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Decision Models for Supplier Selection in Industry 4.0 Era: A Systematic

Literature Review

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ScienceDirect
Procedia Manufacturing 00 (2021) 000–000
www.elsevier.com/locate/procedia

30th International Conference on Flexible Automation and Intelligent Manufacturing (FAIM2021)

15-18 June 2021, Athens, Greece.
Decision Models for Supplier Selection in Industry 4.0 Era: A Systematic
Literature Review
Carlos H. L. Resendea,b, Carla A. S. Geraldesa*, Francisco Rodrigues Lima Juniorb
a
Research Centre in Digitalization and Intelligent Robotics (CeDRI), Instituto Politécnico de Bragança, Santa Apolónia Campus, Bragança 5300-253, Portugal
b
Federal Technological University of Paraná, Av. Sete de Setembro, 3165 - Rebouças, Curitiba - PR, 80230-901, Brazil

* Corresponding author. E-mail address: carlag@ipb.pt

Abstract

Industry 4.0 comprises the application of different technological solutions so that business processes throughout the production
chain are integrated. The supplier’s selection, considering the industry 4.0 requirements, is essential in promoting collaborative
strategies between suppliers and manufacturers. In this context, this study presents a systematic literature review about quantitative
models to support supplier selection in the industry 4.0 era. Fourteen studies were reviewed and characterized in different
perspectives such as modelling, application, and validation of the decision model. The results revealed that most of the decision
models were developed combining multicriteria decision-making (MCDM) with Artificial Intelligence (AI). Among the criteria
related to the Industry 4.0 environment, the most frequent ones were information sharing, technological capacity, digital
collaboration and engagement. The gathered results can be useful to guide researchers and managers in the development of
computational tools to assist decision-making processes for supplier selection in Industry 4.0 era.

© 2020 The Authors. Published by Elsevier Ltd.

This is an open access article under the CC BY-NC-ND license https://creativecommons.org/licenses/by-nc-nd/4.0/)
Peer-review under responsibility of the scientific committee of the FAIM 2020.

Keywords: Supplier Selection; Industry 4.0; Systematic Review; Decision Models; Multicriteria Decision-Making

1. Introduction task management, inventory management, warehouse and

transport strategy throughout the production chain [4]. The
Globalization compelled several industrial sectors to face supply chain 4.0 definition presented by [5] corresponds to “a
adaptations to meet markets with high requirements levels [1]. transformational and holistic approach to supply chain
In this context, Industry 4.0 emerges, which represents the management utilizes industry 4.0 disruptive technologies to
paradigm of fourth stage of industrialization and comprises the streamline supply chain processes, activities and relationships
integration and application of different technological solutions in order to generate significant strategic benefits for all supply
in the establishment of the Internet of Things and Cyber- chain stakeholders”.
Physical Systems, so that business objects and processes are Integration between the links in the supply chain has become
integrated promoting an intelligent environment [2]. Industry essential, as it promotes the development of collaborative and
4.0 is defined by [3] as "the sum of all disruptive innovations synchronized strategies between suppliers and manufacturers
derived and implemented in a value chain to address the trends [6]. This integration is highly relevant in improving member
of digitalization, automation, transparency, mobility, companies’ performance since individual planning without
modularization, network-collaboration and socializing of taking into account partnerships with suppliers can result in
products and processes". goods and service levels with non-competitive costs, which can
The emergence of Supply Chain 4.0 concept represents the affect long-term profitability [7]. Therefore, competitive
use of technologies characteristic of Industry 4.0, to facilitate pressures can be minimized through the development of supply
the management of several activities, such as scheduling and

2351-9789 © 2020 The Authors. Published by Elsevier Ltd.

