Investment strategies in Industry 4.0 for enhanced supply chain resilience: an empirical analysis

Al-Banna, Adnan; Yaqot, Mohamed; Menezes, Brenno C. Article Investment strategies in Industry 4.0 for enhanced supply chain resilience: an empirical analysis Cogent Business & Management Provided in Cooperation with: Taylor & Francis Group Suggested Citation: Al-Banna, Adnan; Yaqot, Mohamed; Menezes, Brenno C. (2024) : Investment strategies in Industry 4.0 for enhanced supply chain resilience: an empirical analysis, Cogent Business & Management, ISSN 2331-1975, Taylor & Francis, Abingdon, Vol. 11, Iss. 1, pp. 1-38, https://doi.org/10.1080/23311975.2023.2298187 This Version is available at: https://hdl.handle.net/10419/325965 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Cogent Business & Management ISSN: 2331-1975 (Online) Journal homepage: www.tandfonline.com/journals/oabm20 Investment strategies in Industry 4.0 for enhanced supply chain resilience: an empirical analysis Adnan Al-Banna, Mohamed Yaqot & Brenno C. Menezes To cite this article: Adnan Al-Banna, Mohamed Yaqot & Brenno C. Menezes (2024) Investment strategies in Industry 4.0 for enhanced supply chain resilience: an empirical analysis, Cogent Business & Management, 11:1, 2298187, DOI: 10.1080/23311975.2023.2298187 To link to this article: https://doi.org/10.1080/23311975.2023.2298187 © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. View supplementary material Published online: 13 Mar 2024. Submit your article to this journal Article views: 2845 View related articles View Crossmark data Citing articles: 7 View citing articles Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=oabm20 MANAGEMENT | REsEARch ARTiclE Cogent Business & ManageMent 2024, VoL. 11, no. 1, 2298187 Investment strategies in Industry 4.0 for enhanced supply chain resilience: an empirical analysis Adnan Al-Bannaa,b , Mohamed Yaqota and Brenno c. Menezesa aDivision of engineering Management and Decision sciences, College of science and engineering, Hamad Bin Khalifa university, Qatar Foundation, Doha, Qatar; bDepartment of Logistics and supply Chain, Milaha, Doha, Qatar ABSTRACT Modern economies grapple with unprecedented challenges that yielded traditional supply chain resilience (scR) ineffective, creating a race towards digital supply chain resilience (DscR) through adopting industry 4.0 (i4.0) strategies and technologies, with the primary goal to fortifying organizations’ capabilities in promptly and efficiently identifying, mitigating, and rebounding from disruptions. This shift highlights the critical differences between traditional scR and the emerging DscR paradigm. Nevertheless, the literature on DscR, especially pertaining to precise investment strategies, remains notably limited. This research seeks to address this critical gap through an empirical investigation leveraging insights from seasoned supply chain experts in academia and industry. Distinguishing itself, the study meticulously navigates investment decisions, aiming for a striking delicate balance between avoiding over-investment risks jeopardizing profitability and steering clear of under-investment pitfalls exposing vulnerabilities. This research stands as a distinctive contribution to existing literature, offering actionable insights into the nuanced realm of DscR, while highlighting the shifting dynamics between traditional scR and emerging DscR strategies. however, while insights from experienced experts offer valuable perspectives, the study is not immune to empirical challenges. individual industry contexts may introduce variability in strategy applicability. Additionally, the dynamic landscape of technology and business practices implies findings may need periodic reassessment. Despite these limitations, the research’s implications are profound, serving as a roadmap for organizations navigating toward DscR complexities, and for policymakers aiming towards providing efficient regulations and ecosystems that allow for harnessing i4.0 powers in enhancing an organization’s DscR within financial constraints. 1. Introduction The increasing integration of industry 4.0 (i4.0) technologies in industry, logistics, and supply chains has revolutionized the way organizations approach new states of production and transportation activities towards the so-called high-performance supply chain management (scM). Nevertheless, digital transformation (DT) projects are complex, interdisciplinary, and costly; hence, it has become an imperative prerequisite to evaluate the optimum i4.0 investments prior to embarking on such intricate projects (Al-Banna etal., 2022). This paper employs an empirical investigation analysis based on a comprehensive survey that received the necessary recognition and approvals from institutional Review Board (iRB) under the number hBKU-iRB-2024-10, with the objective of evaluating perspectives from supply chain (sc) professionals in industrial (market, business) and academic environments about the impact of a wide range of i4.0 technologies on scR. in addition, the paper validates participants’ inputs through a verification channel of seven layers with the acronym ‘GRAciAs’ (Al-Banna etal., 2023), offering valuable insights for organizations marching towards enhancing their DscR. The findings of this paper emphasize the importance of carefully considering the resilience drivers and the aspects of vulnerabilities towards supply © 2024 the author(s). Published by informa uK Limited, trading as taylor & Francis group. CONTACT adnan abdulla al-Banna [email protected] Division of engineering Management and Decision sciences, College of science and engineering, Hamad Bin Khalifa university, Qatar Foundation, Las building, P.o. Box: 34110, education City, Doha, Qatar. https://doi.org/10.1080/23311975.2023.2298187 this is an open access article distributed under the terms of the Creative Commons attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. the terms on which this article has been published allow the posting of the accepted Manuscript in a repository by the author(s) or with their consent. ARTICLE HISTORY Received 16 August 2023 Revised 14 November 2023 Accepted 12 December 2023 KEYWORDS Digital supply chain resilience; industry 4.0; investment REVIEWING EDITOR Rocio Gallego-losada, Universidad Rey Juan carlos - campus de Móstoles: Universidad Rey Juan carlos, spain SUBJECTS Artificial intelligence; supply chain Management; international Business; Management of Technology & innovation; Finance; Project Management 2 A. Al-BANNA ETAl. chain resilience (scR) when making investment decisions in i4.0 technologies, in addition to other critical factors. The results highlight the crucial role that thorough evaluation of the factors contributing to scR plays when investing in advanced technologies. it is important to recognize that scR is a complex and dynamic concept influenced by various internal and external factors, such as regulatory environment, cybersecurity, scalability, among others. it is necessary to consider these and all relevant factors when making investment decisions. Therefore, the resilience drivers and the aspects of vulnerabilities of scR must be taken into account, along with other critical factors, to ensure that investment decisions are well-informed and aligned with long-term strategic goals. This paper contributes to the existing body of knowledge on scR and i4.0 by offering valuable insights on identifying i4.0 investments for enhanced scR that falls within the sc resilience fitness space (RFs). Today’s rapidly changing and unpredictable global markets can have serious impacts on an organization’s operations, reputation, revenues, and even survival. hence, organizations have been investing in enhancing their scR capability to minimize the risk of disruptions and augment their ability to detect, avoid, and recover efficiently and timely from a disruption. however, answering the questions of where to invest and how much to invest remains a challenge for organizations to conquer. Balancing the investment in enhancing scR capabilities with investment and financial considerations is crucial in establishing the ideal RFs. On one hand, a significant investment in enhancing resilience can help organizations better prepare for and respond to disruptions. This can include as an example investing in risk management systems, contingency plans, and training and development programs for employees. While, on the other hand, excessive investment in resilience measures may not be financially feasible for some organizations since it can divert resources away from other critical areas of the business. Within the same context, a dearth of investment in scR, misallocated investments towards unsuitable technologies, or investments of an insufficient scale in appropriate technologies can all engender heightened levels of vulnerability for an organization, impeding its ability to respond effectively to disruptions, whether natural, man-made or the recent black swan of cOViD-19 pandemic (carrillo et al., 2022). The ideal RFs is one where organizations invest in enhancing their scR capability in a way that is both effective and financially responsible (Pettit etal., 2010). This requires organizations to engage in a comprehensive risk assessment process and prioritize their investments based on the likelihood and impact of potential disruptions. By carefully balancing their investment in resilience with financial and sustainable considerations, organizations can achieve a RFs that provides a solid foundation for risk management and response, while also ensuring their long-term financial viability. in summary, the concept of RFs as applied to scM is an important consideration for organizations’ resilience and efficiency throughout raw material (crude oil, minerals, food, etc.) to product (gasoline, copper, etc.) value chains. capitalizing on the previous fundamentals of sc and in the wake of the rapid pace of technological advancements of the i4.0 age, the conventional investment in scR does not suffice in combating today’s ever-increasing and evolving distributions and risks (calabrese & Vervaeke, 2017). hence, investment in i4.0 technologies is a critical necessity in today’s rapidly changing and interconnected global market. investing in i4.0 technologies can help organizations improve their sc visibility, sc structure, sc information sharing, among