Industry 4.0 and digital supplychain capabilities

A framework for understanding digitalisationchallenges and opportunities

Maciel M. QueirozPost Graduate Program in Business Management,Paulista University – UNIP, São Paulo, Brazil

Susana Carla Farias PereiraDepartment of Production and Operations Management,

Fundação Getulio Vargas, FGV – EAESP, São Paulo, Brazil, andRenato Telles and Marcio C. Machado

Post Graduate Program in Business Management,Paulista University – UNIP, São Paulo, Brazil

AbstractPurpose – The Industry 4.0 phenomenon is bringing unprecedented disruptions for all traditional businessmodels and hastening the need for a redesign and digitisation of activities. In this context, the literatureconcerning the digital supply chain (DSC) and its capabilities are in the early stages. To bridge this gap, thepurpose of this paper is to propose a framework for digital supply chain capabilities (DSCCs).Design/methodology/approach – This paper uses a narrative literature approach, based on the mainIndustry 4.0 elements, supply chain and the emerging literature concerning DSC disruptions, to build anintegrative framework to shed light on DSCCs.Findings – The study identifies seven basic capabilities that shape the DSCC framework and six mainenabler technologies, derived from 13 propositions.Research limitations/implications – The proposed framework can bring valuable insights for futureresearch development, although it has not been tested yet.Practical implications – Managers, practitioners and all involved in the digitalisation phenomenon canutilise the framework as a starting point for other business digitalisation projects.Originality/value – This study contributes to advancing the DSC literature, providing a well-articulateddiscussion and a framework regarding the capabilities, as well as 13 propositions that can generate valuableinsights for other studies.Keywords Supply chain disruption, Digital capabilities, Digital supply network,Supply chain digitalizationPaper type Conceptual paper

1. IntroductionThe unprecedented development of information and communication technology (ICT) (Alshawiet al., 2003) has led to a phenomenon known as digital disruption. In this context, traditionalbusiness models based predominantly on physical activities are being disrupted and shiftingtowards digitalisation. The digitalisation process has consequences for all industries(Büyüközkan and Göçer, 2018). Therefore, digital disruption affects not only organisations’business models; it also significantly affects all segments of society, including new relationshipsand interactions with organisations and people (World Economic Forum, 2016a).

Moreover, ICT has enabled a Fourth Industrial Revolution known as Industry 4.0(Barreto et al., 2017; Hofmann and Rüsch, 2017), with its roots in German industry (Hecklauet al., 2016) and supported mainly by the Internet of Things (IoT) and cyber-physical system(CPS) technologies (Qin et al., 2016). This has led organisations around the world toreconsider digitalisation as a necessity for which strategies must be developed.

Benchmarking: An InternationalJournal

© Emerald Publishing Limited1463-5771

DOI 10.1108/BIJ-12-2018-0435

Received 28 December 2018Revised 14 May 2019

6 September 2019Accepted 31 October 2019

The current issue and full text archive of this journal is available on Emerald Insight at:www.emeraldinsight.com/1463-5771.htm

Industry 4.0and DSCCs

Thus ICT also has been supported by the transformation of organisations’ relationshipswith their network. For example, smart cities present challenges for supply chain design(Kumar et al., 2016) in order to support new operations business models, connectingcustomers and organisations more efficiently (Li et al., 2016; Qin et al., 2016).

In this context, digital disruption is already affecting supply chains and requiring newmanufacturing strategies (Holmström and Partanen, 2014), entailing a shift from traditionalproduction planning and control to distributed manufacturing (DM) and from large scale tomicro scale, with multiple manufacturing locations (Srai et al., 2016). Additionally, thedecentralisation of manufacturing with 3D printing applications (Kapetaniou et al., 2018; Mohrand Khan, 2015), also known as additive manufacturing (Strong et al., 2018), is unlocking thepotential for mass customisation (Srai et al., 2016). Thus, traditional supply chains willeventually face the challenge of updating to digital supply chains (DSCs) to support newproductions models, transportation modes, customer experiences and relationships, based on,among other things, real-time information exchange.

Recently, top consulting firms have highlighted the necessity of supply chain digitalisation(A.T. Kearney, 2015; Accenture, 2014; Bain & Company, 2018; Boston Consulting Group, 2018;Deloitte, 2016; Ernst & Young, 2016; McKinsey & Company, 2017; PwC, 2016; Roland Berger,2016). Despite advancements in digitalisation, however, understanding of DSCs is in its earlystages (Büyüközkan and Göçer, 2018). Consequently, previous literature has neither organisednor discussed the digital supply chain capabilities (DSCCs) in order to support organisationsand the digitalisation of their networks. Furthermore, there is scant current literature thatincludes frameworks to further understanding and support both scholars and practitioners inrethinking supply chains in the digital age.

Therefore, the question that guides this paper is: what new capabilities are required tosupport traditional supply chains becoming DSCs? Thus, this study aims to shed light on anunexplored topic in DSCs: “capabilities”. To answer this question, this study draws onliterature covering Industry 4.0, supply chain management (SCM) and DSCs. The mainobjective is to propose a DSCC framework, considering cutting-edge technologies andinteractions with human aspects, to support DSC business models.

This paper contributes to advancing the DSC literature, especially in terms of theintroduction of DSCCs as a new research stream, and with 13 propositions related to DSCCs.The proposed framework can also provide insights for future research on DSC, as well asproviding support to managers, decision-makers and practitioners interested in gaining anin-depth understanding of DSC disruptions.

The rest of this paper is organised as follows: Section 2 presents the theoreticalunderpinning and literature review, covering Industry 4.0, SCM, the digitalisation phenomena,DSCs and DSCCs. In Section 3, an integrative framework is proposed that describes the maininteractions of the DSCCs, considering the basic capabilities and their enabler technologies.Section 4 highlights the main managerial implications, while Section 5 details the theoreticalimplications. The paper concludes with Section 6, which presents the final remarks, limitationsand future research avenues.

2. Theoretical underpinning2.1 Industry 4.0Industry 4.0 is a broad term used to refer to the Fourth Industrial Revolution. There is a setof cutting-edge technologies related to Industry 4.0. In this regard, one of the maincharacteristics of Industry 4.0 is smart applications (Hecklau et al., 2016; Qin et al., 2016), inwhich objects (products) and machines can interact with each other, supported mainly bythe IoT, CPSs, artificial intelligence (AI), big data analytics (BDA), among otherstechnologies (Lee, 2015; Qin et al., 2016; Schumacher et al., 2016). In this vein, it is clear thatthe components of Industry 4.0 (e.g. machines, objects, vehicles, among others) can make

BIJ

their own decisions and operates autonomously (Qin et al., 2016). Industry 4.0 has entailednew relationships concerning workers, objects and systems (Hecklau et al., 2016). Theserelationships have brought great complexity to organisations’ supply chains, mainly interms of rethinking and redesigning their capabilities in the digital age.

In the Industry 4.0 view, new technologies are affecting traditional supply chains andaccelerating the shift towards digitalised supply chains. In this context, the leadingtechnologies affecting the supply chain are, among others: IoT (Bibri, 2018; Kumar et al., 2016);CPSs (Yu et al., 2015; Zhong et al., 2017); BDA (Kache and Seuring, 2017; Strandhagen et al.,2017); cloud computing (CC) (Korpela et al., 2017; Vazquez-Martinez et al., 2018); blockchain(Korpela et al., 2017; Li et al., 2018); and human–robot/machine interaction (Barreto et al., 2017;Oyekan et al., 2017). However, to achieve significant supply chain performance, organisationsmust develop basic capabilities, considering the digitalisation to use these technologies andtheir integration with workers, customers and suppliers through the entire supply chain.

Furthermore, organisations’ strategies are impacted by their resources and capabilities(Grant, 1991). In this perspective, and considering the complexities generated by the digitaldisruption (Kanarachos et al., 2018), all decision-makers and all types of companies arechallenged to understand in depth the supply chain capabilities. However, in a digitalisationage, neither decision-makers nor organisations have complete awareness of what theircapabilities are or how a set of resources and capabilities can be developed and managed tosupport global competition. From the supply chain digital-disruption perspective,capabilities cannot be denied as key to supporting performance improvement.

2.2 Supply chain management (SCM)There is no clear standard definition for the term SCM. Previous studies (LeMay et al., 2017;Lummus and Vokurka, 1999; Mentzer et al., 2001; Stock and Boyer, 2009) have reportedscholars’ efforts to establish a well-articulated definition. For instance, Stock and Boyer(2009) compiled 173 SCM definitions in the literature and provided the following definition:

The management of a network of relationships within a firm and between interdependentorganizations and business units consisting of material suppliers, purchasing, production facilities,logistics, marketing, and related systems that facilitate the forward and reverse flow of materials,services, finances and information from the original producer to final customer with the benefits ofadding value, maximizing profitability through efficiencies, and achieving customer satisfaction.(Stock and Boyer, 2009, p. 706)

Because of the consideration of “network relationships” in the Stock and Boyer’s definition, webelieve that their approach is robust and suitable to understanding the complexities createdby the various relationships in the product/service production process. However, theirdefinition neither encapsulates the digital age transformation complexities nor the disruptioncaused in all SCM. In the next sections, this gap is discussed, and some ideas are suggested toimprove SCM digitalisation awareness in a “smooth way”, based on the DSCC approach.

2.3 Digitalisation vs digitisationThe literature awareness about digitalisation is still limited. There is some confusion regardingthe difference between the terms digitisation and digitalisation. According to Legner et al.(2017), digitisation refers to the process associated with converting analogue signals (physicalactivities) into a digital model, while digitalisation refers to the impact of these technologies,caused by adoption and operation, in organisational and societal perspectives. Figure 1 showsthe interactions between digitisation and digitalisation concepts.

