Industry 4.0 digital transformation and opportunities for supply chain resilience: a comprehensive review and a strategic roadmap

Abstract

Despite interest in opportunities that Industry 4.0 offers for Supply Chain Resilience (SCR), little is known about the underlying mechanisms for such contributions. The study develops a roadmap that explains how supply chains can capitalize on Industry 4.0 SCR functions. The study conducted a content-centric literature review and identified 16 functions through which Industry 4.0 enhances SCR. Results reveal that the Industry 4.0 SCR functions identified are highly interrelated, and supply chain members should align their digitalization strategies with the sequence in which Industry 4.0 delivers these functions. Industry 4.0 contribution to SCR first involves delivering data-centric functions such as supply chain automation, information and communication quality, process monitoring, and visibility. Industry 4.0 further allows supply chain partners to collaborate better on improving supply chain mapping, complexity management, and innovation capabilities. Through these functions and by increasing transparency, flexibility, and agility of supply chain operations, Industry 4.0 delivers more dependent but consequential resilience functions such as supply chain responsiveness, adaptive capability, and continuity management. The roadmap further explains how each pair of Industry 4.0 SCR functions mutually interact while contributing to the overall resilience of the supply chain. The study discusses possible implications and outlines important avenues for future research.

Keywords:

• Industry 4.0
• resilience
• supply chain
• digitalization
• blockchain
• artificial intelligence

1. Introduction

Supply Chain (SC) disruptions that businesses have recently encountered have no parallel in the recent history of the world economy (Bloomberg 2021). The Covid-19 pandemic, radical changes in the global consumer market, and geopolitical shifts are among the primary external causes of SC disruptions (Bahrami and Shokouhyar 2022; Spieske and Birkel 2021). Recent disruptions in national and global SCs have hit numerous nerve centres of the global economy, such as advanced semiconductor chips, medical goods, and energy supply and distribution (Bloomberg 2021). Experts believe most contemporary SCs have overfocused on pushing productivity and efficiency and left resilience by the wayside (Tortorella et al. 2022; Zouari, Ruel, and Viale 2021). Although the concept of Supply Chain Resilience (SCR) is a few decades old, current SC disruption risks have motivated academia to rethink the strategies for rebalancing SC efficiency and resilience (Mubarik, Naghavi, et al. 2021; Naz et al. 2022). The use of modern technological innovation, commonly labelled as the digitalization of SC processes, appears to be one of the most effective strategies for SCR capability building (Tsolakis, Harrington, and Singh Srai 2023; Zouari, Ruel, and Viale 2021). The salient role of digitalization comes as no surprise, given that the business world is witnessing the digital industrial revolution known as Industry 4.0 (Marcucci et al. 2022).

Industry 4.0 is revolutionary because it restructures the organizations and management of industrial value networks (Kayikci et al. 2022). The digital transformation under Industry 4.0 involves the collective implementation of disruptive digital technologies, such as augmented reality, Artificial Intelligence (AI), big data analytics, cloud computing, blockchain, Internet of Things (IoT), robotic systems, and simulation tools, across the value networks (Pozzi, Rossi, and Secchi 2023). In addition to its technological advancements, Industry 4.0 requires SC partners to embrace essential design principles, such as interoperability, real-time capability, modularity, decentralization, and integrability, that enable the concept of a hyperconnected value creation system (Hofmann et al. 2019; Shao et al. 2021). Overall, Industry 4.0 represents a fundamental shift in how value is created, requiring the synergistic utilization of various technologies and the implementation of specific design principles across the value network (Ghobakhloo 2020).

Scholars argue that Industry 4.0 holds immense potential for mitigating SC disruptions and enhancing SCR (Spieske and Birkel 2021). The literature provides some preliminary insights into how technological constituents of Industry 4.0 may contribute to SCR. For example, Dennehy et al. (2021) described how big data analytics promotes SCR by allowing SCs to collect discriminatory information about emerging SC threats effectively. Recent studies have also shown much interest in explaining the enabling functions of AI for SCR (Belhadi, Kamble, Fosso Wamba, et al. 2021; Modgil, Gupta, et al. 2021). The contribution of AI to SCR capability development involves contingency management, SC shock mitigation, prediction accuracy, and demand volatility management, to name a few (Ivanov and Dolgui 2021; Modgil, Singh, and Hannibal 2021; Naz et al. 2022). Alternatively, blockchain enables SCR by increasing SC trust, operational transparency, cybersecurity, and order fulfilment security (Dubey, Gunasekaran, Bryde, et al. 2020; Min 2019). Despite early attempts to explain the SCR opportunities of individual Industry 4.0 technologies, the holistic understanding of the processes through which Industry 4.0, as a collective digital transformation paradigm, would enable SCR is still somewhat lacking. Spieske and Birkel (2021), in their recent systemic literature review, reaffirm this knowledge gap and argue that the literature lacks the empirical assessment of the holistic enabling role of Industry 4.0 in SCR. Similarly, Tortorella et al. (2022, p. 547) argued that while Industry 4.0 technologies have the potential to enable disruption responsiveness through altering SC management, ‘the digital transformation of SCs is still incipient, and literature is particularly sparse when considering the contribution of I4.0 to the resilience of SCs.’

While theory-driven studies explain SCR capabilities and determinants, they are methodologically limited to narrowing the topic area and the resulting conclusions. For example, Bag, Dhamija, et al. (2021) drew on the resource-based view theory and found that big data analytics contributes to SCA by enabling SC partners to develop internal and external risk management capabilities. Bahrami and Shokouhyar (2022) drew on the dynamic capabilities view and empirically revealed that big data analytics contributes to SCA by enhancing innovation capability and information quality across the SC. Belhadi, Mani, et al. (2021) built on the organizational information processing theory and revealed that AI implications for SCR involve better supply chain collaboration and the development of SC adaptive capability. Indeed, we acknowledge that individual technological constituents of Industry 4.0 would offer unique implications for promoting SCR.

Nonetheless, Industry 4.0 does not merely manifest in a single technology as it represents a paradigm shift in value creation, entailing the application of various technologies and the development of specific design principles such as real-time capability or virtualization. Since SCR capability development is challenging and complex (Munoz and Dunbar 2015), the complementarities and synergies among various Industry 4.0 technologies and design principles would expectedly support the inclusive development of SCR capabilities. Despite the logicality of this assumption, scientific research is yet to scrutinize and confirm such a mechanism.

Addressing this knowledge gap entails systemizing the process through which Industry 4.0 holistically introduces resiliency into SC operations. The present study contributes to filling this knowledge gap by identifying the Industry 4.0 SCR functions and modelling how these functions interact to optimally increase SCs’ ability to resist, adapt, or transform in the face of disruptions. To this purpose, the study performs a content-centric review of extant literature to identify Industry 4.0 SCR functions. As the main contribution, the study further draws on Interpretive Structural Modelling (ISM) and experts’ knowledge-based to identify and model the causal relationships among Industry 4.0 SCR functions. The present study develops a resilience roadmap that describes how SC partners can effectively leverage Industry 4.0 to enhance their collective SCR capacity by structuring the resilience functions into a meaningful graph-theoretic hierarchical model and constructing the interpretive logic-knowledge base (ILB). Our research and the resilience roadmap can offer notable implications in three ways. First, the study identifies and describes 16 functions through which Industry 4.0 can promote SCR. Second, the roadmap identifies the optimal sequence for developing these functions to maximize the synergistic SCR gains from Industry 4.0. Third, the study describes the enabling role of each function concerning other functions, offering more profound insight into the micro-mechanisms through which Industry 4.0 can holistically enable SCR.

2. Background

This section provides a brief overview of the Industry 4.0 phenomenon and further reviews the concept and implications of SC resilience.

2.1. Industry 4.0

Industry 4.0, which is reminiscent of the fourth industrial revolution, was first coined in 2011 in Germany. The ten years of Industry 4.0 literature is filled with countless studies exploring the scope, characteristics, and implications of Industry 4.0. Nonetheless, this phenomenon has been proven to be elusive and hard to define (Pozzi, Rossi, and Secchi 2023). Industry 4.0 was first introduced as the digitalization of manufacturing processes at the firm level (Hughes et al. 2022). Later, scholars argued that Industry 4.0 involves the digital transformation of manufacturing value chains (Kazancoglu et al. 2023). Scientific and industrial reports further reveal that the digital revolution under Industry 4.0 is taking place across many industries, such as construction, energy, and healthcare (e.g. Lekan et al. 2022). Industry 4.0 nowadays denotes the paradigm shift across the industrial value chains, involving the digitalization of value creation and delivery processes at the system, corporate, and value network levels. Given the elusive nature of Industry 4.0, defining this phenomenon based on its core building blocks, known as design principles and technology trends, has been a standard procedure within the literature. Following the previous works of Ghobakhloo (2020) and Pozzi, Rossi, and Secchi (2023), the study holds a value chain perspective while defining the scope, design principles, and technology trends of Industry 4.0, introducing the Industry 4.0 archetype as Figure 1. Technology trends of Industry 4.0 include advanced digital, information, and operations technologies, such as AI, additive manufacturing, and Cyber-Physical Systems (CPS), that drive the digital revolution (Marcucci et al. 2022). These technologies have become commercially mature and economically accessible during the past two decades. Design principles of Industry 4.0 are necessary conditions that allow businesses to unlock the Industry 4.0 transition potential (Hermann et al. 2016). Figure 1 lists the most widely accepted design principles of Industry 4.0.

Figure 1. Industry 4.0 archetype. Source: authors.

Viewed from this perspective, the advent of Industry 4.0 disruptive technologies creates the foundation for the development of valuable design principles such as vertical-horizontal integration, real-time capability, decentralization, and virtualization that collectively transform the classic linear SCs into modular, scalable, dynamic, hyper-connected, and data-driven Digital Supply Networks (DSN). The contribution of Industry 4.0 to the development of DSN involves the collective digitalization of SC components, including suppliers, focal manufacturers, logistics channels, distributors, and even customers (Kazancoglu et al. 2023). As an example, the smart factory section of Figure 1 explains how Industry 4.0 technologies such as additive manufacturing, Internet of People (IoP), Internet of Services (IoS), and robotics contribute to the digitalization of manufacturers. Overall, the digitalization of SCs under Industry 4.0 involves implementing idiosyncratic combinations of digital technologies based on each SC component’s strategic needs and priorities. The DSN concept under Industry 4.0 offers essential opportunities for maximizing stakeholder value, examples of which include customer satisfaction, regulatory compliance, improved revenue, or brand responsiveness (Fatorachian and Kazemi 2021).

2.2. Supply chain resilience

Originating from the social psychology theory, SCR denotes the SC's capability to recover after a disruption and to resist future interruptions (Tortorella et al. 2022). The recovery aspect concerns the SC's ability to quickly retain the optimal operational performance after experiencing the disruption (Modgil, Gupta, et al. 2021). The resistance aspect concerns the SC's ability to minimize the impact of disruptions proactively, either through constantly monitoring the environment to avoid the potential disruptions entirely (Modgil, Singh, and Hannibal 2021) or optimally reconfiguring itself to promptly recover from the negative impacts of eminent disruptions (Lohmer, Bugert, and Lasch 2020). SC disruptions are caused by various reasons, from socio-political shifts to natural disasters or economic crises. The Covid-19 crisis and worldwide disruption of SCs across various business sectors perfectly showcased the importance of SC resilience capability building (Peng et al. 2021; Spieske and Birkel 2021). As a result, understanding the mechanism through which SCs can enhance their resilience has recently gained significant attention within the operations and supply chain management disciplines.

Previous studies have introduced a variety of resilience strategies that SCs can implement to enhance their resilience capability. Tortorella et al. (2022) state that SCR strategies can be reactive, proactive, or a combination of both. The widely accepted SCR strategies (also labelled principles, enablers, or drivers) include SC visibility, flexibility, postponement, collaboration, information security, and automation, to name a few (Bag, Dhamija, et al. 2021; Bag, Gupta, et al. 2021; Belhadi, Kamble, Fosso Wamba, et al. 2021; Belhadi, Kamble, Jabbour, et al. 2021; Ivanov and Dolgui 2021; Senna et al. 2023). An emerging stream of research within the SCR discipline examines how SC digitalization can act as a resilience strategy (Zouari, Ruel, and Viale 2021) or facilitate the development of other resilience strategies such as SC collaboration, innovation, information security, or mindfulness (Belhadi, Mani, et al. 2021; Dennehy et al. 2021; Lohmer, Bugert, and Lasch 2020). Scholars have recently begun to study the implications of Industry 4.0 for SCR. Ralston and Blackhurst (2020) explored the interaction between Industry 4.0 and SC resilience. Further, they concluded that although the underlying mechanism through which Industry 4.0 interact with resilience capability is understudied, the productivity outcome of this phenomenon, such as improved product quality, process agility, and lower operational costs, offer essential opportunities for SCR. Under their digital twin-driven SC disruption management framework, Ivanov and Dolgui (2021) theoretically explained how Industry 4.0 technologies and principles such as big data analytics, machine learning, or real-time communication capability can promote SC disruption risk management practices. Spieske and Birkel (2021) conducted a systemic review of Industry 4.0-SC resilience literature and developed a theoretical framework that explained the implications of Individual Industry 4.0 technologies for SCR antecedents (e.g. design, sourcing, velocity) and phases (e.g. response, recovery, and growth). The literature also provides some empirical evidence explaining how SCR can be enabled by individual technological constituents of Industry 4.0, such as AI (Belhadi, Mani, et al. 2021; Modgil, Gupta, et al. 2021; Modgil, Singh, and Hannibal 2021; Naz et al. 2022), blockchain (Dubey, Gunasekaran, Bryde, et al. 2020; Lohmer, Bugert, and Lasch 2020), and big data analytics (Dennehy et al. 2021). Nonetheless, the literature falls short in explaining how Industry 4.0 resilience functions interact to promote SCR.

3. Industry 4.0 SCR functions

The study followed Webster and Watson’s (2002) and Watson and Webster’s (2020) guide and performed a content-centric literature review to identify Industry 4.0 SCR functions. In this study, the term SCR functions refer to the strategic or technical capabilities, opportunities, and outcomes that can be delivered to supply partners due to the collective integration and utilization of advanced technologies and design principles of Industry 4.0, allowing supply chains to adapt and recover from disruptions quickly.

Figure 2 explains the procedure undertaken for identifying the eligible articles and conducting the content-centric review. Step A1 of the review involved the initial identification of related documents. In this step, the initial search of Scopus and Web of Science databases using the search string (explained under step A1 in Figure 2) identified 296 documents. For step A2, we developed three exclusion criteria. Exclusion criterion 1 ensured that only peer-reviewed journal articles were eligible. Exclusion criterion 2 ensured that the main body of text for the shortlisted article should be in English. The exclusion criterion 3 assured that the articles shortlisted in the literature review were relevant to the research question and objectives of the study, offering meaningful insights into the opportunities that Industry 4.0 may offer for SCR. Across step A2, the 296 documents were subjected to exclusion criteria (listed in Figure 2), which removed 279 ineligible documents and shortlisted 17 documents under the primary pool of eligible articles.