This is an open access article under the CC BY-NC-ND license https://creativecommons.org/licenses/by-nc-nd/4.0/)
Peer-review under responsibility of the scientific committee of the FAIM 2020.
2 Author name / Procedia Manufacturing 00 (2019) 000–000

base management strategies, in which suppliers can assist in the the state of the art on the subject, the objective of this article is
improvement of critical areas of the manufacturer [8]. to present a systematic review of studies that proposed
Supplier selection is one of the most important decisions in quantitative decision models to support supplier selection in the
the supply chain management context. The main objective is to Industry 4.0 era. To achieve the proposed objective 14 studies
find the right supplier who can provide the customer with the were collected from IEEEXplore®, Emerald Insight, Science
right quality products or services at the right price, in the right Direct, Scopus, Springer Nature, Taylor & Francis, and Scholar
quantities, and at the right time [9]. As reported by [10, 11], Google databases and then analyzed. The characterization of
several stages of decision-making comprise the process of these studies included the following aspects: year of
selecting suppliers. Firstly, it is necessary to define what is to publication, origin country, techniques and decision metrics
be achieved through supplier selection, and decision-makers used, type of model, the approach used for modeling
must identify purchasing needs and what alternatives are uncertainty, supply chain strategy, application sector, the data
available. Then, it is necessary to formulate the criteria, which source for application, and validation approach.
can be quantitative or qualitative. In the qualification stage, the This paper is organized as follows. In section 2, the
objective is to eliminate inefficient candidates. Finally, one or methodological procedures for studies selection are detailed
more suppliers are selected and orders are allocated between and the aspects considered for analysis and classification of
them. studies. Section 3 presents the characterization of the studies
The literature presents several studies that propose and the discussion of the results. In section 4, several
quantitative decision models to support supplier selection. opportunities for further studies are identified. Finally, section
Given the relevance of this research topic, there are also several 5 presents the conclusion and contributions of this study.
review studies on existing models. In the bibliographic survey
conducted by this study, 15 systematic reviews on this topic 2. Methodological Procedures
were found. As shown in Table 1, these studies characterized
different aspects related to the modelling, application, and 2.1. Selection procedure of studies
validation of decision models, in addition to bibliometric
aspects. However, no previous review studies are focused on The selection of the studies was based on the guidelines for
the characterization of decision models for supplier selection in conducting systematic reviews proposed by [12-14]. Initially,
Industry 4.0 Era. In general, these models are geared towards searches for studies were performed using the string “(supplier
digital supply chains and incorporate metrics from the context OR vendor OR partner) AND (selection OR evaluation) AND
of Industry 4.0 to the decision-making process for selecting ("supply chain 4.0" OR "industry 4.0" OR "digital supply
suppliers. chain" OR “smart supply chain”)”. The studies were collected
Given the need to better characterize the studies and map from Science Direct, Emerald Insight, IEEE Xplore®, Scopus,

Table 1: Review studies related to supplier selection

Time Reviewed
Proposed by Characterized aspects
horizon studies

Ho et al. [16] 2000-2008 78 Decision techniques and performance metrics;

Wu and Barnes [17] 2001-2011 140 Countries, decision techniques, educational institutions, journals and phases of the supplier selection
process;
Chai et al. [18] 2008-2012 123 Decision techniques and journals;
Genovese et al. [19] 1987-2010 28 Countries, decision techniques, journals, performance metrics and type of application;
Igarashi et al. [20] 1991-2011 60 Performance metrics, phases of the supplier selection process, theoretical perspective and type of
research
Govindan et al. [21] 1997-2011 33 Application sector, decision techniques and performance metrics;
Zimmer et al. [14] 1997-2014 143 Application sector, countries, decision situation, decision techniques, data type, dimensions of
sustainability, journals, performance metrics, phases of sustainable supplier management, sourcing,
type of application and validation approach;
Nallusamy et al. [22] 2004-2014 52 Application sector, decision techniques and performance metrics;
Yildiz and Yayla [23] 2001-2014 91 Application sector, decision techniques and performance metrics;
Wetzstein et al. [24] 1990-2015 221 Countries, decision techniques, environmental strategy, journals, operational approach, performance
metrics, supplier selection strategies and stages of the R&D process;
Araújo et al. [25] 1973-2015 119 Decision techniques, journals, methodology, performance metrics, phases of the supplier selection
process and project sector;
Simić et al. [26] 1966-2016 54 Decision techniques;
Ocampo et al. [27] 2006-2016 244 Decision techniques, journals and theme;
Zhang et al. [28] 2009-2020 193 Application sector, countries, decision techniques, journals and performance metrics;
Chai and Ngai [29] 2013-2018 95 Decision techniques;
3 Author name / Procedia Manufacturing 00 (2019) 000–000