This study uses these terms interchangeably, although emphasis is given to the digitalisationphenomena. As reported in Figure 1, digitisation is a subset of the digitalisation concept.Therefore, there are capabilities implications for both aspects. For instance, from the digitisation

Industry 4.0and DSCCs

perspective, there is a closer association with resources capabilities adoption, development andoperations. For digitalisation meanwhile, the capabilities adopted previously impact on thesupply chain (considering all stakeholders) and, consequently, can help improve organisations’competitiveness.

2.4 Digital supply chain (DSC)The DSC concept is still developing (Kayikci, 2018). A DSC can be defined as “an intelligentbest-fit technological system that is based on the capability of massive data disposal andexcellent cooperation and communication for digital hardware, software, and networks tosupport and synchronize interaction between organizations by making services morevaluable, accessible and affordable with consistent, agile and effective outcomes”(Büyüközkan and Göçer, 2018, p. 165). This definition implies a set of ICT resources thatorganisations have to combine with human resources.

In this context, the digitalisation phenomenon is already disrupting all types of supplynetworks (Korpela et al., 2017; Li et al., 2016; Srai et al., 2016). Recent studies havehighlighted the importance of organisations gaining an in-depth understanding of DSC invarious contexts. For instance, Scuotto et al. (2017) studied buyer–supplier relationshipsutilising ICTs in the service sector to support new partnerships and enhance transactions.From the ICT perspective, and considering the impact on DSCs, Korpela et al. (2017) showedhow DSCs can be transformed through blockchain integration. The authors pointed out thesecurity improvements and reduction in transaction costs that organisations applyingblockchain technology can achieve.

Based on the automotive supply industry, Farahani et al. (2017) proposed six dimensionsfor DSCs: digital performance measurement; digital IT and technology; digital suppliers;digital production systems; digital logistics and inventory; and digital customers. One crucial

DigitalisationEmphasis on adoption impacts:

organisational and societal

DigitisationEmphasis on digital

technologies

Source: Based on Legner et al. (2017)

Figure 1.Digitalisation vsdigitisation concepts

BIJ

aspect of DSCs was provided by Büyüközkan and Göçer (2018) in which the DSC impactsproduct development through providing more information, thus leading to better integrationwith customers’ needs and enabling efficiency, both upstream and downstream.

The digitalisation process, supported by ICTs (Li et al., 2016; Scuotto et al., 2017), hasemerged as a driver in organisations achieving competitive advantage in recent years(Korpela et al., 2017). It can be achieved by the introduction of new business models, thanks todigital technology adoption (Martín-Peña et al., 2018). In this sense, several industries havebegun their business digitalisation process, including the retail segment (Hagberg et al., 2016;Plomp and Batenburg, 2010), steel production (Herzog et al., 2017), the food packagingindustry (Vanderroost et al., 2017), manufacturing (Freddi, 2018; Strong et al., 2018) and theconstruction industry (Hautala et al., 2017). As a consequence, organisations have beenchallenged to develop a set of DSC capabilities to support the digitalisation process gradually,but continuously.

2.5 Digital supply chain capability (DSCC)Similar to the lack of clarity regarding the DSC concept, the current literature does not yetoffer a well-articulated definition to DSCC. DSCC, when highlighted by the literature, isdiscussed only from fragmented perspectives. For example, Srai et al. (2016) reported thenecessity for organisations to develop the infrastructural capability to support DM. Moreover,to help the development of all DSCC capabilities, workers’ capability is mandatory and can beconsidered as a critical resource. Consequently, supply chain performance can be enhanced byworkers’ capabilities management (Gunasekaran et al., 2017).

Based on the current DSC literature, and supported by the Industry 4.0’s main ideas, thisstudy defines DSCC as: “A set of ICT resources that an organisation uses to interact withtheir network in order to shift physical activities to digital, applied in an integrated formboth in physical and digital activities to minimise resource consumption and supportproductivity improvement, network visibility, and real-time feedback, including tools forcustom production and suppliers’ cooperation in all stages of the network, supported bystrong data-management techniques and skills”.

This definition implies not only organisations’ internal capabilities development, but alsotakes into account the importance and impact of the organisations’ capabilities, combinedwith the supply chain members. The definition of DSCC encapsulates a set of capabilitiesthat is necessary for organisations to develop, maintain, improve and innovate to increasetheir levels of competitiveness in the digital era.

The level of DSCC competitiveness for any organisation is a function of the internaland external capabilities integration. In this landscape, it is clear that the integration levelof the organisations’ basic capabilities with the supply chain members influences theorganisation’s supply chain performance. In this perspective, one significant consequence ofthis integration is highlighted in Figure 2 as “critical digital supply chain integration”(CDSCI). CDSCI is an intersection of the ICTs’ capabilities, workers’ capabilities andstakeholders’ capabilities.

As shown in Figure 2, for organisations to understand their DSCC, there is a previousstage in which three sets of capabilities have to be developed; these dimensions work as abase to support strategies related to their operations, considering the digitalisationbenefits and complexities. However, if any dimension has not achieved a mature level ofunderstanding and operationalisation, an imbalance will arise. As a consequence, DSCCruns the risk of not meeting the desired performance levels. To implement DSCC in abalanced way, this paper’s proposed integrative framework maps the primary variablesassociated with DSCC taking into account the sustainability perspective. That is,organisations need to develop an integrative view of the relationship between the mainresources (Sangwan et al., 2018).

Industry 4.0and DSCCs

3. MethodologyIn order to develop a framework, we followed a narrative literature review approach(Secundo et al., 2019). This methodology’s main characteristic is the wide approach to theinformation sources available and the research question (Christenson et al., 2017).Traditionally, the narrative review of the literature can be used to develop conceptualframeworks and propositions and to consolidate the literature background (Neumann, 2017).Recently, the narrative review of the literature has been used in several fields with differentpurposes (Apostolakis et al., 2015; Neumann, 2017; Ogbeiwi, 2018; van der Meij et al., 2017).A detailed description of the steps adopted to develop the framework is provided in thefollowing sub-sections.

3.1 Proposing an integrative framework for DSCCWe performed a search on leading databases (i.e. Emerald Insight, ScienceDirect, Taylor &Francis Online and Wiley Online Library), employing the terms “DSC” and “Industry 4.0”.Since our search was “open”, hundreds of articles appeared, and we filtered initiallyconsidering the title and, in sequence, the abstract in which the context had an adherencewith DSC, Industry 4.0 technologies, or logistics. We considered article papers, proceedings,and book chapters published in English. Because these topics are recent, we did not restrictthe search to any specific years.

In order to develop the framework, the following assumptions were considered:

(1) Industry 4.0-related technologies are not consolidated yet. Due to the Industry 4.0technologies recent emergence (Liao et al., 2017), the main technologies are evolvingdaily. In this regard, there are several challenges. For instance, the ICT infrastructurestill not ready to support the companies digital transformation (Xu et al., 2018). Thus,the proposed framework can bring insights about the ICT impact on an organisation’sdigital transformation planning.

(2) The DSC is in the early stages. Following the disruptive phenomenon of Industry4.0, DSC is in its first steps of development. Thus, according to Büyüközkan andGöçer (2018), the main challenges associated with the DSC implementation are lackof planning, collaboration, information sharing and integration and wrong demandforecast. Hence, more DSC literature development is urgently needed.

Information andcommunication

technologies – ICT

Workerscapabilities Stakeholders

CDSCI

Figure 2.Critical digital supplychain integration(CDSCI)

BIJ

(3) The awareness of digitalisation and digitisation are limited in the SCM. Due to the DSCbe a recent approach in SCM, both, scholars and practitioners are in the stage of gainingin-depth concepts to understand and support strategies related to DSC (Büyüközkan andGöçer, 2018).

Figure 3 highlights the approach that supported the emergence of the proposed framework.First, anchored by the Industry 4.0-related literature, we drafted the idea of the DSCC. Afterthat, by employing the DSC emerging literature, wemodelled the final framework containing aset of capabilities and enablers.

Table I shows the capabilities, and the main literature used to derive them. In the sameline of thought, Table II points out the literature to support the enablers. Additionally,following Bartnik and Park (2018), this study offers some research propositions that canbring valuable insights both for both academia and practitioners. DSCCs identified in theliterature analysis are highlighted in Figure 4.

The basic capabilities of the DSCC framework can be classified as ICT policies, workerpolicies, supplier integration, customer integration, warehouse capabilities, transportationand smart production. Besides capabilities, our analysis also identified some enablerswhich support the basic capabilities: BDA; blockchain; AI; CC; CPSs and IoT. Theframework provides an overview concerning the importance of the development of thebasic capabilities required to survive in a digital era; it also indicates that these basiccapabilities require six fundamental enablers that allow high levels of integration withothers supply chain members.

Capabilities and enablers identification

Digital supply chain

literature

Industry 4.0 literature

Figure 3.Digital supply chaincapabilities (DSCC)

frameworkdevelopment

Industry 4.0and DSCCs

3.2 DSCC: basic capabilities3.2.1 ICT policies. ICT is an essential resource for any DSC strategy. From this perspective,in the digital age, ICTs are fundamental not only to digitising current business models; ICTs’true impact lies in their contribution to developing new operations models. Consequently,ICTs enable new relationship models (Scuotto et al., 2017) in which all supply chainmembers can interact with each other faster, improving solving-problem processes andbringing more certain information to support the decision-making process. In the DSCCframework, all basic capabilities interact with each other, and with all enablers. Thus, ICTpolicies are one of the most important drivers in organisations’ digitalisation. Therefore, thisstudy suggests the following proposition:

P1. Organisations’ ICT policies have a positive influence on their DSCCs andconsequently improve supply chain performance.