Figure 2. The process of conducting the content-centric review of the literature.

Under step B1, the backward review of eligible articles was conducted. This step involved analyzing the reference sections of eligible articles shortlisted under step A2 for discovering additional related documents, which led to identifying 68 documents. The 68 newly identified documents were subjected to the exclusion criteria under step B2, which led to the secondary pool of 5 eligible journal articles. Step C1 involved using Google Scholar and Web of Science to discover SCR-related documents that have cited the eligible articles shortlisted throughout steps A2 and B2. In step C2, the 29 newly identified documents in step C1 were subjected to the exclusion criteria, leading to the tertiary pool of 3 eligible journal articles. Steps A1 to C2 collectively led to the final collection of 25 eligible articles for further use in content analysis.

The study followed the existing guides (e.g. Higgins et al. 2019) and developed a detailed assessment protocol to organize the content analysis procedures and ensure the validity and reliability of outcomes. The protocol detailed the coding scheme, data retrieval/archiving, text denoising procedure, and disagreement tracking procedure, enabling two independent content assessors to standardize the content analysis processes and minimize the threat of bias. The qualitative content analysis of eligible articles identified 16 functions through which Industry 4.0 enables SCR. Table 1 explains the distribution of the 16 Industry 4.0 SCR functions across the eligible articles. Table 2 outlines the role of Industry 4.0 technologies and design principles in delivering the SCR functions. Each of the SCR functions is briefly described in the following.

Table 1. Industry 4.0 SCR functions as Perceived by the eligible articles.

Author	Function
	Business Continuity Management (BCM)	Information and Communication Quality (ICQ)	Information and Cyber Security (ICS)	Supply Chain Adaptive Capability (SCAC)	Supply Chain Agility (SCAG)	Supply Chain Automation (SCA)	Supply Chain Collaboration (SCC)	Supply Chain Complexity Management (SCCM)	Supply Chain Flexibility (SCF)	Supply Chain Innovation Capability (SCIC)	Supply chain mapping (SCMP)	Supply Chain Process Monitoring (SCPM)	Supply Chain Responsiveness (SCRP)	Supply Chain Risk Management (SCRM)	Supply Chain Transparency (SCT)	Supply Chain Visibility (SCV)
Bag, Dhamija, et al. (2021)														x
Bag, Gupta, et al. (2021)					x					x			x
Bahrami and Shokouhyar (2022)		x			x					x
Bechtsis et al. (2022)			x		x
Belhadi, Kamble, Fosso Wamba, et al., (2021)					x		X		x					x
Belhadi, Kamble, Jabbour, et al. (2021)		x				x	X							x
Belhadi, Mani, et al. (2021)				x			X
Dennehy et al. (2021)				x									x
Dubey, Gunasekaran, Bryde, et al., (2020)							X								x
Ivanov, Dolgui, and Sokolov, 2019								x	x					x		x
Ivanov and Dolgui (2021)											x	x		x
Lohmer, Bugert, and Lasch (2020)			x		x		x	x	x	x				x		x
Marcucci et al. (2022)				x
Min (2019)											x			x
Modgil, Gupta, et al. (2021)												x
Modgil, Singh, and Hannibal (2021)	x					x					x	x		x		x
Mubarik, Naghavi, et al. (2021)											x					x
Mukherjee et al. (2022)			x												x
Naz et al. (2022)	x	x							x
Peng et al. (2021)	x											x
Ralston and Blackhurst (2020)	x					x								x
Senna et al. (2023)					x									x
Spieske and Birkel (2021)					x		x							x	x	x
Tortorella et al. (2022)				x									x
Zouari, Ruel, and Viale (2021)			x	x			x									x

Table 2. Contributions of Industry 4.0 technologies and design principles to SCR functions.

Technologies and design principles of Industry 4.0	Function
	Business Continuity Management (BCM)	Information and Communication Quality (ICQ)	Information and Cyber Security (ICS)	Supply Chain Adaptive Capability (SCAC)	Supply Chain Agility (SCAG)	Supply Chain Automation (SCA)	Supply Chain Collaboration (SCC)	Supply Chain Complexity Management (SCCM)	Supply Chain Flexibility (SCF)	Supply Chain Innovation Capability (SCIC)	Supply chain mapping (SCMP)	Supply Chain Process Monitoring (SCPM)	Supply Chain Responsiveness (SCRP)	Supply Chain Risk Management (SCRM)	Supply Chain Transparency (SCT)	Supply Chain Visibility (SCV)
Technologies
Additive manufacturing				*		*			*
Artificial intelligence	*		*	*		*			*	*	*	*	*	*
Augmented and virtual reality					*					*				*
Automation and industrial robotics				*	*	*			*
Blockchain		*	*				*				*			*	*	*
Cloud and big data analytics	*	*		*	*	*	*		*	*		*	*	*	*	*
Cyber-physical systems		*			*	*			*			*
Cybersecurity solutions		*	*			*								*
Industrial internet of things	*			*		*		*	*			*			*	*
Internet of people	*	*		*		*	*	*		*	*		*		*
Internet of services		*		*				*	*		*		*		*
Simulation and Modelling (Digital twin)				*	*				*	*
Design principles
Customer and service orientation		*					*						*		*
Decentralisation			*	*	*	*
Horizontal integration	*						*	*							*	*
Interoperability		*				*									*	*
Modularity				*	*				*
Real-time capability		*	*		*	*	*		*	*		*		*	*	*
Technical assistance						*								*
Vertical integration	*	*				*	*	*		*					*	*
Virtualisation		*			*				*	*	*

3.1. Business Continuity Management (BCM)

BCM refers to the firm’s ability to manage difficult situations strategically to avoid potential disruptions or continue to operate after a disaster (Niemimaa et al. 2019). BCM involves defining and identifying potential risks, diagnosing the roots of poor operations, and creating a dynamic governance plan that outlines how business processes should revitalize and stabilize after a crisis (Margherita and Heikkilä 2021). BCM is extremely information-intensive, consisting of numerous data-hungry sub-functions such as incident identification, risk-threat assessment, disaster recovery planning, and business strategy rethinking (Modgil, Singh, and Hannibal 2021; Niemimaa et al. 2019). Industry 4.0 delivers the BCM function through the vertical integration principle and introducing AI, big data, CPS, and the Industrial Internet of Things (IIoT) to business operations (Ivanov and Dolgui 2021; Naz et al. 2022). This process involves integrating information and operations technologies, processes, infrastructure, and human components within and across the operational and functional layers of a business unit (Ghobakhloo, Iranmanesh, et al. 2021; Ghobakhloo, Fathi, et al. 2021b). The resulting digitalized business ecosystem (also called a smart factory for manufacturing firm) offers the utmost level of real-time business monitoring and control, allowing instantaneous access and processing of a large amount of data (Modgil, Singh, and Hannibal 2021; Osterrieder, Budde, and Friedli 2020). Therefore, individual SC partners can draw on Industry 4.0 to develop BCM and improve the resilience of their SC, given that BMC will allow them to increase their strategic recovery capability and reduce the impact of imminent disruptions (Peng et al. 2021; Ralston and Blackhurst 2020).

3.2. Information and Communication Quality (ICQ)

ICQ refers to the ability of an SC to communicate critical and proprietary information across every node of the supply network while ensuring the adequacy, accuracy, credibility, and timeliness of the information (Li and Lin 2006). ICQ has long been acknowledged as a central part of SC productivity and survival (Huo, Haq, and Gu 2021; Narasimhan and Nair 2005). The progressive contributions of technological innovations to the ICQ capability development of SCs are well documented within half a century of literature (Wijewickrama et al. 2021). Industry 4.0 and the underlying digitalization of value chains take ICQ to the next level, primarily via creating a data-driven, connected, and round-the-clock digital community that allows all SC stakeholders to directly and meaningfully communicate and share information in real-time (Müller, Veile, and Voigt 2020). Industry 4.0 enhances information quality by building on smart sensors, IIoT, CPS, and machine learning to automate data collection processes, apply data quality profiling, prevent data synchronization failure, and centralize metadata management (Belhadi, Kamble, Jabbour, et al. 2021; Wollschlaeger, Sauter, and Jasperneite 2017). By increasing the interoperability of SC systems and the systematic application of edge computing and cloud computing, Industry 4.0 can eliminate information silos to remove any communication latencies (Sun et al. 2020). The resulting ICQ function of Industry 4.0 is crucial to SCR as it enhances SC intelligent optimization, end-to-end transparency, and holistic decision-making capabilities (Bahrami and Shokouhyar 2022; Naz et al. 2022).

3.3. Information and Cyber Security (ICS)

Within the increasingly connected business environment, SC partners are always at greater information and cybersecurity risk (Ghadge et al. 2019). Cybersecurity and information security are different concepts. Thus, the ICS function requires every SC to have a collective obligation to (1) defend and protect cyberspace against cyber-attacks and (2) protect information and digital systems from cyber risks to maintain the availability, integrity, confidentiality, and ownership of data and information (Von Solms and Van Niekerk 2013). According to the U.S. National Institute of Standards and Technology (NIST), ICS is not merely concerned with technological concerns, as it profoundly involves knowledge, employees, and process problems (NIST 2018). Viewed from the NIST perspective, Industry 4.0 offers essential implications for ICS, mainly in the form of improved cybersecurity strategy development, mitigating the risk of unauthorized human intervention, blockchain-based end-to-end encryption, continuous support of end-of-life platforms or products, real-time monitoring of IT-OT functionality, and real-time assessment of systems vulnerability (Alotaibi 2019; Jahromi et al. 2021; Tran et al. 2021). Consistently, the ICS function of Industry 4.0 contributes to building SCR by protecting and maintaining production capacity, inspiring confidence in customers, protecting productivity against disruptions, and allowing IT-OT to recover after cyber disruptions rapidly (Bechtsis et al. 2022; Lohmer, Bugert, and Lasch 2020; Mukherjee et al. 2022).

3.4. Supply Chain Adaptive Capability (SCAC)

SCAC refers to the SC’s ability to readjust its design, processes, and operations to respond to new conditions (Zhao, Zuo, and Blackhurst 2019). SCAC also involves supply partners deploying necessary strategies that can adapt to disruptive forces such as market turbulence, disruption of the labour market, socio-political changes, or structural shifts (Dennehy et al. 2021). Experts believe that SC digitalization has been a fundamental element of adaptive supply chains (Tortorella et al. 2022). Consistently, Industry 4.0 and the underlying digital transformation provide modern SCs with immense opportunities for developing adaptive capabilities (Belhadi, Mani, et al. 2021). Industry 4.0 technologies such as AI, big data analytics, digital twin, and IoT address the innovation and knowledge intensity of SCAC and deliver this function by offering immersive insight into SC dynamics, complexity, and uncertainty, thus allowing supply partners to better learn from the external environment (Ivanov and Dolgui 2021; Zouari, Ruel, and Viale 2021). More importantly, the autonomy and modularity design principles of Industry 4.0, as critical enablers of the non-linear digital supply network concept, allow SCs to have the much-needed dynamism to reconfigure their value chain modules (e.g. equipment, operations, processes, or products) to adjust promptly to changes in internal and external conditions (Ghobakhloo, Iranmanesh, et al. 2021; Ivanov and Dolgui 2021). The SCAC function of Industry 4.0 further contributes to SCR significantly, as it allows SC partners to respond to unanticipated disruptions efficiently yet resiliently (Belhadi, Mani, et al. 2021; Dennehy et al. 2021).

3.5. Supply Chain Agility (SCAG)

SCAG refers to the ability of SC partners to adapt their internal SC functions to respond to business environment changes in a timely, effective, and efficient manner (Ayoub and Abdallah 2019; Swafford, Ghosh, and Murthy 2008). To achieve SCAG, supply partners must have the capability to readjust their internal and collaborative operations to reduce manufacturing and delivery lead time, lower the product development cycle, increase order accuracy, and improve customer communication and satisfaction (Dubey et al. 2021). Industry 4.0 delivers the SCA functions in three different ways. First, Industry 4.0 increases the accessibility of relevant information across the DSN, mainly through cloud-based and AI-driven platforms that streamline the continuous real-time exchange of relevant data across SC nodes (Eslami et al. 2021). Second, Industry 4.0 enhances decisiveness, which denotes the supply partner’s capability to make fast and resolute decisions based on the available information (Cisneros-Cabrera et al. 2021). This roots in the decentralization principle of Industry 4.0, which builds on CPS, IIoT, cloud data, and edge computing to allow smart SC components to make independent yet informed decisions closer to the data sources (Ma et al. 2020). Third, Industry 4.0 improves SC planning, as it allows SC partners to abandon sequential planning and benefit from AI, big data analytics, smart enterprise systems, and dynamic simulation to incorporate modern planning systems such as what-if scenario planning or concurrent planning to rapidly assess impacts and alternatives under turbulence or disruption scenarios (Abdirad and Krishnan 2021). Through these features, the SCA function of Industry 4.0 allows supply partners to align their strategies better, respond to SC disruptions cost-effectively, and progressively develop the necessary SCR competencies (Bahrami and Shokouhyar 2022; Bechtsis et al. 2022).

3.6. Supply Chain Automation (SCA)

SCA refers to the ability of the supply partners to automate financial, physical, activity, and information workflows across the SC. It involves integrating disruptive digital and operations technologies that reduce the dependencies of SC operations on human interventions (Belhadi, Kamble, Jabbour, et al. 2021). Industry 4.0 and the underlying digitalization expand beyond the smart factory boundaries, comprising the smartization and automation of warehousing, logistics, and SCM processes (Fatorachian and Kazemi 2021). The contribution of Industry 4.0 to SCA is myriad, involving autonomous warehousing via Autonomous Storage and Retrieval Systems (ASRS), automated guided vehicles/drones, and smart warehouse management systems that allow AI-driven real-time monitoring of warehouse processes, safety, and security (Lee et al. 2018). It further involves using additive manufacturing, augmented reality, CPS, cognitive/autonomous robots, digital twin, embedded IoT, edge computing, and smart sensors to develop an autonomous, agile, and proactive hyper-connected manufacturing ecosystem (Osterrieder, Budde, and Friedli 2020; Wang et al. 2016). Industry 4.0 further automizes customer communication and interactions by maturing and commercializing AI chatbots, smart products, IoP (e.g. social media platforms), or analytical modelling (Frank et al. 2019; Ghobakhloo, Fathi, et al. 2021). Through the resulting autonomy of the SCA function, Industry 4.0 improves SCR in many ways, such as predictive anomaly recognition, adaptability of production-delivery changeovers, predictability of production and delivery capacity, and decision synchronization (Modgil, Singh, and Hannibal 2021; Ralston and Blackhurst 2020).