Springer Nature and Taylor & Francis. Then, an additional the decision techniques used, such as MCDM,
search was performed using the Google Scholar tool. Based on mathematical programming, and AI techniques [13];
[13], the bibliographic search followed the following c) Modeling uncertainty: verifies whether the model adopts
procedure: any approach to deal with decisions in uncertain
environments, which are characterized by use of inaccurate
1) Inserting the search string in each database; data, qualitative assessments and/or subjective judgments
2) Use of a filter to select only studies published from 2011 [13]. It also classifies studies according to the approach
onwards. The search for studies will be carried out from that adopted to deal with uncertainty, such as fuzzy set theory,
date because the term “industry 4.0” first appeared in 2011 pairwise comparison, among others;
[15]; d) Performance metrics: identifies the most common metrics
3) Use of another filter to select only studies published in used by models to assess supplier performance [14];
scientific journals, books, book chapters, and conference e) Supply chain strategy: identifies the competitive strategy
proceedings. In the case of the Google Scholar tool and the adopted by the supply chain in which the buyer and its
Taylor & Francis database, as they do not have this filter, supplier(s) are inserted. Some types of supply chain
this step was performed manually; strategy discussed in the literature are green, sustainable,
4) Sorting the studies by relevance based on criteria contained resilient, lean and agile [13];
on each database (except of Google Scholar, which does not f) Choice of metrics: identifies how the metrics were chosen
have this feature); [13]. While some studies define metrics based on literature
5) Selecting the first 300 results listed; studies, others are based on the opinion of experts’ opinion
6) Analyzing the title, abstract, keywords and, in some cases or the authors themselves;
the content of studies to eliminate those that did not include g) Type of application: considers whether the application
quantitative models to support decision-making for supplier was made based on real data or simulated numerical
selection in the context of industry 4.0; examples [14];
Deleting copies of repeated studies, that is, those that were h) Application sector: identifies the sector in which the
listed and selected in more than one database. As shown in purchasing company participating in the application
Table 2, 14 studies were selected and analyzed. operates, taking into account only applications based on
real data [12];
Table 2: Search results and selection of studies in the databases
i) Source of the data for application: analyzes the source of
Steps the data used to assess supplier performance [14]. It
1 2 3 4 5 6 7 identifies whether they were obtained through historical
Emerald Insight 31,938 18,784 16,467 16,467 300 0 0
data, experts’ judgments, simulated data or combinations
between them;
IEEE Xplore® 18,769 10,703 10,100 10,100 300 2 1
j) Validation approach: Examines whether any procedure
Science Direct 584,679 282,338 235,276 235,276 300 3 3 was applied to validate the results of the study [13], such as
Scopus 65 65 65 65 65 9 4 sensitivity analysis or statistical technique.
Springer Nature 37,263 27,001 12,177 12,177 300 1 1
Scholar Google 14,700 14,200 14,200 14,200 300 14 5 3. Studies Characterization and Results Discussions
Taylor & Francis 1,149,988 380,904 380,904 380,904 300 0 0
Figure 1 shows the distribution of studies over the years.
Total 14
Among the studies analyzed, 12 (85%) were published in the
last two years (from 2019). This shows that interest in the
2.2. Aspects for studies analysis and classification research topic under study is recent and is on the rise. The
distribution of studies according to the authors' origin country
The selected studies were analyzed holistically from some is shown in Figure 2. As some studies were developed by two
structural dimensions. Initially, data related to the year of or more authors from different countries, the frequencies sum
publication and study origin country were collected. Then, the is greater than 14. The countries that most published studies are
studies were characterized according to a set of 10 aspects Turkey, India, United Kingdom, and United States,
related to the modeling, application, and validation of decision respectively.
models. The aspects were based on other systematic reviews of Table 3 summarizes some characteristics of the decision
the literature on topics related to supply chain management [12- models proposed by each study, including the decision
14]. techniques employed and the type of model according to the
nature of the techniques. It is important to highlight that 9
a) Decision technique(s): lists the quantitative decision (64%) of the studies combined two or more techniques in the
technique(s) used by each model. It also classifies in single decision models. Among these, fuzzy logic and its extensions
technique (composed of only one decision technique) or were often used as a component of combination with other
combined techniques (which applies two or more techniques, for example, Analytic Hierarchy Process (AHP),
techniques sequentially) [12, 14]; Technique for Order Preference by Similarity to Ideal Solution
b) Model type: groups the models according to the nature of (TOPSIS), and Best Worst Method (BWM). Table 3 also
4 Author name / Procedia Manufacturing 00 (2019) 000–000