3.2.2 Worker policies. The human factor is also key to supply chain performance(Gunasekaran et al., 2017), and needs to be managed as a strategic resource (Das and Kodwani,2018). From a DSC perspective, although a large number of activities may be digitised,talented professionals remain a strategic resource, and the requirement for new humancapabilities poses a challenge to organisations’ development. For example, how can companiessupport data scientists’ development? What is the role of the organisation following job lossesresulting from the digitalisation process? As reported in Figure 2, human capabilities(Sivathanu and Pillai, 2018) are an essential resource in supporting DSC integration.

Capabilities Derived from

ICT policies Scuotto et al. (2017), Giotopoulos et al. (2017), Bibri and Krogstie (2017),Trentesaux et al. (2016)

Worker policies Gunasekaran et al. (2017), Sivathanu and Pillai (2018), Waibel et al. (2017),Erol et al. (2016)

Supplier integration Korpela et al. (2017), Scuotto et al. (2017), Yu (2015), Chen (2019)Customer integration Li et al. (2016), Zhong et al. (2017), Kunz et al. (2017), Bhattacharjya et al. (2016)Warehouse capabilities World Economic Forum (2016b), Lee et al. (2018), Herzog et al. (2018)Transportation Stock and Seliger (2016), World Economic Forum (2016b), Van

Brummelen et al. (2018)Smart production Waibel et al. (2017), Davis et al. (2015), Kibira et al. (2016), Hozdić (2015)

Table I.Capabilities analyticalsupport

Enablers Derived from

Big data analytics (BDA) Akter et al. (2016), Gupta and George (2016), Jeble et al. (2018), Kache and Seuring(2017), Phillips-Wren and Hoskisson (2015), Queiroz and Telles (2018), Suciu et al.(2016), Verma and Singh (2017), Wamba et al. (2017)

Blockchain (BCT) Al-Saqaf and Seidler (2017), Li et al. (2018), Queiroz and Fosso Wamba (2019),Queiroz et al. (2019), Scott et al. (2017), Toyoda et al. (2017), Wu et al. (2017)

Artificial intelligence (AI) Kanarachos et al. (2018), Van Brummelen et al. (2018), Plastino and Purdy (2018)Cloud computing (CC) Vazquez-Martinez et al. (2018), Yu et al. (2015), Suciu et al. (2016), Caggiano (2018),

Hsu et al. (2014)Cyber-physicalsystems (CPS)

Yu et al. (2015), Lee et al. (2015), Zamfirescu et al. (2013), Zhou et al. (2016),Lu and Xu (2018)

Internet of Things (IoT) Bibri (2018), Porter and Heppelmann (2014), Savarino et al. (2018), Suciu et al.(2016), Romaniuk (2018), Del Giudice (2016)

Table II.Enablers analyticalsupport

BIJ

Consequently, IT skills is a fundamental capability in DSCs (Waibel et al., 2017). Hence, thisstudy suggests the following proposition:

P2. Organisations’ digital policies concerning workers have a positive influence on theirDSCCs and consequently improve supply chain performance.

3.2.3 Supplier integration. The advent of DSCs has led to new models for organisations’integration with all their stakeholders in a supply network. According to Korpela et al.(2017), DSCs enable a multi-stakeholder perspective, in which the competition paradigmshifts towards collaboration. In this sense, DSCs enable effective relationships (Scuotto et al.,2017) and increased transparency and security in transactions (Korpela et al., 2017). Also,organisations’ integration with suppliers is fundamental to value co-creation ( Jääskeläinenand Thitz, 2018). Therefore, this study suggests the following proposition:

P3. Organisations’ digital integration with suppliers has a positive influence on theirDSCCs and consequently improves supply chain performance.

3.2.4 Customer integration. Similar to the supplier integration (Yu, 2015), DSCs enable newforms of customer integration and relationships. DSCs promote not only more informationabout the customers; the most important thing is the increased accuracy of this information(Büyüközkan and Göçer, 2018) thanks to smart-technology adoption. Consequently, DSCs entailsmart connections between organisations and their customers (Li et al., 2016). Furthermore,customer integration remains a vital subject related to organisations’ performance (Afshan andMotwani, 2018). Hence, the current production paradigms are already being disrupted. Thus,this study proposes:

P4. Organisations’ digital integration with customers has a positive influence on theirDSCCs and consequently improves supply chain performance.

3.2.5 Warehouse capabilities. Warehouse capabilities is a significant resource in any DSCstrategy. Shared warehouses (World Economic Forum, 2016b) used in a smart-asset perspectivewill be increasingly available thanks to the digitalisation paradigm. With the intense use ofaugmented reality (Masoni et al., 2017) and virtual reality, DSCs are enabling a new generationof smart warehouses. The virtual picking activity is already a reality, utilising, for example,QR codes and smart glasses. With virtual reality, workers’ training can be more efficient.

Big DataAnalytics

Basic capabilities

ICT’s policiesP1

P2

P3

P4

P5

P6

P7

Workers policies

Suppliers integration

Customers integration

Warehouse

Transportation

Smart production

Enabler technologies Enabler technologies

Digital SupplyChain

Capabilities

Blockchain

ArtificialIntelligenceand workers

CloudComputing

Cyber-PhysicalSystems

Internet ofThings

P11

P12

P13

P8

P9

P10

Figure 4.Digital supply chain

capability (DSCC)framework

Industry 4.0and DSCCs

Also, virtual reality enables the simulation of warehouse operations and enables interaction inreal-time with the supply network. Therefore, this study proposes:

P5. Digitisation of the warehouse has a positive influence on organisations’ DSCCs andconsequently improves supply chain performance.

3.2.6 Transportation. Transportation is one of the most traditional fields of logisticsand SCM. From a DSCC perspective, transportation is now reshaping business models(Van Brummelen et al., 2018). For instance, with shared capabilities (World Economic Forum,2016b), network transportation will utilise resources more efficiently. Regarding internaltransportation, autonomous guided vehicles (AGVs) (Stock and Seliger, 2016) has shifting thetransportation activities frommanual to smart. Moreover, autonomous vehicles such as trucksand drones (World Economic Forum, 2016b) are enabling new capabilities for transportation.Hence, this study suggests the following proposition:

P6. The digitisation of transportation activities has a positive influence on organisations’DSCCs and consequently improves supply chain performance.

3.2.7 Smart production. In a DSCC framework, smart production systems (SPS) can controland monitor the entire products lifecycle (Davis et al., 2015; Kibira et al., 2016). These capabilitiescan transform traditional products into smart products (Savarino et al., 2018), for which theentire lifecycle can be managed (Erol et al., 2016; Stock and Seliger, 2016). In this sense, theproduction systems will be more responsive, implying real-time decisions, according to demandpatterns. Supported mainly by IoT (Bibri, 2018) and CPSs (Zhong et al., 2017), machines andvarious smart connected objects can make their own decisions (Lee, 2015). However, with theincrease in decentralised manufacturing (Kohtala, 2015), traditional production systems are nowchallenged to meet smaller manufacturing demands at various locations (Srai et al., 2016).Therefore, this study suggests the following proposition:

P7. The digitisation of production system activities has a positive influence onorganisations’ DSCCs and consequently improves supply chain performance.

3.3 Enabler technologiesThis study also identified six main enabler technologies of the basic capabilities to support atraditional supply chain becoming a DSC. Although we have identified several DSC enablersin the literature, we have only selected those enabler technologies identified as havingrelationship with DSCCs, namely BDA, blockchain, AI, CC, CPSs and IoT (Büyüközkan andGöçer, 2018; Ivanov et al., 2019).

These technologies can be considered as enablers of DSCCs due to their influence in thedigitalisation process. For instance, BDA has been shown to improve firms’ performance andcompetitive advantage (Akter et al., 2016; Wamba et al., 2017); Blockchain in supply chains(Queiroz and Fosso Wamba, 2019; Queiroz et al., 2019) has been provoking disruptive changegiven in intra- and inter-organisational contexts; regarding interaction between AI and workers,several organisations believe in its potential and are investing large amounts of money.However, we believe that there is a critical and unexplored issue regarding this relationship,namely that CC services is a critical enabler due to its ability to integrate resources in thedigitalisation context, enabling a more accurate monitoring of companies’ internal activities andtheir SCM network (Porter and Heppelmann, 2014). In the Industry 4.0 era, CPS have anessential function of integrating the systems and the physical infrastructure (Wang et al., 2016).In the DSC context, we consider these systems as a fundamental enabler because of the variousinteractions between smart objects. The final enabler identified in this study was IoT, whichis of great importance, especially in providing feedback (Büyüközkan and Göçer, 2018) in

BIJ

terms of the (smart) product’s information and, consequently, contributing to continuousimprovement in organisations. In the following sub-sections, we provide a detailed descriptionof these technologies.

3.3.1 Big data analytics (BDA) policies. In a DSC age, data are one of the most criticalassets. Because of this, techniques for data storage, management and analysis represent afundamental DSCC. Recent literature regarding big data (Phillips-Wren and Hoskisson,2015) and BDA capabilities (BDAC) has achieved a significant maturity level (Akter et al.,2016; Gupta and George, 2016; Jeble et al., 2018; Wamba et al., 2017). In this context, previousstudies have demonstrated the positive impact of BDAC on firm performance (Akter et al.,2016; Wamba et al., 2017). BDA is conceptualised viewed in a 5V’s perspective (volume,velocity, variety, veracity and value) (Queiroz and Telles, 2018; Verma and Singh, 2017;Wamba et al., 2017). Therefore, for an organisation to create value, DSCCs must collect andanalyse data in as short a time as possible, although, for the most organisations, analysingall data generated by their supply network in real-time may seem like a Utopian dream.Thus, this study proposes the following:

P8. Organisations’ BDA policies have a positive influence on their DSCCs andconsequently improve supply chain performance.