3.7. Supply Chain Collaboration (SCC)

SCC is a collective capability that allows supply partners to work closely together to modify SC practices and achieve joint performance improvement (Ralston, Richey, and Grawe 2017). SCC comprises many dimensions and activities, including market information sharing, joint problem solving, collaborative knowledge management, joint production planning, collaborative marketing strategy development, and operations resource sharing (Kumar and Nath Banerjee 2014; Pradabwong et al. 2017). Industry 4.0 delivers the SCC function by allowing supply partners to create a cloud-based collaborative SC platform that streamlines real-time information and knowledge sharing across SC stakeholders (Sundarakani et al. 2021). Through seamless communication and eliminating information silos, Industry 4.0 allows SC partners to work closer to each other in a more integrative manner (Ghobakhloo, Iranmanesh, et al. 2021). Industry 4.0 further allows SC partners to enter the realm of big data analytics and use granular performance metrics to monitor and measure the real-time productivity of various SC nodes (Ivanov, Dolgui, and Sokolov 2019). In particular, Industry 4.0 draws on Distributed Ledger Technology (DLT) to redefine the design and operations of SC collaboration, allowing SC partners to vertically and horizontally collaborate securely and transparently (Lohmer, Bugert, and Lasch 2020). The SCC function of Industry 4.0 is essential to SCR capability building, given that it empowers supply partners to collaboratively recognize threats, develop contingency plans, identify logistical uncertainties, and devise disruption recovery strategies and activities (Belhadi, Kamble, Fosso Wamba, et al. 2021; Fatorachian and Kazemi 2021).

3.8. Supply Chain Complexity Management (SCCM)

SC complexity refers to the degree of interdependencies and interconnectedness throughout an SC where any change in one SC component can affect other components (Olivares Aguila and ElMaraghy 2018). Modern SCs are increasingly growing in complexity as a natural response to the ever-shortening product life-cycle, globalization, growing interest in individualized products, and the urge for faster lead times (Birkie and Trucco 2020). Complexity is not necessarily an undesirable condition as long as supply partners have the capacity to effectively monitor and control the increasing interactions of SC components (Turner, Aitken, and Bozarth 2018). Industry 4.0 delivers the SCCM function by allowing SC partners to manage, evaluate, and optimise the horizontal and vertical relationships that exist across the non-linear modern supply networks (Hofmann et al. 2019). More importantly, Industry 4.0 addresses the SC complexity by orchestrating the integration of various SC elements, including internal and external resources, people, and processes (Hahn 2020). Through the SCCM function, Industry 4.0 allows supply partners to act on end-to-end visibility, build new relationships when necessary, and avoid missteps in SC processes and operations, conditions that directly contribute to SCR (Ivanov, Dolgui, and Sokolov 2019; Lohmer, Bugert, and Lasch 2020).

3.9. Supply Chain Flexibility (SCF)

SFC refers to the collective ability of a focal firm and its suppliers and downstream supply partners to respond to emerging uncertainties, threats, and opportunities cost-effectively and without incurring excessive productivity losses (Huo, Gu, and Wang 2018). Consistently, the SCF function consists of three pillars: upstream supply flexibility, manufacturing flexibility, and downstream supply (logistics and distribution) flexibility (Sreedevi and Saranga 2017). Industry 4.0 offers essential opportunities for upstream and downstream supply flexibility by materializing smart warehousing and logistics (Yavas and Ozkan-Ozen 2020). Industry 4.0 delivers these opportunities in terms of ASRS, sensor-equipped smart goods (material or assembly parts) tracking, visualization of purchasing and delivery options, and autonomous data-driven inventory control platforms (van Geest, Tekinerdogan, and Catal 2021). The contribution of Industry 4.0 to the manufacturing flexibility of SCs involves using disruptive technologies and applications such as intelligent robots, additive manufacturing, high-performance computing computer-aided design (HPC-CAD), digital twinning of new products, smart production planning and control, machine learning-based real-time yield optimisation, and proactive maintenance to improve product modularity, manufacturing lead time, system reliability, and production rate (Margherita and Braccini 2023; Peng et al. 2021). The collective SFC function of Industry 4.0 leads to SCR, as it allows the SC to reduce product lead time, provide product variety, and achieve the flexible capacity to alleviate the impacts of unpredicted disruptions with a minor penalty in productivity and costs (Belhadi, Kamble, Fosso Wamba, et al. 2021).

3.10. Supply Chain Innovation Capability (SCIC)

SCIC is a broad concept comprising a collection of SC-wide competencies in research and development, new business model development, cross-functional business partnerships, ideation and concept development, knowledge acquisition, or resource reconfiguration, to name a few. The contribution of Industry 4.0 and its technological constituents to SCIC building is well documented within the recent literature (e.g. Hahn 2020). This process, for example, involves (1) improving interpersonal communication and inter-functional collaboration via cloud platforms, IoP, and smart wearables, (2) increasing knowledge competencies via augmented/virtual/mixed reality, AI-driven talent management systems, or experienced-based learning tools, and (3) building product or process innovation competencies via real-time information sharing and digital twinning of new ideas and concepts (Ghobakhloo, Iranmanesh, et al. 2021; Hopkins 2021). The SCIC function of Industry 4.0 is crucial to SCR development (Bag, Gupta, et al. 2021), given it empowers SCs to revive or replace declining products rapidly and cost-effectively, better engage with stakeholders, develop new monetization strategies, improve the efficiency of manufacturing and logistics operations, and maintain or even revenue at the time of disruptions (Bahrami and Shokouhyar 2022; Lohmer, Bugert, and Lasch 2020).

3.11. Supply Chain Mapping (SCMP)

SCMP refers to the process through which a focal firm gathers, documents, and dynamically visualizes information on supply partners (individuals or companies) who are directly or indirectly involved in its supply chain (Mubarik, Kusi-Sarpong, et al. 2021). Therefore, SCMP entails creating a global map of the entire supply network that may explain or depict a wide variety of supplier information, such as the geographical positioning of major suppliers, suppliers of suppliers at various tiers, sources of materials, manufacturing sites, transportation channels, or warehouses (Fabbe-Costes, Lechaptois, and Spring 2020). SCMP is an exceptionally complicated process, especially in the case of global SCs, where this process involves tracking and monitoring thousands of materials, parts, or components through hundreds of suppliers across multiple countries (Norwood and Peel 2021). Industry 4.0 delivers SCMP function in two ways: (1) offering AI-driven visualization and virtualization platforms to better identify SC entities (such as wholesalers, distributors, and warehouses) and (2) establishing an efficient communication channel among various SC processes, such as establishing blockchain-driven finance channels or connecting with customers through IoP, IoS, and smart products (Ivanov and Dolgui 2021; Mubarik, Kusi-Sarpong, et al. 2021). This way, the SCMP function of Industry 4.0 contributes to SCR via streamlining SC processes, predicting risks associated with each SC entity, detecting weak link(s) in the SC, and identifying where value is lost across SC (Modgil, Singh, and Hannibal 2021; Mubarik, Naghavi, et al. 2021).

3.12. Supply Chain Process Monitoring (SCPM)

SCPM refers to the overall process of tracking SC operations and actions, from ordering and synthesizing raw materials to delivering final products to the end consumers (Outhwaite and Martin-Ortega 2019). SCPM involves a broad range of activities, including but not limited to monitoring and tracking of production equipment condition, transportation infrastructure condition, production and delivery capacity, human resource personnel, inventory levels, asset maintenance, product quality, transport infrastructure load, and order status (Fatorachian and Kazemi 2021; Xie et al. 2020). SCPM has become a more challenging process as the complexity of modern SCs continuously increases (Birkie and Trucco 2020). Nonetheless, and under Industry 4.0-driven DSN, machine learning, IoT, cloud-based tools, CPS, big data analytics, and edge computing allow SCM platforms to cost-effectively expand the scope of SC monitoring, access real-time and meaningful data about various aspects of SC, analyse massive data volumes to uncover information, and keep a conscious track of every relevant SC operation (Hofmann et al. 2019; Modgil, Singh, and Hannibal 2021). This function of Industry 4.0, in turn, allows supply partners to effectively apply various performance metrics to increase the accuracy of their forecast and better sense upcoming disruptions, abilities that are essential to SCR (Modgil, Gupta, et al. 2021; Peng et al. 2021).

3.13. Supply Chain Responsiveness (SCRP)

SCRP refers to the SC-level capability to respond to market dynamics in a time-effective manner (Ayoub and Abdallah 2019). SCRP is a collective capability, meaning responsiveness comes from the collective efforts of focal firms and their supply partners. SCRP consists of many dimensions, such as customer demand sensitivity or transparency (Giannakis, Spanaki, and Dubey 2019). Therefore, it involves collaborative efforts for better sensing and communicating the changes in the customers’ demand so that the SC can rapidly readjust its product volume, diversity, or delivery strategies (Ayoub and Abdallah 2019; Tortorella et al. 2022). The traditional models of digitalization, such as customer and supplier relationship management applications, could offer some implications for SC collaboration and transparency. Nonetheless, they lack the necessary integrability and alignment to offer the end-to-end real-time visibility that is much needed for the responsiveness of modern complex SCs (Giannakis, Spanaki, and Dubey 2019). Industry 4.0 redefines how SC partners and customers can integrate and reciprocally communicate (Shao et al. 2021). The advent of cloud data platforms, IoS, IoP, and smart products under Industry 4.0 allow SCs to perform the round-the-clock monitoring of customers’ demands. Even customers can effortlessly use IoS platforms to reflect their feedback in real-time (Ghobakhloo 2020). SC can further apply AI and big data analytics to build predictive models of future customer demands and boost their SCRP capability (Bag, Gupta, et al. 2021). Consistently, the SCRP function of Industry 4.0 is vital to SCR building as it enables supply partners to (1) better sense forthcoming disruptions and (2) better communicate and strategize collective responses to the impact of ongoing disruptions (Dennehy et al. 2021).

3.14. Supply Chain Risk Management (SCRM)

Baryannis et al. (2019) define SCRM as the collective and coordinated efforts of all supply partners to identify, monitor, analyze, and alleviate SC risks to mitigate vulnerabilities and ensure the robustness, profitability, and continuity of SC. Supply chains are unprecedently growing in complexity, making them more susceptible to known/unknown internal and external risks (Xu et al. 2020). Internal SC risks involve manufacturing, business process, disruption, planning and control, and contingency management disruption risks, whereas external SC risks consist of supply, demand, environmental, and collaboration disruption risks (Baryannis et al. 2019). As a result, SCs should employ detailed strategies and practices to address the ever-increasing intricacy of SCRM (Tsang et al. 2018). The underlying SCRM strategies and practices, such as the PPRR (prevention, preparedness, response, and recovery) risk management model or logistics contingency plan, are extremely information-intensive, relying on supply-wide transparency, visibility, and monitoring (Kazancoglu et al. 2023; Xu et al. 2020). Industry 4.0 contributions to facilitating these conditions involve leveraging (1) AI and predictive analytics for identifying unknown SC risks, (2) using DLT for smarter, safer, and traceable SC contracts, (3) data visualization techniques for real-time mapping of known SC risks, and (4) cloud data and smart enterprise systems for real-time risk exposure analysis and collaborative development of mitigation strategy (Belhadi, Kamble, Fosso Wamba, et al. 2021; Kayikci et al. 2022; Rogerson and Parry 2020). Industry 4.0 promotes SCR by delivering these SCRM sub-functions, allowing SCs to identify their vulnerabilities and exposures better and shift from risk-blindness to risk-resilience (Peng et al. 2021; Ralston and Blackhurst 2020).

3.15. Supply Chain Transparency (SCT)

SCT centres around SC communication and accountability, defining how internal and external stakeholders can access the necessary information on labour productivity, environmental impacts of products, operational efficiency, or risk points (Zhu et al. 2018). SCT requires key SC actors to enable transparency, communication, and trackability across three different functional levels: intra-organizational transparency, value chain transparency (involving 1st tier customers and suppliers), and end-to-end multitier SC transparency (Bai and Sarkis 2020). Industry 4.0 delivers the SCT function by directly tackling the challenges of achieving a transparent supply chain, such as fragmented infrastructure, information silos, data complexity, and information governance at the SC level (Fatorachian and Kazemi 2021). To enable intra-organizational transparency, Industry 4.0 vertically integrates different business functions, especially by drawing on big data, control systems, IIoT, embedded systems, or smart execution systems to break communication barriers and eliminate information silos (Hofmann and Rüsch 2017). Industry 4.0 further delivers value chain and multitier SC transparency via horizontal integration of shareholders. It is mainly achieved via disruptive technologies such as the IoS, cloud computing platforms, and blockchain, which materialize the techno-functional principles of interoperability and real-time analytics (Ghobakhloo, Iranmanesh, et al. 2021; Kayikci et al. 2022). Through SCT function and offering factual knowledge of the end-to-end SC, Industry 4.0 can promote SCR by empowering supply partners to develop new business models, alleviate reputational risk, reduce disruptions, and adapt to market dynamics (Mukherjee et al. 2022; Spieske and Birkel 2021).

3.16. Supply Chain Visibility (SCV)

SCV refers to the capability of the supply chain to perform the forward and backward tracing of raw materials, subassemblies, or final goods, as they circulate across suppliers, manufacturers, distributors, and consumers (Mubarik, Naghavi, et al. 2021). SCV is mainly enabled by SCM technologies that offer near-real-time access to logistics and SC operations data (Rogerson and Parry 2020). SCV is vital to SCR as it provides the ability to respond quickly to changes and disruptions, better understand supply chain relationships, and reduce the complexity of SC relationships, especially at the global scale (Dubey, Gunasekaran, Childe, et al. 2020). Industry 4.0 and the underlying technologies and principles take SCV to the next level. Big data, blockchain, cloud computing, and IoT, complemented by the horizontal-vertical integration and interoperability principles of Industry 4.0, allow supply partners to have real-time access to a massive volume of understandable data across every node in the SC (Ivanov, Dolgui, and Sokolov 2019; Rogerson and Parry 2020). Thus, the SCV function of Industry 4.0 promotes SCR by providing SC managers with the ultimate visibility across the entire SC, improving SC decision processes, better compliance with regulatory requirements, and enhancing customer acquisition, satisfaction, or retention (Modgil, Singh, and Hannibal 2021; Tortorella et al. 2022).