other scR drivers, allowing them to quickly identify and respond to disruptions. Furthermore, to gain a comprehensive understanding of the benefits of investing in i4.0 technologies for supply chain resilience, it is essential to consider the perspectives of supply chain experts. While some organizations may be hesitant to invest in new technologies due to uncertainty about their potential benefits and costs, empirical research can provide valuable insights into these issues. By surveying supply chain experts, organizations can gain a better understanding of the specific benefits that i4.0 technologies can provide, and how investment in these technologies can improve their supply chain resilience. Empirical research, including survey investigations, has long been recognized as a valuable methodology for gaining insights into complex phenomena (creswell & creswell, 2017). in the context of supply chain management, an empirical survey investigation can provide a unique opportunity to tap into the collective knowledge of supply chain experts and gain a deeper understanding of i4.0 technologies and investment in DscR. cOGENT BUsiNEss & MANAGEMENT 3 One of the key advantages of empirical survey investigations is their ability to generate quantitative data that can be analyzed using statistical methods to identify patterns and relationships between variables (Wolf et al., 2016). in the case of our research, a survey investigation can provide a wealth of quantitative data on supply chain experts’ perceptions of i4.0 technologies and their impact on DscR. By analyzing this data, we can gain a clearer understanding of the factors that influence investment decisions in digital transformation for enhanced scR and identify opportunities for improving supply chain performance through the adoption of i4.0 technologies. Moreover, a well-designed survey investigation can enable us to draw inferences about the wider population of supply chain experts from which our sample is drawn. This can be achieved by using rigorous sampling techniques to ensure that our sample is representative of the population of interest, and by employing statistical techniques to control bias and confounding factors. By doing so, we can generate results that are generalizable beyond our sample since these results can contribute to the wider body of knowledge on the topic. Another advantage of survey investigations is their ability to capture rich qualitative data through open-ended questions or structured interviews (Fowler, 2013). This allows us to gain a more nuanced understanding of supply chain experts’ perceptions and experiences, and to explore the reasons behind their attitudes and behaviors. in the context of our research, open-ended questions can be used to probe deeper into the factors that influence investment decisions in digital supply chain resilience and the challenges that organizations face when adopting i4.0 technologies. Finally, survey investigations can be a cost-effective way to collect data from a large and diverse group of respondents (Dillman et al., 2014). This is particularly important in the context of our research, where we aim to gather perspectives from a wide range of sc experts with different backgrounds and experiences. By administering the survey online, we can reach a geographically dispersed group of respondents and minimize the costs associated with data collection. This paper endeavors to elicit the perspectives of erudite professionals and scholars in the fields of DscR and i4.0 regarding the interplay between scR and scV drivers, as well as i4.0 technologies in the context of enhancing DscR. One of the salient contributions of this paper to the extant knowledge base in this area is the presentation of a comprehensive and methodical roadmap that provides both academic and business domains with robust guidelines for progressing towards their DscR objectives within the resilience fitness space (RFs). The guidelines emanate from the expertise and erudition of a cohort of supply chain and logistics professionals hailing from aviation, shipping, maritime, and logistics industries, along with distinguished academics and scholars from the field, acquired via a structured survey. in order to ensure the validity and reliability of the survey, a thorough survey design and pilot study was conducted before the main data collection stage took place. The survey was designed based on a thorough review of the literature and input from experts in the field of supply chain resilience and i4.0 technologies. A sample of 30 supply chain professionals participated in the pilot study. The thirty participants where split equally between industry and academy, where 15 participants from each domain globally. The industry participants were split among; airlines, airports, shipping companies, seaports, and land transport. The authors have worked with renowned organizations in these sectors that enabled such a widespread and global reach. Each respondent was met individually, either physically or virtually for about fifteen minutes where detailed discussions took place about the survey background, motivation, design, the target perspectives to be addressed, the similarity among various scR drivers and how to distinguish them for the survey respondents. similarly, the pilot discussed the similarities among the various scV drivers and ways to simplify them for the survey respondents. Next, the i4.0 technologies were discussed with the 30 pilot respondents, as well as the GRAciAs verification channels in detail (Al-Banna et al., 2023). The pilot survey assessment yielded a number of recommendations such as providing definitions for the scR, scV and GRAciAs. Next, another recommendation was to provide detailed definitions of i4.0 technologies, as the respondents believe that different sc experts could have different perceptions about each i4.0 technologies. in addition to the aforementioned path the construction of the proposed survey (see in Appendix), this critical phase of the survey design identified and addressed some areas of improvements with respect to issues with survey wording, structure, and response gathering options. For the purpose of this 4 A. Al-BANNA ETAl. paper, these cognitive interviews pilot survey approach were deemed the most appropriate methodology for evaluating survey question quality and identifying areas for improvement, given the valuable insights obtained by the sc experts. in section 2, a literature review is presented followed by a discussion of the i4.0 technologies considered for the empirical investigation analysis. in section 3, the empirical investigation methodology is addressed. in section 4, the empirical investigations, findings, and discussions are presented. in section 5, the research implications and managerial perspectives are discussed. Finally, section 6 presents the paper conclusions. The designed survey is found as Appendix. 2. Literature review and overview of the industry 4.0 technologies considered for the empirical investigation analysis 2.1. Literature review Prior to delving into the key i4.0 technologies, the paper presents a summary of key papers that addresses and provides valuable contribution to the knowledge base of this topic. For example, Xu et al. (2021) provide several key takeaways and recommendations regarding the implementation and perception of i4.0 and industry 5.0 (i5.0) paradigms. Firstly, it highlights that i4.0 represents a transformative concept integrating advanced technologies such as the internet of Things (ioT), cyber-physical systems (cPs), and Big Data Analytics (BDA), offering potential benefits such as increased productivity, improved quality, and enhanced customization capabilities. secondly, the paper emphasizes that i5.0 builds upon i4.0 by recognizing the importance of human-machine collaboration and the integration of human skills with advanced technologies. it underscores the role of Ai in facilitating seamless interaction between humans and machines. The paper further emphasizes the need to address challenges and barriers to successfully implement i4.0 and i5.0. it highlights concerns related to data security and privacy, which need to be adequately addressed. Furthermore, the importance of investing in workforce upskilling and training is stressed to ensure a smooth transition to these new paradigms. Furthermore, the perception and adoption of i4.0 and i5.0 are acknowledged to vary across industries and countries. consequently, the paper recommends that organizations consider industry-specific needs, market conditions, and technological readiness when formulating implementation strategies. collaboration and knowledge sharing among researchers, practitioners, and decision-makers are deemed crucial to drive the understanding and adoption of these paradigms. Platforms facilitating the exchange of best practices, lessons learned, and case studies are suggested to facilitate successful implementation. lastly, the paper advises organizations to continuously monitor technological advancements and market trends to stay abreast of developments and adapt strategies accordingly. Regular assessment and evaluation of i4.0 and i5.0 initiatives are encouraged to identify areas for improvement and refine implementation approaches. On the other hand, singh et al. (2023) offer valuable insights and recommendations regarding the use of Digital Twin technology to improve resilience and sustainability in food supply chains. The study employs a grey causal modelling (GcM) approach to analyze the relationships and causalities within the supply chain network. The key insights from this research include the identification of critical factors that impact the resilience and sustainability of food supply chains. The authors highlight the importance of understanding the complex interactions between these factors and propose the use of Digital Twin technology as a valuable tool for modeling and simulating supply chain operations. The study demonstrates that Digital Twin technology can provide a dynamic and real-time representation of the supply chain, enabling stakeholders to anticipate and respond to disruptions more effectively. Furthermore, the paper emphasizes the significance of resilience and sustainability in the context of food supply chains. it highlights the need for proactive strategies to mitigate risks and enhance the