Fig. 1. Distribution of studies by publication year

Fig. 2. Distribution of studies grouped by the authors’ countries

identifies the most frequent types of models. The combination based purely on AI (14.2%) or MCDM (14.2%) techniques.
of MCDM techniques with AI techniques is the most frequent, Figure 3 presents different approaches used in the studies to
totalizing 7 (50%) of the studies analyzed. Next are models deal with decision-making processes under uncertainty.
Table 3: Characterization of the models analyzed according to the decision techniques and model type.

Proposed by Techniques Types of model

Interval Valued Intuitionistic Fuzzy Multi-Objective MCDM combined with AI
Büyüközkan and Göçer [30]
Optimization on the basis of Ratio Analysis (MOORA)

Single technique Ghadimi et al. [31] Multi-Agent System AI

(5) Tozanli et al. [32] Goal Programming Mathematical programming
Özek and Yildiz [33] Interval Type-2 Fuzzy TOPSIS MCDM combined with AI
Zekhnini et al. [34] Adaptive Neuro Fuzzy Inference System (ANFIS) AI
Interval Valued Intuitionistic Fuzzy AHP and Interval MCDM combined with AI
Büyüközkan and Göçer [35] Valued Intuitionistic Fuzzy Addial Ratio Assessment
(ARAS)
Pythagorean Fuzzy AHP and Pythagorean Fuzzy MCDM combined with AI
Büyüközkan and Göçer [36]
Complex Proportional Assessment (COPRAS)
MCDM combined with AI and
Hasan et al. [37] Fuzzy TOPSIS e Multi-choice Goal Programming
Mathematical programing
BWM e VIseKriterijumska Optimizacija I Kompromisno MCDM
Combined Kusi-Sarpong et al. [38]
Resenje (VIKOR)
techniques (9)
Liao et al. [39] Hesitant Fuzzy BWM and Hesitant Fuzzy ARAS MCDM combined with AI
Intuitionistic Fuzzy Set TOPSIS and Shannon’s Entropy MCDM combined with AI
Sachdeva et al. [40]
Method
Çalık [41] Pythagorean Fuzzy AHP and Pythagorean Fuzzy TOPSIS MCDM combined with AI
Fuzzy AHP, TOPSIS and Data Envelopment Analysis MCDM combined with AI and
Kaur and Singh [42]
(DEA) Mathematical programing
BWM e Weighted Aggregated Sum Product Assessment MCDM
Torkayesh et al. [43]
(WASPAS)
5 Author name / Procedia Manufacturing 00 (2019) 000–000