3.3.2 Blockchain-related policies. The blockchain is disrupting all traditional business models(Scott et al., 2017). Based on the disintermediation process, costs minimisation and processefficiency are transforming organisations and their traditional business models (Wang et al.,2019; Queiroz et al., 2019). Blockchain technology refers to a distributed digital ledger(Al-Saqaf and Seidler, 2017) in which all transactions are shared within a tamper-proofnetwork, i.e. the transactions cannot be modified. The blockchain is transforming thetraceability of goods (Wu et al., 2017) and improving anti-counterfeiting measures (Toyodaet al., 2017). Another blockchain application is smart contracts. With smart contracts, supplynetworks can be more agile, responsive and more economical, due to disintermediation.Because of this, several industries have been incorporating blockchain in their DSC strategies.Hence, this study proposes the following:

P9. Organisations’ blockchain-related policies have a positive influence on their DSCCsand consequently improve supply chain performance.

3.3.3 Artificial intelligence (AI) and interaction with workers. According to the OxfordEnglish Dictionary, AI refers to “The theory and development of computer systems able toperform tasks normally requiring human intelligence, such as visual perception, speechrecognition, decision-making, and translation between languages”. In the context of thispaper, this means machines learning autonomously and behaving similarly to humans.However, in terms of DSCCs, skilled workers continue to be a critical and scarce resource.Recently, Barreto et al. (2017) highlighted the societal transformation caused by Industry 4.0and the necessity of organisations rethinking their processes, considering human–machineintegration and interaction. In this context, Oyekan et al. (2017) proposed a method ofreal-time manufacturing collaboration involving various teams at various locations. Themethod can enable organisations to digitise human activities. Consequently, world-class,real-time manufacturing will be a reality in the coming years (Oyekan et al., 2017). It is asignificant disruption associated with production systems environment, reflecting howorganisations must rethink and optimise collaboration between workers and machines.Thus, a worker in a DSC is continuously challenged to develop new skills, as IT capabilities(Waibel et al., 2017). Thus, this study suggests the following proposition:

P10. The degree of the interaction between workers and AI can bring positive outcomeson organisations’ DSCCs and consequently improve supply chain performance.

Industry 4.0and DSCCs

3.3.4 Cloud computing (CC). CC refers to outsourcing services associated with informationdata management, including a large amount of data transactions generated by products andservices (Vazquez-Martinez et al., 2018). From this perspective, it can be seen that CC is acritical resource for any organisation, since, in a DSC environment, all process and productshave to be smart. Additionally, previous capabilities as BDA, IoT and CPSs require muchintegration with CC services, including exchanging data over the supply network. Because ofthis, CC has to offer high levels of integration throughout the entire product lifecycle. From theproduction systems’ perspective, CC enables manufacturing-on-demand production in thevalue chain (Li et al., 2018; Yu et al., 2015). Furthermore, CC can support more control, bothinternally and throughout the network (Porter and Heppelmann, 2014) via remote monitoring.Thus, safety levels can be enhanced by smart and decentralised maintenance (Waibel et al.,2017); it is also expected that cloud integration will improve cost optimisation in DSCs(Korpela et al., 2017). Therefore, this study proposes the following:

P11. Organisations’ CC policies have a positive influence on their DSCCs andconsequently improve supply chain performance.

3.3.5 Cyber-physical system (CPS) policies. A CPS refers to the infrastructure that integratesboth physical and cyber components. Thus, CPSs provide a network infrastructure withembedded devices (sensors) that can self-manage their operations process and feedback withthe physical world (Wang et al., 2016). Due to smart objects and machines, CPSs can exchangeinformation. Consequently, CPSs and IoT applications have great synergy. In a DSCC context,CPSs represent a crucial capability for organisations to enhance control and monitoringoperations. For instance, AGVs in various production systems is a standard CPS application, inwhich it performs a set of jobs in a particular network, interacting with the physicalenvironment and with the cyber world. Therefore, a SPS, also known as smart factory, withCPS capabilities can connect different objects, machines, and conveyors (Erol et al., 2016;Lee, 2015) with the IoT. In addition, for CPSs to achieve high levels of production, incorporatingimprovements to safety and information sharing over the supply network, a CC capability ismandatory for support the processes and operations. Hence, this study proposes the following:

P12. Organisations’ CPS policies have a positive influence on their DSCCs andconsequently improve supply chain performance.

3.3.6 Internet of Things (IoT) policies. Literature regarding IoT in the capabilities context isscarce. The IoT enables a set of objects to communicate with each other, without humaninteraction. Consequently, traditional production systems can shift to a SPS. In this context,SPS capabilities can optimise a set of resources because the products that are manufacturedare also smart. Consequently, products and services have information (Büyüközkan andGöçer, 2018), not only to optimise their production, planning and control from an internalperspective, but also including feedback throughout the supply network covering the entireproduct lifecycle. IoT capabilities also enhance production safety because of the reduction inhuman interactions (Suciu et al., 2016). According to the DSCC framework, as many tasks aspossible should be performed using IoT or a combination of human–IoT interaction toimprove DSCC performance. Thus, this study proposes the following:

P13. Organisations’ IoT policies have a positive influence on their DSCCs and consequentlyimprove supply chain performance.

4. Managerial and practical implicationsFrom a practical perspective, the proposed DSCC framework can bring essential managerialinsights for practitioners and decision-makers, as well as anyone involved in the process or

BIJ

interested in gaining a more in-depth understanding of the digitalisation phenomena. TheDSCC framework highlights seven basic DSC capabilities and six enabler technologies thatinteract and support these capabilities. Thus, it offers impactful, practical implications topractitioners and decision-makers. For instance, managers need to recognise which are theirDSCCs required to operate in order to achieve improved performance and how thesecapabilities integrate with their network. Thus, the framework provides a valuablemanagerial contribution, in which organisations need to understanding in-depth theirinternal resources and how it could be integrated with external partners. Thus, theframework provides a valuable managerial contribution, as may help managers andorganisations to develop an in-depth understanding of their internal resources and how itcould be integrated with their external partners.

Moreover, the DSCC framework points out that in the digital age era, managers arechallenged constantly to improve their awareness of these cutting-edge technologies, in orderto facilitate the adoption, implementation, and diffusion of the main technologies that supportour DSC in the SCM. In addition, since we highlighted the seven capabilities as “basiccapabilities”, managers need to understand the needs to incorporate other cutting-edgetechnologies harmoniously.

Thus, managers and practitioners should concern the need to understand the dynamicsand complexities (e.g. barriers and facilitators) involved in supply chain digitalisationadoption. That is, a critical aspect is regarding the lack of decision-makers’ awareness atthe digital disruption age, related to resources, capabilities, technologies and itsintegration. In this context, the DSCC framework contributes to shedding light on thebusiness digitalisation phenomena for all practitioners and highlights the necessity toremodel traditional business models.

Also, supply chain digitalisation needs a high-level integration of organisations’ internalcapabilities and those of their supply chain members. Consequently, decision-makers arechallenged to develop these capabilities in an optimised way. Although the frameworkdepicts the main capabilities that interact in a DSC, decision-makers have to consider theorganisation’s culture complexities when shifting their traditional business model to adigitalised model. For example, workers’ resistance to adoption can arise at various stagesof the process. Besides, the proposed framework represents a valuable tool that can helporganisations generate insights and develop their supply chain digitalisation plan; however,for the project to be successful, the organisation’s framework needs to integrate with theirnetwork. Additionally, managers have to consider the costs and risks associated with adigitalisation project.

Besides, from an Industry 4.0 perspective (Hecklau et al., 2016; Qin et al., 2016), theframework emphasises that production systems are shifting towards a small productionscale in different nodes of the network. Consequently, digital capabilities in productionsystems are essential for monitoring production in different locations. Ultimately, the finalimplication is derived from the fact that the workers’ capabilities gap can negatively impacta DSC project in term so of making it more expensive and, consequently, not achieving theexpected performance. In order to avoid this, it is suggested that organisations, prior toinitiating a DSC project, must develop a deeper understanding of the role of the “digital”workers and their required capabilities.

In summary, from a practical perspective, the proposed framework can provide insightsfor the supply chain digitalisation process. DSCCs, with their enablers identified, haveimportant practical implications for supply chain digitalisation and digital transformationprojects. We believe that the proposed framework can be a good starting point formanagers, consultants, and practitioners involved in supply chain digitalisation projects.Moreover, the DSCC framework can be improved in the interaction between supply chainmembers and adapted to the reality of a specific organisation’s needs and capabilities.

Industry 4.0and DSCCs

5. Theoretical implicationsThis study has important theoretical implications for scholars interested in the digitalisationphenomena in organisations and supply networks (Büyüközkan and Göçer, 2018). The firstsignificant implication concerns the opportunity to shed light on the discussion regardingDSCC practices. Considering the recent influential literature in digitalisation (Büyüközkanand Göçer, 2018; Legner et al., 2017), our study provides significant a contribution byproposing a rational vision of the digital transformation process and capabilities in the SCMfield. Furthermore, supported by previous studies (Büyüközkan and Göçer, 2018; Kayikci,2018; Legner et al., 2017), we have developed the concept and framework of DSCCs and theirrespective enablers. To the best of our knowledge, this is the first attempt to formalise theDSCC concept. In this view, it represents an important contribution to theory.

Second, this study argues that, to better understand DSCCs, an in-depth understandingof organisations’ internal capabilities and their relationships with supply chain members inmandatory. In this regard, previous literature has highlighted only the buyer–supplierrelationships, with ICTs aiding and supporting improvements in these transactions (Scuottoet al., 2017). Therefore, our study significantly expands on the importance of ICTs within theSCM field.

