accepted papers with at least one 
keyword belonging to set A or to set B. 
By operating in this way, we obtained a list of 767 filtered citations. 
3.2.2. Relevance assessment through citation graph analysis 
The 767 citations and the original set of 678 papers were joined 
together and used as input for Gephi©, a freeware software application 
for the creation of citation networks. The simplified version of the 
generated network, where only connections among the main nodes are 
displayed, is shown in Fig. 1. In the network, nodes correspond to pa-
pers, and arcs indicate citations among them. More precisely, green 
nodes are the source of a citation, whereas blue nodes are the papers that 
received at least one citation from the other ones. Also, the nodes’ size is 
an indication of importance, evaluated as the number of received 
citations. 
Using this relevance criterion, we decided to add to the original list 
all the nodes having at least three incoming arcs in the citation graph. As 
an example, let us consider the blue node labeled as A in Fig. 1. This 
Fig. 1. The simplified citation networks (sources of citation in green, cited papers in blue). 
M. Bertolini et al. 
https://dev.elsevier.com/sc_apis.html
Expert Systems With Applications 175 (2021) 114820
5
node, whose size has been enlarged for display purposes, refers to Shiue 
(2009), a work that did not belong to the original list of the collected 
papers. However, the citation graph allowed us to include A in the list 
too, as A is cited, and thus connected, with three relevant works that 
were already part of the list. These works, namely Priore et al. (2010), 
Shiue et al. (2011), and Shiue et al. (2012), correspond to the green 
nodes (or citation sources) labeled as B, C, and D, in Fig. 1. 
By operating in this way, the original list increased from 678 to 714 
papers. 
3.3. Abstract analysis and final screening of the selected works 
Lastly, to refine the selection, all the abstracts were read and filtered 
using three additional inclusion criteria: 
- C4 – Only works with an informative abstract clearly stating the 
papers’ contributions and industrial results are considered; 
- C5 – Studies must be unique, copies (or very similar papers) are 
removed; 
- C6 – Purely theoretical or conceptual studies were not considered. 
Specifically, to be included, studies should present industrial appli-
cations tested on experimental data or, at least, tested on accessible 
datasets (used as a benchmark by the research community). 
By doing so, mainly due to the application of criteria C4 and C6, 569 
papers were considered of low operating value and were discarded, 
leaving a final corpus of 147 papers. The full list of the selected papers 
can be found in Tables 3a–3d of Section 4, where the papers are analyzed 
in detail. 
4. Systematic review 
4.1. Preliminary classification 
To answer the first three research questions, all papers were carefully 
read and classified in terms of their: 
- Application Domain (AD) – The industrial area or process considered 
in the paper, 
- ML Area (MLA) – The SL, UL, and RL clusters, as described in Section 
2, to which the adopted algorithms belong to. 
In line with the content of the articles that were collected during the 
search, we tried to define clusters of comparable size containing papers 
sufficiently detailed and homogeneous. 
In light of this, a good compromise was reached considering the 
following four ADs: 
1. Maintenance Management (MM), which includes 23 papers dealing 
with: 
- Failure modes classification and prediction (6), 
- Condition monitoring and fault detection (14), 
- Downtime minimization and maintenance planning (3). 
2. Quality Management (QM), which includes 53 papers dealing with: 
- On-line quality control (10), 
- Defects detection and classification (33), 
- Image recognition for defect identification (9), 
- Life cycle management (1). 
3. Production Planning and Control (PPC), which includes 49 papers 
dealing with: 
- Performance prediction and maximization (18), 
- Job scheduling and dispatching (15), 
- Dynamic process control (16). 
4. Supply Chain Management (SCM), which includes 19 papers dealing 
with: 
- Demand planning and forecasting (6), 
- Inventory management (4), 
- Supply chain modeling and coordination (9). 
The above-mentioned classification is graphically displayed in Fig. 2, 
where the distribution of the papers in terms of AD and of MLA is clearly 
shown. 
Please note that the histogram chart includes an additional category, 
namely Engineering Design (ED), that was purposely introduced to 
insert three relevant papers, in the field of technical design (Cholette 
et al., 2017; Loyer et al., 2016; Stocker et al., 2019), that could not have 
been put in any other category. Also, note that the sum of the bars of a 
certain AD may be greater than the number of papers belonging to the 
same AD. This is because, quite frequently, there are papers that use 
and/or compare different methodologies to solve the same problem. 
As can be seen, the number of ML applications to the industrial 
problem is relevant and, most of all, in terms of the application domain, 
(i.e., Research Question #1) applications are distributed fairly evenly 
among the various fields of operations management. Only SCM is not yet 
a much-explored domain, a fact that can be probably explained 
considering that most of the time, SCM involves strategic optimization 
models, requiring complex and less known approaches, such as Deep 
Learning and/or Reinforcement Learning. A further discussion on this 
Fig. 2. Number of papers for Application Domain (AD) and Machine Learning Area (MLA). 
M. Bertolini et al.Expert Systems With Applications 175 (2021) 114820
6
topic is postponed to Section 4.4, where a detailed analysis of the 
collected paper is given. 
For some additional statistics, the interested reader is referred to 
Appendix B, where a bibliometric analysis (in terms of journals with 
most publications, authors with more citations, etc.) is provided. 
4.2. Trend analysis 
The trend in the number of publications, for each AD, is shown in 
Fig. 3, which incontrovertibly responds to Research Question #2. 
Indeed, after an initial phase of latency, in which only some pioneering 
works have been occasionally published, scientific and industrial in-
terest in ML applications has exploded. Especially over the last five 
years, the growing trend of publications is evident, with a very high 
spike in 2019. 
Concerning the evolution through time of the application areas (i.e., 
Research Question #3), a clear picture is given by Fig. 4, which shows 
the evolution of the distribution on the published papers, in terms of 
MLAs, for each of the four 5-year-periods from 2000 to 2019. 
In line with the overall increase of published papers, the trend is 
positive in each of the three MLAs, and it is particularly pronounced for 
SL approaches. This is not surprising because, historically, SL methods 
have always been the most studied and applied ones. Indeed, thanks to 
the ground-truth information (recorded in the training data set), they 
fully exploit available data, and they are also easier to interpret. 
Due to the relevance of SL approaches, the trend analysis is deepened 
in Fig. 5, which shows the trend of Neural Networks (NN)s, Support 
Vector Machine (SVM), and Tree-Based (TB) techniques (i.e., Decision 
Trees, Random Forests and Gradient Boosting), which have shown to be 
the most used techniques belonging to this ML area. As it is clear from 
the chart, SVM was the prominent technique until 2010, and although 
its use has not faded away, lately it has been overtaken by NNs. Albeit 
informally, the start of the Deep Learning era can be approximately 
placed around 2010–2012, and indeed, in the last 7–8 years, the use of 
Neural Network (especially of deep architectures), has been prominent. 
Nonetheless, collecting and labeling data is expensive and time- 
consuming, and this explains why, more recently, UL methods are 
increasingly being used too. As shown in Fig. 4, although they were 
Fig. 4. Time distributions of ML Areas (MLA). 
Fig. 3. The trend of publications, for each Application Domain (AD). 
M. Bertolini et al. 
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almost absent till 2014, in the last five years they account for about 1/5 
of the total, with a very rapid growth trend, as clearly highlighted by the 
black line displayed in Fig. 5. 
Conversely, after a modest peak toward the end of the first decade of 
2000, RL has stabilized at a lower growth rate. Probably, notwith-
standing recent breakthrough developments in such areas and the 
greater understanding of RL’s potentialities, the high complexity of 
reinforcement learning algorithms is still a hurdle for its full acceptance 
and industrial applicability. 
The above-mentioned analyses are summarized in Table 1, which 
shows the number of published papers in terms of MLA and AD. Please 
note that, as for the histogram of Fig. 3, also the rows’ sum of the values 
of Table 1, may be higher than the corresponding number of papers. 
4.3. Keywords analysis 
From the corpus of the investigated papers, we extracted around 350 
different keywords. Getting rid of the obvious ones (e.g., Machine 
Learning, Supervised Learning, Unsupervised Learning, etc.), and 
combining the remaining ones by synonyms, a total of 61 basic key-
words remained. Of these, 32 concern the application domain, the other 
29 refer to the adopted ML techniques. 
The total count is graphically shown in Figs. 6a and 6b, where three 
fictional macro-keywords, namely ‘Metaheuristics’, ‘Statistic Tech-
niques’, and ‘Neural Networks’, have been added to group similar and 
recurrent items. 
As can be seen, following the results reported in the previous sec-
tions, NNs and SVMs are very common, together with RL and Meta-
heuristic, that occur quite frequently too. Relatively to the application 
domain, ‘Diagnosis & Fault Detection’, ‘Additive Manufacturing’, and 
‘Manufacturing Processes’ are, by far, the most frequent keywords. 
Immediately after, other interesting fields follow, such as: ‘Supply Chain 
Management’, ‘Big Data’, ‘Intelligent Manufacturing’, ‘Production 
Planning & Control’, ‘Quality Control’ and ‘Simulation’. 
For more in-depth information, a Word Cloud representation of the 
20 most relevant keywords is also provided in Fig. 6c. As it is evident, 
there is a very good matching between the most occurring keywords and 
the Applications Domains that were used to classify the investigated 
papers. Apart from this rather predictable result, the presence of the 
‘Intelligent Manufacturing’ is a strong indication of how important 
machine learning techniques are considered to obtain a competitive 
edge in the Industry 4.0 era. Lastly, it is also worth noting that the term 
Fig. 5. Publication trend of papers dealing with NNs, SVM, and TB algorithms. 
Table 1 
Rq. 3 – Trend Analysis: results summary. 
Unsupervised Learning Reinforcement Learning Supervised Learning 
NNs SVM TB Other SL 
Maintenance Management (23) 3 4 13 9 7 6 
Failure Mode Analysis (6) 1 [–] 5 2 1 2 
Condition Monitoring (14) 2 [–] 7 6 6 3 
Downtime Minimization (3) [–] 4 1 1 [–] 1 
Quality Management (53) 16 1 27 24 19 24 
On-Line Quality Control (10) 3 1 7 4 [–] [–] 
Defect Detection & Class. (33) 12 [–] 16 14 15 20 
Image Recognition (9) 1 [–] 3 5 3 4 
Life Cycle Management (1) [–] [–] 1 1 1 [–] 
Prod. Planning & Control (49) 10 12 22 10 8 10 
Performance Prediction (18) 6 2 7 4 3 7 
Scheduling (16) 1 7 6 2 5 2 
Process Control (15) 3 3 9 4 [–] 1 
Logistic & Supply Chain (19) [–] 10 3 5 3 3 
Demand Forecasting (6) [–] [–] 3 4 1 1 
Inventory Management (4) [–] 5 [–] [–] [–] [–] 
Modelling & Coordination (9) [–] 5 [–] 1 2 2 
Engineering Design (3) [–] 1 [–] 2 [–] 1 
M. Bertolini et al. 
Expert Systems With Applications 175 (2021) 114820
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‘neural network’ is the only one that explicitly refers to a particular ML 
algorithm. This is a further indication of the prominence and importance 
attached by researchers to this specific technique. However, the pres-
ence of the ‘feature extraction’ term suggest that the practice of data pre- 
processing and data engineering is still common and dominant. This fact 
is in partial contrast with the development and dissemination of Deep 
Learning techniques that, as known, can exploit raw data, without 
needing sophisticated feature extraction techniques. Although the 
presence of the ‘feature extraction’ term is probably due to the older 
works (that used standard ML techniques), it may also indicate a rather 
immature approach to Deep Learning technique, which is still influ-
enced by the most popular approaches in the recent past. 
4.3.1. Current trends and hot topics 
To get a better idea of the current trends, and to give an answer to 
Research Question # 4, we also organized keywords in the 3D bubble 
chart of Fig. 7. Each keyword k (denoted using the same abbreviations 
used in Fig. 6b) is identified with a triplet of data (age, trend, size), and it 
is plotted as a sphere, with volume proportional to the size, and centrally 
located at coordinates(x, y) corresponding to ‘age’ and ‘trend’, 
Fig. 6a. Keywords relative to the adopted ML technique. 
Fig. 6b. Keywords relative to the solution domain. 
M. Bertolini et al. 
Expert Systems With Applications 175 (2021) 114820
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respectively. 