4. Research methodology

The present study aims to identify the hierarchical relationships among the Industry 4.0 SCR functions and develop the visual model of Industry 4.0-driven SC resilience, which requires the application of a suitable decisions analytics technique to construct and visualize such a model. Indeed, there are several robust decision analytics techniques for addressing complex decision problems, such as AHP, ANN, DEMATEL, and ISM. We benefitted from ISM to fulfil our research goals. As with any decision analytics technique, ISM is associated with advantages and disadvantages. ISM is a reliable tool for exploratory studies that seek theory development (Mathivathanan et al. 2021). ISM involves the iterative application of graph theory to identify the contextual relationships between elements of a complex system or phenomenon (Rajesh 2017). This method is interpretive in the sense that groups (experts in most scenarios) collectively decide whether, why, and how elements of a complex system are interrelated (Ali et al. 2020). The decision to implement and use ISM in the study roots in various advantages of this method, such as the ability of ISM to transmute vaguely articulated and uncertain mental models of a particular phenomenon or system into coherent, meaningful, and visualized models (Bianco et al. 2023). Previous studies have extensively used ISM for the causal modelling of the firm SC resilience capabilities (Rajesh 2017), SC implementation of Six Sigma (Ali et al. 2020), SC adoption of big data (Bag, Gupta, and Wood 2022), and barriers to SC adoption of the blockchain (Mathivathanan et al. 2021).

In addition, this study involves drawing on the experts’ opinions to establish a strategy roadmap that explains the micro-mechanisms by which Industry 4.0 might promote SCR. Developing a strategy roadmap with such goals entails fulfilling two requirements. The first requirement concerns establishing the precedence relationships among the functions. The second requirement involves experts collaboratively deciding on the causal direction of contextual relationships. Compared to the alternate approaches such as ANN, AHP, TISM, and their fuzzy versions, ISM has the best compatibility with these strategy roadmapping requirements (Ghobakhloo et al. 2022). This fact has been supported by comparable works such as Ng et al.’s (2022) strategy roadmap to Industry 4.0-driven sustainable manufacturing. While ISM serves strategy roadmapping best, it is limited in two ways within this context. First, it is over-reliant on expert opinion, which might increase the threat of bias, especially in inequitable expert contributions to collective decision-making. We addressed this possible limitation by carefully organizing and moderating the expert panel meetings. The second limitation concerns the inefficacy of ISM in explaining the implications of contextual relationships among functions (Sushil 2012). We borrowed from the TISM background (Nasim 2011) and drew on the experts’ opinions to construct the ILB, representing the collective experts’ interpretation of each contextual relationship.

Following the ISM literature (e.g. Bag, Gupta, and Wood 2022; Bianco et al. 2023), the study applies the widely accepted steps shown in Figure 3 to develop the structural interpretive model of Industry 4.0-driven SC resilience.

Figure 3. Steps applied for conducting the ISM.

4.1. Collecting expert opinion

ISM seeks the opinions and views of experts or working professionals with hands-on experience in the system domain of interest (Mathivathanan et al. 2021). To fulfil this requirement, the study followed existing guides (Bogner, Littig, and Menz 2009; Hertzum 2014) and executed a rigorous expert selection and idea collection protocol. The protocol allowed the research team to minimize the thread of method and cognitive bias and further warrant the validity and reliability of ISM results. Following this protocol, the study focused on European SC experts for two reasons. Firstly, the study was funded by the Horizon 2020 program with a priority on understanding the implications of Industry 4.0 for SCR from a European perspective. Secondly, the research project’s European academic and industry partners made it practical to approach leading European experts to contribute to our expert panel meetings. Accordingly, the research team defined eligibility requirements for academic and industry experts and followed a specific protocol in approaching them. Notable contributions to Industry 4.0 and SC literature and involvement in European projects related to the Industry 4.0-SCR phenomenon were the eligibility criteria for academic experts. Alternatively, a minimum of 10 years of experience as an SC manager, expertise in the digital supply network context, and direct involvement in the digital transformation of SC under the Industry 4.0 framework were among the eligibility requirements for industry experts. After collaborating with academic and industry partners, we shortlisted 17 eligible experts. We used a questionnaire to measure experts’ familiarity with Industry 4.0 and SCR concepts to determine their suitability for the expert panel meetings. This procedure led to shortlisting ten experts to participate in online group brainstorming meetings. The ten experts consisted of 4 academicians, two senior SC consultants, two senior SC managers, an SC operations manager, and a senior SC planning specialist.

The study used the Nominal Group Technique (NGT) as the central small-group discussion and brainstorming method. NGT is a structured method for collaborative small-group brainstorming, which unequivocally collects information from individual experts and renders them into a collective decision concerning an idea (Harvey and Holmes 2012). NGT involves preventing domination of the discussion by individual experts. To do so, NGT requires the moderator(s) to ensure that experts impartially contribute to a specific discussion (MacPhail 2001). NGT output represents the collective experts’ opinions as this technique systematically encourages participants to reach a shared consensus on a given matter (Ng et al. 2022). Consistently, the ten shortlisted experts attended a series of five NGT-based meetings and collectively identified how Industry 4.0 may improve SC resilience. Across the first and second NGT-based meetings, experts assessed the validity and completeness of the 16 Industry 4.0 SCR functions. Experts highlighted a few revisions to the titling and description of the functions, which led to the finalized version of Industry 4.0 SCR functions (including the title and description) presented in Section 3. Experts collaboratively identified and further elucidated the causal relationships among each pair of Industry 4.0 SCR functions across the remaining NGT-based meetings, leading to the development of ILB. An ILB concerns the interpretation of pairwise relationships among the elements of a system (Yadav 2014). Under ILB, when experts collectively decide that a contextual relationship exists among a pair of system elements, they are requested to interpret the implications of such relationships (Rajesh 2017). The collection of all interpretations concerning the identified contextual relationships constitutes the ILB (Mathivathanan et al. 2021). Similarly, the ILB in the present study (listed in the appendix) describes the functionality of each contextual relationship among the Industry 4.0 SCR functions as collectively identified by the experts.

4.2. Establishing contextual relationships

Drawing on the ISM methodology (e.g. Bianco et al. 2023), the relationship between each pair of functions can be defined based on the following coding system.

V: SCR function i determines SCR function j;

A: SCR function i is determined by SCR function j;

X: SCR functions i and j determine each other;

O: SCR functions i and j are independent.

By applying the abovementioned coding scheme to each pair of relationships among resilience functions identified by experts, the Structural Self-Interaction Matrix (SSIM) of the study is constructed as Table 3. The SSIM, for example, represents the BCM-SCV entry by the symbol O, meaning these two functions are independent of each other.

Table 3. The SSIM for Industry 4.0 SCR functions.

SCR function	SCV	SCT	SCRM	SCRP	SCPM	SCMP	SCIC	SCF	SCCM	SCC	SCA	SCAG	SCAC	ICS	ICQ	BCM
BCM	O	O	A	O	A	O	A	O	O	O	O	O	A	A	O	–
ICQ	V	V	V	O	V	O	V	O	O	O	A	O	O	A	–
ICS	O	O	V	O	O	O	O	O	O	O	X	O	O	–
SCAC	O	O	O	A	O	A	O	O	A	O	O	O	–
SCAG	A	A	O	V	O	O	V	A	A	A	O	–
SCA	V	V	O	O	V	O	O	V	V	O	–
SCC	A	X	V	V	O	A	V	O	O	–
SCCM	A	A	V	O	A	A	O	O	–
SCF	A	O	V	O	A	O	A	–
SCIC	O	O	O	V	O	O	–
SCMP	O	A	V	O	A	–
SCPM	V	O	O	O	–
SCRP	O	O	A	–
SCRM	A	A	–
SCT	A	–
SCV	–

4.3. Establishing the initial reachability Matrix

The third step in applying ISM involves developing the Initial Reachability Matrix (IRM). The IRM is a binary matrix developed by replacing the V, A, X, and O symbols of the SSIM with 0 or 1 values based on the following replacement rules (Ali et al. 2020; Rajesh 2017). By applying the following replacement rules to the SSIM, the IRM of the study is developed in Table 4.

Table 4. The IRM for Industry 4.0 SCR functions.

SCR function	BCM	ICQ	ICS	SCAC	SCAG	SCA	SCC	SCCM	SCF	SCIC	SCMP	SCPM	SCRP	SCRM	SCT	SCV
BCM	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
ICQ	0	1	0	0	0	0	0	0	0	1	0	1	0	1	1	1
ICS	1	1	1	0	0	1	0	0	0	0	0	0	0	1	0	0
SCAC	1	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0
SCAG	0	0	0	0	1	0	0	0	0	1	0	0	1	0	0	0
SCA	0	1	1	0	0	1	0	1	1	0	0	1	0	0	1	1
SCC	0	0	0	0	1	0	1	0	0	1	0	0	1	1	1	0
SCCM	0	0	0	1	1	0	0	1	0	0	0	0	0	1	0	0
SCF	0	0	0	0	1	0	0	0	1	0	0	0	0	1	0	0
SCIC	1	0	0	0	0	0	0	0	1	1	0	0	1	0	0	0
SCMP	0	0	0	1	0	0	1	1	0	0	1	0	0	1	0	0
SCPM	1	0	0	0	0	0	0	1	1	0	1	1	0	0	0	1
SCRP	0	0	0	1	0	0	0	0	0	0	0	0	1	0	0	0
SCRM	1	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0
SCT	0	0	0	0	1	0	1	1	0	0	1	0	0	1	1	0
SCV	0	0	0	0	1	0	1	1	1	0	0	0	0	1	1	1

If (i, j) entry of SSIM is symbolised as V, then entries (i, j) and (j, i) in the IRM are, respectively, set to 1 and 0.

If (i, j) entry of SSIM is symbolised as A, then entries (i, j) and (j, i) in the IRM are, respectively, set to 0 and 1.

If (i, j) entry of SSIM is symbolised as X, then entries (i, j) and (j, i) in the IRM are both set to 1.

If (i, j) entry of SSIM is symbolised as O, then entries (i, j) and (j, i) in the IRM are both set to 0.

4.4. Establishing Final Reachability Matrix

The fourth step in ISM involves developing the Final Reachability Matrix (FRM) by applying the transitivity rule to the IRM (Bag, Gupta, and Wood 2022). The transitivity rule accounts for the indirect causality, stating that if function X directly determines function Y, and function Y directly determines function Z, then function X would be considered the direct determinant of function Z. The FRM of the study is presented in Table 5, in which ‘1*’ entries represent the presence of the transitivity rule. For example, the entry ICQ-BCM in Table 5 is represented by value 1*. In interpreting this value, it should be noted that the ICQ-BCM entry in the IRM (Table 4) is 0. However, ICQ causes SCIC (ICQ-SCIC entry in IRM is 1), and SCIC causes BCM (SCIC-BCM entry in IRM is 1). Therefore, the transitivity rule is applied to the ICQ-BCM relationship, rendering its value as 1* in the FRM. Table 5 also includes the driving power and dependence power of each Industry 4.0 SCR function. The driving power value for a given function is calculated as the number of functions it determines directly or indirectly (via the transitivity rule). In contrast, the dependence power equals the number of functions that directly or indirectly determine the given function (Mathivathanan et al. 2021).

Table 5. The FRM for Industry 4.0 SCR functions.

SCR function	BCM	ICQ	ICS	SCAC	SCAG	SCA	SCC	SCCM	SCF	SCIC	SCMP	SCPM	SCRP	SCRM	SCT	SCV	Driving power	Rank
BCM	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	11
ICQ	1*	1	0	0	1*	0	1*	1*	1*	1	1*	1	1*	1	1	1	13	2
ICS	1	1	1	0	0	1	0	1*	1*	1*	0	1*	1*	1	1*	1*	12	3
SCAC	1	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	2	10
SCAG	1*	0	0	1*	1	0	0	0	1*	1	0	0	1	0	0	0	6	7
SCA	1*	1	1	1*	1*	1	1*	1	1	1*	1*	1	0	1*	1	1	15	1
SCC	1*	0	0	1*	1	0	1	1*	1*	1	1*	0	1	1	1	0	11	4
SCCM	1*	0	0	1	1	0	0	1	0	1*	0	0	1*	1	0	0	7	6
SCF	1*	0	0	0	1	0	0	0	1	1*	0	0	1*	1	0	0	6	7
SCIC	1	0	0	1*	1*	0	0	0	1	1	0	0	1	1*	0	0	7	6
SCMP	1*	0	0	1	1*	0	1	1	0	1*	1	0	1*	1	1*	0	10	5
SCPM	1	0	0	1*	1*	0	1*	1	1	0	1	1	0	1*	1*	1	11	4
SCRP	1*	0	0	1	0	0	0	0	0	0	0	0	1	0	0	0	3	9
SCRM	1	0	0	1*	0	0	0	0	0	0	0	0	1	1	0	0	4	8
SCT	1*	0	0	1*	1	0	1	1	0	1*	1	0	1*	1	1	0	10	5
SCV	1*	0	0	1*	1	0	1	1	1	1*	1*	0	1*	1	1	1	12	3
Dependence power	16	3	2	12	11	2	7	9	9	11	7	4	12	12	8	5
Rank	1	9	10	2	3	10	6	4	4	3	6	8	2	2	5	7

4.5. Developing the hierarchy levels

This step involves identifying the hierarchy level of each Industry 4.0 SCR function by implementing the iterative extraction procedures. For the iterative extraction to happen, the reachability, antecedent, and intersection sets of each Industry 4.0 SCR function should be established first. The reachability and antecedent sets correspond to the driving power and dependence power identified in FRM. Therefore, for a given Industry 4.0 SCR function, the reachability set consists of the functions that directly it determines, whereas the antecedent set includes functions that determine it. The intersection set for each function represents the intersection of the reachability and antecedent sets, including all the functions of the reachability set that also belong to the antecedent set and vis versa. After establishing each function’s reachability, antecedent, and intersection sets, the first extraction takes place, which involves identifying functions with identical reachability and intersection sets and extracting them. Table A1 represents the hierarchal levels for SC resilience functions of Industry 4.0. The table explains that BCM is the only function with identical reachability and intersection sets for the first iteration. Thus, it should be extracted in iteration I. After excluding (removing) the function(s) extracted in the first iteration, the extraction process repeats itself in the second iteration, and this procedure continues to identify hierarchical levels of the remaining functions iteratively. Table A1 explains how the hierarchy levels of the 16 Industry 4.0 SCR functions are iteratively identified across 11 iterations.

4.6. Developing the interpretive structural model

The interpretive structural model of the study is developed and presented in Figure 4. Firstly, developing the interpretive structural model involves structuring the functions based on identified hierarchy levels. In constructing the structural model, the number of placement levels equals the number of hierarchy levels identified (Bianco et al. 2023). Thus, the model presented in Figure 4 consists of 11 placement levels, given that the hierarchy levels of Industry 4.0 SCR functions have been identified across 11 iterations within Table A1. Overall, the placement order within the interpretive structural model is the inverse of the iterative extraction order, meaning the most dependent function(s) identified in iteration one should be positioned at the last placement level within the structural model and vice versa (Ali et al. 2020). Accordingly, the BCM function extracted in iteration 1 is positioned at placement level 11 of Figure 4, whereas ICS and SCA functions extracted in iteration 11 are positioned at placement level 1. The second step in developing the structural model entails representing the contextual relationships between each pair of functions with a vector arrow positioned within the consecutive placement levels. While the model in Figure 4 follows this role, there are a few expectations. For example, SCMP in placement level 5 of Figure 4 is not caused by SCV in placement level 4. Thus, it is connected by a vector arrow to the closest enablers in lower placement levels, which would be SCPM in placement level 3. The third and final step involves removing transitivity effects among functions. Therefore, none of the vector arrows in Figure 4 represent the transitivity effect.