overall performance of the supply chain network. The authors suggest that Digital Twin technology can support decision-making processes by providing insights into potential bottlenecks, vulnerabilities, and opportunities for improvement. Based on their findings, the paper provides several recommendations for practitioners and policymakers. Firstly, organizations in the food industry should consider adopting Digital Twin technology as a means to enhance resilience and sustainability in their supply chains. cOGENT BUsiNEss & MANAGEMENT 5 This technology can facilitate real-time monitoring, predictive analytics, and scenario-based simulations, enabling proactive decision-making and rapid response to disruptions. secondly, collaboration among stakeholders is crucial for effective implementation of Digital Twin technology. This includes sharing data and knowledge, aligning goals and objectives, and establishing partnerships to address common challenges in the food supply chain. collaboration can help build a more resilient and sustainable ecosystem by fostering information exchange and coordinated decision-making. lastly, policymakers are urged to create an enabling environment for the adoption of Digital Twin technology in the food industry. This includes developing supportive regulatory frameworks, incentivizing investments in digitalization, and promoting research and development activities in the field. Policymakers should also consider the ethical and privacy implications associated with the use of digital technologies in supply chain operations. Furthermore, Ghobakhloo (2020) present key insights, value adds, and recommendations regarding the intersection of i4.0, digitization, and sustainability. The study explores the potential of i4.0 technologies to contribute to sustainable development and addresses the challenges and opportunities associated with their adoption. One of the key insights of the paper is the recognition that i4.0 technologies, such as the ioT, BDA, and Ai have the potential to enable significant sustainability improvements across various industries. These technologies offer opportunities for optimizing resource utilization, enhancing energy efficiency, reducing waste, and improving environmental performance. By leveraging digitalization, organizations can achieve more sustainable and environmentally friendly operations. The paper also highlights the value adds of adopting i4.0 technologies in terms of sustainability. it emphasizes that the integration of digital technologies with sustainable practices can lead to enhanced operational efficiency, increased competitiveness, and improved environmental and social outcomes. For example, real-time monitoring and data analytics can enable better decision-making and resource management, leading to reduced energy consumption and greenhouse gas emissions. The automation and connectivity enabled by i4.0 technologies can also contribute to safer working environments and improved labor conditions. Based on their analysis, the authors provide several recommendations for practitioners and policymakers. Firstly, organizations are encouraged to embrace a holistic approach to digital transformation, integrating sustainability considerations into their digitalization strategies. This includes adopting energy-efficient technologies, implementing circular economy principles, and prioritizing sustainable supply chain practices. By aligning digitalization efforts with sustainability goals, organizations can maximize the positive impact of i4.0 technologies. secondly, collaboration and knowledge sharing among stakeholders are emphasized as critical enablers for sustainable i4.0 implementation. Partnerships between industry, academia, government, and civil society can foster innovation, facilitate technology transfer, and address challenges related to skills development and workforce transition. The paper suggests the creation of collaborative platforms, networks, and policy frameworks to support the exchange of best practices and promote collective action towards sustainability. lastly, policymakers are urged to develop supportive regulations and incentives to encourage the adoption of sustainable i4.0 practices. This includes the establishment of standards for energy efficiency, waste reduction, and environmental performance. Policy interventions should also promote the development and deployment of sustainable technologies, provide financial support for sustainable initiatives, and encourage sustainable practices through regulatory frameworks. in the same connection, Rajesh (2023) presents valuable inputs and recommendations with regards to the prediction of environmental sustainability performances of firms using a trigonometric grey prediction model. The study aims to develop a forecasting model that can assist in predicting the future environmental sustainability performances of firms, thereby supporting decision-making and facilitating sustainability planning. One of the key insights of the paper is the application of a trigonometric grey prediction model as a tool for predicting environmental sustainability performances. The authors highlight the importance of accurate prediction models in assessing and monitoring the environmental impact of firms. By utilizing the trigonometric grey prediction model, the study demonstrates the potential to forecast environmental sustainability performances and anticipate future trends, enabling proactive measures and informed decision-making. The value adds of this research lie in its contribution to the field of environmental sustainability performance prediction. The paper introduces a novel approach that combines trigonometric functions and grey forecasting techniques to improve the accuracy of predictions. The trigonometric grey prediction model offers a flexible and reliable framework for capturing 6 A. Al-BANNA ETAl. the complex dynamics and interrelationships involved in environmental sustainability performances. The study also provides empirical evidence of the effectiveness of the proposed model through the analysis of real-world data. Based on their findings, the authors provide several recommendations for practitioners and policymakers. Firstly, firms are encouraged to adopt proactive measures in assessing and improving their environmental sustainability performance. The trigonometric grey prediction model can serve as a valuable tool for firms to monitor their progress, identify areas for improvement, and set realistic targets for sustainable practices. By leveraging predictive modeling, firms can anticipate future challenges and develop appropriate strategies to mitigate environmental impacts. secondly, policymakers are urged to incorporate predictive modeling techniques, such as the trigonometric grey prediction model, into their policy-making processes. Accurate predictions of environmental sustainability performances can inform the development of effective regulations, incentives, and support mechanisms that encourage firms to adopt sustainable practices. Policymakers should also focus on fostering collaboration and knowledge sharing among firms to facilitate the implementation of sustainability initiatives and promote industry-wide environmental improvements. lastly, the paper emphasizes the importance of further research and development in the field of environmental sustainability performance prediction. The authors suggest exploring advanced modeling techniques and integrating additional factors, such as social and economic indicators, into the predictive models. continual refinement and validation of prediction models will enhance their reliability and applicability, enabling more accurate assessments of firms’ environmental sustainability performances. lastly, sony and Naik (2020) provide key insights, value adds, and recommendations regarding the evaluation of i4.0 readiness for organizations. The study aims to identify the essential components that contribute to an organization’s preparedness for i4.0 implementation through a comprehensive review of the existing literature. One of the key insights of the paper is the identification of key ingredients or factors that determine an organization’s readiness for i4.0. The authors emphasize that i4.0 readiness is not solely dependent on technological capabilities but also encompasses organizational, human, and strategic aspects. The study identifies a range of factors such as leadership commitment, organizational culture, digital infrastructure, data analytics capabilities, talent development, and strategic alignment that play a crucial role in determining an organization’s readiness for i4.0. The value adds of this research lie in its contribution to the understanding of i4.0 readiness evaluation. By synthesizing and analyzing the literature, the study provides a comprehensive framework that encompasses the multiple dimensions of readiness. This framework enables organizations to assess their strengths and weaknesses across various readiness factors, facilitating informed decision-making and targeted interventions to enhance their readiness for i4.0 adoption. Based on their analysis, the authors provide several recommendations for practitioners and researchers. Firstly, organizations are encouraged to conduct a thorough assessment of their readiness for i4.0 using a holistic approach. This involves evaluating not only technological aspects but also organizational and strategic factors. Organizations should leverage assessment tools and frameworks developed based on the identified key ingredients to gain insights into their current state and prioritize areas for improvement. secondly, the paper highlights the importance of leadership commitment and organizational culture in fostering i4.0 readiness. leaders and executive management should demonstrate a clear vision, commitment, and support for the digital transformation journey. They should also foster an innovative and agile culture that embraces change, experimentation, and continuous learning. Developing a shared understanding of the benefits and implications of i4.0 within the organization is crucial for successful implementation. lastly, the study emphasizes the need for ongoing research and collaboration in the field of i4.0 readiness evaluation. Further empirical studies and case analyses are recommended to validate and refine the identified key ingredients