Specifically, age, trend, and size are defined as follows: 
- Size (Sk) – The total number of occurrences of k, 
- Age (Ak) – The number of years since the first occurrence of k, 
- Trend (Tk) – The percentage misalignment of the Centre Of Gravity 
(COG) of k, as defined in Eqs. (1) and (2): 
T k = ((t (COG, k) − (t n − 0.5A k)))/A k = ((t (COG, k) − t k))/A k
(1) 
t (COG, k) = (
∑
(i = 1)n(s (i, k)∙t i))/(
∑
is (i, k))
= (
∑
(i = 1)n(s (i, k)∙t i))/S k (2) 
where: tn is the current year, t
−
k is the midpoint of the life of k, si,k is the 
number of occurrences of k at year i, and tCOG,k is the ‘temporal’ coor-
dinate of the COG of k. 
Specifically, for a consolidated and stable keyword k, tCOG,k should 
lay at the midpoint of its life (i.e., tCOG,k = t
−
k) and Tk should be close to 
zero. Instead, a positive value of Tk indicates a keyword that is being 
used more and more frequently, or that has come back into vogue, after a 
period of latency. Conversely, a negative value of Tk denotes a keyword 
that is out of fashion or no longer in use. 
Using these metrics, five main clusters can be identified. These are: 
1. Question Marks (Low Age and Negative Trend) – Recently introduced 
topics, that have not got a follow-up, yet. Thermography (THER), 
Cyber-Physical Systems (CPS), and Design For (D4) belong to this 
category. 
2. Hot Topics (Low Age and Negative Trend) – Very recent topics of 
booming interest. At present, none of the keywords properly belong 
to this category. Yet, Additive Manufacturing (ADD_MN), Prediction 
& Prognostic (PR_PR), and Industry 4.0 (I4.0) are those who come 
closest to this category. For this reason, they have been labeled as 
‘new promises’. 
3. Consolidated (Medium Age and Stable Trend) – Not recent topics, 
which are still studied, but without the initial spike of interests. 
Topics such as Supply Chain Management (SCMI), Flexible 
Manufacturing Systems (FMS), Inventory Control (INV_CTRI), and 
Tool Monitoring (TLL_MN) belong to this category. 
4. Stars (High Age and Positive Trend) – Old and consolidated topics 
that are still attracting increasing research interest. Topics such as 
Diagnosis and Fault Detection (DG_FLT), Manufacturing Process 
(MN_PR), Intelligent Manufacturing (INT_MN), and Big Data analysis 
(BD_DM) certainly belong to this class. Probably, Simulation (SIM) 
and the Internet of Things (IoT) are on their way to become stars. 
5. Obsoletes (High Age and Negative Trend) – Old topics that have never 
received much scientific interest and that have almost disappeared 
from the technical literature. Due to the recent introduction of ML, 
for operation management, no keywords can be classified as obso-
letes yet. However, Order Management (OM) and, probably, also 
Feature Extraction (FT_EX) are moving toward this class. 
We also note that, as indicated by the direction arrows shown in 
Fig. 7, according to a standard evolutionary trajectory, question marks 
should become consolidated topics, moving diagonally from the bottom 
left corner to the center of the graph. However, in case of rapid success, 
question marks can move vertically to reach the hot topics area and, if 
Fig. 7. Topics’ evolution map measured in terms of age and trend. 
Fig. 6c. Word cloud of the 20 most relevant keywords. 
M. Bertolini et al. 
Expert Systems With Applications 175 (2021) 114820
10
the growing trend continues, they can proceed straightly toward the 
stars’ area. In this regard, three additional clusters, namely New 
Promises, Emerging Trends, and Young Stars can be identified. The first 
one contains recent topics that have already overcome the initial phase 
of uncertainty and that are likely to remain of interest in the years to 
come. As already noted, Additive Manufacturing (ADD_MN), Prevision 
& Prognostic (PR_PR), and Industry 4.0 (I4.0) belong to this cluster. The 
second one contains rather recent topics growing in popularity, which 
can be expected to become consolidated or even star topics in the next 
few years. Production Planning & Control (PPC) and Defect Detection 
(Q_DF) and Signal Processing (SIGN_PR) are the main topics in this area. 
The last one contains young and consolidates topics that are still in a 
booming phase. Automation (AUT) and Process Control (Q_PR) are the 
main topics in this area. 
4.3.2. Gaps’ investigation 
To partially explain the difference between Stars and Question 
Marks, a gap analysis is provided in Table 2. Specifically, the occurrence 
of each ML algorithm is reported, both for the items classified as Stars 
and Question Marks. 
Some interesting differences, between the two groups, are clear. 
Indeed: 
- Applications of Reinforcement Learning algorithms are completely 
missing in the Question Marks group. 
- As far as the Supervised Learning algorithms are concerned, the 
number of algorithms applied by the Question Marks group is lower 
and limited to the most classic and widespread techniques. Several 
gaps are noted, even in case of some very common techniques, such 
as NN, RF and SVM, that are little used, if not completely ignored. 
- A similar gap can also be found in terms of Unsupervised Learning 
techniques. The gap is particularly marked in the Quality 
Management area, where Unsupervised learning is widely investi-
gated by Stars, but it is totally neglected in the Question Marks group 
Is therefore evident that, in case of Stars the whole spectrum of 
possible ML solutions has been tested and, to emerge in this group, 
where research is almost mature, researchers have to resort to innova-
tive and frontier techniques. Conversely, concerning Question Marks, 
ML applications are still a niche and only standard and consolidate ML 
techniques have been tested. There is therefore room for further in-
vestigations, which could certainly lead to a positive development in all 
the involved Application Domains. 
4.4. Detailed analysis of selected papers 
To answer to Research Questions #5 and #6, all selected papers were 
analyzed in detail. For each of the four ADs defined in Section 4.1, re-
sults are summarized by providing a brief description of the papers 
deemed more significant and innovative and a summary table that 
highlights the main features of all the analyzed papers. 
Specifically, for each paper, the following fields are quantified: 
- Article – The reference to the described paper. 
- Sub Area -The sub-area to which the described paper belongs to. 
- # Citations – The number of obtained citations. 
- Alg_Class – The class (i.e., Supervised, Unsupervised, and Reinforced 
Learning) to which the algorithms used in the paper belong to. 
Algorithm 
- The full list of the adopted algorithms. Please note that, for reason 
of space, algorithms are indicated with an acronym; the full list is re-
ported in Table A1 in the appendix section. 
- Sim_Based – A Boolean field that is equal to one for the papers based 
on discrete event simulation. If none of the articles belonging to a 
specific AD is based on simulation, this field is not considered. 
- CPS – A Boolean field that is equal to one for the papers dealing with 
a Cyber-Physical System. Also in this case, if this field is missing, 
none of the papers deals with a CPS. 
- Goals & Approaches – A small summaryof the papers’ methodologies 
and objectives. 
4.4.1. Maintenance management 
Maintenance management concerns administrative, financial, and 
technical approaches for assessing and planning maintenance opera-
tions, on a scheduled basis. The objective is to keep assets and machines 
at a full operating state, so that production proceeds effectively, and no 
money is wasted due to inefficiencies. 
Papers belonging to this area are listed in Table 3a, from which it is 
easy to see that ML perfectly fits this area, especially within the SL 
framework, for condition monitoring and failure analysis (i.e., faults 
detection and classification). Indeed, the problem can be easily inter-
preted as a prediction task, where historical data are collected on the 
production floor, and faulty and non-faulty events are used as ‘ground- 
truth data’ against which a prediction model can be trained. NNs and 
SVM are commonly used, with a total of thirteen and nine applications, 
respectively. Although SVM was generally considered as the best per-
forming techniques (see for example the review by Widodo and Yang, 
2007), thanks to the introduction of new sophisticated algorithms 
(generally taken from the Deep Learning area), in the last decade their 
popularity has started to decrease, in favor of more promising NN ap-
proaches. Most of the papers dealing with ‘Failure Mode Analysis’ employ 
NNs to efficiently determine the cause of failures of both equipment and 
machines. For instance, Prieto et al. (2013) proposed a novel approach 
for on-line fault detection of electrical machines, which considers both 
local and distributed defects. The model integrates a curvilinear 
Table 2 
Gap analysis of consolidated and new emerging clusters. 
Question Marks Stars 
ED MM PPC QM MM PPC QM 
Reinforcement Learning 
Deep Q Learning 1 
Proximal Policy Optimization 1 
Trust Region Policy 
Optimization 
1 
Supervised Learning 
Boosting 1 1 1 
Decision Tree 1 3 6 
Linear Discriminant Analysis 1 1 
Logistic Regression OGIT 1 
Linear Regression 1 1 
Neighbor Based Clustering 2 2 
Neural Network 1 2 10 3 13 
Quadratic Discriminant 
Analysis 
1 
Random Forests 2 2 
Rough Set Algorithm 2 3 
Support Vector Data 
Description 
2 
Super Vector Machines 2 2 6 12 
Unsupervised Learning 
Gaussian Density Estimation 1 
Gaussian Mixture Modelling 1 3 
Hierarchical Clustering 1 
K-Means/K-Median 1 
K-Means clustering 1 3 
K Nearest Neighbors 1 1 3 
Local Outlier Factor 2 
Non-negative Matrix Factoriz. 1 
Principal Comp. Analysis 4 
Parzen Windows 3 
Self-Organizing Maps 1 1 
M. Bertolini et al. 
Expert Systems With Applications 175 (2021) 114820
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Table 3a 
Maintenance Management Papers. 
Sub-Area Article Received 
Citations 
Algorithm 
Classification 
Main 
Algorithm 
Simul. 
Based 
Goals & Approach 
Condition 
Monitoring 
Cho et al., (2005) 62 Supervised Learning SVM 0 Multiple sensors are used to record cutting forces and power 
consumption of milling machines. Using a Super Vector 
Regression, the tool breakage detection rate is increased, with a 
huge impact on manufacturing performance. 
Condition 
Monitoring 
Saxena and Saad, 
(2007) 
116 Supervised Learning NN, GA 0 A Genetic algorithm is coupled to a NN for feature selection and 
topology search. The NN is used for fault detection of roller 
bearing health monitoring. Data were collected on-site, from 
three accelerometers and one acoustic sensor. 
Condition 
Monitoring 
Kankar et al., 
(2011) 
100 Supervised Learning NN, SVM 0 NN and SVM are compared on a dataset of ball bearings’ faults, 
that have been pre-processed, for dimensionality reduction. 
Results show that an automated diagnosis system is feasible. 
Condition 
Monitoring 
Azadeh et al., 
(2013) 
38 Supervised Learning SVM, NN, GA 0 A flexible algorithm based on an ensemble of SVM, NN, and 
metaheuristics is used for condition monitoring and fault 
detection. The ensemble is tested against noisy and corrupted 
data of centrifugal pumps. 
Condition 
Monitoring 
Zhang et al., 
(2015) 
31 Supervised Learning SVM, ACO 0 An Ant Colony Optimization metaheuristic is applied for features 
selection and hyperparameters optimization of an SVM for 
intelligent fault diagnosis. The method is evaluated on a rotor 
system and locomotive roller bearings. 
Condition 
Monitoring 
Li et al., (2017) 0 Supervised Learning CNN 0 A novel fault diagnosis algorithm, leveraging on an ensemble of 
Deep Convolutional NN, is presented. The algorithm is tested on 
a public database of bearings’ failure data. 
Condition 
Monitoring 
Syafrudin et al., 
(2018) 
3 Supervised & 
Unsupervised Learning 
RF 0 A two-steps approach for fault detection is presented. First, the 
DBSCAN algorithm is used to detect possible outliers, next a 
random forest is used to predict possible faults. 
Condition 
Monitoring 
Liu et al. (2018b) 2 Supervised Learning LDA, 
Clustering 
0 Acoustic emissions signals, collected from additive 
manufacturing machines, are used to recognize different 
operating states. To this aim, data are pre-processed through 
LDA (both in time and frequency domains) and clustered with 
unsupervised methods. 
Condition 
Monitoring 
Hesser and 
Markert (2019) 
0 Supervised Learning NN 0 A programmable prototype platform, equipped with onboard 
sensors, is coupled with a NN to make existing milling machines 
compliant to the Industry 4.0 standards. 
Condition 
Monitoring 
Wang et al., 
(2019) 
3 Supervised Learning NN 0 A newly developed deep heterogeneous GRU model is used with 
local feature extraction for long-term prediction of equipment 
deterioration. 
Condition 
Monitoring 
Li et al., (2019) 0 Supervised & 
Unsupervised Learning 
PCA, DT, RF, 
KNN, SVM 
0 A tool wearing detection framework is proposed, based on audio 
signal processing. A compression stage based on PCA is followed 
by a classification stage that makes use of standard ML 
techniques 
Condition 
Monitoring 
Bukkapatnam 
et al., (2019) 
1 Supervised Learning Balanced -RF 0 The paper introduces a non-parametric random forest 
(Manufacturing system-wide Balanced RF), that takes into 
account complex dynamic dependencies among parts and 
failures. The approach allows a long-term prognosis of machine 
breakdowns and greatly reduces prediction error. 