Figure 4. The structural model of Industry 4.0 contributions to SCR.

4.7. MICMAC analysis

MICMAC (A French term commonly interpreted as cross-impact matrix multiplication applied to classification) is the final and complementary step in applying ISM, which involves the visual comparative analysis of the relational scope of each function (Mathivathanan et al. 2021). MICMAC develops a cartesian coordinate system that categorizes the system elements (Industry 4.0 SCR functions in this study) into four quadrants based on their respective driving and dependence powers. Figure 5 presents the results of the MICMAC analysis. The cartesian system in this figure comprises the driving power axis and the dependence power axis. Each axis in this system is divided by 16 equal points since there are 16 functions in the study. While building the MICMAC matrix, each function is positioned based on its unique coordinate derived from its driving and dependence powers (calculated in the FRM).

Figure 5. MICMAC analysis for assessment of driving and dependence powers of SC resilience functions.

The autonomous quadrant consists of functions with weak driving and dependence powers. Figure 5 explains that none of the Industry 4.0 SCR functions are categorized under the autonomous quadrant. The lack of autonomous functions signals the complexity of precedence relationships that exist within SCR functions. The driver quadrant comprises functions with strong driving power and weak dependence power. SCA, ICS, ICQ, SCPM, SCV, SCC, and SCMP are categorized as driver functions, highlighting their driving role in enabling other functions. The linkage quadrant comprises functions with strong driving and dependence powers. Referring to Figure 5, SCT is the only linkage function of the study. The dependent quadrant includes functions with weak driving power but strong dependence power. Consistently, SCCM, SCIC, SCF, SCAG, SCRM, SCRP, SCAC, and BCM are categorized as dependent Industry 4.0 SCR functions, meaning their existence relies on the favourable presence of driver and linkage functions.

5. Discussion

The ISM results reveal that Industry 4.0 contributes to promoting SCR via 16 functions. ISM identified complex precedence relationships among the Industry 4.0 SCR functions, indicating that Industry 4.0 contributions to SCR involve following a specific order in delivering resilience functions. The ISM in Figure 4 and the MICMAC matrix in Figure 5 collectively reveal that the enabling role of Industry 4.0 for SCR first involves increasing the information and cyber security of SC operations (ICS function) and automating a wide variety of SC activities (SCA function). Industry 4.0 streamlines SC communication through ICS and SCA functions and increases the quality (e.g. reliability, timeliness, completeness, or objectivity) of information exchanged across the SC (ICQ function). ICQ, in turn, enables SCPM function by allowing SC partners to effectively track the SC micro-elements such as human resource productivity, product quality, equipment condition, or inventory levels, even under severe SC complexity. Under the SCPM function, real-time product and process tracking and on-demand SC performance monitoring offer unique opportunities for forward and backward tracking of materials, components, or subassemblies across the SC (SCV function). Therefore, the more immediate contribution of Industry 4.0 to SCR involves creating a data-driven hyperconnected SC ecosystem that promotes ICS, SCA, ICQ, SCPM, and SCV, the driving functions that heavily rely on real-time and continuous data collection and information exchange across the SC. By doing so, Industry 4.0 allows SC partners to gain a shared perspective to closely collaborate on maximizing SC cost-effectiveness and performance (SCC function). It further enables SC partners to create a dynamic and detailed map of the entire SC to address existing blind spots (SCMP function) and identify and communicate factually backed detail of their operations internally and externally to promote SC transparency and trust (SCT function).

SCC, SCMP, and SCT functions, positioned at placement level 5 of Figure 4, allow SC members to effectively deal with the ever-increasing interdependencies, uncertainties, variability, and volatility of SC operations (SCCM function). By effectively managing SC administration, information, and operational complexities, the SCCM function allows SC partners to optimize and enhance their flexibility (SCF function) and increase their competencies to develop new business models or excel in product or process innovation (SCIC function), albeit indirectly. SCF function further allows SCs to respond to market disruptions more effectively (SCAG function) and implement necessary strategies for managing SC routine and unique risks (SCRM). SCF delivers these enabling roles through various mechanisms, such as increasing SC partners’ ability to change the product mix efficiently or readjust manufacturing processes and capacity to reduce process risk. SCAG and SCRM, in turn, grant the SC partners a collective capability to proactively sense and communicate imminent changes in consumer demand and promptly respond to market dynamics (SCRP function). Through the SCRP function, Industry 4.0 allows SC partners to implement needed strategies to readjust their designs and processes and adapt to disruptions caused by environmental turbulence, such as socio-political shifts (SCAC function). In turn, SCAC enables SC partners to develop proactive disruption response strategies to avoid future disruption when possible or continue to function effectively after experiencing a disruption (BCM function).

Figure 4 outlines the logical interdependencies between SCR functions, indicating that a specific function cannot be adequately developed until its preceding functions are delivered by Industry 4.0. As the most driving functions, ICS and SCA are the most accessible and immediate functioning outcomes of SC digitalization under Industry 4.0. By ascending towards placement level 11 of Figure 4, the dependence power of SCR functions significantly increases. Consistently, highly dependent SCR functions such as SCRP, SCAS, and BCM are the most remote and inaccessible resilience outcomes of Industry 4.0, given their development relies on the favourable presence of many preceding SCR functions. Nonetheless, drawing on Industry 4.0 to develop these highly dependent functions is indispensable to SCs seeking resilience since SCAS and BCM are among the most critical enablers of SCR in the absolute sense.

5.1. The roadmap to Industry 4.0-driven SCR

ISM is a valuable and rigorous method since it results in a digraph of contextual relationships for a complex system comprising many elements. While any interpretive structural model can be interpreted at the two levels of nodes and links, ISM is methodically limited to interpreting the nodes and merely defining the elements of the system. It means ISM does not involve interpreting how a direct link operates and should be interpreted. To address this limitation, the study developed a strategy roadmap (Figure 6) that explains the implications of each contextual relationship.

Figure 6. Industry 4.0-driven SCR roadmap.

A strategic roadmap is an effective tool for developing the framework of a transformation phenomenon and aiding stakeholders in understanding the necessary actions and activities required to achieve the transformation objectives. The roadmap should explicitly identify and describe the specific actions and activities that need to be taken and the relationships between them. Additionally, it must explore and explain the implications of how these actions and activities interact with one another and contribute to achieving the overall transformation goal. To effectively promote SCR under Industry 4.0, it is essential to identify and describe the underlying strategic or technical capabilities, opportunities, and outcomes of Industry 4.0 (called functions in this study) that support this transformation. To this end, our research thoroughly synthesized the relevant Industry 4.0 literature and identified 16 functions through which Industry 4.0 boosts SCR. Further, we applied the ISM technique to determine the relational organization of these functions, providing a clear understanding of the relationships and dependencies among them.

Additionally, we drew on an ILB to interpret the meaning and implications of the interactions between each pair of Industry 4.0 SCR functions, thus providing valuable insights into the strategic roadmap for achieving SCR under the Industry 4.0 framework. As explained previously, ILB is a type of knowledge representation used in ISM, a methodology for analyzing contextual relationships within a complex system. ILB is developed through a process of expert consultation and knowledge synthesis. Experts provide opinions on the relationships between each pair of elements or components, which are then analyzed and synthesized to create the ILB. This knowledge base represents and describes the contextual relationships between the system elements, outlining the properties and characteristics of contextual relationships. To identify the meaning and implications of each contextual relationship and understand the enabling role of each function concerning other functions, we drew on the experts’ opinions collected across the NGT-based expert panel meetings and constructed the ILB for the pair-wise comparison of the Industry 4.0 SCR functions. This is a common practice within the Total ISM technique, which helps address ISM limitations in interpreting contextual links. The ILB of the study is presented in Table A2, in which the direct relationships among pairs of functions correspond to the relationships identified within the SSIM (Table 3). Therefore, we drew on the functions identified within the content-centric review, hierarchical level and sequential relationships of functions established via ISM, and implications of each contextual relationship from the ILB to develop the roadmap to Industry 4.0-driven SCR, as shown in Figure 6.

5.2. Interpreting the roadmap

The strategic roadmap in Figure 6 aims to provide a transformation framework for SC stakeholders, bridging the gap between the vision of Industry 4.0-driven SCR and the actions required to achieve it. The roadmap offers a visual representation that outlines the sequential execution of Industry 4.0 SCR functions to achieve vital results that contribute synergistically to SCR. The roadmap is complex because it outlines the pairwise contextual relationships among 16 functions. Each contextual relationship, visually shown by a vector arrow, has an idiosyncratic meaning and implications collectively inferred by the experts. Given the limitations posed by visual and spatial constraints, the interpretation of each contextual relationship could not be accommodated within Figure 6. Therefore, a code has been assigned to each relationship, and the corresponding details are provided within the ILB presented in Table A2. Consequently, Table A2 and Figure 6 collectively illustrate each function’s driving role and dependency power and how they interact to achieve the ultimate goal of SCR. For example, Figure 6 shows that SCRM directly leads to the development of the BCM function (coded as SCRM→BCM). According to Table A2, this code implies that SCRM enables BCM via (i) better identification and management of continuity risks, (ii) prioritization of SC flow risks such as reputation, price, quality, or delivery risks, and (iii) development of effective risk prevention and risk mitigation strategies. By using this roadmap, SC actors can better understand how the different Industry 4.0 SCR functions work together to boost resilience and can plan and execute the necessary actions to achieve it. Overall, the roadmap provides a valuable tool for stakeholders looking to enhance their SCR in the context of Industry 4.0.

Overall, the structural model in Figure 4 and the Industry 4.0-driven SCR roadmap presented in Figure 6 and Table A2 describe the role of Industry 4.0 and the digitalization of supply networks in enabling SCR. Industry 4.0 enables SCR via a significantly complex mechanism that involves the development of 16 intertwined resilience functions. Referring to Figure 5, one may argue that SCA is the most critical SCR function of Industry 4.0, given that it has a very weak dependence power while directly enabling seven SCR functions of ICQ, ICS, SCCM, SCF, SCPM, SCT, and SCV. Such a conclusion is accurate in terms of prioritization, meaning SC digitalization under Industry 4.0 should first lead to the automation of SC operations to further contribute to developing more dependent SCR functions. Nonetheless, dependent functions such as SCAC and BCM are undoubtedly essential pillars of SCR (e.g. Dennehy et al. 2021; Ralston and Blackhurst 2020), and it is indispensable for SC partners to leverage Industry 4.0 opportunities to develop all Industry 4.0 SCR functions identified, given they each play a unique role in enabling SCR.

Following, we explain how the roadmap describes the contributions of Industry 4.0 to SCR. While we merely explain the contextual relationships between consecutive placement levels for brevity, each contextual relationship in Figure 6 can be interpreted based on the information provided in Table A2. Overall, the roadmap explains that Industry 4.0 contribution to SCR first involves automating supply chain operations and improving the safety and security of autonomous systems across the SC via the SCA and ICS functions. The roadmap highlights that these two functions are mutually interlinked, meaning SCA and ICS facilitate each other. The facilitating role of SCA for ICS involves the streamlined implementation of cybersecurity solutions (e.g. SOAR) and better response time to cyber threats. Alternatively, the enabling role of ICS for SCA entails functional and operational safety of cyber-physical systems against accidents or deliberate intrusions. SCA and ICS, located at placement level 1 of the roadmap, further improve the quality of information shared across the SC (ICQ function). SCA promotes ICQ using automated technologies such as robotic process automation to improve seamless connectivity or enhance data integration, acquisition, and standardization. ICS's enabling role for ICQ is myriad, involving the security of cloud platforms for communication and information sharing, information infrastructure operability, and data reliability.

ICQ, in turn, allows SC partners to monitor their operations more efficiently (SCPM function). ICQ promotes SCPM by increasing data accessibility, data compatibility, and performance metrics comparability. Improved process monitoring further leads to SC-wide visibility by enabling the SCV function. Indeed, SCPM contributions to SCV entail seamless logistics monitoring, product tracking, continuous SC-wide condition monitoring, and reduction of human errors. SCV, in turn, plays a critical role in facilitating the functions positioned at placement level 5 of the roadmap, namely SCC and SCT. SCV offers several implications for enabling SCC, from data harmonization and process interconnectedness to SC-wide data synchronization and seamless information sharing capability. SCV is vital to promoting SCT as it facilitates the acquisition and communication of relevant information within the SC while ensuring the accuracy of data collection and identification across SC nodes/links.

Within placement level 5, SCT directly leads to the development of SCMP. This enabling role of SCT involves the visualization of SC variability points, identification of information gaps, and a deeper understanding of end-to-end SC. The roadmap and underlying results further reveal that SCMP offers valuable implications for managing the ever-increasing complexity of contemporary SCs, mainly via promoting the SCCM function. SCCM delivers these implications by increasing the transparency of internal and external collaborations as well as preventing SC complexity by offering a better understanding of SC value propositions. Alternatively, the collaboration capabilities of Industry 4.0, manifested in the SCC function, increase the innovation capability of SC partners (SCIC function). SCC delivers this enabling role in several ways, such as streamlining innovation investments, collaborative innovation training strategy development, and innovation-friendly SC governance systems. SCIC, in turn, improves the flexibility of SC (SCF function), as it empowers partners to leverage disruptive technologies in support of SC processes and introduce resource efficiency and eco-friendlies into the new product designs. The roadmap explains that the enabling role of SCF for SCRM concerns lower process risks via flexible manufacturing capacity as well as reduced supply cost risks due to multisourcing-driven order flexibility. SCF is indispensable to SC agility and risk management since it promotes the SCAG and SCRM functions. SCF is essential to SCAG because it allows businesses to effectively implement changes to their product mix, procurement operations, and production schedules. The resulting agility paves the way for higher responsiveness of SC by enabling the SCRP function. Indeed, SCAG promotes SCRP by empowering partners to speed up implementing changes caused by disruptions. It further allows for the adjustability of operational tactics, expandability of products, and decisiveness in responding to new opportunities, changes, or threats. SCRP, in turn, improves SC adaptive capability (SCAC function) by empowering proactive demand responsiveness, the responsiveness of logistics operations, and strategic intuition for disruption management. Finally, yet importantly, SCAC allows SC partners to ensure their business continuity (BCM function) in several ways, from building strategies that support adaptability to unforeseen changes or higher modularity of SC operations to supporting more proactive disruption response strategies.