Third, the CDSCI (see Figure 2) introduces to the DSC literature the necessity oforganisations considering CDSCI as a success factor. Fourth, this paper introduces to theliterature a robust framework that integrates cutting-edge technologies to gain an in-depthunderstanding of DSC complexities and, more specifically, to generate insights for scholarsin terms of rethinking the necessity of organisations developing new business models and,consequently, the necessity to understand, develop, monitor and control these newcapabilities. In this context, the DSCC framework represents a starting point for scholarsand academics in the general advancement of the DSCC literature.

The fifth and final implication is related to the 13 propositions related to the framework.The DSCC framework, anchored in the emerging DSC and Industry 4.0 literature, presentsseveral opportunities for scholars and practitioners to empirically test it in various scenarios(e.g. emerging and developed countries, small, medium and large organisations, etc.). Table IIIpresents a summary of the propositions.

6. Final remarks and future researchThis study has proposed a framework to shed light on DSCCs in response to rethinkingsupply chain complexities in the age of digitalisation. This study contributes to theliterature and is also of benefit to practitioners, generating insights into the decision-makingprocess by developing a robust framework (DSCC) to improve the awareness of all involvedin the business digitalisation phenomena. Additionally, the DSCC framework emphasisesthat, in the digital age, organisations must make significant efforts to integrate theircapabilities with their supply chain members. This implies that, if organisations focus onlyon internal capabilities development, this will likely result in an unbalanced DSC that,consequently, will not achieve the desired performance. Although DSCs involve significantdigitalisation of physical activities, ICTs can be viewed as a critical (resource) enablertechnologies (Scuotto et al., 2017), implying that organisations need to develop policies toensure and provide the necessary (key) resources. In this regard, the framework highlightsthat, for a DSC project be successful; the human factor (Gunasekaran et al., 2017) remains asthe leading resource. Because of this, the study suggests that organisations begin DSCprojects by fist considering the human capabilities requirements. Moreover, the propositionsof this study bring a relevant contribution to the theory and practice, as also have thepotential to unlock the DSCC research stream.

Whilst this study makes significant academic and practical contributions, there are somelimitations that future studies can address. The first is related to the fact that the DSCC

BIJ

framework has not yet been tested. Future studies have the opportunity to develop andimplement a conceptual model to test this framework empirically. Second, the proposedframework does not consider the particular characteristics of the organisations that operatein emerging and developed countries. Thereafter, scholars can expand the framework byconsidering supply network differences in these countries. Third, the study does not discussthe barriers to adoption related to the capabilities that comprise the DSCC framework. Also,our work deals only technological enablers approach.

Researchers can, therefore, aim to identify these barriers to support the framework’sadoption. Finally, it would be a valuable contribution to the literature can be interesting toadvance the literature if the critical success factors (Kumar and Singh, 2018) in DSC projectsand their relationship with the DSCC framework could be identified.

Proposition Description Adapted from

P1 Organisations’ ICT policies have a positive influenceon their DSCCs and consequently improve supplychain performance

Scuotto et al. (2017)

P2 Organisations’ digital policies concerning workershave a positive influence on their DSCCs andconsequently improve supply chain performance

Gunasekaran et al. (2017), Waibel et al.(2017)

P3 Organisations’ digital integration with suppliers has apositive influence on their DSCCs and consequentlyimproves supply chain performance

Korpela et al., (2017), Scuotto et al. (2017)

P4 Organisations’ digital integration with customers hasa positive influence on their DSCCs and consequentlyimprove supply chain performance

Li et al. (2016)

P5 Digitisation of the warehouse has a positive influenceon organisations’ DSCCs and consequently improvessupply chain performance

World Economic Forum (2016b)

P6 The digitisation of transportation activities has apositive influence on organisations’ DSCCs andconsequently improves supply chain performance

Stock and Seliger (2016), WorldEconomic Forum (2016b)

P7 The digitisation of production system activities has apositive influence on organisations’ DSCCs andconsequently improves supply chain performance

Davis et al. (2015), Kibira et al. (2016), Lee(2015), Srai et al. (2016)

P8 Organisations’ BDA policies have a positive influenceon their DSCCs and consequently improve supplychain performance

Queiroz and Telles (2018), Wamba et al.(2017)

P9 Organisations’ blockchain-related policies have apositive influence on their DSCCs and consequentlyimprove supply chain performance

Akter et al. (2016), Al-Saqaf and Seidler(2017), Scott et al. (2017), Wu et al. (2017)

P10 The degree of the interaction between workers androbots can bring positive outcomes on organisations’DSCCs and consequently improve supply chainperformance

Barreto et al. (2017), Oyekan et al. (2017)

P11 Organisations’ cloud computing (CC) policies have apositive influence on their DSCCs and consequentlyimprove supply chain performance

Li et al. (2018), Vazquez-Martinez et al.(2018), Yu et al. (2015)

P12 Organisations’ cyber-physical system (CPS) policieshave a positive influence on their DSCCs andconsequently improve supply chain performance

Erol et al. (2016), Lee (2015), Wang et al.(2016)

P13 Organisations’ Internet of Things (IoT) policies havea positive influence on their DSCCs and consequentlyimprove supply chain performance

Suciu et al. (2016)

Note: DSSCs, digital supply chain capabilities

Table III.Summary of the

propositions

Industry 4.0and DSCCs

References

Accenture (2014), “The digital supply network: a new paradigm for supply chain management”,available at: www.accenture.com/t20150708T025455__w__/fr-fr/_acnmedia/Accenture/Conversion-Assets/DotCom/Documents/Local/fr-fr/PDF_5/Accenture-Digital-Supply-Network-New-Standard-Modern-Supply-Chain-Management.pdf (accessed 15 December 2018).

Afshan, N. and Motwani, J. (2018), “The mediating role of customer related performance outcomes onthe relationship between customer integration and firm performance”, Benchmarking: AnInternational Journal, Vol. 25 No. 7, pp. 2184-2197, doi: 10.1108/BIJ-11-2016-0178.

Akter, S., Wamba, S.F., Gunasekaran, A., Dubey, R. and Childe, S.J. (2016), “How to improve firmperformance using big data analytics capability and business strategy alignment?”, InternationalJournal of Production Economics, Vol. 2 No. 3, pp. 113-131, doi: 10.1016/j.ijpe.2016.08.018.

Al-Saqaf, W. and Seidler, N. (2017), “Blockchain technology for social impact: opportunities andchallenges ahead”, Journal of Cyber Policy, Vol. 2 No. 3, pp. 1-17, doi: 10.1080/23738871.2017.1400084.

Alshawi, S., Irani, Z. and Baldwin, L. (2003), “Editorial”, Benchmarking: An International Journal,Vol. 10 No. 4, pp. 312-324, doi: 10.1108/14635770310484953.

Apostolakis, G., van Dijk, G. and Drakos, P. (2015), “Microinsurance performance – a systematicnarrative literature review”, Corporate Governance (Bingley), Vol. 15 No. 1, pp. 146-170, availableat: http://doi.org/10.1108/CG-08-2014-0098

A.T. Kearney (2015), “Digital supply chains: increasingly critical for competitive edge – European A.T.Kearney/WHU Logistics Study 2015”, available at: www.atkearney.com/documents/20152/435077/Digital%2BSupply%2BChains.pdf/82bf637e-bfa9-5922-ce03-866b7b17a492 (accessed15 December 2018).

Bain & Company (2018), “Build a digital supply chain that is fit for the future”, available at: www.bain.com/Images/BAIN_BRIEF_Digital_Supply_Chain_Trends.pdf (accessed 15 December 2018).

Barreto, L., Amaral, A. and Pereira, T. (2017), “Industry 4.0 implications in logistics: an overview”,Procedia Manufacturing, Vol. 13, pp. 1245-1252, doi: 10.1016/j.promfg.2017.09.045.

Bartnik, R. and Park, Y. (2018), “Technological change, information processing and supply chainintegration”, Benchmarking: An International Journal, Vol. 25 No. 5, pp. 1279-1301, doi: 10.1108/BIJ-03-2016-0039.

Bhattacharjya, J., Ellison, A. and Tripathi, S. (2016), “An exploration of logistics-related customer serviceprovision on Twitter: the case of e-retailers”, International Journal of Physical Distribution andLogistics Management, Vol. 46 Nos 6/7, pp. 659-680, available at: https://doi.org/10.1108/IJPDLM-01-2015-0007

Bibri, S.E. (2018), “The IoT for smart sustainable cities of the future: an analytical framework forsensor-based big data applications for environmental sustainability”, Sustainable Cities andSociety, Vol. 38, October, pp. 230-253, doi: 10.1016/j.scs.2017.12.034.

Bibri, S.E. and Krogstie, J. (2017), “ICT of the new wave of computing for sustainable urban forms: theirbig data and context-aware augmented typologies and design concepts”, Sustainable Cities andSociety, Vol. 32, pp. 449-474, available at: https://doi.org/10.1016/j.scs.2017.04.012

Boston Consulting Group (2018), “Turning visibility into value in digital supply chains”, available at:http://image-src.bcg.com/Images/BCG-Turning-Visibility-into-Value-in-Digital-Supply-Chains-Jan-2018_tcm9-181967.pdf (accessed 15 December 2018).

Büyüközkan, G. and Göçer, F. (2018), “Digital supply chain: literature review and a proposedframework for future research”, Computers in Industry, Vol. 97, pp. 157-177, doi: 10.1016/j.compind.2018.02.010.