Condition 
Monitoring 
Kammerer et al., 
(2019) 
0 Supervised Learning DT, RF, NN 0 The work considers two data sets (taken from Industry 4.0 
scenarios) and has the goal to detect sensor data anomalies. The 
focus is on the collection and processing steps, whereas analysis 
is performed using standard machine learning techniques. 
Condition 
Monitoring 
Alegeh et al., 
(2019) 
0 Supervised Learning SVM, DT, 
KNN 
0 The paper focus on the “product-service system” (PSS). 
Specifically, a case study is discussed where the manufacturer of 
a 5 axes gantry machine monitors the degradation of the 
equipment (using sensor data) and use the analysis to offer 
maintenance services. 
Downtime 
Minimization 
Susto et al., 
(2015) 
20 Supervised Learning SVM, KNN 1 A multi-classifier is proposed to optimize a cost-based 
maintenance decision system. Each classifier can deal with high- 
dimensional censored data and is trained with different 
prediction horizons. 
Downtime 
Minimization 
Wan et al., (2017) 2 Supervised Learning NN 0 A NN is proposed to predict the remaining lifetime of mechanical 
components, subjected to specific processing conditions. Using 
the NN in a big-data system, an active preventive maintenance is 
developed. 
Downtime 
Minimization 
Kuhnle et al. 
(2018) 
0 ReinforcementLearning 
DQN, VPF, 
TRPO, PPO 
1 Downtime reduction and lower maintenance costs are achieved 
using a Reinforcement Learning approach, based on the 
Proximal Policy Optimization algorithm. 
Failure analysis Prieto et al., 
(2013) 
95 Supervised Learning NN 0 The paper considers 6 bearing scenarios, in 25 operating 
conditions. After feature selection and dimensionality reduction 
(for physical interpretation), a NN is used for the 
multiclassification task. 
Failure analysis Perzyk et al., 
(2014) 
5 Supervised Learning DT, RST, 
NBC, NN, 
SVM 
0 The paper shows how simple statistical methods, such as 
contingency tables, may perform similarly or better, than ML 
techniques in detecting the main parameters for fault diagnosis. 
Failure analysis 0 Supervised Learning CNN 1 
(continued on next page) 
M. Bertolini et al. 
Expert Systems With Applications 175 (2021) 114820
12
component analysis (used for dimensionality reduction) with a final 
classifier based on a two-level hierarchical NN. Li et al. (2017) used an 
ensemble of Convolutional Neural Networks (CNN) for bearings’ fault 
diagnosis and classification. The same problem was also tackled by Sobie 
et al. (2018), who used Dynamic Time Warping along with CNN’s. In 
doing so, they demonstrated that a training dataset generated through 
high-resolution simulations may be effectively used to integrate or even 
to replace missing and/or insufficient data. This is an important 
achievement because all precedent works (concerning fault detection 
and classification) were highly dependent on historical data collected on 
the field; a clear detrimental fact for their adoption in the industry. 
Instead, unsupervised techniques are less frequent, and they are gener-
ally limited to defects’ classification, as in the work by Wu et al. (2019), 
where a Self-Organizing Map, based on acoustic data, is used to cluster 
filaments in terms of different failure modes. 
Even in the field of ‘Condition Monitoring’ NNs are, by far, the most 
applied techniques and, in this case, the most common applications 
concern condition monitoring of rotating mechanical systems (Saxena 
and Saad, 2007; Zhang et al., 2015) and rolling bearings (Kankar et al., 
2011). In both cases, the problem is solved using vibrations and/or 
acoustic signals as classifiers inputs, for faulty and non-faulty prediction. 
It is interesting to note that, to exploit the information content of the 
acoustic signal, the oldest works paid close attention to feature selec-
tions, hyper-parameters optimization, and dimensionality reduction. 
More recent ML techniques, instead, have eliminated part of these lim-
itations and especially Deep Learning allows an ‘As Is’ use of the original 
data set, without requiring careful data pre-processing. In this regard, 
Azadeh et al. (2013), proposed an ensemble of Deep NNs and SVM for 
condition monitoring of centrifugal pumps and effective maintenance 
management. The ensemble, optimized with a novel metaheuristic, has 
been proved to be particularly resilient concerning corrupted or noisy 
data. On the other side, Deep Learning requires a very massive dataset, 
that is not always available. For this reason, historical data are often 
enriched with additional data generated through simulation, as in Sobie 
et al. (2018) and in Kuhnle et al. (2018) where an innovative approach 
for downtime reduction and lower maintenance costs, is proposed based 
on four different Reinforcement Learning algorithms. 
Similar approaches can also be found in the field of ‘Downtime 
Minimization’, as in Susto et al. (2015), who used an ensemble of SVM 
and k-Nearest Neighbors to plan predictive maintenance tasks in a way 
that minimizes all the costs generated by unexpected breakdowns and/ 
or by machine unexploited lifetime. Their interesting approach was 
successfully tested on a well-known semiconductor manufacturing 
maintenance problem. 
4.4.2. Quality management 
Quality Management, a major area within the field of operation 
management, can be defined as the process of achieving and maintain-
ing a certain level of business excellence so that products and/or services 
are consistent with what customers want and are willing to pay for. 
From this perspective, quality management is not limited to product 
and/or service compliance, but it also encompasses all the processes that 
are needed to achieve the desired quality level, such as quality planning, 
quality assurance, quality control, and quality improvement. 
As shown by Table 3b, in the context of ML the focus is mainly on 
quality assurance and quality control and, overall, the main aim is to 
understand what customers want and, more in general, which are the 
true drivers for better quality. 
A typical example is that of quality monitoring and ‘Defects’ Detection 
and Classification’, a topic that counts several applications in the elec-
tronic industry. Typically, to discriminate between defective and non- 
defective items, manufacturing data are collected from sensors, PLCs, 
and Manufacturing Executions Systems, and they are used as decision 
variables of an ensemble of classifiers. Lenz et al. (2013) used an 
ensemble of Decision Trees, NNs, and SVM to tackle a virtual metrology 
problem, that is to predict the thickness of dielectric layers deposited 
during the manufacturing of semiconductor wafers. Saucedo-Espinosa 
et al. (2014) used sound analysis to detect defective bearings in home 
appliances and showed that Random Forests are the most effective 
classification techniques. Liu et al. (2017) implemented a Deep Belief 
Network (a composition of Restricted Boltzmann Machines) for fault 
detection and isolation and demonstrated that this peculiar network 
topology can capture highly discriminative semantic features; indeed, 
impressive accuracy levels, up to 100%, was obtained. 
It is interesting to note that, when the aim is to detect defective items, 
the so-called imbalance problem is frequently found. Indeed, this issue is 
rather common when the objective is to discriminate positive events 
from negative and rare ones, such as defects. A detailed discussion of this 
problem can be found in Lee et al. (2016) and in Kim et al. (2018), who 
compared a comprehensive set of ML classification techniques showing 
that, in case of heavily unbalanced data sets, Random Forests offer the 
best results. Other relevant works are those by Ye et al. (2013) and by Ko 
et al. (2017). The first one proposed an ensemble of NNs and SVMs 
(based on a weighted majority vote), for functional diagnosis of printed- 
circuit boards. The ensemble was successfully applied to a highly un-
balanced manufacturing dataset that was artificially augmented with 
synthetic data. The second work presented a framework to detect 
anomalies of heavy machinery engines, based on manufacturing, in-
spection, and after-sales data. Specifically, it was shown that in the case 
of unbalanced data, Gaussian Mixture Models and Parzen Window 
Density Estimation are very effective, compared to other techniques 
such as Principal Component Analysis or K-Means Clustering. 
Besides the assessment of product compliance, ML has also been used 
to implement ‘On-Line Quality Control’ systems, thus enabling more 
Table 3a (continued ) 
Sub-Area Article Received 
Citations 
Algorithm 
Classification 
Main 
Algorithm 
Simul. 
Based 
Goals & Approach 
Sobie et al., 
(2018) 
Statistical methods are compared with Convolutional NN for 
bearing fault classification. Data are generated from high- 
resolution simulations and a novel application of Dynamic Time 
Warping is also presented. 
Failure analysis Liu et al. (2018a) 0 Supervised Learning, 
Unsupervised Learning 
NN, SVM, AE 0 A Denoising Auto-Encoder is usedto extract meaningful 
representations of failure modes, and newly generated data is 
compared to historical ones, using KL-divergence. The approach 
emphasizes new fault modes while maintaining a dynamic and 
compensatory behavior. 
Failure analysis Ren et al., (2018) 4 Unsupervised Learning AE DNN 0 To predict the remaining useful life of a rolling bearing, a Deep 
Auto-Encoder and a Deep Neural Network are used. Specifically, 
they are coupled with a novel eigenvector-based method and can 
accurately reproduce the bearings’ degradation process. 
Failure analysis Wu et al., (2019) 5 Unsupervised Learning SOM 0 The paper proposes a data-driven monitoring method, based on 
acoustic emissions, for online process failure diagnosis of fused 
filament fabrication. Specifically, the diagnosis of different 
failure modes is formalized using a self-organizing map. 
M. Bertolini et al. 
Expert Systems With Applications 175 (2021) 114820
13
Table 3b 
Quality Management Papers. 
Sub-Area Article Received 
Citations 
Algorithm 
Classification 
Main Algorithm Simul. Based Goals & Approach 
Defect 
Detection 
Kusiak and 
Kurasek, (2001) 
47 Supervised Learning RST, DT 0 Data mining techniques are used to identify the cause 
of solder-ball defects, in circuit board manufacturing. 
The Rough Set algorithm is used because it can provide 
explicit rules, in contrast with NN or Linear 
Regression. 
Defect 
Detection 
Kim et al., (2012) 16 Supervised & 
Unsupervised 
Learning 
GDE, GMM, PW, 
KMC, SVM, PCA 
0 Aiming to detect faulty wafers, 7 different ML 
algorithms, and 3 dimensionality reduction methods 
are used. 
Defect 
Detection 
Çaydaş and Ekici 
(2010) 
48 Supervised Learning SVM, NN 0 SVM and NN are compared to estimate the surface 
roughness of stainless steel. SVM shows the best 
performances, but the NN is very shallow, and only 
three input variables are used. 
Defect 
Detection 
Ye et al., (2013) 36 Supervised Learning SVM, NN 0 An ensemble of NN and SVM, based on majority 
voting, is applied both to defects detection (of three 
complex boards) and to propose repair suggestions. 
Defect 
Detection 
Lenz et al., (2013) 1 Supervised Learning DT, NN, SVM 0 Using 27 features from process data, Decision Trees, 
NN, and SVM are compared for predicting the 
thickness of dielectric layers in a semiconductor 
manufacturing scenario. 
Defect 
Detection 
Tan et al., (2015) 13 Supervised Learning Evolutionary NN 0 An evolutionary Neural Network is applied to an 
imbalanced data set (of semiconductor 
manufacturing) for defect detection. Based on the 
adaptive resonance theory, it combines a fuzzy set and 
stability-plasticity characteristic. It is benchmarked 
against other cost sensitive NN and non-cost sensitive 
ML algorithms. 
Defect 
Detection 
Adly et al., (2015) 5 Supervised Learning SVM, NN 0 A novel regression algorithm is introduced and 
compared to state-of-the-art ML methods for the 
identification of defects in wafer manufacturing. 
Results show comparable performance, with the 
benefit of a reduced computational footprint. 
Defect 
Detection 
Gao et al., (2016) 4 Unsupervised 
Learning 
NMF 0 A sparsity-adaptive sparse non-negative matrix 
factorization is proposed to detect defects in an 
unsupervised way, without requiring manual selection 
of specific frequencies. Experimental tests are made on 
metal manufacturing data. 
Defect 
Detection 
Lee et al., (2016) 0 Supervised Learning SVM, DT, Bagging, 
Boosting, RF, KNN 
0 The performance of three sampling-based algorithms, 
four ensemble algorithms, four instance-based 
algorithms, and two support vector machine 
algorithms are compared to effectively tackle the 
imbalance problem for the development of high- 
performance fault detection systems. 
Defect 
Detection 
Mohammadi and 
Wang, (2016) 
0 Supervised Learning SVM 0 Based on data collected throughout an abrasion- 
resistant material manufacturing process, product 
quality prediction of burned balls is achieved using 
Support Vector Machine. 