5.3. Comparative analysis of results

The study’s results agree with most prior research on the relationship between Industry 4.0 and SCR. Nevertheless, our results also provide fresh and, at times, contentious perspectives on how Industry 4.0's transformational effects may promote SCR. Overall, Industry 4.0-SCR literature is embryonic, limited to a few initial studies that mainly provided the theoretical understanding of the opportunities that Industry 4.0 technologies may offer for SCR. The present study provides empirical support for the works of Spieske and Birkel (2021) and Tortorella et al. (2022) that each Industry 4.0 technology uniquely contributes to SCR. However, the results extend the previous studies by considering the SCR implications of various Industry 4.0 technologies such as industrial automation and robotics, cybersecurity solutions, IoP, and IoS. While we support previous studies that acknowledge the critical role of individual Industry 4.0 technologies for SCR (e.g. Dilyard et al. 2021; Qader et al. 2022), our findings imply that Industry 4.0 represents a paradigm shift involving the collective implementation of disruptive digital technologies and valuable design principles. Results show that technological constituents and design principles of Industry 4.0 are interrelated, overlapping, and complementary, and their collective implementation can synergistically deliver SCR functions. While scholars such as Qader et al. (2022) propose a linear SC ecosystem in which Industry 4.0 technologies, such as big data and IoT, directly lead to the SCR, our findings intend to side with Ralston and Blackhurst (2020) arguing that Industry 4.0 transformation acts a capability enhancer, boosting firm’s technical and strategic capabilities to be more resilient to SC disruptions. Indeed, the findings of the study showed that Industry 4.0 enables 16 functions that are critical to the firm’s SCR. These SCR functions represent strategic/technical capabilities, opportunities, and outcomes that collectively emerge from integrating and utilising Industry 4.0 technologies and design principles. By leveraging these functions, SC partners can swiftly adapt and recover from disruptions, providing significant opportunities for SCR.

The results illustrated that the Industry 4.0 SCR functions are highly interrelated, and precedence relationships exist among them, which supports the recent study by Mubarik, Naghavi, et al. (2021). Nonetheless, the roadmap proposes that SC mapping does not directly affect SC visibility, which contradicts the recent findings from Mubarik, Naghavi, et al. (2021) on the causal direction of mapping → visibility. Instead, our findings showed that SCV indirectly promotes SCMP via the intermediary role of SCT. Thus, contrary to Mubarik, Naghavi, et al. (2021), our results showed that visibility precedes mapping capability within the Industry 4.0-driven SCR context. Overall, our results and the associations identified within the roadmap offer support for comparable studies within the literature. For example, the ISM results confirm the causal directions of flexibility → agility → responsiveness proposed by Shekarian, Reza Nooraie, and Parast (2020), flexibility → agility proposed by (Irfan, Wang, and Akhtar 2019), and innovation → risk management capabilities proposed by Kwak, Seo, and Mason (2018) within the SC context.

6. Concluding remarks

The study aimed to explore and explain how SC digitalization under Industry 4.0 can lead to SCR. To this purpose, the study designed and executed a content-centric literature review and identified 16 unique functions through which Industry 4.0 enables SCR. To explain how the functions identified can lead to SCR, the research team captured the opinion of SC experts and executed the ISM methodology. ISM results revealed that the SCR functions identified are highly interrelated, and the contribution of Industry 4.0 to SCR involves the sequential development of these functions. Through the structural model and Industry 4.0-driven SCR roadmap, the study detailed the order in which Industry 4.0 should deliver each function while describing existing pair-wise interaction among functions. The results and findings are believed to offer valuable theoretical and practical implications.

6.1. Theoretical implications

The theoretical contribution of the study is threefold. First, the study identified 16 unique functions through which Industry 4.0 increases the resilience of SCs. In doing so, the study provides a detailed conceptualization of each function and further explains the underlying mechanism through which each function enables SCR.

Second, previous studies have been more inclined to view the SCR contributions of Industry 4.0 and its technological constituents through a specific theoretical lens. The present study drew on the fact that Industry 4.0 expands beyond implementing individual technologies. The Industry 4.0 archetype showed how Industry 4.0 capitalizes on various emerging technological innovations to develop fundamental design principles that materialize the concept of hyperconnected DSN. While acknowledging the synergistic and complementary effects of Industry 4.0 technologies and principles, the study explored the implications of this phenomenon for SCR and provided a holistic but detailed overview of Industry 4.0 contribution to SCR via 16 unique functions. While acknowledging and supporting the findings of previous studies that attempted to explain how emerging technologies allow SC partners to enhance SCR capability, the present study opens a new perspective on how SCs should approach SCR under ongoing digital industrial transformation.

Third, the study identified the precedence relationships among the functions and explained the sequence in which they are delivered by Industry 4.0. By doing so, the study addressed a few theoretical and knowledge gaps that exist within the literature. For example, there is a lack of consensus in the positioning of SC flexibility, agility, and responsiveness within the SCM literature. While some scholars argue that these concepts are distinguishable (Ayoub and Abdallah 2019; Shekarian, Reza Nooraie, and Parast 2020), others acknowledge that these concepts can be used interchangeably or collectively as a part of unified constructs (e.g. Yu et al. 2019). Drawing on the ISM findings, the present study sides with the former and acknowledges the SCF → SCAG → SCRP chain of relationships. Results explain that SCF refers to SC partners’ ability to provide a cost-effective response to emerging threats and opportunities, whereas SCAG denotes SC partners’ ability to adapt internal functions and respond promptly and efficiently to business environment changes. SCF enables SCAG via reduced complexity of SC operations, the flexibility of product mix, and dynamic production schedules. While SCF and SCAG are mostly firm-level reactive abilities, SCRP is primarily characterized as a collective SC capability that is proactive, even amid disruptions. Findings show that the enabling role of SCAG for SCRP involves the ability to respond to new opportunities and threats decisively, adjust operational tactics, and implement SC changes promptly and efficiently.

6.2. Managerial and practical implications

Industry 4.0 epitomizes a paradigm shift involving the digital transformation of industrial value chains. The scope of Industry 4.0 expands beyond organizational digitization projects. Digitalization under this phenomenon involves the synergetic implementation of disruptive digital technologies such as AI, augmented reality, blockchain, big data analytics, cloud computing, industrial robots, IoT, and digital twin throughout the value chain and achieving certain techno-functional principles such as horizontal integration, interoperability, decentralization, modularity, and virtualization. Through constructing a data-driven and hyperconnected SC ecosystem, Industry 4.0 offers 16 functions through which modern SCs can significantly enhance their resilience. Contrary to the legacy information technologies and last-gen SCM systems, the integrative and inclusive nature of Industry 4.0 provides SC partners with unique opportunities to achieve various SCR functions synchronously. Although each technological constituent of Industry 4.0 is instrumental to particular SCR functions, their complementarities allow SC partners to develop all the necessary functions and maximize their resilience.

Our findings show that businesses should understand and account for the domino effect of Industry 4.0 digital transformation for SCR, as highlighted through the hierarchy and contextual relationships identified across the strategic roadmap. Industry 4.0 is a transformative framework that can offer 16 interrelated functions that help SC partners become resilient towards disruptions. Organizations should realize the domino effect of Industry 4.0 for SCR as a cascading effect triggered by adopting the Industry 4.0 framework. Under this framework, the sequence in which the 16 functions of Industry 4.0 are delivered and leveraged is critical to achieving SCR. For example, the roadmap implies that invaluable strategic SCR functions such as SCAC and BCM are not directly dependent on technical driver functions such as ICQ. Nonetheless, firms cannot enjoy the dependent functions (e.g. SCAC and BCM) without first leveraging the technical SCR functions of Industry 4.0, given that likes of ICQ are enablers of intermediary SCR functions like SCIC and SCRM that directly enable SCAC and BCM. Therefore, the domino effect of Industry 4.0 for SCR should be understood as a series of interdependent functions that empower organizations to achieve resilience in their SC operations. As such, SC partners must ensure that their digitalization strategies align with the sequence in which the 16 functions of Industry 4.0 are delivered to maximize benefits synergistically. By following this sequence, organizations can effectively leverage Industry 4.0 to enhance their SCR capabilities and achieve greater operational efficiency.

In this regard, results revealed that Industry 4.0 contributions to SCR first involve using technologies such as cognitive robots, IoT, edge computing, and CPS to automate physical activities, information workflows, and decision processes across the SC to reduce human interventions. The resulting automation and data centricity naturally put SC partners at more significant cybersecurity risks. Industry 4.0 addresses this concern via the ICS function, which involves many practices such as real-time assessment of vulnerabilities or elimination of unauthorized human interventions. Industry 4.0 synchronizes decision processes through these practices, protects equipment and technological infrastructure against disruptions, enhances production and delivery capacity reliability, and boosts IT and OT recovery, allowing SC partners to strengthen their disruption resilience.

Industry 4.0 further enables the real-time and continuous tracking of SC operations, from ordering raw materials to delivering final products. Industry 4.0 delivers this function via IoT, industrial controllers, cloud technologies, enterprise systems, and machine learning to allow SC partners to vertically and horizontally integrate their internal operations and engage in real-time information exchange. These circumstances enable SC partners to seamlessly access a large volume of meaningful data across SC nodes and efficiently perform the forward and backward tracing of SC material flow. Consistently and through SCPM and SCV functions, Industry 4.0 leads to utmost visibility across the SC and allows partners to use numerous performance metrics to better sense disruptions and increase the efficiency of their decision processes.

Industry 4.0 implications for SCR further involve facilitating SC collaboration and integration by eliminating information silos and streamlining real-time information and knowledge sharing. Improved SC collaboration under Industry 4.0, complemented by AI-driven visualization of SC map and SC-wide accessibility of vital information concerning environmental impacts of products and processes, risks points, or labour issues, enhances the transparency of SC operations. This condition, in turn, boosts SCR via empowering SC partners to recognize threats collaboratively, identify logistical uncertainties, address weak SC links, and mitigate reputational risk. More importantly, the resulting SC visibility, transparency, and mapping capability enhance SC partners’ capacity to better capitalize on the technological constituents of Industry 4.0 to orchestrate the integration of SC resources such as people, knowledge base, and processes. These interactions provide the necessary means for managing the ever-increasing complexity of modern SC. By doing so, Industry 4.0 delivers the SCCM function, enabling SC partners to gain resilience by building transparent relationships and acting on the resulting end-to-end visibility.

After developing data-driven Industry 4.0 SCR functions such as SCMP, SCT, and SCCM, SC partners should capitalize on Industry 4.0 to materialize the concept of smart factory, warehousing, and logistics to increase the flexibility of upstream/downstream supply and manufacturing operations. Furthermore, Industry 4.0 technologies such as IoP, extended reality, smart wearables, or simulation tools can enable SC members to improve inter-functional communication, collaboration, and knowledge competencies to enhance their product and process innovation capability. In turn, the resulting SCF and SCIC functions improve SCR by enabling members to increase the cost-efficiency of manufacturing and logistics operations, lower product lead time, and revive or replace declining products swiftly. Through the enabling role of these functions, SCs should further draw on Industry 4.0 to decentralize decision processes, implement dynamic SC planning systems, identify unknown SC risks, perform real-time mapping of SC risks, and collaboratively develop SC risk mitigation strategies. The resulting SC agility and risk management capabilities boost SCR by allowing partners to recognize their vulnerabilities, respond to SC disruptions cost-effectively, and become risk-resilient.

By going through the complex procedures explained above and developing SCAG and SCRM functions, SC partners can capitalize on Industry 4.0 to perform the round-the-clock monitoring of market dynamics and construct predictive models of future customer demands to respond to environmental turbulence and future disruptions proactively. The resulting responsiveness, complemented by the Industry 4.0-driven SC autonomy and modularity, allows SC partners to promptly restructure their modules and adapt to internal and external condition changes. In turn, the follow-on adaptive capability capitalizes on the digitalized business ecosystem of Industry 4.0 and allows SC members to strategically manage and survive inevitable disruptions and continue operating properly after a disaster has occurred. Consistently, the three complicated SCR functions of SCRP, SCAC, and BCM enhance the resiliency of SC partners by allowing them to sense imminent disruptions, forecast future threats, collectively strategize the necessary responses, and develop strategic recovery capabilities.

6.3. Limitations and future research directions

The present study provided a hypothetical description of the process through which Industry 4.0 enhances the resilience of SC. Delivering this goal involved identifying 16 Industry 4.0 SCR functions and drawing on the ISM to model the interrelationships among them. While the study followed the standard procedures to identify functions and perform ISM, it is limited in some respects. First, the study held an optimistic view of the SCR opportunities that Industry 4.0 offers and illustrated a best-case scenario within which Industry 4.0 operates positively throughout the SCM operations. Nevertheless, the literature widely acknowledges the complexity of SC digitalization under Industry 4.0, arguing that not all businesses have the capacity to capitalize on this disruptive phenomenon. Digitalization under Industry 4.0 is significantly resource and knowledge-intensive, requiring SC partners to have a certain degree of digital maturity, technological alignment, infrastructural readiness, and strategic competencies, to name a few requirements. Indeed, most ordinary SC partners rarely have the needed capacity to embark on the all-inclusive Industry 4.0 digital transformation. For most businesses, transitioning to Industry 4.0 is a gradual process, starting with the selective implementation of a few disruptive technologies that align with the strategic priorities. While the study provides a holistic but detailed explanation of the opportunities that Industry 4.0 offers for SCR, it could not conceivably explain how the limited implementation of Industry 4.0 technologies, and various combinations of technologies that might occur, can impact SCR. Future research can address this gap by developing dynamic decision models that idiosyncratically outline the best course of digitalization for individual SCs while considering their resilience goals and digitalization competencies.

Second, Industry 4.0 can negatively impact SCR if governed improperly. For example, under the ICS function, the study explained how Industry 4.0 improves the cybersecurity of SC operations via various practices such as real-time monitoring of IT-OT functionalities. Nonetheless, every connected device within Industry 4.0 hyperconnected ecosystem represents a potential cyber risk. In addition, the breadth of Industry 4.0 that involves vertical and horizontal integration of SC operations increases the cyberattack surface significantly. Capitalizing on Industry 4.0 to develop ISC function hypothetically requires SC partners to have many competencies, such as operational security knowledge, techniques for secure integration with legacy infrastructure, and expertise in applying security functionalities of Industry 4.0 technologies. Although the study explained how Industry 4.0 enables each of the SCR functions, it could not conceivably explain other competencies required by them. Consistently, Industry 4.0 can be a double-edged sword, acting against many functions such as ICS, SCC, SCCM, and SCAC when managed improperly. Addressing this knowledge gap requires future studies to thoroughly investigate the requirements (knowledge, skills, competencies, or strategies) SC partners should have to leverage Industry 4.0 to successfully develop each SCR function.

Acknowledgements

This research has been a part of the IN4ACT project that received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 810318.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This research has been a part of the IN4ACT project that received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 810318.

Notes on contributors

Morteza Ghobakhloo

Morteza Ghobakhloo is an Associate Professor at the Division of Industrial Engineering and Management, Uppsala University, Sweden, and a collaborator with the IN4ACT project at the School of Economics and Business, Kaunas University of Technology, Lithuania. He holds a Ph.D. in Industrial Engineering. His research interests include the strategic management of digital industrial transformation in the Industry 4.0 era, hyper-connected manufacturing ecosystems, and corporate sustainability performance. His research has been published in many leading journals, such as BSE, IJPR, TFSC, and JCLP.