and their impact on organizational readiness. Researchers are encouraged to explore the interdependencies and interactions among different readiness factors and develop practical tools and methodologies for assessing i4.0 readiness. The intricate and nuanced nature of these interdependencies, often delicately poised, seems to have been overlooked by scholarly scrutiny. Addressing and disentangling such complexities becomes notably challenging due to their elusive and frequently misconstrued character. This intricate landscape is further compounded by a prevailing dearth of comprehensive knowledge within both academic and business domains, a circumstance elucidated by Al-Banna et al. (2023). The multifaceted interactions and dependencies between various elements necessitate a thorough exploration and understanding, wherein the cOGENT BUsiNEss & MANAGEMENT 7 dynamics of these relationships are intricately interwoven with both scholarly and practical dimensions. As we navigate this intricate terrain, it becomes evident that the limited comprehension of these interdependencies in academic and business contexts adds layers of intricacy to an already intricate web of relationships. The resulting confluence of factors underscores the critical need for a more nuanced and expansive comprehension of these intricacies to inform both scholarly inquiry and practical endeavors. This calls for an intensified focus on interdisciplinary collaboration and rigorous investigation to unravel the subtleties inherent in the interplay of factors, fostering a more comprehensive understanding that transcends conventional boundaries. in summary, the explicated literature review proffers profound insights and discerning guidance for executives, practitioners, and researchers endeavoring to assess and augment their organizational preparedness for the adoption of i4.0. This comprehensive examination not only facilitates the strategic positioning of organizations but also imparts the requisite proficiency to adeptly navigate the intricate landscape rife with challenges and opportunities emblematic of the digital transformation era. By conscientiously adhering to the delineated recommendations, organizations can adroitly negotiate the intricate nuances associated with both i4.0 and i5.0, thereby harnessing the transformative potential inherent in these paradigms to achieve elevated levels of operational performance and competitive prowess. concomitantly, the literature unearths a discernible lacuna in the existing knowledge repository. This research, therefore, seeks to fill this void by undertaking a meticulous investigative approach, delving into the intricate terrain of investment decisions within the contextual ambit of i4.0. The literature notably lacks research that strikes a judicious equilibrium between the benefits, costs, and challenges intrinsic to i4.0 adoption. consequently, this research assumes the onus of redressing this consequential gap, proffering recommendations imbued with value that can guide organizations in realizing their digital transformation objectives. This endeavor is particularly germane in mitigating the inherent risks associated with over-investing, which poses a potential compromise to organizational profitability, while concurrently averting the pitfalls of under-investment that might expose organizations to heightened vulnerabilities. As a corollary, the research significantly contributes to the scholarly discourse by furnishing indispensable guidance for policymakers and decision-makers. it provides them with the necessary tools to cultivate resilient strategies, bespoke for the exigencies of i4.0, thereby deftly addressing the multifaceted challenges arising from the intersection of digital transformation and financial constraints. in essence, this scholarly endeavor stands as a distinctive and pragmatic contribution to the extant literature, offering nuanced insights into the realm of digital supply chain resilience within the expansive context of industry 4.0 and its evolutionary trajectory. 2.2. Industry 4.0 technologies considered for the empirical study industry 4.0 (i4.0), also referred to as the Fourth industrial Revolution, represents the integration of advanced digital technologies into traditional industrial processes. i4.0 is characterized by the widespread adoption of technologies such as artificial intelligence (Ai), the internet of things (ioT), additive manufacturing (AM), cloud computing (cc), blockchain (Bc), big data analytics (BDA), among others. These technologies are changing the way businesses interact with their customers, employees, and suppliers, leading to new opportunities and challenges (hsu etal., 2022). in this paper, we will examine the details, potential applications, advantages, risks, and recommendations for each of these i4.0 technologies. 2.2.1. Artificial intelligence Artificial intelligence (Ai) is the development of computer systems that can perform tasks that typically require human intelligence, such as visual perception, speech recognition, decision-making, and language translation. Ai has a wide range of potential applications in various industries, including healthcare, finance, manufacturing, and retail. in healthcare, this can be used to diagnose diseases and analyze patient data to provide personalized treatment plans. in finance, Ai can be used to identify fraud and predict market trends. in manufacturing, Ai can optimize production processes, reduce waste, and improve quality control. in retail, it can provide personalized shopping experiences, improve scM 14 A. Al-BANNA ETAl. posed by the lack of a clear understanding of digital transformation, transforming this ostensibly promising venture into a profound dilemma and a substantial risk for the misallocation of time and resources. This challenge is further accentuated by Gartner’s forecast (Gartner, 2018), which warns that a staggering 85% of artificial intelligence projects in 2022 may face failure due to various reasons. These encompass data inconsistencies, inappropriate algorithms, inefficiencies in human capital utilization, and critical factors such as a lack of alignment among leadership teams, micromanagement versus mismanagement, limited control over vendors, inadequate training and competencies, and insufficient understanding of technologies and their capabilities, which amplify the risks inherent in digital transformation initiatives. The paramount significance of this topic for decision-makers is underscored by multiple factors. Firstly, the intricate and costly nature of digital transformation demands meticulous consideration. secondly, the anticipated surge in global investment in industry 4.0 further elevates the urgency of understanding and navigating the complexities of digital transformation. To contextualize this urgency, projections by sava (2022) estimate that global spending on digital transformation in 2026 is anticipated to surpass a Us$ 3.4 trillion. The convergence of these factors accentuates the imperative for policy and decision-makers to approach digital transformation with a nuanced understanding, strategic foresight, and a comprehensive grasp of the potential pitfalls to ensure the realization of its promises and the mitigation of inherent risks. 3.2. The empirical investigation methodology The research aims to source the perceptions of industrial (market, business) and academic experts in the fields of DscR and i4.0 with respect to the interconnectedness between supply chain resilience (scR) and supply chain vulnerabilities (scV) drivers, as well as i4.0 technologies within the domain of enhancing DscR. One of this paper addition to the base of knowledge in this field is that it presents the views and perceptions of both business and academic domains with robust guidelines that represent a structured and methodological roadmap for organizations marching toward achieving their DscR that falls within the resilience fitness space (RFs). The guideline is built upon the intelligence and expertise of tens of the logistics and sc professionals from aviation, shipping, maritime and logistics industries, in addition to distinguished academics and scholars from the field through a structured survey. 3.2.1. Pilot survey and methodology assessment To ensure the validity and reliability of the survey and respondents, a thorough survey design and pilot study were conducted prior to the main data collection. The survey was designed based on a comprehensive review of the literature and input from experts in the field of supply chain resilience and industry 4.0 technologies. The pilot study involved a sample of 30 supply chain professionals who were asked to complete the survey and provide feedback on the clarity and relevance of the questions. The authors have had extensive work experience in a range of industries that included oil and gas, aviation, shipping, logistics and warehousing, among others. This breadth of experience has empowered the authors to engage with numerous seasoned executives in the business sector. Additionally, throughout the research duration, the authors have established direct connections with esteemed academic experts. This unique combination of practical industry knowledge and academic insights enriches the depth and perspective of the pilot survey and the overall research. survey pilot results assessment could be conducted through a number of methodologies, such as descriptive statistics Rauch et al. (2009), reliability analysis Alshawi et al. (2017), and validity analysis Mohr and Webb (2005). in the context of this paper, cognitive interviews were identified as the ideal methodology to evaluate the quality of survey questions and identify areas for improvement, as it could provide valuable insights into the comprehension and relevance of the survey questions among supply chain experts. Given the complexity and technical nature of the topic of digital supply chain resilience and investment in industry 4.0 technologies, cognitive interviews are especially relevant for assessing the pilot results responses. supply chain academic and professional experts possess unique insights and perspectives that can help improve the survey instrument, including refining the wording and structure of the questions, identifying cOGENT BUsiNEss & MANAGEMENT 15 potential areas of confusion or misinterpretation, and providing feedback