Defect 
Detection 
Saucedo-Espinosa 
et al., (2017) 
1 Supervised Learning SVM, NN, NBC, 
KNN, DT 
0 Home appliances with defective embedded bearings 
are detected using ML algorithms for sound signals 
analysis. Results show that intuitive and simple 
methods yield high performance. 
Defect 
Detection 
Ko et al., (2017) 0 Supervised & 
Unsupervised 
Learning 
GMM, PW, LOF, K- 
MEANS, PCA, k- 
PCA, SVDD 
0 A novel method for feature extraction is proposed for 
the manipulation of multidimensional time-series 
data. Specifically, the method is tested on after-sales 
data of heavy machine engines. 
Defect 
Detection 
Tušar et al., (2017) 0 Supervised Learning DT, RF 0 A quality prediction framework based on machine 
vision, Decision Tree-based algorithms, and 
evolutionary optimization algorithms are studied in 
terms of overfitting problems, and authors show that, 
in some cases, over-optimization leads to overfitting. 
Defect 
Detection 
Liu et al., (2017) 0 Unsupervised 
Learning 
RBM 0 A Deep Belief Network is employed to capture 
different semantic representations of the voltage signal 
for fault detection and isolation system. The method 
proved to be superior to traditional feature extraction 
methods. 
Defect 
Detection 
Kim et al., (2018) 4 Supervised Learning DT 0 The paper deals with defect detection and focuses on 
the imbalance problem. Using a die-cast data set, it is 
shown that the AdaC2 algorithm, a cost-sensitive 
Decision Tree algorithm, outperforms other classifiers 
in case of unbalanced data. 
Defect 
Detection 
Khanzadeh et al., 
(2017) 
7 Unsupervised 
Learning 
SOM 0 A Self-Organizing Map is employed for measuring 
geometric accuracy, with fewer data and avoiding the 
need to define custom landmark features. Identified 
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Table 3b (continued ) 
Sub-Area Article Received 
Citations 
Algorithm 
Classification 
Main Algorithm Simul. Based Goals & Approach 
clusters correspond to specific types of deviation from 
the ideal shape. 
Defect 
Detection 
Manohar et al., 
(2018) 
1 Unsupervised 
Learning 
SS, PCA 0 ML is employed to learn, from past data, the 
distribution of shim gaps in aircraft assembly. ML is 
coupled with optimized sparse sensing to gather new 
data. Also, Robust Principal Component Analysis is 
used for dimensionality reduction. 
Defect 
Detection 
Khanzadeh et al., 
(2018) 
12 Supervised Learning DT, KNN, SVM, LDA, 
QDA 
0 ML algorithms are used to regress defect occurrence 
from melt pool characteristics, in additive 
manufacturing. DT shows the lowest type II error, 
while KNN achieves the highest accuracy. The 
combination of a morphological model with 
supervised learning techniques outperforms state-of- 
the-art results. 
Defect 
Detection 
Zhu et al., (2018) 3 Supervised Learning Gauss. PR 0 A multi-task Gaussian Process is employed to analyze 
in-plane geometric deviations from an additive 
manufacturing process to estimate geometric 
deviation. 
Defect 
Detection 
Carvajal Soto et al. 
(2019) 
3 Supervised Learning GrB NN 1 A Multi-layer Perceptron, a Random Forest, and a 
Gradient Boosting algorithm are applied to build a 
real-time online failure identification solution. 
Decision Tree-based methods outperform the NN, 
mainly due to data unbalance. 
Defect 
Detection 
Peres et al., (2019) 2 Supervised Learning NBC, KNN, XGB, RF, 
SVM 
0 Different methods are compared to recognize productdimensional variability, for defect detection in a real 
automotive multistage assembly line. 
Defect 
Detection 
Stoyanov et al., 
(2019) 
0 Supervised Learning SVM 0 SVM is employed for failure testing in electronics 
manufacturing. The objective is to develop an 
intelligent optimization of the tests’ sequence and a 
reduced number of tests. 
Defect 
Detection 
Chen et al., (2019), 
Chen et al. (2019b) 
0 Supervised Learning NN, SVM 0 NN and SVM are compared for automatic detection 
and classification of welding defects. Applied to a 
dataset of galvanized steel sheets, NN outperformed 
SVM. 
Defect 
Detection 
Kim and Kang, 
(2019) 
0 Supervised Learning NN, DT, KNN 0 NN, DT, and KNN are compared for defect detection, 
using data set containing irrelevant variables. KNN 
shows the maximum degradation, while DT is more 
resilient. 
Defect 
Detection 
Ruiz et al., (2019) 0 Supervised Learning KNN, RF, NN 0 Three methodologies are compared to detect breakage 
during the drawing of steel. The imbalance problem is 
tackled using different techniques (under-sampling, 
oversampling, SMOTE). 
Defect 
Detection 
Caggiano et al., 
(2019) 
3 Supervised Learning CNN 0 A Deep Convolutional NN is used for online defects 
detection. Specifically, the NN is trained to analyze in- 
process images of Selective Laser Melting 
manufacturing process. 
Defect 
Detection 
Tsutsui and 
Matsuzawa, (2019) 
1 Supervised Learning DNN 0 Deep Learning models are applied to Optical Emission 
Spectroscopy for predicting measurements of ongoing 
semiconductor process. The proposed network 
topology outperforms standard models for image 
analysis. 
Defect 
Detection 
Imoto et al., (2019) 0 Supervised Learning CNN, TL 0 A Convolutional NN, trained on a real semiconductor 
fabrication dataset, is used for defect classification, 
based on the analysis of electron microscope images. 
To reduce the amount of data of the training step a 
Transfer Learning algorithm is also used. 
Defect 
Detection 
Oh et al. (2019b) 0 Supervised Learning ASVM 0 The paper presents a framework for on-line-quality 
control of a sunroof assembly line. Thanks to an 
iterating loop between a data pre-processing module 
and an SVM learning module, the defect classifier 
continuously learns from past experiences. 
Defect 
Detection 
Yacob et al., (2019) 2 Supervised & 
Unsupervised 
Learning 
SVM, DT, KNN 0 The aim is to detect parts’ anomalies, based on surface 
characteristics, and categorize them as systematic and 
random variations. To reduce the number of physical 
parts needed to train the models, also digital twins 
(Skin Model Shapes) are used. This has the additional 
benefit of avoiding biases and unbalancing problems. 
Defect 
Detection 
Papananias et al., 
(2019) 
2 Supervised Learning Bayesian R., ANOVA 0 The paper develops a probabilistic model, based on 
Bayesian linear regression, for flatness tolerance 
evaluation. Two case studies demonstrate the 
effectiveness of the probabilistic model. 
Defect 
Detection 
Saqlain et al., 
(2019) 
5 Supervised Learning NN, LogR, GrB, RF 0 The paper proposes a soft voting ensemble classifier 
with multi-types features, to identify wafer map defect 
patterns in semiconductor manufacturing. Four 
classifiers are used, and results are combined assigning 
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Table 3b (continued ) 
Sub-Area Article Received 
Citations 
Algorithm 
Classification 
Main Algorithm Simul. Based Goals & Approach 
higher weights to the classifiers with higher prediction 
accuracy. 
Defect 
Detection 
Iqbal et al., (2019) 3 Supervised Learning AE, Clustering 1 The paper presents a novel approach for automated 
Fault Detection and Isolation. Deep Auto Encoders 
coupled with other ML tools (hierarchical clustering 
and Markov Chains) model the spatial/temporal 
patterns found in the data and successfully diagnose 
and locate multiple classes of faults. 
Image 
Recognition 
Ravikumar et al., 
(2011) 
28 Supervised Learning NBC, DT 0 A Decision Tree and a Naïve Bayes classifier are 
compared relative to an image classification task, for 
automated visual inspection. Feature pre-processing is 
performed to generate images’ histogram features 
used as input for the classifiers. 
Image 
Recognition 
El-Bendary et al., 
(2015) 
10 Supervised & 
Unsupervised 
Learning 
LDA, PCA, SVM 0 The aim is to classify tomato ripeness based on their 
color. SVM, Linear Discriminant Analysis, and 
Principal Components Analysis are combined and 
tested on a sample of 250 images. Results are validated 
with 10-fold cross-validation. 
Image 
Recognition 
Chen et al., (2016) 2 Supervised Learning SVM 0 Aiming to enhance yield and to reduce defect rate, an 
automatic optical inspection system is proposed. The 
system makes use of an SVM classifier, which is 
strengthened by a similarity approach capable to 
reduce the number of false alarms. 
Image 
Recognition 
Yang et al., (2018) 2 Supervised Learning CNN 0 A Convolutional Neural Network, coupled with a 
three-point circle fitting method, is used for automatic 
aperture detection of LED cups. 
Image 
Recognition 
Gobert et al., 
(2018) 
5 Supervised Learning SVM 0 To enable in-process re-melting and defects correction 
(of an additive manufacturing process), an in-situ 
defect detection protocol is proposed. Using SVM, 
digital single-lens images are pre-processed and 
classified, with an accuracy rate of around 80%. 
Image 
Recognition 
Yuan et al., (2018) 2 Supervised Learning CNN 0 A Convolutional NN is trained (in a supervised 
fashion) to analyze 10 ms video clips of laser powder 
additive manufacturing. The CNN can predict LPBF 
track widths and track continuity, from in situ video 
data. 
Image 
Recognition 
Scime and Beuth, 
(2018) 
1 Supervised Learning CNN 0 The input layer of a Convolutional NN is modified to 
allow the NN to learn the appearance of the powder 
bed anomalies and key contextual information with 
the scale-invariance property. This alteration improves 
accuracy and mitigates human biases. 
Image 
Recognition 
Penumuru et al. 
(2019) 
0 Supervised Learning SVM, DT, RF, LogR, 
KNN 
0 Alternative methodologies are compared in the 
recognition of metallic materials from surface images. 
The robustness of the classifiers is checked for various 
camera orientations, illuminations angle, and focal 
length. 
Image 
Recognition 
Scime and Beuth, 
(2019) 
12 Supervised & 
Unsupervised 
Learning 
SIFTS, SVM 0 The goal is to detect keyholing porosity and balling 
instabilities in laser powder bed fusion additive 
manufacturing. A scale-invariant description of the 
melting pool morphology is constructed applying the 
“Bag-of-Words” technique to features extracted using 
Scale Invariant Feature Transforms. SVM is then 
applied to classify the observed melt pools. 
Life cycle 
Management 
Jennings et al., 
(2016) 
2 Supervised Learning RF, NN, SVM 0 The aim is to predict the obsolescence risk level at a 
certain stage of the lifecycle of a device. Specifically, 
NN, Random Forests, and SVM are compared, to 
partition “active” and “obsolete” smartphones. 
Online Quality 
Control 
Ribeiro, (2005) 47 Supervised Learning SVM, NN 0 The work compares NN and SVM to predict product 
quality using process’ data. Using the real-data of a 
molding injection process, the paper shows that both 
methods can quickly react to unexpected disturbances. 
Online Quality 
Control 
Lin et al., (2011) 16 Supervised Learning SVM, NN 0 Support Vector Machine and Neural Networks are 
compared to effectively classify seven different control 
charts patterns for specific causes. SVM results less 
prone to overfitting and more robustto background 
noise. 
Online Quality 
Control 
Wuest et al. (2013) 16 Supervised & 
Unsupervised 
Learning 
HC SVM n.a. 
(theoretical) 
Hierarchical Clustering and SVM are used to analyze 
multidimensional data (of the product’s state along the 
whole manufacturing process) and to trigger 
corrective actions if needed. 
Online Quality 
Control 
Yang and Zhou, 
(2015) 
2 Supervised Learning NN, LVQ 0 This study proposes a NN, ensemble-enabled, 
autoregressive, and coefficient-invariant control chart 
patterns recognition model. Each NN is trained to 
recognize CCP with a specific autoregressive 
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M. Bertolini et al. 
Expert Systems With Applications 175 (2021) 114820
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flexible processes with the ability to automatically take corrective ac-
tions as soon as possible. For instance, Wuest et al. (2013), proposed a 
hybrid approach, namely Semi-Supervised Learning, to estimate the 
product’s state along its manufacturing process. At the first stage, an 
unsupervised hierarchical clustering is used to label the records of the 
training set. Next, labeled data are processed by a supervised layer, 
based on SVM, which performs the final classification. In this way, the 
need for a manually labeled data set is avoided, with great benefit in 
terms of development time and classification accuracy. Similarly, 
Nakata et al. (2017) proposed a Big-Data based, long-term automated 
monitoring system of micro-conductors manufacturing, which allowed 
production engineers to obtain significant yield enhancements. 