Mohammad Iranmanesh

Mohammad Iranmanesh is an Associate Professor attached to the La Trobe Business School, La Tobe University. His research interests are at the interface of sustainability and Information Systems (IS), focusing on issues related to digital transformation, sustainable manufacturing, and sustainable development. He has published more than 100 articles in a range of leading academic journals and conferences. Mohammad was named in the Top 40 Australia’s early achievers (Rising Stars) of 2020 by research cited in The Australian newspaper.

Behzad Foroughi

Behzad Foroughi is an Assistant Professor in the Program in International Business Administration at I-Shou University, Taiwan. His main areas of research are International Marketing, Tourism & Hospitality, and Information Technology. He is an active researcher and has published widely in several peer-reviewed journals. He has published his research in various journals such as Journal of Travel Research, Journal of Destination Marketing & Management, and Business Strategy & the Environment. Currently, he serves as an Associate Editor at Asia-Pacific Journal of Business Administration.

Ming-Lang Tseng

Ming-Lang Tseng (Scopus: h-index: 65; Google h-index: 82) is Chair Professor and Director of Institute of Innovation and Circular Economy in Asia University. Prof. Tseng was a research fellow in the Institute of Applied Ecology at Chinese Academy of Sciences, China (2012–2013); visiting scholar at University of Derby, United Kingdom (2015); Adjunct Distinguished Prof, Universiti Kebangsaan Malaysia, Malaysia (2019–2021); Honorary Professor, Graduate School of Business, Universiti Sains Malaysia (2019–2021); and Visiting Prof., University of Glasgrow, United Kingdom (2023). Prof Tseng is the Editor in Chief of Journal of Production of Engineering (Taylor & Francis).

Davoud Nikbin

Davoud Nikbin is a Senior Lecturer in Marketing at the School of Business & Law, University of Brighton, UK. He holds a PhD in Services Marketing from Universiti Sains Malaysia. He teaches a wide range of courses in marketing. His research interests are services marketing, consumer behavior, tourism and hospitality marketing, digital marketing and innovation management. He has published his research in various journals such as Journal of Knowledge Management, Journal of Consumer Behaviour, Journal of Travel and Tourism Marketing, Journal of Air Transport Management, etc.

Ahmad A. A. Khanfar

Ahmad A. A. Khanfar is a lecturer at Edith Cowan University. Ahmad has received his Master Degree by Research in Project Management from Edith Cowan University, Bachelor degree in Computer Science from the University of Jordan, and has many years of experience in information technology systems and IT project Management. His research interests are mainly in the area of information systems and its applications in various fields.

Appendices

 

Table A1. Hierarchy level for Industry 4.0 SCR functions.

Factors	Reachability set	Antecedent set	Intersection set	level
Iteration I
BCM	BCM	BCM, ICQ, ICS, SCAC, SCAG, SCA, SCC, SCCM, SCF, SCIC, SCMP, SCPM, SCRP, SCRM, SCT, SCV	BCM	I
ICQ	BCM, ICQ, SCAG, SCC, SCCM, SCF, SCIC, SCMP, SCPM, SCRP, SCRM, SCT, SCV	ICQ, ICS, SCA	ICQ
ICS	BCM, ICQ, ICS, SCA, SCCM, SCF, SCIC, SCPM, SCRP, SCRM, SCT, SCV	ICS, SCA	ICS, SCA
SCAC	BCM, SCAC	SCAC, SCAG, SCA, SCC, SCCM, SCIC, SCMP, SCPM, SCRP, SCRM, SCT, SCV	SCAC
SCAG	BCM, SCAC, SCAG, SCF, SCIC, SCRP	ICQ, SCAG, SCA, SCC, SCCM, SCF, SCIC, SCMP, SCPM, SCT, SCV	SCAG, SCF, SCIC
SCA	BCM, ICQ, ICS, SCAC, SCAG, SCA, SCC, SCCM, SCF, SCIC, SCMP, SCPM, SCRM, SCT, SCV	ICS, SCA	ICS, SCA
SCC	BCM, SCAC, SCAG, SCC, SCCM, SCF, SCIC, SCMP, SCRP, SCRM, SCT	ICQ, SCA, SCC, SCMP, SCPM, SCT, SCV	SCC, SCMP, SCT
SCCM	BCM, SCAC, SCAG, SCCM, SCIC, SCRP, SCRM	ICQ, ICS, SCA, SCC, SCCM, SCMP, SCPM, SCT, SCV	SCCM
SCF	BCM, SCAG, SCF, SCIC, SCRP, SCRM	ICQ, ICS, SCAG, SCA, SCC, SCF, SCIC, SCPM, SCV	SCAG, SCF, SCIC
SCIC	BCM, SCAC, SCAG, SCF, SCIC, SCRP, SCRM	ICQ, ICS, SCAG, SCA, SCC, SCCM, SCF, SCIC, SCMP, SCT, SCV	SCAG, SCF, SCIC
SCMP	BCM, SCAC, SCAG, SCC, SCCM, SCIC, SCMP, SCRP, SCRM, SCT	ICQ, SCA, SCC, SCMP, SCPM, SCT, SCV	SCC, SCMP, SCT
SCPM	BCM, SCAC, SCAG, SCC, SCCM, SCF, SCMP, SCPM, SCRM, SCT, SCV	ICQ, ICS, SCA, SCPM	SCPM
SCRP	BCM, SCAC, SCRP	ICQ, ICS, SCAG, SCC, SCCM, SCF, SCIC, SCMP, SCRP, SCRM, SCT, SCV	SCRP
SCRM	BCM, SCAC, SCRP, SCRM	ICQ, ICS, SCA, SCC, SCCM, SCF, SCIC, SCMP, SCPM, SCRM, SCT, SCV	SCRM
SCT	BCM, SCAC, SCAG, SCC, SCCM, SCIC, SCMP, SCRP, SCRM, SCT	ICQ, ICS, SCA, SCC, SCMP, SCPM, SCT, SCV	SCC, SCMP, SCT
SCV	BCM, SCAC, SCAG, SCC, SCCM, SCF, SCIC, SCMP, SCRP, SCRM, SCT, SCV	ICQ, ICS, SCA, SCPM, SCV	SCV
Iteration 2
ICQ	ICQ, SCAG, SCC, SCCM, SCF, SCIC, SCMP, SCPM, SCRP, SCRM, SCT, SCV	ICQ, ICS, SCA	ICQ
ICS	ICQ, ICS, SCA, SCCM, SCF, SCIC, SCPM, SCRP, SCRM, SCT, SCV	ICS, SCA	ICS, SCA
SCAC	SCAC	SCAC, SCAG, SCA, SCC, SCCM, SCIC, SCMP, SCPM, SCRP, SCRM, SCT, SCV	SCAC	II
SCAG	SCAC, SCAG, SCF, SCIC, SCRP	ICQ, SCAG, SCA, SCC, SCCM, SCF, SCIC, SCMP, SCPM, SCT, SCV	SCAG, SCF, SCIC
SCA	ICQ, ICS, SCAC, SCAG, SCA, SCC, SCCM, SCF, SCIC, SCMP, SCPM, SCRM, SCT, SCV	ICS, SCA	ICS, SCA
SCC	SCAC, SCAG, SCC, SCCM, SCF, SCIC, SCMP, SCRP, SCRM, SCT	ICQ, SCA, SCC, SCMP, SCPM, SCT, SCV	SCC, SCMP, SCT
SCCM	SCAC, SCAG, SCCM, SCIC, SCRP, SCRM	ICQ, ICS, SCA, SCC, SCCM, SCMP, SCPM, SCT, SCV	SCCM
SCF	SCAG, SCF, SCIC, SCRP, SCRM	ICQ, ICS, SCAG, SCA, SCC, SCF, SCIC, SCPM, SCV	SCAG, SCF, SCIC
SCIC	SCAC, SCAG, SCF, SCIC, SCRP, SCRM	ICQ, ICS, SCAG, SCA, SCC, SCCM, SCF, SCIC, SCMP, SCT, SCV	SCAG, SCF, SCIC
SCMP	SCAC, SCAG, SCC, SCCM, SCIC, SCMP, SCRP, SCRM, SCT	ICQ, SCA, SCC, SCMP, SCPM, SCT, SCV	SCC, SCMP, SCT
SCPM	SCAC, SCAG, SCC, SCCM, SCF, SCMP, SCPM, SCRM, SCT, SCV	ICQ, ICS, SCA, SCPM	SCPM
SCRP	SCAC, SCRP	ICQ, ICS, SCAG, SCC, SCCM, SCF, SCIC, SCMP, SCRP, SCRM, SCT, SCV	SCRP
SCRM	SCAC, SCRP, SCRM	ICQ, ICS, SCA, SCC, SCCM, SCF, SCIC, SCMP, SCPM, SCRM, SCT, SCV	SCRM
SCT	SCAC, SCAG, SCC, SCCM, SCIC, SCMP, SCRP, SCRM, SCT	ICQ, ICS, SCA, SCC, SCMP, SCPM, SCT, SCV	SCC, SCMP, SCT
SCV	SCAC, SCAG, SCC, SCCM, SCF, SCIC, SCMP, SCRP, SCRM, SCT, SCV	ICQ, ICS, SCA, SCPM, SCV	SCV
Iteration 3
ICQ	ICQ, SCAG, SCC, SCCM, SCF, SCIC, SCMP, SCPM, SCRP, SCRM, SCT, SCV	ICQ, ICS, SCA	ICQ
ICS	ICQ, ICS, SCA, SCCM, SCF, SCIC, SCPM, SCRP, SCRM, SCT, SCV	ICS, SCA	ICS, SCA
SCAG	SCAG, SCF, SCIC, SCRP	ICQ, SCAG, SCA, SCC, SCCM, SCF, SCIC, SCMP, SCPM, SCT, SCV	SCAG, SCF, SCIC
SCA	ICQ, ICS, SCAG, SCA, SCC, SCCM, SCF, SCIC, SCMP, SCPM, SCRM, SCT, SCV	ICS, SCA	ICS, SCA
SCC	SCAG, SCC, SCCM, SCF, SCIC, SCMP, SCRP, SCRM, SCT	ICQ, SCA, SCC, SCMP, SCPM, SCT, SCV	SCC, SCMP, SCT
SCCM	SCAG, SCCM, SCIC, SCRP, SCRM	ICQ, ICS, SCA, SCC, SCCM, SCMP, SCPM, SCT, SCV	SCCM
SCF	SCAG, SCF, SCIC, SCRP, SCRM	ICQ, ICS, SCAG, SCA, SCC, SCF, SCIC, SCPM, SCV	SCAG, SCF, SCIC
SCIC	SCAG, SCF, SCIC, SCRP, SCRM	ICQ, ICS, SCAG, SCA, SCC, SCCM, SCF, SCIC, SCMP, SCT, SCV	SCAG, SCF, SCIC
SCMP	SCAG, SCC, SCCM, SCIC, SCMP, SCRP, SCRM, SCT	ICQ, SCA, SCC, SCMP, SCPM, SCT, SCV	SCC, SCMP, SCT
SCPM	SCAG, SCC, SCCM, SCF, SCMP, SCPM, SCRM, SCT, SCV	ICQ, ICS, SCA, SCPM	SCPM
SCRP	SCRP	ICQ, ICS, SCAG, SCC, SCCM, SCF, SCIC, SCMP, SCRP, SCRM, SCT, SCV	SCRP	III
SCRM	SCRP, SCRM	ICQ, ICS, SCA, SCC, SCCM, SCF, SCIC, SCMP, SCPM, SCRM, SCT, SCV	SCRM
SCT	SCAG, SCC, SCCM, SCIC, SCMP, SCRP, SCRM, SCT	ICQ, ICS, SCA, SCC, SCMP, SCPM, SCT, SCV	SCC, SCMP, SCT
SCV	SCAG, SCC, SCCM, SCF, SCIC, SCMP, SCRP, SCRM, SCT, SCV	ICQ, ICS, SCA, SCPM, SCV	SCV
Iteration 4
ICQ	ICQ, SCAG, SCC, SCCM, SCF, SCIC, SCMP, SCPM, SCRM, SCT, SCV	ICQ, ICS, SCA	ICQ
ICS	ICQ, ICS, SCA, SCCM, SCF, SCIC, SCPM, SCRM, SCT, SCV	ICS, SCA	ICS, SCA
SCAG	SCAG, SCF, SCIC	ICQ, SCAG, SCA, SCC, SCCM, SCF, SCIC, SCMP, SCPM, SCT, SCV	SCAG, SCF, SCIC	IV
SCA	ICQ, ICS, SCAG, SCA, SCC, SCCM, SCF, SCIC, SCMP, SCPM, SCRM, SCT, SCV	ICS, SCA	ICS, SCA
SCC	SCAG, SCC, SCCM, SCF, SCIC, SCMP, SCRM, SCT	ICQ, SCA, SCC, SCMP, SCPM, SCT, SCV	SCC, SCMP, SCT
SCCM	SCAG, SCCM, SCIC, SCRM	ICQ, ICS, SCA, SCC, SCCM, SCMP, SCPM, SCT, SCV	SCCM
SCF	SCAG, SCF, SCIC, SCRM	ICQ, ICS, SCAG, SCA, SCC, SCF, SCIC, SCPM, SCV	SCAG, SCF, SCIC
SCIC	SCAG, SCF, SCIC, SCRM	ICQ, ICS, SCAG, SCA, SCC, SCCM, SCF, SCIC, SCMP, SCT, SCV	SCAG, SCF, SCIC
SCMP	SCAG, SCC, SCCM, SCIC, SCMP, SCRM, SCT	ICQ, SCA, SCC, SCMP, SCPM, SCT, SCV	SCC, SCMP, SCT
SCPM	SCAG, SCC, SCCM, SCF, SCMP, SCPM, SCRM, SCT, SCV	ICQ, ICS, SCA, SCPM	SCPM
SCRM	SCRM	ICQ, ICS, SCA, SCC, SCCM, SCF, SCIC, SCMP, SCPM, SCRM, SCT, SCV	SCRM	IV
SCT	SCAG, SCC, SCCM, SCIC, SCMP, SCRM, SCT	ICQ, ICS, SCA, SCC, SCMP, SCPM, SCT, SCV	SCC, SCMP, SCT
SCV	SCAG, SCC, SCCM, SCF, SCIC, SCMP, SCRM, SCT, SCV	ICQ, ICS, SCA, SCPM, SCV	SCV
Iteration 5
ICQ	ICQ, SCC, SCCM, SCF, SCIC, SCMP, SCPM, SCT, SCV	ICQ, ICS, SCA	ICQ
ICS	ICQ, ICS, SCA, SCCM, SCF, SCIC, SCPM, SCT, SCV	ICS, SCA	ICS, SCA
SCA	ICQ, ICS, SCA, SCC, SCCM, SCF, SCIC, SCMP, SCPM, SCT, SCV	ICS, SCA	ICS, SCA
SCC	SCC, SCCM, SCF, SCIC, SCMP, SCT	ICQ, SCA, SCC, SCMP, SCPM, SCT, SCV	SCC, SCMP, SCT
SCCM	SCCM, SCIC	ICQ, ICS, SCA, SCC, SCCM, SCMP, SCPM, SCT, SCV	SCCM
SCF	SCF, SCIC	ICQ, ICS, SCA, SCC, SCF, SCIC, SCPM, SCV	SCF, SCIC	V
SCIC	SCF, SCIC	ICQ, ICS, SCA, SCC, SCCM, SCF, SCIC, SCMP, SCT, SCV	SCF, SCIC	V
SCMP	SCC, SCCM, SCIC, SCMP, SCT	ICQ, SCA, SCC, SCMP, SCPM, SCT, SCV	SCC, SCMP, SCT
SCPM	SCC, SCCM, SCF, SCMP, SCPM, SCT, SCV	ICQ, ICS, SCA, SCPM	SCPM
SCT	SCC, SCCM, SCIC, SCMP, SCT	ICQ, ICS, SCA, SCC, SCMP, SCPM, SCT, SCV	SCC, SCMP, SCT
SCV	SCC, SCCM, SCF, SCIC, SCMP, SCT, SCV	ICQ, ICS, SCA, SCPM, SCV	SCV
Iteration 6
ICQ	ICQ, SCC, SCCM, SCMP, SCPM, SCT, SCV	ICQ, ICS, SCA	ICQ
ICS	ICQ, ICS, SCA, SCCM, SCPM, SCT, SCV	ICS, SCA	ICS, SCA
SCA	ICQ, ICS, SCA, SCC, SCCM, SCMP, SCPM, SCT, SCV	ICS, SCA	ICS, SCA
SCC	SCC, SCCM, SCMP, SCT	ICQ, SCA, SCC, SCMP, SCPM, SCT, SCV	SCC, SCMP, SCT
SCCM	SCCM	ICQ, ICS, SCA, SCC, SCCM, SCMP, SCPM, SCT, SCV	SCCM	VI
SCMP	SCC, SCCM, SCMP, SCT	ICQ, SCA, SCC, SCMP, SCPM, SCT, SCV	SCC, SCMP, SCT
SCPM	SCC, SCCM, SCMP, SCPM, SCT, SCV	ICQ, ICS, SCA, SCPM	SCPM
SCT	SCC, SCCM, SCMP, SCT	ICQ, ICS, SCA, SCC, SCMP, SCPM, SCT, SCV	SCC, SCMP, SCT
SCV	SCC, SCCM, SCMP, SCT, SCV	ICQ, ICS, SCA, SCPM, SCV	SCV
Iteration 7
ICQ	ICQ, SCC, SCMP, SCPM, SCT, SCV	ICQ, ICS, SCA	ICQ
ICS	ICQ, ICS, SCA, SCPM, SCT, SCV	ICS, SCA	ICS, SCA
SCA	ICQ, ICS, SCA, SCC, SCMP, SCPM, SCT, SCV	ICS, SCA	ICS, SCA
SCC	SCC, SCMP, SCT	ICQ, SCA, SCC, SCMP, SCPM, SCT, SCV	SCC, SCMP, SCT	VII
SCMP	SCC, SCMP, SCT	ICQ, SCA, SCC, SCMP, SCPM, SCT, SCV	SCC, SCMP, SCT	VII
SCPM	SCC, SCMP, SCPM, SCT, SCV	ICQ, ICS, SCA, SCPM	SCPM
SCT	SCC, SCMP, SCT	ICQ, ICS, SCA, SCC, SCMP, SCPM, SCT, SCV	SCC, SCMP, SCT	VII
SCV	SCC, SCMP, SCT, SCV	ICQ, ICS, SCA, SCPM, SCV	SCV
Iteration 8
ICQ	ICQ, SCPM, SCV	ICQ, ICS, SCA	ICQ
ICS	ICQ, ICS, SCA, SCPM, SCV	ICS, SCA	ICS, SCA
SCA	ICQ, ICS, SCA, SCPM, SCV	ICS, SCA	ICS, SCA
SCPM	SCPM, SCV	ICQ, ICS, SCA, SCPM	SCPM
SCV	SCV	ICQ, ICS, SCA, SCPM, SCV	SCV	VIII
Iteration 9
ICQ	ICQ, SCPM	ICQ, ICS, SCA	ICQ
ICS	ICQ, ICS, SCA, SCPM	ICS, SCA	ICS, SCA
SCA	ICQ, ICS, SCA, SCPM	ICS, SCA	ICS, SCA
SCPM	SCPM	ICQ, ICS, SCA, SCPM	SCPM	IX
Iteration 10
ICQ	ICQ	ICQ, ICS, SCA	ICQ	X
ICS	ICQ, ICS, SCA	ICS, SCA	ICS, SCA
SCA	ICQ, ICS, SCA	ICS, SCA	ICS, SCA
Iteration 11
ICS	ICS, SCA	ICS, SCA	ICS, SCA	XI
SCA	ICS, SCA	ICS, SCA	ICS, SCA	XI