on the overall relevance of the survey topics. Furthermore, cognitive interviews can also help identify potential gaps in knowledge or understanding that may exist among the supply chain experts. This can inform future research directions and highlight the need for targeted education and training initiatives to enhance supply chain experts’ understanding of digital supply chain resilience and industry 4.0 technologies. The cognitive interviews methodologies have been widely used in this domain, for example schmidt and Rossmann (2019) used the cognitive interview methodology to evaluate the understanding of supply chain executives about digital transformation and its impact on supply chains. The researchers used the results to identify common misconceptions and areas of confusion that needed to be addressed in future research, such as the need for more education and training on industry 4.0 concepts and technologies. similarly, indorf and hinz (2018) used the cognitive interview methodology to evaluate the usability of digital supply chain management systems. The authors used the results to identify potential issues and improvements for the systems, such as the need for better visualization tools and improved data quality. Furthermore, Thomése etal. (2016) used the same methodology to evaluate the usability of digital supply chain management systems. The researchers used the results to identify potential issues and improvements for the systems, such as the need for better visualization tools and improved data quality. The same is summarized in Table 1. These examples demonstrate the versatility and effectiveness of the cognitive interview methodology in evaluating the understanding and perceptions of respondents about complex concepts and technologies related to digital supply chain resilience, investment, and industry 4.0. hence, this paper adopted the cognitive interview methodology to assess the initial pilot responses and results of thirty sc experts from academic and business domains. This paper researchers were able to identify potential issues and areas of improvement, as well as ensure that the research questions are accurately understood by respondents. The cognitive interviews have proven to be valuable methodology for assessing the pilot results responses and enable the research to source deeper knowledge and insights from the supply chain academic and professional experts. This provides the enhanced survey updated questions with higher levels of clarity, comprehension, and relevance of the survey questions, as well as identify potential areas for improvement and future research directions. 3.2.2. Survey structure and its three distinct parts Post conducting 30 comprehensive and thorough cognitive interviews with sc experts from the academic and business domains, the enhanced survey is built with three distinct parts, as illustrated in Appendix. Table 1. survey pilot results assessment methodologies. Descriptive statistics it involves analyzing the basic statistical properties of the survey results, such as means, standard deviations, and frequencies, to gain an overall understanding of the data’s distribution and variation. it was utilized by Rauch et al. (2009) examining entrepreneurial orientation and business performance. Reliability analysis and internal consistency it examines the consistency and stability of the survey instrument’s measurements by calculating Cronbach’s alpha, a measure of internal consistency. this analysis helps to identify items that may need to be revised or removed to improve the survey’s reliability. it was utilized by alshawi et al. (2017) to investigate digital transformation in organizations Validity analysis it examines the extent to which the survey instrument measures what it intends to measure by assessing the survey’s content validity, construct validity, and criterion-related validity. it was used by Mohr and Webb (2005) while studying the impact of corporate social responsibility on customer loyalty. Factor analysis it involves grouping similar survey items into distinct factors or dimensions, which can help to identify underlying patterns and relationships within the data. it was utilized by Kim and Le (2021) while examining the job satisfaction and employee turnover. Cognitive interviews it involves conducting individual interviews with pilot survey respondents to assess their comprehension of the survey questions, their interpretation of the response options, and their thought processes when answering the questions. it was utilized by Hamari et al. (2014) while examining the impact of gamification on learning outcomes, the authors used cognitive interviews to assess the pilot survey respondents’ understanding of the survey questions and their interpretation of the gamification elements used in the study. it was also used by schmidt and Rossmann (2019) to evaluate the understanding of supply chain executives about digital transformation and its impact on supply chains. it was also utilized by indorf and Hinz (2018) evaluate the usability of digital supply chain management systems, and by thomése et al. (2016) evaluate the usability of digital supply chain management systems. 16 A. Al-BANNA ETAl. The first part of the survey focused on the criteria of inclusions and exclusions, where the target participants were shortlisted to be sc experts who either have knowledge about DscR or i4.0, who have at least a bachelor’s degree, and have at least six years of experience, whom their organizations consider DscR or i4.0 as strategic priorities to at least a minor extent. Respondents who do not fall within the aforementioned categories are ignored. The second part of the survey requests the participants to identify the i4.0 technologies recommended to be invested in, and adopt to interconnect particular scVs with the optimum scR. The survey capitalizes on the research of Pettit et al. (2010) on the RFs and Zhang et al. (2021) on striking a balance between scR and scV in the cross-border e-commerce sc. The outcome of this part of the survey is a table with seven rows and four columns, where a) the rows are the scR drivers, namely; sc agility, sc structure, sc visibility, information sharing, Risk and revenue sharing, sc geographical distribution, and collaboration with sc partners. While b) the columns are scV drivers, namely: supply side risk, Operation process risk, Demand side risk, and Environmental risk. This paper does not stop at obtaining the experts insights. however, the third part of the survey provides applies the previously introduced 7-layers verification channels, which carry the acronym GRAciAs developed by Al-Banna et al. (2023), which encompasses the following: • G: Golden Triangle; refers to having the right people, the right Process, and the right technologies to guarantee investment and implementation success. • R: Regulatory Environment: refers to the organization’s operating environment, governing laws, regulations, and tax structure, among others • A: Age of the asset: refers to the point at which investment is considered with respect to the overall asset life. • c: cybersecurity: refers to the security of data creation, sharing, and storing in digital cyberspace, in relation to isO 27001. • i: investment: refers to the expected return on investment, payback period, and other financial aspects of the considered technology. • A: Agnosticism: refers to the solution’s ability to integrate, interact, exchange data and information and operate seamlessly with the organization enterprise resources planning (ERP) system. • s: scalability: refers to the importance of building future expansion capability in the soon-to-be-acquired digital technologies and/or eco-system. The paper emphasizes that making digital transformation decisions without taking into consideration these verification channels, risks an organization of directly falling into profits erosions or getting exposed to increased risks. 4. Empirical investigations findings and discussions The survey attracted sc experts professionals from academic and industrial domains, in total 174 respondents answered the survey, virtually through the surveyMonkey portal, and physically through face-to-face meetings, and remotely through phone calls and e-meetings conducted via Ms Teams, Webex and Zoom platforms. Majority of the survey respondents belong to the category that consists of sc professionals who are also sc educated, which are 83 respondents. This high number provides a higher level of confidence in the survey outcome, as it represents professionals who are sc educated, and are working in organizations and occupations that are engaged in sc, in aviation, shipping, logistics, maritime, among others. The second biggest category of respondents belongs to the sc professionals, who are respondents working in sc occupations and organizations but are not sc educated, at 46 respondents. The third category of respondents are sc academics who are not working in sc occupations nor organizations, at 33 respondents. The last category of respondents does not belong to any of the aforementioned categories; hence they are excluded, at 12 respondents. The survey respondents are shortlisted to 162 respondents. As illustrated in Figure 1. All the 162 respondents have at least a Bachelor’s degree, have at least 6 years of experience and are working in organizations that consider DscR and i4.0 as strategic priorities, at least at minor levels. The cOGENT BUsiNEss & MANAGEMENT 17 respondents’ geographical distribution is illustrated in Figure 2. Most of the respondents are from the Gulf cooperation council, at 34 respondents, which comprises Kuwait, saudi Arabia, Qatar, Bahrain, United Arab Emirates and Oman. This concentration could be attributed to the fact that the authors belong to organizations that reside in this geographical location. The subsequent three major geographical categories are North America, Western Europe, southeast Asia, at 22, 16, and 15 respectively. 