More recently, visual quality inspection, supported by automated 
‘Image Recognition’, is emerging as a promising field for defects identi-
fication and classification. For instance, Chen et al. (2016) used an 
automatic optical inspection system, based on an SVM classifier with a 
similarity approach, to reduce the false alarm rate (of defect classifica-
tion) in the production of CMOS image sensors. El-Bendary et al. (2015) 
proposed the application of machine learning techniques to assess to-
mato ripeness. Posed as a multi-class classification task, the problem was 
solved with a hybrid classifier (based on SVM and Linear Discriminant 
Analysis), supported by Principal Component Analysis for feature 
extraction. Other interesting works concern the use, increasingly com-
mon, of CNNs for image recognition and visual control, as in the work by 
Yuan et al. (2018) and Scime and Beuth (2018), who used this neural 
network topology to detect anomalies in laser powder additive 
manufacturing. 
Other interesting applications propose integrating ML and statistical 
control charts, to understand if drift in operating parameters is taking 
place. Notable examples can be found in Lin et al. (2011) and in Yang 
and Zhou (2015), who used an ensemble of NNs to handle 
autocorrelated data in control chart patterns. The model can detect up to 
seven types of unnatural patterns and drifts and can be used by quality 
managers to promptly identify the root causes of processes’ anomalies. 
4.4.3. Production Planning & Control (PPC) 
As noted in Section 4.3, in terms of ML applications, Process Planning 
and Control is an emerging trend that is attracting much academic and 
industrial interest in the last decade. Mainly, it includes all the activities 
that are needed to manage a manufacturing process and to improve its 
operating performance; as shown in Table 3c, ‘Performance Prediction 
and Optimization’ is the most studied problem. 
For instance, Arredondo and Martinez (2010) proposed an RL 
approach based on Local Weighted Regression, to implement an order 
acceptance policy, similar to Workload Control. In particular, jobs can 
either be put in a rejection or in an acceptance set and, in this way, the 
average revenues can be maximized relative to the installed capacity. 
Doltsinis et al. (2012) used RL for production ramp-up optimization. To 
this aim, they formulated the problem as a sequence of technical de-
cisions needed to progress the system toward the desired steady state, in 
the shortest amount of time. Other interesting contributions can be 
found in Li et al. (2016), who combined Q-Learning and SVM to reduce 
the electricity consumption of an automated manufacturing system, and 
in Agarwal et al. (2019), who used an autoencoder to find the best set of 
process parameters for optimizing process productivity and profit. 
‘Scheduling’ is the second most studied problem. This topic has al-
ways attracted a lot of industrial and academic interest, not only for its 
immediate practical implications but also because it is extremely chal-
lenging from a research perspective. Indeed, scheduling problems are 
known to be NP-hard (almost ever) and, for this reason, they create a 
fertile ground for the application of novel ML algorithms. One of the first 
works is that by Priore et al. (2001), who studied the implementation of 
Table 3b (continued ) 
Sub-Area Article Received 
Citations 
Algorithm 
Classification 
Main Algorithm Simul. Based Goals & Approach 
coefficient. The outputs are combined through 
Learning Vector Quantization. 
Online Quality 
Control 
Nakata et al., 
(2017) 
0 Supervised Learning, 
Unsupervised 
Learning 
CNN, K-means 0 A Convolutional NN is applied to classify wafers’ 
failure map patterns. It is integrated into a three-stage 
automated monitoring system fed with real-time 
massive manufacturing data. 
Online Quality 
Control 
Zhang et al., 
(2019a), Zhang 
et al. (2019b) 
0 Supervised Learning LSTM 0 A Long-Short Term Memory NN takes as input 
temperature and vibration data of an additive 
manufacturing process and predicts the tensile 
strength of the manufactured item. Layer-wise 
Relevance Propagation is used to assess parameters’ 
influence. 
Online Quality 
Control 
Oh et al. (2019a) 1 Supervised Learning SVM 0 A cost-effective SVM is used for online QC of a 
manufacturing process. The SVM incorporates 
inspection-related expenses and error types and is 
tested against an automotive door-trim manufacturing 
process. Design of Experiment is carried out to perform 
sensitivity analysis. 
Online Quality 
Control 
Zhu et al., (2019) 2 Reinforcement 
Learning 
QLrn, TS 0 Acoustic emissions sensing, through fiber brag grating, 
is coupled with Q-Learning and Taboo Search for 
quality monitoring of an additive manufacturing 
process. 
Online Quality 
Control 
Yu (2019) 0 Supervised Learning SDAE 0 An enhanced stacked denoising autoencoder (ESDAE), 
with manifold regularization, is used for wafer map 
pattern recognition (WMPR). The approach, which can 
be used for on-line detection of map defects, has been 
successfully validated using a real-world wafer map 
dataset. 
Online Quality 
Control 
Yu et al., (2019b) 4 Supervised & 
Unsupervised 
Learning 
SDAE 1 A Stacked Denoising Autoencoder is used for pattern 
recognition. SDAE denoises the input signal and 
extracts the important features used as input of a final 
classification layer. SDAE layers are trained in an 
unsupervised way, whereas the regression is fine- 
tuned with a supervised approach. By doing so SDAE 
greatly improves its generalization performance and 
can learn more robust and compact features. 
M. Bertolini et al. 
Expert Systems With Applications 175 (2021) 114820
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Table 3c 
Production Planning and Control Papers. 
Sub-Area Article Rec. 
Cit. 
Algorithm Class. Main Alg. Sim. 
Based 
Cyber 
Ph. Sys. 
Goals & Approach 
Performance 
Prediction 
Arredondo and 
Martinez, (2010) 
20 Reinforc. 
LearningLWR 1 0 A locally weighted regression is used to learn the value of 
accepting or rejecting a production order. The approach 
maximizes the average revenue obtained per unit cost of the 
installed capacity. 
Performance 
Prediction 
Meidan et al., (2011) 19 Superv. 
Learning 
NBay 1 0 Using Conditional Mutual Information Maximization, a 
selective Naïve Bayesian Classifier is used to select the most 
discriminative features for cycle time prediction. 
Performance 
Prediction 
Doltsinis et al., 
(2012) 
2 Reinforc. 
Learning 
QLrn 0 0 A Q-Learning algorithm supports decision-making during 
production ramp-up. It significantly reduces the time needed to 
reach a stable state. 
Performance 
Prediction 
Duan et al., (2015) 0 Superv. 
Learning 
SVM, DT, 
Bay.R 
0 0 Based on production capacity and orders’ properties and 
requirements, a tree-based classifier is used to accept or reject 
incoming orders, to maximize profit. 
Performance 
Prediction 
Heger et al., (2016) 5 Superv. 
Learning 
Gauss. PR 1 0 Gaussian Process Regression predicts the best parameters’ 
settings, conditioned on current system status. Results showed a 
significant mean tardiness reduction. 
Performance 
Prediction 
Delgoshaei and 
Gomes, (2016) 
1 Superv. 
Learning 
SA, NN 1 0 A hybrid model based on NN and Simulated Annealing is used 
to optimize the prediction mix. The focus is on a cellular 
shopfloor with parallel machines and bottlenecks 
Performance 
Prediction 
Li, (2016) 0 Superv. & 
Reinfor. 
Learning 
SVM, QLrn 1 0 To reduce the electricity consumption of a multi-route 
transportation system, SVM and Q-learning algorithms are 
proposed. The approach is validated through simulation. 
Performance 
Prediction 
Diaz-Rozo et al., 
(2017) 
0 Unsuper. 
Learning 
K-Mean, HC, 
GMM 
0 1 A Cyber-Physical system is described, and 3 clustering 
algorithms are compared, to group high throughput machining 
cycle conditions. 
Performance 
Prediction 
Rude et al. (2015) 0 Unsuper. 
Learning 
HMM 0 0 An unsupervised Hidden Markov Model, used for recognition of 
worker activity in manufacturing processes, shows comparable 
results with supervised techniques, thus reducing the need for 
labeled data. 
Performance 
Prediction 
Chan et al., (2018) 0 Superv. 
Learning 
LASSO, 
Cluster. 
1 0 The aim is to estimate the costs of new jobs, based on historical 
data and technical features. A model based on dynamic 
clustering for model selection, coupled with Lasso and/or 
Elastic Regression is proposed. 
Performance 
Prediction 
Ghadai et al., (2018) 0 Superv. 
Learning 
CNN 0 0 Difficult-to-manufacture geometries are predicted with a 3D 
Convolutional NN. A second method is proposed to explain the 
causes of non-manufacturability. 
Performance 
Prediction 
Tulsyan et al., (2018) 3 Superv. 
Learning 
Gauss. PR 1 0 The paper addresses the “Low-N” problem, relatively to a batch 
manufacturing process for which scarce historical data are 
available. The problem is tackled using a multi-dimensional 
approach based on Gaussian Processes. 
Performance 
Prediction 
Gyulai et al., (2018) 2 Superv. 
Learning 
RF, SVM 0 1 Analytical and ML techniques are applied, within a Digital Data 
Twin, to predict Lead Time. The focus is on flow-shops with 
frequent changes in customer demand. Frequent retraining and 
on-line learning are adopted. 
Performance 
Prediction 
Silbernagel et al., 
(2019) 
0 Superv. & 
Unsuperv. 
Learning 
AE, PCA, K- 
mean 
0 0 A Convolutional Autoencoder is used to cluster images of the 
processing of pure copper in a laser powder bed fusion printer. 
The quality of each cluster is mapped manually, to the original 
set of the process parameter. 
Performance 
Prediction 
Stathatos and 
Vosniakos, (2019) 
0 Superv. 
Learning 
NN 0 0 Three NNs are used to predict, given a laser trajectory, the 
evolution of temperature and density. The trajectory is 
decomposed using a custom method that provides a local 
description relative to the surroundings. 
Performance 
Prediction 
Agarwal et al. (2019) 0 Superv. &, 
Unsup. Learning 
AE, NN, SVM 1 0 The paper presents 2 approaches to find the ranges of process 
inputs optimizing process productivity and profit. Supervised 
and unsupervised deep learning techniques are investigated, 
and the layer-wise relevance propagation algorithm is used to 
prune the inputs of the NNs. 
Performance 
Prediction 
Jang et al., (2019) 0 Superv. 
Learning 
NN 0 0 The paper presents a model to predict the yield of new wafer 
maps. The approach is based on a deep NN and exploits spatial 
relationships relative to the positions of dies (on a wafer) and 
die-level yield variations. 
Performance 
Prediction 
Gurgenc et al., 
(2019) 
0 Superv. 
Learning 
NN 0 0 A deep NN is used to estimate the machining times of a CNC 
milling machine. Design and manufacturing parameters are 
used as input and the network is trained with an extreme 
learning machine (ELM), with optimal results. 
Process Control Chinnam, (2002) 49 Superv. 
Learning 
SVM, NN 0 0 NNs and SVM are applied to recognize quality drifts in related 
and unrelated manufacturing processes. It is shown that even 
simple linear kernels perform better than Statistical Process 
Control techniques. 
Process Control Sun et al., (2004) 70 Superv. 
Learning 
NN, SVM 0 0 A tool condition monitoring system, based on acoustic emission 
sensing, is presented. NN and SVM are used to classify the tool 
state; the performance evaluation is based on manufacturing 
loss, due to misclassification. 
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Table 3c (continued ) 
Sub-Area Article Rec. 
Cit. 
Algorithm Class. Main Alg. Sim. 
Based 
Cyber 
Ph. Sys. 
Goals & Approach 
Process Control Shin et al., (2012) 13 Reinfor. 
Learning 
AHC 1 0 A fuzzy Reinforcement Learning system is applied for 
manufacturing control. The agent has the ability of self- 
regulating in response to the system’s changes. It can also 
dynamically re-set its goal. 
Process Control García Nieto et al. 
(2012) 
20 Superv. 
Learning 
SVM, NN 0 0 SVM and NN are used to control the manufacturing process of a 
paper mill. NN and SVM are chosen, given their capabilities to 
reproduce non-linear relationships among explanatory 
variables. 
Process Control Wang et al., (2018) 1 Superv. 
Learning 
CNN 0 0 A Convolutional NN is applied for continuous human motion 
analysis. The aim is to infer human actions and future intentions 
for human-robot collaboration. Experiments showed a 96% 
accuracy. 
Process Control Maggipinto et al., 
(2018) 
0 Supervised 
Learning 
CNN 0 0 A Convolutional NNN is employed to avoid the feature 
extraction phase that is generally needed for image processing 
in virtual metrology. It is applied to optical emission 
spectroscopy data. 