Table A2. The ILB and the interpretation of contextual relationships among Industry 4.0 SCR functions.

Causal relationship	Enabling role
Supply Chain Automation (SCA)
SCA → ICQ	Data acquisition standardisation and accuracy; Improved data integration; seamless connectivity.
SCA → ICS	Improved response time to cyber threats; Supporting security orchestration, automation, and response (SOAR) systems; Simplifying the implementation of cyber protection solutions.
SCA → SCCM	Automation of SC routine operations; Automation of complex administrative tasks (e.g. employee lifecycle management or customer engagement management); logistics (transportation, delivery, and distribution) automation; SC resource orchestration.
SCA → SCF	Reconfigurable SC system components; Autonomous business planning processes; Automation of repetitive tasks and reduction of human interventions; Streamlined product development.
SCA → SCPM	Sensor-enabled measurement of influential components; Autonomous benchmarking of SC performance indices; Continuous, real-time, and consistent data feeds.
SCA → SCT	Centralized and ubiquitous information access; Streamlining the intelligent workflows; End-to-end product traceability.
SCA → SCV	Real-time access to meaningful data/information; Autonomous on-site inspections; Integrability of SC elements; Decentralised and autonomous decision-making.
Information and Cyber Security (ICS)
ICS → BCM	Safeguarding mission-critical digital services; Uninterrupted data access; Protection against data loss; IT-OT safety and recoverability.
ICS → ICQ	Secure cloud communications; Data reliability and protection across mobile, big, corporate, and cloud data environments; Security and operability of information infrastructure.
ICS → SCA	Protection against deliberate automation control networks intrusions; Built-in high security of autonomous systems; Functional safety of cyber-physical systems.
ICS → SCRM	Cyber threats mitigation across the lifecycle of IT-OT systems; Higher security of employee practices across the SC; Mitigating information and cyber security risks and vulnerabilities across tiers of supplier systems.
Information and Communication Quality (ICQ)
ICQ → SCIC	Improved communication for the idea and information sharing; More comprehensive and detailed overview of SC innovation processes; Data-driven early trend recognition; More agile innovation processes.
ICQ → SCPM	Compatibility and coherence of data; Data accessibility for influential components and factors; Reliability and comparability of performance indicators.
ICQ → SCRM	Effective management of SC data points; Increased operational efficiencies via data alignment; Data-driven response orchestration; Data alignment with regulatory requirements; Facilitating the end-to-end assessments of risk processes.
ICQ → SCT	Communication of factually backed SC information; Data safety and ethical compliance; Improved stakeholder communication.
ICQ → SCV	Accuracy, reliability, and timeliness (real-time or near real-timeliness) of data shared across SC partners; Better identification of SC pain points; Elimination of information and functional silos; Higher information trustworthiness.
Supply Chain Process Monitoring (SCPM)
SCPM → BCM	Better synchronisation of SC processes, applications, and technological infrastructure; Facilitating continuous operations under disruption scenarios.
SCPM → SCCM	Optimizing procurement and shipment schedules; Better compliance with everchanging regulations and legal restrictions; Minimized data disinformation or manipulation.
SCPM → SCF	Efficient diversification of supplier base; Higher control on third-party logistics companies’ activities; Information access for business planning model development; Condition monitoring of SC automation tools.
SCPM → SCMP	Real-time tracking of SC changes; Value chain-wide accessibility of SC element (e.g. processes, shipment, equipment, raw material) information; Remote information verification for every SC tier.
SCPM → SCV	Real-time product location tracking; Logistics and transportation performance monitoring; Constant condition monitoring across SC; Automating reporting processes; Reduced chance of human errors.
Supply Chain Visibility (SCV)
SCV → SCAG	Improved inventory accuracy; reconfigurability of supplier base and distribution networks; Faster action plan development
SCV → SCC	Data synchronisation across SC nodes; Data harmonisation; real-time information sharing capability; Process interconnectedness; People (e.g. customer, managers) interconnectedness.
SCV → SCCM	Unnecessary complexity avoidance; Identifying processes that require simplification; Higher integrity across SC relationships.
SCV → SCF	Increased ease (mobility) of supply chain reconfiguration; Timely understanding of occurring/forthcoming disruptions; SC-wide information systems alignment; Streamlining the re-alignment or reinvention of the SC.
SCV → SCRM	Clearer shared understanding of SC risks; Streamlining SC integration; Better tracking of shifts in customer/market demands; Determining and handling bad data
SCV → SCT	Accurate data identification and collection across the SC nodes and links; Facilitating the internal/external communication of relevant information.
Supply Chain Collaboration (SCC)
SCC → SCAG	Higher operational alignment; SC-openness to change; Higher market sensitivity; Strategic orientation; Improved demand management.
SCC → SCIC	SC governance system openness to accommodating innovation; Streamlining the innovation/R&D investments; Joint innovation training strategies.
SCC → SCRP	Collective situational awareness; SC-wide communication of changes to production/delivery schedules; Coordination for technological alignment and heterogeneity; Collaborative planning and forecasting.
SCC → SCRM	Collective understanding of risk/failure implications and impacts; Constant tracking of changes in SC risk factors; Dynamic SC risk recognition, ranking, and prioritisation; Collective risk mitigation planning.
SCC → SCT	Active collaboration on common SC issues; Collective materiality assessment and SC priority identification; Collective establishment of disclosure levels.
Supply chain mapping (SCMP)
SCMP → SCC	Identifying areas where SC partners have a solid footing for collaboration; Optimised SC partner selection (in terms of, e.g. strategic alignment or shared value); Stronger collaboration via effective benefit-sharing models.
SCMP → SCCM	Visual control of SC element interactions; Better understanding of SC processes and their impacts; Identification of SC complexity sources; Functional SC complexity control.
SCMP → SCAC	Better SC localisation and diversification strategy development; Rationalisation of the supplier base; Detailed assessment of SC product and process compliance with new trends.
SCMP → SCRM	Better identification of disruption risks; Improved SC vulnerability assessment; SC-wide compliance and risk exposure assessment; Streamlined supplier segmentation.
Supply Chain Transparency (SCT)
SCT → SCAG	Ease of information access and sharing; Improved flow of information across partners and customers; Higher dependability of logistics systems.
SCT → SCC	Promoting trust across SC; Identifying resources needed for effective collaboration; More detailed design of collaboration program and underlying responsibilities.
SCT → SCCM	Detailed understanding of SC value propositions for complexity avoidance; Better understanding of SC partners and customers; Transparency of internal and external collaboration.
SCT → SCMP	A deeper understanding of end-to-end SC; Visual representation of variability points across supply networks; Identification of information gaps throughout supply, manufacturing, and distribution nodes.
SCT → SCRM	Improved accuracy of SC data used for risk assessment; Satisfying SC quality and capacity requirements; Better recognition of risk sources and risk drivers; Streamlined implementation of risk mitigation strategies.
Supply Chain Complexity Management (SCCM)
SCCM → SCAC	Freeing up working capital; Reducing obsolescence throughout the SC; Cost efficiency; Higher responsiveness to internal and external disruptive forces.
SCCM → SCAG	Faster implementation of action plans and SC process changes; Improved efficiency of reactive decision-making; Awareness of imminent/happening disruptions, competitions, or opportunities.
SCCM → SCRM	Better integration of risk compliance into SCM functions; Detailed understanding of impact speed, disruption scope, and financial implications of various risk/failure factors; Better understanding of SC partners’ risk potential (product, operational, process, and operating region risks).
Supply Chain Flexibility (SCF)
SCF → SCAG	Ability to efficiently change the production mix; Procurement capability to supply required resources for changing production schedules; Reduced complexity of SC operations.
SCF → SCRM	Lower supply cost risks via the ability to shift order quantities across multiple active suppliers; Lower process risk due to the flexibility of manufacturing processes and capacity; Lower demand risks by enabling postponement strategy.
Supply Chain Innovation Capability (SCIC)
SCIC → SCF	Ability to leverage disruptive technologies in support of SC processes; Better organisation of manufacturing operations; Introducing new (resource-efficient and eco-friendly) product design.
SCIC → BCM	New ways of threat discovery and analysis; Development and implementation of novel business impact analysis tools; Better designing, testing, and upgrading of the business continuity plan.
SCIC → SCRP	Streamlined shareholder interactions for satisfying complex/dynamic environmental (especially customers’) expectations; Lower time to market of new products; Countering environmental changes via novel business and monetisation models.
Supply Chain Risk Management (SCRM)
SCRM → BCM	Better identification and management of continuity risks; Prioritisation of SC flow risks (e.g. reputation, price, quality, or delivery risks); Development of effective risk prevention and risk mitigation strategies.
SCRM → SCRP	SC partner performance assessment for reducing supply and delivery risks; Better fulfilment of customer demands/requirements; Improved reliability of SC operations; Maintaining SC functions via early risk detection.
Supply Chain Agility (SCAG)
SCAG → SCIC	Reduced SC costs and resource availability for innovation; Streamlined SC innovation processes (e.g. dynamic R&D team building); Better articulation of SC innovation strategy; Better alignment of innovation strategy with SC overall strategy.
SCAG → SCRP	Swiftness in the efficient implementation of SC changes; Adjustability of operational tactics; Expandability in products, regions, or business models; Decisiveness in responding to new opportunities, changes, or threats.
Supply Chain Responsiveness (SCRP)
SCRP → SCAC	Data-driven proactive demand responsiveness; SC infrastructure and management alignment for effective value creation and delivery; Strategic intuition for SC disruption management; Improved responsiveness of logistics operations and processes.
Supply Chain Adaptive Capability (SCAC)
SCAC → BCM	Deployment strategy to adapt to ongoing or unforeseen changes (e., customer demand, regulations, labour rates, or raw material rates); SC modularity for proactive disruption response strategy.