Caggiano, A. (2018), “Cloud-based manufacturing process monitoring for smart diagnosis services”,International Journal of Computer Integrated Manufacturing, Vol. 31 No. 7, pp. 612-623, availableat: https://doi.org/10.1080/0951192X.2018.1425552

Chen, C. (2019), “Value creation by SMEs participating in global value chains under Industry 4.0 trend :Case Study of Textile Industry in Taiwan”, Journal of Global Information TechnologyManagement,Vol. 22 No. 2, pp. 120-145, available at: https://doi.org/10.1080/1097198X.2019.1603512

BIJ

Christenson, J.K., O’Kane, G.M., Farmery, A.K. and McManus, A. (2017), “The barriers and drivers ofseafood consumption in Australia: a narrative literature review”, International Journal ofConsumer Studies, Vol. 41 No. 3, pp. 299-311, available at: http://doi.org/10.1111/ijcs.12342

Das, R. and Kodwani, A.D. (2018), “Strategic human resource management: a power based critique”,Benchmarking: An International Journal, Vol. 25 No. 4, pp. 1213-1231, doi: 10.1108/BIJ-09-2016-0143.

Davis, J., Edgar, T., Graybill, R., Korambath, P., Schott, B., Swink, D., Wang, J. and Wetzel, J. (2015),“Smart manufacturing”, Annual Review of Chemical and Biomolecular Engineering, Vol. 6 No. 1,pp. 141-160, doi: 10.1146/annurev-chembioeng-061114-123255.

Del Giudice, M. (2016), “Discovering the Internet of Things (IoT) within the business processmanagement”, Business Process Management Journal, Vol. 22 No. 2, pp. 263-270, available at:https://doi.org/10.1108/BPMJ-12-2015-0173

Deloitte (2016), “The rise of the digital supply network: Industry 4.0 enables the digital transformationof supply chains”, available at: www2.deloitte.com/content/dam/insights/us/articles/3465_Digital-supply-network/DUP_Digital-supply-network.pdf (accessed 15 December 2018).

Ernst & Young (2016), “Digital supply chain: it’s all about that data”, available at: www.ey.com/Publication/vwLUAssets/Digital_supply_chain_-_its_all_about_the_data/$FILE/EY-digital-supply-chain-its-all-about-that-data-final.pdf (accessed 15 December 2018).

Erol, S., Jaeger, A., Hold, P., Ott, K. and Sihn, W. (2016), “Tangible industry 4.0: a scenario-basedapproach to learning for the future of production”, Procedia CIRP, Vol. 54, pp. 13-18, doi: 10.1016/j.procir.2016.03.162.

Farahani, P., Meier, C. and Wilke, J. (2017), “Digital supply chain management agenda for theautomotive supplier industry”, in Oswald, G. and Kleinemeier, M. (Eds), Shaping the DigitalEnterprise, Springer, Cham, pp. 157-172.

Freddi, D. (2018), “Digitalisation and employment in manufacturing: pace of the digitalisation processand impact on employment in advanced Italian manufacturing companies”, AI and Society,Vol. 33 No. 3, pp. 393-403, doi: 10.1007/s00146-017-0740-5.

Giotopoulos, I., Kontolaimou, A., Korra, E. and Tsakanikas, A. (2017), “What drives ICT adoption bySMEs? Evidence from a large-scale survey in Greece”, Journal of Business Research, Vol. 81,pp. 60-69, available at: https://doi.org/10.1016/j.jbusres.2017.08.007

Grant, R.M. (1991), “The resource-based theory of competitive advantage: implications forstrategy formulation”, California Management Review, Vol. 33 No. 3, pp. 114-135, doi: 10.2307/41166664.

Gunasekaran, A., Subramanian, N. and Rahman, S. (2017), “Improving supply chain performancethrough management capabilities”, Production Planning and Control, Vol. 28 Nos 6-8,pp. 473-477, doi: 10.1080/09537287.2017.1309680.

Gupta, M. and George, J.F. (2016), “Toward the development of a big data analytics capability”,Information and Management, Vol. 53 No. 8, pp. 1049-1064, doi: 10.1016/j.im.2016.07.004.

Hagberg, J., Sundstrom, M. and Egels-Zandén, N. (2016), “The digitalization of retailing: an exploratoryframework”, International Journal of Retail and Distribution Management, Vol. 44 No. 7,pp. 694-712, doi: 10.1108/IJRDM-09-2015-0140.

Hautala, K., Järvenpää, M.E. and Pulkkinen, P. (2017), “Digitalization transforms the constructionsector throughout asset’s life-cycle from design to operation and maintenance”, Stahlbau, Vol. 86No. 4, pp. 340-345, doi: 10.1002/stab.201710474.

Hecklau, F., Galeitzke, M., Flachs, S. and Kohl, H. (2016), “Holistic approach for human resourcemanagement in industry 4.0”, Procedia CIRP, Vol. 54, pp. 1-6, doi: 10.1016/j.procir.2016.05.102.

Herzog, K., Winter, G., Kurka, G., Ankermann, K., Binder, R., Ringhofer, M., Maierhofer, A. and Flick, A.(2017), “The digitalization of steel production”, BHM Berg- und Hüttenmännische Monatshefte,Vol. 162 No. 11, pp. 504-513, doi: 10.1007/s00501-017-0673-9.

Herzog, V.N., Buchmeister, B., Beharic, A. and Gajsek, B. (2018), “Visual and optometric issues withsmart glasses in Industry 4.0 working environment”, Advances in Production Engineering &Management, Vol. 13 No. 4, pp. 417-428, available at: https://doi.org/10.14743/apem2018.4.300

Industry 4.0and DSCCs

Hofmann, E. and Rüsch, M. (2017), “Industry 4.0 and the current status as well as future prospects onlogistics”, Computers in Industry, Vol. 89, pp. 23-34, doi: 10.1016/j.compind.2017.04.002.

Holmström, J. and Partanen, J. (2014), “Digital manufacturing-driven transformations of service supplychains for complex products”, Supply Chain Management, Vol. 19 No. 4, pp. 421-430, doi: 10.1108/SCM-10-2013-0387.

Hozdić, E. (2015), “Smart factory for industry 4.0: a review”, International Journal of AdvancedManufacturing Technology, Vol. 7 No. 1, pp. 28-35.

Hsu, P.F., Ray, S. and Li-Hsieh, Y.Y. (2014), “Examining cloud computing adoption intention, pricingmechanism, and deployment model”, International Journal of Information Management, Vol. 34No. 4, pp. 474-488, available at: https://doi.org/10.1016/j.ijinfomgt.2014.04.006

Ivanov, D., Dolgui, A. and Sokolov, B. (2019), “The impact of digital technology and Industry 4.0 on theripple effect and supply chain risk analytics”, International Journal of Production Research,Vol. 57 No. 3, pp. 1-18, doi: 10.1080/00207543.2018.1488086.

Jääskeläinen, A. and Thitz, O. (2018), “Prerequisites for performance measurement supportingpurchaser-supplier collaboration”, Benchmarking: An International Journal, Vol. 25 No. 1,pp. 120-137, doi: 10.1108/BIJ-08-2016-0121.

Jeble, S., Dubey, R., Childe, S., Papadopoulos, T., Roubaud, D. and Prakash, A. (2018), “Impact of bigdata & predictive analytics capability on supply chain sustainability”, International Journal ofLogistics Management, Vol. 29 No. 2, pp. 513-538, doi: 10.1108/IJLM-05-2017-0134.

Kache, F. and Seuring, S. (2017), “Challenges and opportunities of digital information at the intersectionof big data analytics and supply chain management”, International Journal of Operations &Production Management, Vol. 37 No. 1, pp. 10-36, doi: 10.1108/IJOPM-02-2015-0078.

Kanarachos, S., Christopoulos, S.-R.G. and Chroneos, A. (2018), “Smartphones as an integrated platformfor monitoring driver behaviour: the role of sensor fusion and connectivity”, TransportationResearch Part C: Emerging Technologies, Vol. 95, pp. 867-882, doi: 10.1016/j.trc.2018.03.023.

Kapetaniou, C., Rieple, A., Pilkington, A., Frandsen, T. and Pisano, P. (2018), “Building the layers of anew manufacturing taxonomy: how 3D printing is creating a new landscape of productioneco-systems and competitive dynamics”, Technological Forecasting and Social Change, Vol. 128,pp. 22-35, doi: 10.1016/j.techfore.2017.10.011.

Kayikci, Y. (2018), “Sustainability impact of digitization in logistics”, Procedia Manufacturing, Vol. 21,pp. 782-789, doi: 10.1016/j.promfg.2018.02.184.

Kibira, D., Morris, K. and Kumaraguru, S. (2016), “Methods and tools for performance assurance ofsmart manufacturing systems”, Journal of Research of the National Institute of Standards andTechnology, Vol. 121, pp. 282-313, doi: 10.6028/jres.121.013.

Kohtala, C. (2015), “Addressing sustainability in research on distributed production: an integrated literaturereview”, Journal of Cleaner Production, Vol. 106, pp. 654-668, doi: 10.1016/j.jclepro.2014.09.039.

Korpela, K., Hallikas, J. and Dahlberg, T. (2017), “Digital supply chain transformation towardblockchain integration”, In Proceedings of the 50th Hawaii International Conference on SystemSciences, Hawaii, pp. 4182-4191, doi: 10.24251/HICSS.2017.506.

Kumar, M., Graham, G., Hennelly, P. and Srai, J. (2016), “How will smart city production systemstransform supply chain design: a product-level investigation”, International Journal ofProduction Research, Vol. 54 No. 23, pp. 7181-7192, doi: 10.1080/00207543.2016.1198057.

Kumar, R. and Singh, H. (2018), “Exploring the success factors for examining the potential of manufacturingsystem output”, Benchmarking: An International Journal, Vol. 25 No. 4, pp. 1171-1193, doi: 10.1108/BIJ-10-2016-0156.