Process Control Mezzogori et al. 
(2020) 
0 Superv. 
Learning 
NN 1 0 Deep NN and Linear Regression are used to predict throughput 
time, given the current system’s state. The system is regulated 
by Workload Control and the aim is to define reliable due dates 
reducing the % of tardy jobs. 
Process Control Zan et al., (2019) 5 Superv. 
Learning 
CNN, NN 1 0 A 1-D Convolutional NN is applied to a dataset with 6 control 
chart patterns. The CNN performance is compared to manual 
feature extraction methods and a simple NN, showing 
advantages. 
Process Control Ma et al., (2019) 4 Reinfor. 
Learning 
DDPG 1 0 Deep Deterministic Policy Gradient is applied to chemical 
process control. Many operating features, such as action 
boundaries and reward definitions are discussed. 
Process Control Joswiak et al., 
(2019) 
0 Superv. & 
Unsuperv. 
Learning 
PCA, t-SNE, 
UMAP 
0 0 16 dimensionality reduction techniquesare compared using 
data sets of chemical plants. UMAP (Uniform Manifold 
Approximation and Projection) outperforms other methods 
Process Control Zhang et al., 
(2019a), Zhang et al. 
(2019b) 
1 Unsuperv. 
Learning 
K-mean 0 0 A K-Means with Davies-Bouldin Criterion is used to decompose 
the surface of additive manufactured parts, to optimize the 
build orientation dynamically. 
Process Control Gardner et al., 
(2019) 
2 Superv. 
Learning 
NN, GrB. 0 0 The work proposes a combined approach (based on NN and 
Gradient Boosting) for optimal parameters’ selection depending 
on the location of a 3D printing process. 
Process Control Chen et al., (2019), 
Chen et al. (2019b) 
1 Superv. & 
Unsuperv. 
Learning 
NN 0 0 A Deep Neural Network is applied for energy consumption 
modeling, which usually relies on abundant labeled data. The 
NN is trained with a semi-supervised approach, to better exploit 
non-labeled data. An experimental study on furnace energy 
consumption data is described. 
Process Control Dornheim et al. 
(2019) 
0 Reinforc. 
Learning 
QLrn 1 0 The paper proposes a self-learning optimal control algorithm 
(based on Q Learning), for manufacturing processes subject to 
nonlinear dynamics and stochastic influences. It accounts for 
stochastic variations of the process conditions and can cope 
with partial observability. 
Process Control Denkena et al., 
(2019) 
0 Superv. 
Learning 
SVM, SA 1 0 The aim is to optimize the operating parameters of a grinding 
process of helical flutes. The model integrates simulation, SVM, 
and an optimizer (based on simulated annealing) to fine-tune 
both the cutting feed and the speed of the grinder. 
Scheduling Aydin and Oztemel, 
(2000) 
96 Reinforc. 
Learning 
QLrn 1 1 An RL agent learns how to select the most appropriate 
dispatching rule and performs dynamic scheduling based on 
available information. An extension of the Q-learning 
algorithm, called Q-III, is also presented. 
Scheduling Priore et al., (2001) 7 Superv. 
Learning 
DT 1 1 Decision Trees are used to identify, the best dispatching rule for 
flow and job shop systems. Results are good, but many 
simulation runs are needed to generate training examples. 
Scheduling Mönch et al., (2006) 40 Superv. 
Learning 
DT, NN 1 0 Decision Trees and NN are used to fine-tune a simple heuristic 
for dispatching rule selection. Data is generated via simulation. 
Scheduling Priore et al., (2006) 58 Superv. 
Learning 
DT, NN, CBR 1 0 Inductive Learning, NNs, and Case-Based Reasoning are 
compared to find the best dispatching rule. Testes performed 
via simulation 
Scheduling Csáji et al., (2006) 19 Reinforc. 
Learning 
SA, TD, NN 1 0 Simulated Annealing, Temporal Difference Learning, and NNs 
are used to solve a dynamic job-shop scheduling problem in a 
distributed and iterative way. Each machine and job is 
associated with an agent, which has the role of selecting the best 
schedule. 
Scheduling Shiue, (2009) 14 Superv. 
Learning 
DT 1 1 A Decision Tree is applied to a two-stage real-time scheduling 
scenario with a nonstationary product mix: first, a knowledge 
base class is selected, then a scheduling rule is chosen. 
Scheduling Gaham and 
Bouzouia, (2009) 
3 Superv. 
Learning 
NN, GA 1 0 A Genetic Algorithm is used to solve a flexible job shop 
scheduling, while 2 NNs are used for machine allocation for 
priority assignment. 
Scheduling Priore et al., (2010) 5 Superv. 
Learning 
SVM 1 1 SVM is used to find the best dispatching rule. Tests are made 
with simulated data. 
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M. Bertolini et al. 
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a Decision Tree algorithm, (namely C.4.5) to select the best dispatching 
rule, depending on the current system state. The work was subsequently 
extended and refined with the addition of other ML techniques, and it 
was shown that SVM is the best approach when the objective is the 
minimization of the mean tardiness and of the mean flow time (Priore 
et al., 2006, 2010). Similar work was made by Heger et al. (2016), who 
investigated the application of Gaussian Processes to dynamically 
readjust the parameters of dispatching rules, based on current shop-floor 
conditions. As a result, their approach drastically reduced jobs’ mean 
tardiness. 
More recently, scientific interest has moved from the selection of the 
best dispatching rule to the definition of fully automated adaptive and 
real-time schedulers. Multiple and diverse approaches have been pro-
posed, ranging from Self-Organizing Maps (SOM), that autonomously 
extrapolate the suitable classes which best explain the data (as in Shiue 
et al., 2011), to hybrid models based on simulation, population-based 
metaheuristics, and NNs (as in Gaham & Bouzouia, 2009) that make it 
possible to dynamically regenerate optimal scheduling sequences, 
anytime certain manufacturing events take place. In some cases, even 
hybrid models, based on metaheuristics and RL, are used. For instance, 
Csáji et al. (2006) proposed a combination of a Simulated Annealing, RL, 
and NN to implement an adaptive iterative distributed scheduling al-
gorithm for market-based production, in which each machine and job is 
seen as an agent and can participate in a bid system with the global aim 
of minimizing the total production time. Palombarini and Martínez 
(2012), implemented a Q-Learning algorithm to reschedule jobs anytime 
an unforeseen event (e.g. arrival of a rush order or breakdown of a 
working machine) takes place on the shop floor. 
Many works have also dealt with the problem of ‘Process Control’, a 
field closely related to ‘Online Quality Control’, in which the objective is 
that to govern a manufacturing process (regulating and fine-tuning its 
main operating parameters) so that it behaves as planned with little or 
none non-conforming situations. Shin et al. (2012) proposed an RL- 
based approach to build a self-adapting manufacturing system. The RL 
model, based on two collaborating neural networks, makes the 
manufacturing system completely autonomous, as it becomes able to set 
its goals and reconfigures itself to changing environmental conditions. 
Next, similar work was proposed by Dornheim et al. (2019), who used a 
Q-Learning method for optimal and automatic control of manufacturing 
processes characterized by non-linear dynamics. To conclude, we cite 
the interesting and very recent work by Joswiak et al. (2019) who 
tackled process control following a more practitioner-oriented 
approach. In particular, the authors compared different dimensionality 
reduction algorithms to create a dashboard of meaningful process data 
aimed to support and to enhance human decision-making. 
4.4.4. Supply chain management 
SCM is the process of planning, controlling, and executing all logistic 
flows, from the acquisition of raw materials to the delivery of end 
products, in the most streamlined and cost-effective way. In this sense, 
SCM encompasses a diversified set of activities that broadly includes: 
demand planning, sourcing, inventory management, and trans-
portations. As shown by the rather limited number of papers included in 
Table 3d, in terms of ML applications, SCM is not yet a much-explored 
domain, as already confirmed by the keywords’ analysis of Section 
4.3, which revealed that, although SCM is a consolidated field, with a 
recently increasing trend, it is not a star topic yet. 
‘Modelling and Coordination’ is the most studied topic of the SCM 
area. Generally, a two-stage supply chain with non-stationary demand is 
considered and a variegated set of performance indicators is optimized 
using a multi-agent-based simulated scenario. One of the first works of 
this kind is that of Kim et al. (2008), who used an Action Value-basedRL 
algorithm to optimize and to compare a centralized and a decentralized 
supply chain, whose state is described by customer-demand patterns. 
Chaharsooghi et al. (2008) tested the Q-Learning algorithm using, as a 
simulative setting, that of the famous Beer Game (Coppini et al. 2010), 
showing that purchasing orders generated by the RL based decision 
system greatly reduce the bullwhip effect of the supply chain. More 
recently, a similar approach was used by Mortazavi et al. (2015) who 
used Q-Learning to coordinate a four-echelons supply chain with non- 
stationary demand. Other than using RL based models, some recent 
papers applied SL approaches to coordinate the supply chain. For 
instance, Cavalcante et al. (2019) used K-Nearest Neighbours and Lo-
gistic Regression for suppliers’ selection, while Priore et al. (2019) used 
a Decision Tree to dynamically select the best replenishment model for 
each tier of the supply chain. 
‘Demand Forecasts’, a cornerstone of SMC, is the second most 
considered topic, with applications in different settings and demand 
Table 3c (continued ) 
Sub-Area Article Rec. 
Cit. 
Algorithm Class. Main Alg. Sim. 
Based 
Cyber 
Ph. Sys. 
Goals & Approach 
Scheduling Shiue et al., (2011) 2 Unsuper. 
Learning 
SOM 1 1 A Self-Organizing Map is used to select multiple scheduling 
rules. The SOM outperforms, in the long run, traditional 
approaches based on a single scheduling rule. 
Scheduling Palombarini and 
Martínez, (2012) 
9 Reinforc. 
Learning 
QLrn 1 1 A Q-Learning system is proposed for adaptive rescheduling, to 
respond to non-planned events such as new order or equipment 
failures. 
Scheduling Shiue et al., (2012) 5 Superv. 
Learning 
NN, DT, 
Bagging SVM, 
GA 
0 1 A real-time scheduling system is proposed. NNs, SVM, and 
Decision Tree (based on Bagging) are integrated, and a Genetic 
algorithm is used for feature selection. The approach is 
evaluated using 10-fold cross-validation. 
Scheduling Drakaki and Tzionas, 
(2017) 
0 Reinforc. 
Learning 
QLrn 1 0 An order-picking scheduling problem is tackled through a Q- 
learning algorithm (without a Neural Network function 
approximator) coupled with hierarchical Colored Petri Nets. 
Scheduling Priore et al., (2018) 2 Superv. 
Learning 
Bagging, 
Boosting 
1 1 Bagging, boosting, and stacking methods are tested for 
dispatching rule selection. Mean tardiness and mean flow time 
are improved. 
Scheduling Tan et al., (2019) 1 Reinforc. 
Learning 
QLrn 1 1 A Multi-agent reinforcement learning approach for dynamic 
planning and scheduling is proposed. The focus is on robot 
assembly lines, to minimize the makespan. 
Scheduling De Jong et al., 
(2019) 
1 Superv. 
Learning 
CNN 1 0 A CNN is proposed for quick and accurate makespan forecast, 
both for job and shop floor systems. A visual representation of 
the layout and the system’s state is also provided as an 
additional input. 
Scheduling Lin et al., (2019) 2 Reinforc. 
Learning 
DQN 1 1 The paper integrates Deep Q-Learning with edge computing to 
solve complex scheduling problems requiring different 
dispatching rules 
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Expert Systems With Applications 175 (2021) 114820
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Table 3d 
Logistic and Supply Chain Management Papers. 
Sub-Area Article Received 
Citations 
Algorithm 
Classification 
Main 
Algorithm 
Simul. 
Based 
Goals & Approach 
Modeling and 
Coordination 
Chi et al., (2007) 19 Supervised 
Learning 
SVM, GA 1 SVM for regression is compared to a DOE approach to predict 7 
performance measures of Vendor Managed Inventory. The optimal 
input settings are generated using a genetic algorithm. The main 
benefit of SVM is the possibility to avoid system disruption, as 
opposed to the DOE approach. 
Modeling and 
Coordination 
Kim et al., (2008) 12 Reinforcement 
Learning 
AVL 1 An asynchronous RL agent is used for inventory control in a serial 
supply chain. Time-varying rewards are used, and the approach is 
tested either for centralized and decentralized supply chains. 