4.1. SCR and SCV drivers’ intersections in today’s fast-paced and volatile business environment, the ability of scs to respond to disruptions and changes aptly and effectively is critical for maintaining a competitive advantage. The conducted survey of sc professionals from the academic and industrial fields aims to better understand how i4.0 technologies can be deployed with the objective to address a pre-identified scV aspect by augmenting a particular scR driver. Using the results of this survey, seven stacked-column graphs are created that plot the relative sc academic and industrial professionals’ preferences of the seven i4.0 technologies against each of the four sc vulnerabilities for each of the seven sc domains. Our findings indicate that the use of i4.0 technologies can significantly enhance scR by mitigating the various risks associated with each sc vulnerability. stacked column graphs are selected to plot the survey respondents’ data for the scR drivers, scV drivers, and i4.0 technologies for several reasons: 83 46 33 12 SC Prof. + Acad.SC Prof.SC Acad.None Figure 1. survey respondents’ category. Figure 2. survey respondents’ geographical distribution. 18 A. Al-BANNA ETAl. a. comparison: effective in visually comparing the relative importance of different categories or subcategories. in the case of the research on scR and vulnerabilities, the stacked column graphs can clearly show which drivers are considered more important by the survey respondents and how the subcategories are weighted. b. clarity: easy to read and interpret, as the bars are visually distinct from each other and are clearly labeled with the corresponding subcategory. c. Efficiency: compact way to represent data for multiple categories and subcategories in a single graph, making them efficient in terms of space and time. Overall, stacked column graphs are a versatile and effective tool for communicating research data, particularly when comparing multiple categories and subcategories. They can clearly and efficiently communicate complex information to a broad audience. in the domain of sc agility, majority of the respondents perceived Ai as the optimum i4.0 technology to consider for investment. Followed by additive manufacturing, and internet of things (ioT). This result confirms the strong and intricate relationship between sc agility and emerging technologies such as artificial intelligence, additive manufacturing, and ioT. These technologies enable scs to be more flexible, efficient, and responsive to changes in demand, market conditions, and disruptions. Artificial intelligence enables sc managers to predict demand and optimize inventory levels, while additive manufacturing allows for faster and more cost-effective production of customized products. ioT provides real-time visibility into the movement of goods and the condition of assets, allowing for greater control and optimization of sc operations. Together, these technologies can transform scs into agile and resilient systems that can adapt to changing circumstances and deliver greater value to customers. As illustrated in Figure 3. in the domain of sc structure, majority of the respondents perceived cloud computing (cc) as the optimum i4.0 technology to consider for investment. This is followed by Big Data Analytics (BDA) and Artificial intelligence (Ai). This result emphasis on the fundamental importance of sc structure to the implementation and success of i4.0 technologies. i4.0 technologies require an interconnected and integrated sc structure that can support the collection, analysis, and utilization of data. A flexible, responsive, and agile sc structure is essential to meet the demands of i4.0 technologies, allowing for seamless communication and collaboration between different components and systems. By integrating i4.0 technologies, sc managers can optimize their operations, improve efficiency, reduce costs, and enhance customer satisfaction. however, the implementation of i4.0 technologies requires significant changes in the sc structure, including the adoption of new processes, skills, and organizational structures. Therefore, a strong relationship exists between the structure of a sc and the successful implementation of i4.0 technologies. As illustrated in Figure 4. 57 45 53 39 44 41 39 42 45 44 42 37 42 42 39 37 41 39 42 44 42 24 34 24 42 39 36 39 0 20 40 60 80 100 120 140 160 AI IoTAMCCBCBDA CPS Supply Side risk Operation Process Risk Demand Side Risk Env. Risk Figure 3. Recommended i4.0 technologies for sC agility. cOGENT BUsiNEss & MANAGEMENT 19 in the domain of sc visibility, majority of the respondents perceived ioT as the optimum i4.0 technology to consider for investment. iT is followed by cc and BDA. sc visibility and i4.0 technologies are intrinsically linked. i4.0 technologies such as ioT, Ai, and BDA provide the tools to collect, process, and analyze vast amounts of data in real-time. By integrating these technologies into a sc, managers can gain greater visibility into their operations and sc partners, allowing them to make informed decisions based on real-time information. The improved visibility provided by i4.0 technologies can help sc managers identify bottlenecks, track inventory, monitor product quality, and ensure timely delivery of goods. This enhanced visibility can lead to improved efficiency, reduced lead times, and better customer service. however, the successful implementation of i4.0 technologies requires collaboration between sc partners, and the sharing of data across the sc. Therefore, a strong relationship exists between sc visibility and i4.0 technologies, with visibility acting as a critical enabler for the successful implementation of these technologies in scM. As illustrated in Figure 5. in the domain of sc information sharing, majority of the respondents perceived internet of Things as the optimum i4.0 technology to consider for investment. This is followed by cc and BDA. The respondents highlight the strong interconnectedness between sc information sharing and i4.0 technologies. 49 44 45 57 42 53 42 39 44 37 36 41 42 41 44 39 41 42 39 39 37 31 36 39 28 41 28 42 0 20 40 60 80 100 120 140 160 AI IoTAMCCBCBDA CPS Supply Side risk Operation Process Risk Demand Side Risk Env. Risk Figure 4. Recommended i4.0 technologies for sC structure. 53 62 45 57 45 49 47 42 63 41 53 44 29 44 39 19 41 29 41 45 47 28 18 36 23 32 39 24 0 20 40 60 80 100 120 140 160 AI IoT AM CC BC BDACPS Supply Side risk Operation Process Risk Demand Side Risk Env. Risk Figure 5. Recommended i4.0 technologies for sC visibility. 20 A. Al-BANNA ETAl. i4.0 technologies, such as ioT, BDA, and Ai require access to large amounts of data from multiple sources to function effectively. sharing information across the sc can provide a comprehensive view of operations, enabling stakeholders to make informed decisions based on real-time information. By leveraging i4.0 technologies to share information across the sc, managers can optimize inventory levels, reduce lead times, and improve customer service. The sharing of information can also enhance collaboration and coordination between sc partners, leading to improved efficiency and reduced costs. however, the successful sharing of information requires trust, transparency, and security, which can be achieved through the implementation of appropriate data privacy and security measures. Therefore, the importance of sc information sharing is significant, and it is closely related to the successful implementation of i4.0 technologies in scM. As illustrated in Figure 6. in the domain of sc Risk & Revenue sharing, majority of the respondents perceived ioT as the optimum i4.0 technology to consider for investment. This is followed by Ai and Blockchain (Bc). sc Risk & Revenue sharing are critical components of a successful business strategy. Given that sc is a complex network of suppliers, manufacturers, distributors, and customers that must work together seamlessly to ensure the timely delivery of products and services. Managing sc risk is crucial to avoid disruptions that can result in delays, increased costs, and lost revenue. Revenue sharing is equally important as it allows all parties in the sc to share in the financial benefits of a successful collaboration. i4.0 technologies have the potential to transform sc risk management and revenue sharing. The respondents considered ioT as the most optimum i4.0 technology for this scR driver because ioT sensors and devices can be used to track and monitor sc processes in real-time, providing valuable data that can be used to identify and mitigate risks. This technology can help organizations monitor the movement of goods, track inventory levels, and ensure that products are delivered on time. in addition, Ai, can be used to analyze vast amounts of data and identify patterns and trends that may not be immediately apparent to humans. This technology can help organizations identify potential sc risks and take proactive measures to mitigate them. Additionally, Ai can be used to optimize revenue sharing by analyzing sales data and identifying opportunities to improve collaboration among sc partners. in the same connection, Bc technology can be used to create a secure and transparent sc network. Each transaction can be recorded on the blockchain, providing a tamper-proof record of all sc activities. This technology can help organizations reduce the risk of fraud and increase transparency, which can improve collaboration and revenue sharing among sc partners. As illustrated in Figure 7. in the domain of sc geographical distribution, majority of the respondents perceived ioT as the optimum i4.0 technology to consider for investment. it is followed by cc and Bc. The respondents feedback emphasis on the increasing necessity for scs coordination with suppliers and distributors located around 55 57 39 52 36 45 41 42 42 44 55 44 52 39 39 44 42 29 39 36 42 26 19 37 26 44 29 41 0 20 40 60 80 100 120 140 160 AI IoT AM CC BC BDA CPS Supply Side risk Operation Process RiskDemand Side Risk Env. Risk Figure 6. Recommended i4.0 technologies for sC information sharing. cOGENT BUsiNEss & MANAGEMENT 21 the world to ensure timely and cost-effective delivery of products and services. Managing a geographically dispersed sc can be challenging and requires careful planning, coordination, and execution to ensure products are delivered on time and at the right cost. i4.0 technologies have the potential to transform the way organizations manage their geographically dispersed scs. The three perceived optimum i4.0 technologies to enhance sc geographical distribution are ioT as it provides real-time tracking and monitoring of products and inventory. ioT sensors can be attached to products, containers, and vehicles to track their location, temperature, humidity, and other