Kunz, W., Aksoy, L., Bart, Y., Heinonen, K., Kabadayi, S., Ordenes, F.V., Sigala, M., Diaz, D. andTheodoulidis, B. (2017), “Customer engagement in a big data world”, Journal of ServicesMarketing, Vol. 31 No. 2, pp. 161-171, available at: https://doi.org/10.1108/JSM-10-2016-0352

Lee, C.K.M., Lv, Y., Ng, K.K.H., Ho, W. and Choy, K.L. (2018), “Design and application of internet ofthings-based warehouse management system for smart logistics”, International Journal ofProduction Research, Vol. 56 No. 8, pp. 2753-2768, available at: https://doi.org/10.1080/00207543.2017.1394592

BIJ

Lee, J. (2015), “Smart factory systems”, Informatik-Spektrum, Vol. 38 No. 3, pp. 230-235, doi: 10.1007/s00287-015-0891-z.

Lee, J., Bagheri, B. and Kao, H.A. (2015), “A cyber-physical systems architecture for industry 4.0-basedmanufacturing systems”, Manufacturing Letters, Vol. 3, pp. 18-23, available at: https://doi.org/10.1016/j.mfglet.2014.12.001

Legner, C., Eymann, T., Hess, T., Matt, C., Böhmann, T., Drews, P., Mädche, A., Urbach, N. andAhlemann, F. (2017), “Digitalization: opportunity and challenge for the business and informationsystems engineering community”, Business and Information Systems Engineering, Vol. 59 No. 4,pp. 301-308, doi: 10.1007/s12599-017-0484-2.

LeMay, S., Helms, M., Kimball, B. and McMahon, D. (2017), “Supply chain management: the elusiveconcept and definition”, The International Journal of Logistics Management, Vol. 28 No. 4,pp. 1425-1453, doi: 10.1108/IJLM-10-2016-0232.

Li, F., Nucciarelli, A., Roden, S. and Graham, G. (2016), “How smart cities transform operations models:a new research agenda for operations management in the digital economy”, Production Planningand Control, Vol. 27 No. 6, pp. 514-528, doi: 10.1080/09537287.2016.1147096.

Li, Z., Wang, W., Liu, G., Liu, L., He, J. and Huang, G. (2018), “Toward open manufacturing a cross-enterprises knowledge and services exchange framework based on blockchain and edgecomputing”, Industrial Management and Data Systems, Vol. 118 No. 1, pp. 303-320, doi: 10.1108/IMDS-04-2017-0142.

Liao, Y., Deschamps, F., Loures, E.d.F.R. and Ramos, L.F.P. (2017), “Past, present and future ofIndustry 4.0 – a systematic literature review and research agenda proposal”, International Journalof Production Research, Vol. 55 No. 12, pp. 3609-3629, doi: 10.1080/00207543.2017.1308576.

Lu, Y. and Xu, X. (2018), “Resource virtualization: a core technology for developing cyber-physicalproduction systems”, Journal of Manufacturing Systems, Vol. 47, pp. 128-140, available at:https://doi.org/10.1016/j.jmsy.2018.05.003

Lummus, R.R. and Vokurka, R.J. (1999), “Defining supply chain management: a historical perspectiveand practical guidelines”, Industrial Management and Data Systems, Vol. 99 No. 1, pp. 11-17,doi: 10.1108/02635579910243851.

McKinsey & Company (2017), “Digital transformation: raising supply-chain performance to newlevels”, available at: www.mckinsey.com/business-functions/operations/our-insights/digital-transformation-raising-supply-chain-performance-to-new-levels (accessed 15 December 2018).

Martín-Peña, L.M., Díaz-Garrido, E. and Sánchez-López, J.M. (2018), “The digitalization andservitization of manufacturing: a review on digital business models”, Strategic Change, Vol. 27No. 2, pp. 91-99, doi: 10.1002/jsc.2184.

Masoni, R., Ferrise, F., Bordegoni, M., Gattullo, M., Uva, A.E., Fiorentino, M., Carrabba, E. andDonato, M.D. (2017), “Supporting remote maintenance in industry 4.0 through augmentedreality”, Procedia Manufacturing, Vol. 11, pp. 1296-1302, doi: 10.1016/j.promfg.2017.07.257.

Mentzer, J.T., DeWitt, W., Keebler, J.S., Min, S., Nix, N.W., Smith, C.D. and Zacharia, Z.G. (2001),“Defining supply chain management”, Journal of Business Logistics, Vol. 22 No. 2, pp. 1-25,doi: 10.1002/j.2158-1592.2001.tb00001.x.

Mohr, S. and Khan, O. (2015), “3D printing and its disruptive impacts on supply chains of the future”,Technology InnovationManagement Review, Vol. 5 No. 11, pp. 20-25, doi: 10.5437/08956308X5606193.

Neumann, F. (2017), “Antecedents and effects of emotions in strategic decision-making: a literaturereview and conceptual model”, Management Review Quarterly, Vol. 67 No. 3, pp. 175-200,available at: http://doi.org/10.1007/s11301-017-0127-1

Ogbeiwi, O. (2018), “General concepts of goals and goal-setting in healthcare: a narrative review”, Journalof Management and Organization, pp. 1-18, available at: http://doi.org/10.1017/jmo.2018.11

Oyekan, J., Prabhu, V., Tiwari, A., Baskaran, V., Burgess, M. and Mcnally, R. (2017), “Remote real-timecollaboration through synchronous exchange of digitised human–workpiece interactions”,Future Generation Computer Systems, Vol. 67, pp. 83-93, doi: 10.1016/j.future.2016.08.012.

Industry 4.0and DSCCs

Phillips-Wren, G. and Hoskisson, A. (2015), “An analytical journey towards big data”, Journal ofDecision Systems, Vol. 24 No. 1, pp. 87-102, doi: 10.1080/12460125.2015.994333.

Plastino, E. and Purdy, M. (2018), “Game changing value from Artificial Intelligence: eight strategies”,Strategy and Leadership, Vol. 46 No. 1, pp. 16-22, doi: 10.1108/SL-11-2017-0106.

Plomp, M.G.A. and Batenburg, R.S. (2010), “Measuring chain digitisation maturity: an assessment ofDutch retail branches”, Supply Chain Management, Vol. 15 No. 3, pp. 227-237, doi: 10.1108/13598541011039983.

Porter, M.E. and Heppelmann, J.E. (2014), “How smart, connected products are transforming competition”,Harvard Business Review, Vol. 92 No. 11, pp. 64-88, doi: 10.3182/20110828-6-IT-1002.02913.

PwC (2016), “Industry 4.0: how digitalization makes the supply chain more efficient, agile, andcustomer-focused”, available at: www.strategyand.pwc.com/media/file/Industry4.0.pdf(accessed 15 December 2018).

Qin, J., Liu, Y. and Grosvenor, R. (2016), “A categorical framework of manufacturing for industry 4.0and beyond”, Procedia CIRP, Vol. 52, pp. 173-178, doi: 10.1016/j.procir.2016.08.005.

Queiroz, M.M. and Fosso Wamba, S. (2019), “Blockchain adoption challenges in supply chain: anempirical investigation of the main drivers in India and the USA”, International Journal ofInformation Management, Vol. 46, pp. 70-82, doi: 10.1016/j.ijinfomgt.2018.11.021.

Queiroz, M.M. and Telles, R. (2018), “Big data analytics in supply chain and logistics: an empiricalapproach”, International Journal of Logistics Management, Vol. 29 No. 2, pp. 767-783, doi: 10.1108/IJLM-05-2017-0116.

Queiroz, M.M., Telles, R. and Bonilla, S.H. (2019), “Blockchain and supply chain managementintegration: a systematic review of the literature”, Supply Chain Management: An InternationalJournal, doi: 10.1108/SCM-03-2018-0143.

Roland Berger (2016), “2016 logistics study on digital business models”, available at: www.rolandberger.com/publications/publication_pdf/roland_berger_logistics_final_web_251016.pdf(accessed 15 December 2018).

Romaniuk, R.S. (2018), “IoT – review of critical issues”, International Journal of Electronics andTelecommunications, Vol. 64 No. 1, pp. 95-102, available at: https://doi.org/10.24425/118153

Sangwan, K.S., Bhakar, V. and Digalwar, A.K. (2018), “Sustainability assessment in manufacturingorganizations”, Benchmarking: An International Journal, Vol. 25 No. 3, pp. 994-1027, doi: 10.1108/BIJ-08-2017-0227.

Savarino, P., Abramovici, M., Göbel, J.C. and Gebus, P. (2018), “Design for reconfiguration as fundamentalaspect of smart products”, Procedia CIRP, Vol. 70, pp. 374-379, doi: 10.1016/j.procir.2018.01.007.

Schumacher, A., Erol, S. and Sihn, W. (2016), “A maturity model for assessing Industry 4.0 readinessand maturity of manufacturing enterprises”, Procedia CIRP, Vol. 52, pp. 161-166, available at:https://doi.org/10.1016/j.procir.2016.07.040

Scott, B., Loonam, J. and Kumar, V. (2017), “Exploring the rise of blockchain technology: towards distributedcollaborative organizations”, Strategic Change, Vol. 26 No. 5, pp. 423-428, doi: 10.1002/jsc.2142.

Scuotto, V., Caputo, F., Villasalero, M. and Del Giudice, M. (2017), “A multiple buyer–supplierrelationship in the context of SMEs’ digital supply chain management”, Production Planning &Control, Vol. 28 No. 16, pp. 1378-1388, doi: 10.1080/09537287.2017.1375149.

Secundo, G., Toma, A., Schiuma, G. and Passiante, G. (2019), “Knowledge transfer in open innovation”,Business Process Management Journal, Vol. 25 No. 1, pp. 144-163, doi: 10.1108/BPMJ-06-2017-0173.