Modeling and 
Coordination 
Chaharsooghi et al., 
(2008) 
37 Reinforcement 
Learning 
QLrn 1 Q-Learning is proposed to coordinate a multi-agent supply chain 
(with 4 tiers) and to minimize the bullwhip effect. The 
environment state is described by inventory position, ordering size 
to the upstream level, and distribution amount at each level and 
the objective is to minimize the total inventory costs. 
Modeling and 
Coordination 
Zarandi et al. 
(2012) 
2 Reinforcement 
Learning 
TD 1 A fuzzy-inference system is used to approximate the value 
function returned by a Reinforcement Learning approach for 
inventory control. Specifically, the agent models a supplier and 
determines the number of orders for each retailer, with supply 
capacity constraints. 
Modeling and 
Coordination 
Mortazavi et al., 
(2015) 
7 Reinforcement 
Learning 
QLrn 1 Q-Learning algorithm is used, in an agent-based simulation of a 4- 
echelon chain, with non-stationary demand. The objective is to 
coordinate the ordering processes. The Value-at-Risk methodology 
is also applied both for risk evaluation and sensitivity analysis. 
Modeling and 
Coordination 
Cavalcante et al., 
(2019) 
0 Supervised 
Learning 
LogRT, KNN 1 Simulation and ML are combined for supplier selection in resilient 
chains. K-nearest neighbors and Logistic Regression are compared 
for the classification task. 
Modeling and 
Coordination 
Priore et al., (2019) 3 Supervised 
Learning 
DT 1 A dynamic framework for automated inventory management is 
proposed. Specifically, a Decision Tree periodically selects the best 
inventory model for a node of the supply chain according to its 
state and the network state. 
Modeling and 
Coordination 
Du and Jiang, 
(2019) 
0 Reinforcement 
Learning 
QLrn 1 A multi-agent reinforcement learning approach is proposed. The 
aim is to optimize the manufacturer’s strategies, in a dynamic 
supply chain, mitigating the risk of the supplier. The approach is 
successfully validated in a simulated environment with a single 
manufacturer and a single supplier. 
Modelling and 
Coordination 
González Rodríguez 
et al. (2019) 
0 Supervised Fuzzy Inf 
System + Tree 
0 The paper proposes a decision support system to coordinate a 
Closed-Loop Supply Chain in presence of uncertainties. The 
support system makes use of a Fuzzy Inference Systems, whose 
rules are automatically generated with a regression tree. One of 
the main contributions is the ability to limit the impact, on 
inventories, of imbalances in the rest of the chain. 
Demand 
Forecasting 
Carbonneau et al., 
(2007) 
6 Supervised 
Learning 
SVM, NN, 
RNN 
0 ML algorithms are compared to statistical methods for demand 
forecasting in supply chains. Tests showed that ML techniques are 
generally outperformed when applied to single feature time- 
series. The performance of ML rapidly increases using multi- 
dimensional time-series. 
Demand 
Forecasting 
Villegas et al., 
(2018) 
0 Supervised 
Learning 
SVM 0 SVM is used to select the best forecasting model, based on the 
prediction output of each model. Also, a comprehensive feature 
selection analysis was carried out. 
Demand 
Forecasting 
Mezzogori and 
Zammori (2019) 
0 Supervised 
Learning 
AE RNN 0 An Entity Embedding based neural network is used to learn vector 
representation of past and current product. The vectorial 
representations are exploited to trace similarities of the current 
product to past products, so to build pseudo-time-series, analyzed 
by an RNN based network to predict the quantity sold for each 
product at the end of a sales campaign 
Demand 
Forecasting 
Fu and Chien 
(2019) 
0 Supervised 
Learning 
KNN, SVM, 
NN 
0 Machine Learning and temporal aggregation mechanism areintegrated to forecast the demand for intermittent products. The 
proposed framework is tested using the data of a semiconductor 
distributor. 
Demand 
Forecasting 
Ji et al., (2019) 0 Supervised 
Learning 
XGB 0 A novel XGBoost algorithm is proposed and tested against classical 
ARIMA models, to forecast sales of an e-commerce platform. 
Demand 
Forecasting 
Wu, (2010) 45 Supervised 
Learning 
SVM, PSO 0 A hybrid approach based on Particle Swarm Optimization and 
SVM is tested on a dataset of car sales. The aim is to optimize the 
reorder points of each tier of the supply chain. 
Inventory 
Management 
Kim et al., (2005) 41 Reinforcement 
Learning 
AVL 1 Centralized and non-centralized inventory models are proposed to 
manage a supply chain with one supplier and multiple retailers. 
Specifically, an action-value based algorithm is proposed to 
constantly react to the changes in customers’ demand. 
Inventory 
Management 
Kwon et al., (2008) 7 Reinforcement 
Learning 
CBR 1 A case-based RL approach is presented to control inventory (at 
supply chain scale) in case of non-stationary customer e. 
Specifically, the case-based reasoning discretizes the state space, 
thus reducing the number of possible configurations to be learned. 
Inventory 
Management 
Jiang and Sheng, 
(2009) 
37 Reinforcement 
Learning 
CBR 1 
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Expert Systems With Applications 175 (2021) 114820
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patterns. For instance, Carbonneau et al. (2007), compared classical 
statistical methods against ML and concluded that for multidimensional 
time-series, the accurateness of the newest methods is evident. Wu 
(2010) showed that a combination of Particle Swarm Optimization and 
SVM can be used to optimize reorder points at each level of a supply 
chain. Villegas et al. (2018) used SVM to select the best forecasting 
model among a diverse pool of predictive models for non-stationary 
and/or lumpy demand. Ji et al. (2019) presented a hybrid forecasting 
model based on XGBoost and Arima and successfully tested it against an 
e-commerce dataset. Mezzogori and Zammori (2019) integrated an 
Entity Embedding and a Recurrent Neural Network for demand forecast 
in the fashion market. 
Finally, a limited number of works applied RL for ‘Inventory Control’. 
The first paper in this area is that by Kim et al. (2005), who used an 
Action Value RL algorithm to optimize centralized and non-centralized 
inventory models. Kwon et al. (2008) applied a case-based myopic 
approach to a Vendor Managed Inventory model, to minimize the 
probabilities of infringements of the contracted service level. Next, Jiang 
and Sheng (2009) expanded this model to study a simulated multi-agent 
supply-chain with 80 retailers and 10 customers, in which each tier of 
the supply chain is modeled as an independent reinforcement learning 
agent. Lastly, Kara and Dogan (2018) focused on perishable products, 
with random demand and deterministic lead-time. The aim was to 
minimize the retailer’s total cost of a retailer, using two alternative in-
ventory policies optimized either with Q-Learning and SARSA algo-
rithms. Specifically, the latter ensured better results if applied to items 
with a short lifetime and lumpy demand. 
4.4.5. Models’ complexity, Input-Output variables 
To give a better idea of the complexity and variety of the considered 
problems, we deemed it useful to include Table 4, which provides some 
indications concerning the variables that are commonly used per each 
application domain and sub-area. Specifically, this information is 
exemplified by the list of the input and output variables of the dataset 
used by most representative papers, belonging to each investigated sub- 
area. 
4.4.6. Concluding remarks 
As already mentioned, given the sudden rise of ML and DL applica-
tions, the vastness of scientific literature that is being produced can be 
confounding if not even misleading, both for researchers and practi-
tioners aiming to apply these methodologies to specific industrial tasks. 
Apart from this general criticality, some operational issues, that could 
hamper the diffusion of ML in the industry, have also emerged from the 
literature review (i.e., Research Question #5). Generally, problems are 
related to the data set needed to train the ML models. Indeed, if data are 
collected directly on the field, issues related to missing, dirty, or even 
insufficient data are frequently encountered. Apart from the standard 
ways used for data pre-processing (e.g., imputation using most frequent, 
zero/constant and k-NN), many papers (see for example Sobie et al., 
2018) demonstrated that this problem can be reduced using a training 
dataset generated using high-resolution simulations or using generative 
methods, such as Generative Adversarial Networks (Douzas and Bacao, 
2018). Also, and perhaps more important, Deep Learning methodologies 
allow working on almost raw data with little or no need for data pre- 
Table 4 
Overview of dataset characteristics. 
Area Sub-Area Article Input Variables Output Variable(s) # of 
samples 
Maintenance 
Management 
Condition 
Monitoring 
Saxena and Saad, 
(2007) 
38 statistical features of accelerometer and microphone 
data 
Type of fault predicted 1152 
Downtime 
minimization 
Susto et al., (2015) 125 statistical moments calculated of 31 time series (i.e. 
current, deceleration, position, pressure). 
Predicted faultiness class 3671 
Failure Analysis Prieto et al., (2013) 25 statistical-time features calculated from vibration signal Prediction of 6 bearing 
status 
120 
Quality Management Defect Detection Kusiak and Kurasek, 
(2001) 
14: stencil composition, stencil thickness, …, paste 
application, position 
Presence of solder defect 2052 
Image Recognition Ravikumar et al., 
(2011) 
20 histogram features of image data 3 different component 
status 
300 
Life Cycle 
Management 
Jennings et al., (2016) 18 product characteristics (weight, screen resolution, etc.) Prediction of product 
discontinuity 
7000 
Online Quality 
Control 
Ribeiro, (2005) 26 sensors readings (temperature, pressures, etc.) 6 kind of plastic part fault 200 
Production Planning & 
Control 
Performance 
Prediction 
Arredondo and 
Martinez, (2010) 
4 order type attributes (size, composition, due date, arrival 
date) 
Order value N.A. 
Process Control Sun et al., (2004) 9 cutting conditions (speed, depth, feed rate), and statistics 
of band power 
3 tool states N.A. 
Scheduling Mönch et al., (2006) 4 batch machine factor, due dates tightness, due date 
variance, ready time tightness 
Scheduling look-ahead 
parameter 
N.A. 
Supply Chain 
Management 
Modeling and 
Coordination 
Priore et al., (2019) 7 firm and supply state variables Replenishment model 2000 
Demand Forecasting Mezzogori and 
Zammori (2019) 
26 product attributes Product demand 
prediction 
1020 
Inventory 
Management 
Kara and Dogan, 
(2018) 
4 state variables measuring product remaining life and 
inventory position 
Action value N.A. 
Table 3d (continued ) 
Sub-Area Article Received 
Citations 
Algorithm 
Classification 
Main 
Algorithm 
Simul. 
Based 
Goals & Approach 
Under a nonstationary demand simulated scenario, a case-based 
RL approach is tested, both in a periodical review order-up-to 
system and in an order-quantity reorder-point system. 
Inventory 
Management 
Kara and Dogan, 
(2018) 
0 Reinforcement 
Learning 
QLrn, SARSA 1 The Q-learning algorithm and the Sarsa method are compared for 
solving an inventory management problem with perishable 
products. RL shows better results with high variance demand of 
short lifetime products. 
M. Bertolini et al.Expert Systems With Applications 175 (2021) 114820
22
processing (Azadeh et al., 2013). Even in the case of very noisy data 
(especially for signal and/or image processing), data can be optimally 
denoised using stacked autoencoders, as in Yu et al. (2019). Certainly, 
on the other one side, Deep Learning techniques and, more, in general, 
all the NN based approaches, are difficult to be interpreted and could be 
negatively seen as a black box, by most of the practitioners. However, 
new and effective techniques, such as the ‘layer-wise relevance propa-
gation’ and ‘Grad-cam’, can be effectively used either to interpret a 
concept learned by a NN or for producing visual explanation for de-
cisions made by CNN’s (see, for example, Ayodele and Yussof, 2019; 
Montavan et al., 2018; Selvaraju et al., 2017). Thus, also considering the 
extreme flexibility of Deep-learning techniques, and the outstanding 
results that have been obtained in seemingly unrelated applications, 
such as Natural Language Processing (Vaswani et al., 2017), their use in 
operation management is expected to further increase. It is not a wild 
guess to speculate that, in the next future, deep learning could find its 
way in many industrial fields where these techniques are still shallowly 
explored (i.e., Research Question #6). The first evidence comes from 
SCM, a domain area that, although still little explored, is rapidly 
growing, thanks to the adoption of Deep and Reinforcement Learning 
techniques that make it possible to model and optimize complex prob-
lems of strategic nature. It is not difficult to predict that a similar 
approach could be helpful to obtain concrete improvements over state- 
of-the-art results in traditional industrial problems, such as scheduling 
and inventory management. 
Finally, the so-called data unbalancing problem is worth mentioning. 
This issue is typical for quality and/or defect classification tasks when 
the objective is to discriminate positive events from negative and rare 
ones. Also, in this case, standard methods exist, ranging from classical 
under-sampling (e.g. Near Miss algorithm) and oversampling ap-
proaches (e.g. Synthetic Minority Oversampling techniques) to more 
elaborated techniques based on Competitive NNs (Nugroho et al., 2002). 