conditions in real-time. This allows businesses to monitor the movement of goods, identify any delays, and take corrective action to ensure that products are delivered on time and in the right condition. By collecting and analyzing data from sensors and other sources, businesses can gain valuable insights into sc processes and identify areas for optimization. in addition, ioT can help businesses to reduce costs and improve efficiency by optimizing sc operations. By monitoring the performance of vehicles and other assets in real-time, businesses can identify opportunities to reduce fuel consumption, improve route planning, and reduce maintenance costs. in addition, cloud computing can be used to provide real-time data and analytics to businesses operating in geographically dispersed scs. cloud-based platforms can be used to share data and collaborate with sc partners, providing visibility into sc operations and allowing businesses to quickly identify and respond to issues. Next, blockchain technology can be used to create a secure and transparent sc network. The fact that each transaction is recorded on the blockchain provides a tamper-proof log of all relevant value chain activities. This technology can help businesses reduce the risk of fraud and increase transparency. As illustrated in Figure 8. in the domain of collaboration with sc partners, majority of the respondents perceived cc as the optimum i4.0 technology to consider for investment. This is followed by Bc and BDA. collaboration with sc partners is a critical aspect of scM. The degree of collaboration and cooperation among sc partners, including suppliers, manufacturers, distributors, and retailers, can significantly impact the overall efficiency and effectiveness of the scR. collaboration can help organizations to reduce costs, improve quality, and enhance the customer experience. The respondents perceived cloud computing as the optimum i4.0 technology as it enables data sharing and active collaboration with sc partners in real-time. cloud-based platforms provide a secure and accessible way to store and share data, allowing organizations to exchange information quickly and easily with their partners. in addition, next, blockchain technology can be used to create a secure and transparent sc network, where all transactions are recorded on the blockchain, providing a tamper-proof log of all sc activities, which provides strong immunity against data and records fraud, which in turns provides increased transparency, and collaboration among sc partners. Next, big data analytics is considered a critical technology as it involves the use of advanced analytics tools to analyze large datasets and extract valuable insights. in the context of sc partners collaborations, big data analytics can 55 60 39 42 53 36 42 42 37 42 39 44 45 44 39 45 44 37 42 39 41 26 19 37 44 23 42 36 0 20 40 60 80 100 120 140 160 AI IoT AM CC BC BDA CPS Supply Side risk Operation Process RiskDemand Side Risk Env. Risk Figure 7. Recommended i4.0 technologies for sC risk & revenue sharing. 22 A. Al-BANNA ETAl. be used to collect and analyze data from various sources, including sensors, ioT devices, social media, and transactional data. By analyzing this data, organizations can gain insights into sc processes and identify areas for improvement. Big data analytics can help to enhance collaboration with sc partners by providing real-time insights into sc operations. For example, organizations can use big data analytics to monitor the performance of suppliers and identify any bottlenecks or delays in the sc. This information can be shared with sc partners to improve coordination and collaboration. in addition, big data analytics can be used to forecast demand and optimize inventory levels. By analyzing historical sales data and other relevant information, organizations can predict future demand and adjust inventory levels accordingly. This can support to reduce costs and improve the efficiency of the sc. As illustrated in Figure 9. in summary, i4.0 technologies have the potential to transform traditional sc models by creating a more connected and collaborative ecosystem. By integrating various systems and processes, companies can achieve greater efficiency and transparency, enabling them to respond more quickly to disruptions and minimize the impact of disruptions on their operations. One of the key benefits of i4.0 technologies is their ability to enable predictive maintenance. With the help of ioT devices and advanced analytics, businesses can identify potential equipment failures before they occur and take preventive measures to avoid disruptions to production. This not only reduces the risk of downtime but also helps to lower maintenance costs and increase the lifespan of equipment. 42 41 42 52 44 45 36 42 44 44 49 36 37 45 36 39 41 39 44 39 41 42 39 36 23 39 41 41 0 20 40 60 80 100 120 140 160 AI IoT AM CC BC BDA CPS Supply Side risk Operation Process RiskDemand Side Risk Env. Risk Figure 9. Recommended i4.0 technologies for sC partners collaboration. 39 37 36 53 49 41 41 42 57 60 58 53 39 45 39 39 39 37 37 42 36 42 29 28 13 23 41 41 0 20 40 60 80 100 120 140 160 AI IoT AM CC BC BDA CPS Supply Side risk Operation Process RiskDemand Side Risk Env. Risk Figure 8. Recommended i4.0 technologies for sC geographical distribution. cOGENT BUsiNEss & MANAGEMENT 23 Another significant advantage of i4.0 technologies is their ability to improve inventory management. By using real-time data analytics and automated solutions, businesses can better manage their inventory levels, reduce the risk of stockouts, and avoid over-stocking. This, in turn, can improve customer satisfaction by ensuring timely delivery of products and services. Furthermore, i4.0 technologies can enable more efficient logistics operations (Tsipoulanidis & Nanos, 2022). By using sensors and GPs tracking systems, businesses can monitor the movement of goods in real-time and optimize routes to reduce transportation costs and improve delivery times. This allows businesses to improve their competitiveness by offering faster and more reliable delivery services to customers. in conclusion, i4.0 technologies have the potential to enhance scR by providing businesses with greater visibility, agility, and collaboration. By leveraging these technologies, businesses can improve their ability to respond to disruptions, reduce costs, and improve customer satisfaction. As such, it is essential for businesses to embrace i4.0 technologies to stay competitive in today’s rapidly changing business environment. The aforementioned findings are utilized to construct a comprehensive guide about the various scR and scV drivers’ intersection areas, that are filled with the suggested and perceived as most optimum i4.0 technology based on the responses of tens of sc professionals from industry and academia. As illustrated in Table 2. 4.2. I4.0 technologies examined through GRACIAS verification channels The experts’ perspectives are further analyzed and examined, where each i4.0 technology is plotted in a radar chart. A radar chart, also known as a spider chart or web chart, is a graphical representation of multivariate data in the form of a two-dimensional chart with multiple quantitative variables displayed on axes emanating from a central point. it is often used to compare the relative strengths or performance of different categories, such as products, individuals, or organizations. in a radar chart, each axis represents a different variable, and the data for each category is plotted as a series of points that are connected by a line to create a polygonal shape. The area inside the shape is then shaded or colored to make it easier to visually compare the categories. Radar charts are useful when comparing data that is spread across multiple categories and allow easy identification of categories that perform well or poorly across multiple variables. however, they can become cluttered and difficult to read when there are too many variables or categories, and they can also be prone to distortion if the scales of the axes are not consistent. The spider charts are used to evaluate i4.0 technologies degree of fitness-for-purpose through the proprietary 7-layer verification channels that are encompassed in the acronym ‘GRAciAs’ from Al-Banna et al. (2023). The use of radar charts to analyze the relative strengths and weaknesses of various technologies against specific parameters is a useful tool in understanding how these technologies can be deployed in real-world scenarios. in this particular case, the technologies of additive manufacturing, blockchain, artificial intelligence, cloud computing, cyber physical systems, big data analytics, and the internet of things have been plotted against a set of seven parameters, including the golden triangle, regulatory environment, agnostic technology, age of asset, cybersecurity, return on investment, and scalability. The golden triangle, which refers to the right people, right processes, and the right technologies, is an important consideration for any technology deployment. The regulatory environment is another key consideration, particularly in highly regulated industries such as healthcare or finance. Blockchain technology, for example, offers significant potential in terms of data security and transparency, but it may be Table 2. survey based Roadmap: Connecting sCR & sCV drivers through i4.0. sCV drivers sCR drivers supply side Risk operation Process Risk Demand side Risk env. Risk sC agility ai iot BDa CC sC structure CC iot ai CPs sC Visibility iot iot CPs BDa information sharing iot CC iot BC Risk & Revenue sharing iot BDa iot CC geographical Distribution CC aM BDa ai Collaboration with sC partners CC CC BC ai 30 A. Al-BANNA ETAl. implementing robust cybersecurity measures, conducting regular risk assessments, information technology (iT) and operations technology (OT) ecosystems penetration vulnerability testing, and establishing effective supplier relationship management practices. By adopting a proactive approach to risk management, organizations can safeguard against potential disruptions and ensure the resilience of their supply chains. in addition, the managerial value of this study lies in its practical implications for organizations across various industries. For instance, in the manufacturing sector, organizations can leverage technologies such as ioT-enabled sensors and advanced analytics to monitor equipment performance, predict maintenance needs, and optimize production schedules. This leads to increased operational efficiency, reduced downtime, and improved customer satisfaction. in the retail industry, the adoption of i4.0 technologies can facilitate demand forecasting and inventory optimization. By using Ai algorithms and big data analytics, retailers can accurately forecast customer demand, optimize inventory levels, and ensure product availability. This not only reduces stock-outs and excess inventory but also enhances customer satisfaction and profitability. 