Initially, 12 (85.7%) of the studies proposed approaches for this

purpose. It appears that the most recurrent approaches are fuzzy
set theory (35.7%) and fuzzy set theory combined with
pairwise comparison (35.7%). The widespread fuzzy set theory
adoption may be due to the possibility that it allows the
linguistic terms use quantified by fuzzy numbers to assess the
weight of the metrics and the scores of the alternatives [44].
The metrics used in the assessment of suppliers are shown
in Table 4. The metrics used by only one study were
suppressed. The restrictions used by the mathematical
programming models were not counted as metric. In summary, Fig. 3. Approaches for model uncertainty
all studies contemplated the associated use of operational
metrics with digital ones, that is, metrics aligned with the
Table 4: Metrics adopted by the models for supplier selection

Metrics Proposed by Frequency

Information sharing [30, 33, 35, 39, 42, 43] 6

Cost [31, 34, 36, 37, 40] 5

Digital collaboration [35, 36, 39, 43] 4

Digital engagement [35, 36, 39, 43] 4

Flexibility [35, 37, 39, 43] 4

Fig. 4. Distribution of type of supply chain strategy
Product quality [31, 34-36, 41] 4
Technological capacity [34-36, 40] 4
industry 4.0 environment. Otherwise, the grouping of digital
metrics with environmental metrics was present in the studies
Service quality [31, 39, 41, 43] 4 by [31, 34, 36, 38, 41]. Besides, only the study by [36] used
Supplier sustainability [31, 34, 36, 41] 4 social metrics associated with digital metrics.
As shown in Table 4, metrics related to operations
Use of analytical tools [30, 35, 41, 42] 4 performance continue to be highly relevant, such as cost,
Cloud computing [33, 41, 42] 3 flexibility, quality, and delivery. The most frequent metrics
Cybersecurity [33, 37, 42] 3
related to Industry 4.0 era are information sharing,
technological capacity, digital collaboration, and engagement,
Delivery [31, 34, 40] 3
cloud computing, respect for privacy, and cybersecurity. The
Financial stability [35, 36, 43] 3 development of these metrics reflects the advances of digital
IoT Infrastructure [33, 41, 42] 3 technology in the construction and implementation of Industry
Privacity respect [35, 36, 43] 3 4.0. Due to the incipience of the theme, attempts to develop
metrics and investigate how to evaluate different aspects of the
Real time visibility [30, 33, 35] 3
use of technology are still limited [45].
3D printing and [33, 41] 2
augmented reality The metrics chosen by the analyzed studies are also related
Automation [33, 37] 2 to competitive supply chain strategies. Although all models
Additive Manufacturing [33, 42] 2
analyzed incorporate metrics from Industry 4.0, other types of
supply chain strategies are also considered in some of the
Collaboration level [30, 37] 2
studies. As illustrated in Figure 4, the studies by [31, 34, 38]
Digital customization [35, 36] 2 also focused on developing sustainable supply chains. [37]
and personalization
proposed a model aimed at resilient supply chains. [41] focused
Financing efficiency [39, 43] 2
on green supply chains. Finally, [32] focused on resilient and
Information and [33, 37] 2
sustainable supply chains.
communication
technologies Figure 5 presents the approach used by the authors to choose
Smart logistics [33, 42] 2 the metrics to use in the supplier selection process. In 6
Reputation [35, 36] 2
(42.8%) studies, the metrics were defined by decision-makers
based on a list extracted from previous studies. For 5 (35.7%)
Robotics [33, 41] 2
of the studies, the metrics were extracted based on previous
Security [36, 43] 2 studies. Less frequently, there are cases in which the metrics
Support service [35, 36] 2 were defined by the study authors themselves.
Technological [35, 36] 2 Regarding the type of application, 72% (10) made the
integration application in real cases. In contrast, 21% (3) applied the model
6 Author name / Procedia Manufacturing 00 (2019) 000–000