However, as noted by Ko et al. (2017) and by Kim et al. (2018), even the 
use of ensemble methods (of Random Forest in the simplest case) is 
frequently enough to overcome this criticality. 
5. Conclusions and directions for future works 
The hype surrounding Machine Learning and Deep Learning algo-
rithms is ever-growing and, given recent breakthrough developments, 
their use has been experiencing a steep increase in many fields. This 
trend is very marked in the industry, especially in the operation man-
agement area, as revealed by the literature analysis herein described. 
The number of published papers is very large and covers the whole 
spectrum of operation management. Moreover, all the application do-
mains considered in this study show a steady and significant increase in 
the number of publications (especially in the last two years), thus further 
demonstrating an ever-growing interest in such applications. Histori-
cally, in terms of application domains, studies concerning Maintenance 
and Quality appeared first, followed by applications in Production 
Planning and Control and, lastly, in Supply Chain Management. Quality 
management, as of today, is the most studied topic, probably given its 
relevance on total sales and, consequently, its quicker return on in-
vestment. Recently, the investigated domain has been extended with the 
introduction of new research fields such as Cyber-physical systems, 
Additive Manufacturing and, more generally, Industry 4.0. These fields 
seem to be promising for ML applications, and preliminary results are 
encouraging. However, this enthusiasm is not certain to be followed up 
and the initial interest could rapidly fade off, as already happened in 
other fields. A typical example is ‘Order Management’ that, after an 
initial boom, is now displaying a rapid decrease in interest. Probably, 
this is due to the use of boundary algorithms that, although appealing for 
the academic community, are of scarce interest for industrial practi-
tioners. This fact highlights the need to find a trade-off between novelty 
and industrial applicability; a trade-off that is particular critical espe-
cially for ‘young domains’ (or question marks) where, to foster 
acceptance it may be preferable to leverage on simple and more 
consolidated techniques, rather than on novel and complex one that, 
conversely, might even have a detrimental effect. 
Concerning the adopted techniques, the most explored ones are 
based on Supervised Learning, closely followed by Unsupervised 
Learning algorithms, fast-rising especially in the last decade. Rein-
forcement Learning methods, given their higher complexities, are still 
few, but they are also increasing (with a spike in 2018–2019), mainly in 
the SCM area. 
Anyhow, this positive trend and the even distribution of ML appli-
cations in many different industrial areas confirm the flexibility of ML 
methodologies and their high potentialities for operation management 
tasks. It is also important to note that enabling technologies are now 
mature and that only a few operational problems must still be solved, for 
the definitive dissemination of these methods. As discussed, most of the 
problems concern either the generation of meaningful benchmark 
datasets or the low interpretability of the obtained results. However, as 
clearly discussed in the paper, thanks to recently emerging techniques, 
both problems can already be satisfactory solved. Perhaps, the only real 
problem that still needs to be solved, is to provide practitioners with a 
proper key to interpret and to choose appropriate ML methods, without 
getting lost in the vastity of scientific works published in the subject 
matter. Hence, we hope that this systematic literature review, which 
classified the existing corpus of works in a structured and operative way, 
could be of help to solve this problem. For the same reason, a topics’ 
trend analysis has also been made, aiming to give precious indications 
on the research areas on which academic researchers should focus, 
depending on the tasks at hand and the scope of their study. Surely, it 
can be presumed that more effort should be placed on topics classified as 
‘Question Marks’ and ‘Hot Topics’ that, being the youngest and least 
explored, are the ones where the bigger innovations can be made. On the 
other hand, if the problem falls within the ‘Consolidated’ or ‘Stars’ 
category, then the innovation rate will be lower, but a solid corpus of 
works can be found with precise indications of the implementation 
strategy. In this regard, we note that the creation and sharing of open 
datasets (of real industrial data) could be very helpful to further accel-
erate the diffusion and acceptance process of ML methods. Indeed, this 
would allow practitioners to develop, test, and compare new algorithms, 
leveraging common datasets. 
Our belief is that many opportunities and potentials are yet to be 
discovered in the application and/or integration of ML methods to 
existing operational management techniques. In a certain sense, the 
adoption and hybridization of standard operation management ap-
proaches with ML algorithms could further strengthen the smart 
manufacturing concept. Just to name a few examples, embedding ML 
models within discrete event simulations (or in Digital Twins), could 
exploit the concept of cyber-physical system, boosting operating per-
formance and bringing to light new and interesting results. Similarly, 
Reinforcement Learning techniques should be studied not only for 
classic ‘hard’ applications in the field of robotics and automation, but 
also for more ‘soft’ tasks, such as expert systems and/or decision support 
systems. Other fields worthinvestigating could be the applicability of 
ML methods in a real-world environment, in terms of computing power 
and excessive latency. Also, economical assessments of the impact of ML 
techniques could be helpful to further show the utility of such methods. 
All these could be interesting topics for future streams of research. 
To conclude we note that, due to the vastness of the considered 
domain, our analysis was mainly of explanatory nature. The aim, in fact, 
was to assess the current diffusion of ML and the potential it offers and/ 
or it may offer to solve problems typical of the operation management 
field. We have simply tracked which algorithms are used in which field, 
without any ambition of establishing which are the best ones. This was 
not the aim of our works and, frankly speaking, we do not think it is 
possible to do so as the problems analyzed are so varied and specific that 
it would be difficult to make a fair comparison. Certainly, by narrowing 
the field (for instance to one of the sub areas identified in the paper) such 
M. Bertolini et al. 
Expert Systems With Applications 175 (2021) 114820
23
a detailed analysis and comparison could be made and would be 
extremely useful for a further advancement of ML in the industry. Hence, 
this could be another interesting field for future research. 
CRediT authorship contribution statement 
Massimo Bertolini: Conceptualization, Supervision. Davide Mez-
zogori: Data curation, Writing - original draft. Mattia Neroni: Visual-
ization, Resources. Francesco Zammori: Methodology, Writing - 
review & editing. 
Declaration of Competing Interest 
The authors declare that they have no known competing financial 
interests or personal relationships that could have appeared to influence 
the work reported in this paper. 
Appendix A Acronyms of the algorithms cited in the literature 
review 
Table A1 
Glossary of the Acronyms of the cited algorithms. 
Acronym Full Name Explanation 
AHC Adaptive Heuristic Critic Reinforcement Learning Algorithm 
ANOVA Analysis Of Variance Mean Test among groups 
AVL Action Value Reinforcement Learning Algorithm 
ASVM Adaptive Super Vector Machine Adaptive Classification 
ACO Ant Colony Optimization Metaheuristic for optimization 
AuNN Autoencoder Neural Network Network for Dimensionality Reduction 
Bay.R Bayesian Regression Non-Parametric Regression model 
Boosting Boosting Ensemble learning technique 
Bagging Bootstrap Aggregating Resampling Technique for Variance Reduction 
CBR Case Base Reasoning Using past knowledge to solve new problems 
CNN Convolutional Neural Network Feed Forward Network 
DT Decision Tree Classification and Regression 
DDPG Deep Deterministic Policy Gradient Reinforcement Learning Algorithm 
DQN Deep Q Learning Reinforcement Learning Algorithm 
GMM Gaussian Mixture Modelling Clustering 
Gauss. PR Gaussian Process Regression Non-Parametric Regression model 
GA Genetic Algorithm Metaheuristic Optimization 
GDE Gaussian Density Estimation Gaussian Distribution estimation method 
GrB Gradient boosting Ensemble Technique for decision trees 
HMM Hidden Markov Model Prediction and Prognostic Model 
HC Hierarchical Clustering Clustering 
KNN K nearest neighbor Clustering 
K-PCA Kernel Principal Component Analysis Dimensionality Reduction 
K-Means K-Means Clustering 
KMC K-Means/K-Median Clustering 
LASSO Lasso Regression Regression model 
LVQ Learning Vector Quantization Classification (labeled data) 
LDA Linear Discriminant Analysis Classification and Patter recognition 
LOF Local Outlier Factor Anomaly Detection 
LWR Locally Weighted Regression Regression 
LogR Logistic Regression Parametric Regression model (for probabilities) 
LSTM Long-Short Term Memory Recurrent Neural Network 
Nbay Naive Bayes Classification Technique 
NBC Neighbor Based Clustering Clustering 
NN Neural Network (Multilayers perc.) Standard Feed Forward Network 
NMF Non-Negative Matrix Factorization Matrix Factorization 
PSO Particle Swarm Optimization Optimization Metaheuristic 
PCA Principal Component Analysis Dimensionality Reduction 
PPO Proximal Policy Optimization Reinforcement Learning Algorithm 
PW Parzen Windows Unsupervised Density Estimation 
QLrn Q-Learning Reinforcement Learning Algorithm 
QDA Quadratic Discriminant Analysis Classification and Patter recognition 
RnF Random Forest Ensemble of Decision Tree 
RNN Recurrent Neural Network Neural Networks for time series analysis 
RBM Restricted Boltzmann Machine Network for Probability Distribution Learning 
RST Rough Set Algorithm Rule Mining Algorithm 
SIFT Scale Invariant Feature Transform Computer Vision Feature Detection technique 
SOM Self-Organizing Maps Network for Dimensionality Reduction 
SA Simulated Annealing Optimization Heuristic 
SS Sparse Sensing Signal Processing Technique 
SDA Stacked Denoising Auto Encoder Network for Dimen. Reduction and data denoising 
SARSA State–action–reward–state–action Reinforcement Learning Algorithm 
SVM Super Vector Machine Classification and Regression 
SVDD Support Vector Data Description Classification technique for unbalanced datasets 
TS Taboo Search Optimization Heuristic 
t-SNE t-distributed stochastic neighbor embedding Dimensionality Reduction 
TD Temporal Difference Learning Reinforcement Learning Algorithm 
TL Transfer Learning Storing past learning to solve new problems 
TRPO Trust Region Policy Optimization Reinforcement Learning Algorithm 
UMAP Uniform Manifold Approx. and Projection Dimensionality Reduction 
VPF Variable Picket Fence Harmonic Analysis 
XGB XGBoost Parallel Tree Gradient Boosting technique 
M. Bertolini et al. 
Expert Systems With Applications 175 (2021) 114820
24
Appendix B Bibliometric analysis 
Some additional statistic, of bibliosmetric nature, are reported 
herein. Specifically, the following figures and Table B1a–B1d show: 
- the top journals, evaluated both in terms of number of publications 
and number of obtained citations, 
- the most cited authors, for each application domain. 
Figs. B1 and B2 
Fig. B2. Top 25 Journals measured in number of received citations. 
Fig. B1. Top ten Journals measured in terms of number of published papers. 
M. Bertolini et al. 
Expert Systems With Applications 175 (2021) 114820
25
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Satish C. Sharma 1 100 
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Table B1c 
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Mark A. Tschopp 2 19 
Table B1d 
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Chengzhi Jiang 1 37 
Jafar Heydari 1 37 
S. Hessameddin Zegordi 1 37 
S. Kamal Chaharsooghi 1 37 
Zhaohan Sheng 1 37 
Chang Ouk Kim 3 19 
Herbert Moskowitz 1 19 
Hoi-Ming Chi 1 19 
Ick-Hyun Kwon 2 19 
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	Machine Learning for industrial applications: A comprehensive literature review
	1 Introduction
	2 A brief introduction of Machine Learning theory
	2.1 Machine Learning areas
	2.1.1 Supervised Learning (SL)
	2.1.2 Unsupervised Learning (UL)
	2.1.3 Reinforcement Learning (RL)
	3 Searching methodology
	3.1 Initial query-based search
	3.2 Search enlargement
	3.2.1 Cross-reference analysis
	3.2.2 Relevance assessment through citation graph analysis
	3.3 Abstract analysis and final screening of the selected works
	4 Systematic review
	4.1 Preliminary classification
	4.2 Trend analysis
	4.3 Keywords analysis
	4.3.1 Current trends and hot topics
	4.3.2 Gaps’ investigation
	4.4 Detailed analysis of selected papers
	4.4.1 Maintenance management
	4.4.2 Quality management
	4.4.3 Production Planning & Control (PPC)
	4.4.4 Supply chain management
	4.4.5 Models’ complexity, Input-Output variables
	4.4.6 Concluding remarks
	5 Conclusions and directions for future works
	CRediT authorship contribution statement
	Declaration of Competing Interest
	Appendix A Acronyms of the algorithms cited in the literature review
	Appendix B Bibliometric analysis
	References