Sivathanu, B. and Pillai, R. (2018), “Smart HR 4.0 – how industry 4.0 is disrupting HR”, Human ResourceManagement International Digest, Vol. 26 No. 4, pp. 7-11, doi: 10.1108/HRMID-04-2018-0059.

Srai, J.S., Kumar, M., Graham, G., Phillips, W., Tooze, J., Ford, S., Beecher, P., Raj, B., Gregory, M.,Tiwari, M.K., Ravi, B., Neely, A., Shankar, R., Charnley, F. and Tiwari, A. (2016), “Distributedmanufacturing: scope, challenges and opportunities”, International Journal of ProductionResearch, Vol. 54 No. 23, pp. 6917-6935, doi: 10.1080/00207543.2016.1192302.

BIJ

Stock, J.R. and Boyer, S.L. (2009), “Developing a consensus definition of supply chain management: aqualitative study”, International Journal of Physical Distribution & Logistics Management,Vol. 39 No. 8, pp. 690-711, doi: 10.1108/09600030910996323.

Stock, T. and Seliger, G. (2016), “Opportunities of sustainable manufacturing in industry 4.0”, ProcediaCIRP, Vol. 40, pp. 536-541, doi: 10.1016/j.procir.2016.01.129.

Strandhagen, J.O., Vallandingham, L.R., Fragapane, G., Strandhagen, J.W., Stangeland, A.B.H. andSharma, N. (2017), “Logistics 4.0 and emerging sustainable business models”, Advances inManufacturing, Vol. 5 No. 4, pp. 359-369, doi: 10.1007/s40436-017-0198-1.

Strong, D., Kay, M.G., Conner, B., Wakefield, T.P. and Manogharan, G. (2018), “Hybrid manufacturing –integrating traditional manufacturers with additive manufacturing (AM) supply chain”,Additive Manufacturing, Vol. 21, pp. 159-173, doi: 10.1016/j.addma.2018.03.010.

Suciu, G., Vulpe, A., Martian, A., Halunga, S. and Vizireanu, D.N. (2016), “Big data processing forrenewable energy telemetry using a decentralized cloud M2M system”, Wireless PersonalCommunications, Vol. 87 No. 3, pp. 1113-1128, doi: 10.1007/s11277-015-2527-7.

Toyoda, K., Mathiopoulos, P.T., Sasase, I. and Ohtsuki, T. (2017), “A novel blockchain-based productownership management system (POMS) for anti-counterfeits in the post supply chain”,IEEE Access, Vol. 5, pp. 17465-17477, doi: 10.1109/ACCESS.2017.2720760.

Trentesaux, D., Borangiu, T. and Thomas, A. (2016), “Emerging ICT concepts for smart, safe andsustainable industrial systems”, Computers in Industry, Vol. 81, pp. 1-10, available at: https://doi.org/10.1016/j.compind.2016.05.001

Van Brummelen, J., O’Brien, M., Gruyer, D. and Najjaran, H. (2018), “Autonomous vehicle perception:the technology of today and tomorrow”, Transportation Research Part C: EmergingTechnologies, Vol. 89, pp. 384-406, doi: 10.1016/j.trc.2018.02.012.

van der Meij, M.G., Broerse, J.E.W. and Kupper, F. (2017), “Conceptualizing playfulness for reflectionprocesses in responsible research and innovation contexts: a narrative literature review”,Journal of Responsible Innovation, Vol. 4 No. 1, pp. 43-63, available at: http://doi.org/10.1080/23299460.2017.1326258

Vanderroost, M., Ragaert, P., Verwaeren, J., De Meulenaer, B., De Baets, B. and Devlieghere, F. (2017),“The digitization of a food package’s life cycle: existing and emerging computer systems in thelogistics and post-logistics phase”, Computers in Industry, Vol. 87, pp. 15-30, doi: 10.1016/j.compind.2017.01.004.

Vazquez-Martinez, G.A., Gonzalez-Compean, J.L., Sosa-Sosa, V.J., Morales-Sandoval, M. and Perez, J.C.(2018), “Cloud chain: a novel distribution model for digital products based on supply chainprinciples”, International Journal of Information Management, Vol. 39, pp. 90-103, doi: 10.1016/j.ijinfomgt.2017.12.006.

Verma, N. and Singh, J. (2017), “An intelligent approach to big data analytics for sustainable retailenvironment using Apriori-MapReduce framework”, Industrial Management & Data Systems,Vol. 117 No. 7, pp. 1503-1520, doi: 10.1108/IMDS-09-2016-0367.

Waibel, M.W., Steenkamp, L.P., Moloko, N. and Oosthuizen, G.A. (2017), “Investigating the effects ofsmart production systems on sustainability elements”, Procedia Manufacturing, Vol. 8, October,pp. 731-737, doi: 10.1016/j.promfg.2017.02.094.

Wamba, S.F., Gunasekaran, A., Akter, S., Ren, S.J., Dubey, R. and Childe, S.J. (2017), “Big data analyticsand firm performance: effects of dynamic capabilities”, Journal of Business Research, Vol. 70,pp. 356-365, doi: 10.1016/j.jbusres.2016.08.009.

Wang, S., Wan, J., Li, D. and Zhang, C. (2016), “Implementing smart factory of industry 4.0: anoutlook”, International Journal of Distributed Sensor Networks, Vol. 12 No. 1, pp. 1-10, doi: 10.1155/2016/3159805.

Wang, Y., Hungh, H.J. and Paul, B.-D. (2019), “Understanding blockchain technology for future supplychains a systematic literature review and research agenda”, Supply Chain Management:An International Journal, Vol. 24 No. 1, pp. 62-84, doi: 10.1108/SCM-03-2018-0148.

Industry 4.0and DSCCs

World Economic Forum (2016a), “World economic forum white paper digital transformations ofindustries: in collaboration with Accenture – societal implications”, available at: http://reports.weforum.org/digital-transformation/wp-content/blogs.dir/94/mp/files/pages/files/dti-societal-implications-white-paper.pdf (accessed 15 December 2018).

World Economic Forum (2016b), “World economic forum white paper digital transformations ofindustries: in collaboration with Accenture – logistics industry”, available at: http://reports.weforum.org/digital-transformation/wp-content/blogs.dir/94/mp/files/pages/files/wef-dti-logisticswhitepaper-final-january-2016.pdf (accessed 15 December 2018).

Wu, H., Li, Z., King, B., Ben Miled, Z., Wassick, J. and Tazelaar, J. (2017), “A distributed ledgerfor supply chain physical distribution visibility”, Information, Vol. 8 No. 4, pp. 1-18, doi: 10.3390/info8040137.

Xu, L.D., Xu, E.L. and Li, L. (2018), “Industry 4.0: state of the art and future trends”, InternationalJournal of Production Research, Vol. 56 No. 8, pp. 1-22, doi: 10.1080/00207543.2018.1444806.

Yu, C., Xu, X. and Lu, Y. (2015), “Computer-integrated manufacturing, cyber-physical systems andcloud manufacturing – concepts and relationships”, Manufacturing Letters, Vol. 6, pp. 5-9,doi: 10.1016/j.mfglet.2015.11.005.

Yu, W. (2015), “The effect of IT-enabled supply chain integration on performance”, ProductionPlanning and Control, Vol. 26 No. 12, pp. 945-957, doi: 10.1080/09537287.2014.1002021.

Zamfirescu, C.B., Pirvu, B.C.-T., Schlick, J. and Zuehlke, D. (2013), “Preliminary insides for ananthropocentric cyber-physical reference architecture of the smart factory”, Studies inInformatics and Control, Vol. 22 No. 3, pp. 269-278.

Zhong, R.Y., Xu, X., Klotz, E. and Newman, S.T. (2017), “Intelligent manufacturing in the context ofindustry 4.0: a review”, Engineering, Vol. 3 No. 5, pp. 616-630, doi: 10.1016/J.ENG.2017.05.015.

Zhou, K., Liu, T. and Liang, L. (2016), “From cyber-physical systems to Industry 4.0: make futuremanufacturing become possible”, International Journal of Manufacturing Research, Vol. 11No. 2, pp. 167-188, available at: https://doi.org/10.1504/IJMR.2016.078251

Further reading

Lai, K.H., Wong, C.W.Y. and Cheng, T.C.E. (2010), “Bundling digitized logistics activities and itsperformance implications”, Industrial Marketing Management, Vol. 39 No. 2, pp. 273-286,doi: 10.1016/j.indmarman.2008.08.002.

Oxford (2018), “Oxford dictionaries”, available at: https://en.oxforddictionaries.com/definition/artificial_intelligence (accessed 15 December 2018).

Wu, D., Ren, A., Zhang, W., Fan, F., Liu, P., Fu, X. and Terpenny, J. (2018), “Cybersecurity for digitalmanufacturing”, Journal of Manufacturing Systems, Vol. 48, pp. 3-12, doi: 10.1016/j.jmsy.2018.03.006.

Wu, L., Yue, X., Jin, A. and Yen, D. (2016), “Smart supply chain management: a review and implicationsfor future research”, The International Journal of Logistics Management, Vol. 27 No. 2,pp. 395-417, doi: 10.1108/IJLM-02-2014-0035.

Wu, S.J., Melnyk, S.A. and Flynn, B.B. (2010), “Operational capabilities: the secret ingredient”, DecisionSciences, Vol. 41 No. 4, pp. 721-754, doi: 10.1111/j.1540-5915.2010.00294.x.

Corresponding authorMaciel M. Queiroz can be contacted at: maciel.queiroz@docente.unip.br

For instructions on how to order reprints of this article, please visit our website:www.emeraldgrouppublishing.com/licensing/reprints.htmOr contact us for further details: permissions@emeraldinsight.com

BIJ