Fig. 5. Choice of the metrics used by the analyzed models

Fig. 6. Company sectors participating in real applications

Fig. 7. Source of data used as inputs to the decision models

in illustrative cases. The model proposed by [34] was the only

one not validated through the application of a real or illustrative 4. Research Opportunities
case. Concerning real cases, Figure 6 reveals the application
sector. It stands out in the automotive, aviation, and textile From the mapping of the studies, some research
sectors. Applications were also identified in the food, opportunities on this topic were identified:
agricultural, shipping, and retail sectors.
Figure 7 shows studies classification regarding the source of a) There is a lack of application of AI techniques with learning
the input data used in the models’ application. This data can be capacity. Although the application of ANFIS model has
used to assess suppliers’ performance. While 9 (64.2%) of the been identified, applications using artificial neural
studies used judgments from experts to apply the model, 2 networks such as Multilayer perceptron, Kohonen, and
(14.2%) used historical data, and 2 (14.2%) used historical data Fuzzy Adaptive Resonance Theory (ART) have not been
combined with experts’ judgment. identified;
Finally, about the validation of the models analyzed, Figure b) There are no applications of stochastic models for supplier
8 shows that 7 (50%) studies use some technique for this selection in industry 4.0 era, which may be necessary to
purpose. On the contrary, 6 (42.8%) of the studies only deal with probabilistic uncertainties;
performed the real or simulated application of the model. c) There is an absence of models based on Fuzzy c-means,
Among the models that performed the validation of the results, Fuzzy cognitive maps, Fuzzy neutrosophic sets, Hesitant
4 (28.5%) performed a sensitivity analysis to verify the impact fuzzy (VIKOR), Pythagorean fuzzy VIKOR, Fuzzy
of introducing small changes in the specific input parameters Analytic Network Process (ANP), Fuzzy cognitive maps,
on the model results [46]. Furthermore, 2 (14.2%) analyzed the Fuzzy Decision Making Trial and Evaluation Laboratory
consistency of the results by comparing them with the results (DEMATEL), Fuzzy NGT, ANP, ELimination Et Choice
obtained in different techniques. Only 1 study performed the Translating REality (ELECTRE), Measuring
sensitivity analysis and the comparative analysis Attractiveness by a Categorical Based Evaluation
simultaneously. Technique (MACBETH), Organization Rangement Et
Synthese De Donnes Relationnelles (ORESTE), Preference
Ranking Organization Method for Enrichment Evaluation
7 Author name / Procedia Manufacturing 00 (2019) 000–000

Fig. 8. Approaches used by the models analysed for validating results

The development of this study has provided some

(PROMETHEE), and Simple Multi Attribute Rating contributions to the literature. Aspects previously ignored by
Technique (SMART). Therefore, the development of new previous systematic review studies on models for supplier
models using such techniques has the potential to selection were considered, including uncertainty modeling,
incorporate new functionalities into the decision models for choice of metrics, the data source for application, and
supplier selection in the industry era 4.0. validation approach. The results of this study allowed us to
d) There are no appropriate decision models to support draw an overview of the state of the art regarding this research
decision-making in supplier selection in supply chains that topic and to identify some opportunities for the development of
combine digital strategy with lean and agile strategy; future studies. Besides, the results presented can be useful to
e) Few decision models combine environmental and social guide researchers and managers in the development of
metrics with metrics related to industry 4.0; computational tools to assist in decision-making processes.
f) No applications were found in the sectors of civil Finally, regarding the limitations of this study, although
construction, chemical, energy, metal-mechanical, rigorous research procedures have been adopted for the
pharmaceutical, footwear, and furniture; selection and analysis of studies, it is possible that some studies
g) There is a lack of quantitative decision models that support have not been included in the sample. Therefore, this review
the choice of supplier evaluation metrics considering their can be complemented by new studies.
interrelationships with industry 4.0 requirements;
h) There was also a lack of comparative studies between Acknowledgements
techniques that identify the advantages and disadvantages
of use when applied to supplier selection in industry 4.0 era; To Fundação para a Ciência e a Tecnologia (FCT) for
i) Besides, no study has proposed a procedure for validating supporting this research project.
the results using statistical techniques, such as hypothesis
tests and Analysis Of Variance (ANOVA). References

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