5.3. Progression of the knowledge base This study contributes to the progression of the knowledge base regarding DscR by providing empirical insights from supply chain experts. By capturing perspectives from both academic and industrial domains, the study enhances the understanding of optimal enablers for achieving resilient supply chains. The findings serve as a foundation for future research and development, guiding academia, and industry in the development of comprehensive frameworks, best practices, and innovative approaches for DscR implementation. in summary, this study has significant implications for policy and decision makers, offering insights into the adoption of specific i4.0 technologies and the investment magnitude required for achieving DscR. By addressing organizational and managerial predicaments, organizations can proactively navigate the complexities of the digital transformation era and enhance their supply chain resilience. 6. Conclusions in conclusion, the Fourth industrial Revolution is transforming the way organizations manage and operate their activities, albeit its sc and logistics management. i4.0 technologies, like ioT, BDA and cc are at the forefront of this digital and business transformation, and their adoption is essential for organizations that are keen to remain competitive in today’s fast-paced and ever-changing business environment. By digitizing processes and automating manual tasks, businesses can achieve greater efficiency, reduce costs, and increase agility. Furthermore, i4.0 technologies can help businesses build DscR, enabling them to withstand and recover from disruptions caused by business disruptions, natural disasters, cyber-attacks, epidemics, pandemics, among others. Disruptions can have a significant impact on the sc, leading to delayed deliveries, lost revenue, and a negative impact on the customer experience. Therefore, it is crucial for businesses to build DscR through optimum investment in i4.0 technologies that are perceived fir-for-purpose for the particular organizational requirement, operating environment, and industry. The adoption of i4.0 technologies permits businesses to build DscR in several ways. Firstly, it can enable businesses to monitor their scs in real-time, providing visibility into potential disruptions and allowing businesses to take proactive measures to mitigate their impact. For example, by using ioT sensors, businesses can monitor their inventory levels, production processes, and logistics operations in real-time, enabling them to respond quickly to any issues that arise. secondly, i4.0 technologies support businesses automate manual tasks, reducing the risk of human error and improving the accuracy and speed of sc operations. By automating tasks such as order processing, inventory management, and shipping, businesses can reduce the risk of delays and errors in their sc. Finally, i4.0 technologies allow businesses to build flexibility and agility into their scs, enabling them to quickly adapt to changing circumstances. Nevertheless, to achieve maximum benefits from i4.0 technologies, businesses must invest in them efficiently and effectively. The golden triangle of the right people, right process, and right technology is crucial for the success of any investment and implementation of digital transformation projects and i4.0 technologies. cOGENT BUsiNEss & MANAGEMENT 31 The right people refer to having the necessary talent and skills to manage and operate the technologies effectively. This includes recruiting and training staff with the skills and expertise required to manage and operate i4.0 technologies, such as data scientists, software engineers, and automation experts. in addition, it is essential to create a culture of innovation and experimentation within the organization, where employees are encouraged to explore new technologies and ways of working (Berawi etal., 2020). The right process refers to having the necessary processes in place to maximize the benefits of i4.0 technologies. This includes developing a clear strategy and roadmap for digital transformation, identifying the key areas where i4.0 technologies can deliver the most significant benefits, and implementing processes to manage and monitor the adoption of these technologies. in addition, businesses must also consider the impact of i4.0 technologies on their existing processes and systems and take steps to ensure that they integrate seamlessly. The right technology refers to selecting and implementing the most appropriate technologies that align with the business’ needs and goals. This includes evaluating the range of i4.0 technologies available and selecting those that are best suited to the business’ requirements. it is also important to consider factors such as scalability, compatibility with existing systems, and the total cost of ownership when selecting industry 4.0. in conclusion, an empirical survey investigation can be a valuable methodology for gaining insights into supply chain experts’ perceptions of industry 4.0 technologies and investment in digital supply chain resilience. By generating quantitative and qualitative data that is generalizable and cost-effective, survey investigations can provide a rich source of information for improving supply chain performance and enhancing the resilience of digital supply chains. 7. Limitations and future research This research, while providing valuable insights into the complex terrain of digital supply chain resilience (DscR), is not without its limitations. Firstly, the industry specificity of the study raises concerns about the generalizability of the findings across various sectors. Each industry possesses unique nuances and dynamics that can significantly impact the applicability of the proposed strategies. For instance, supply chain strategies that prove highly effective in the automotive sector may not directly translate to the healthcare industry due to variations in regulatory constraints, demand patterns, and criticality of supply chain operations. Moreover, the temporal dynamics inherent in the study design pose a significant challenge. This research captures a snapshot of the supply chain landscape at a specific moment, and given the rapid pace of technological advancements and evolving business practices, the relevance of the findings may diminish over time. For example, a strategy that was effective at the time of the study may become obsolete due to emerging technologies or shifts in customer preferences. To address this limitation, future research should consider adopting a more dynamic and responsive research approach that accommodates the evolving nature of the supply chain ecosystem. Another limitation stems from the potential expertise bias present in this study, as the insights heavily rely on seasoned professionals. While their perspectives undoubtedly enrich the qualitative aspect of the research, there might be a bias towards certain viewpoints, potentially overlooking emerging perspectives from newer entrants in the field. A more balanced approach would involve a wider spectrum of supply chain professionals, from established experts to newcomers, to ensure that the insights encompass a broad and diverse range of perspectives. Furthermore, it is important to note that the study’s recommendations are grounded in the state of technology at the time of the investigation. As technology evolves, the efficacy of the proposed strategies may be subject to change. For instance, the adoption of new technologies like quantum computing or advanced machine learning algorithms may render existing DscR strategies outdated. Future research should anticipate and address this challenge by staying abreast of emerging technologies and their implications for supply chain resilience. With regards to potential future research directions, several promising avenues of research can contribute to a more comprehensive and adaptable understanding of DscR, including -but not limited tothe following: 7.1. Cross-industry comparative analyses To enhance generalizability, researchers can conduct cross-industry comparative analyses. By comparing and contrasting DscR strategies across different sectors, studies can identify sector-specific best practices 32 A. Al-BANNA ETAl. and challenges. For example, by examining how DscR strategies differ between the automotive and pharmaceutical industries, researchers can provide insights that are transferable across sectors while recognizing sector-specific nuances. 7.2. Longitudinal studies longitudinal studies tracking the effectiveness of DscR strategies over an extended period can offer dynamic insights into their impact and adaptability. By examining how specific strategies evolve and perform over time, researchers can provide businesses with guidance on the long-term viability of their DscR investments. 7.3. Exploration of emerging technologies As industry 4.0 continues to evolve, researchers should explore emerging technologies and trends shaping the future of DscR. This could involve investigating the role of artificial intelligence, blockchain, or other industry 4.0 advancements in enhancing scR. By staying ahead of the curve, research can inform businesses on the most cutting-edge strategies. 7.4. Quantitative validation of DSCR strategies Future research can focus on supplementing qualitative insights with quantitative data to validate the efficacy of specific DscR strategies. This may involve developing metrics and key performance indicators (KPis) for resilience assessment. By quantifying the impact of strategies, researchers can provide businesses with data-driven decision-making tools. 7.5. Global perspectives To gain a more global perspective, research can extend its scope to include a diverse range of global perspectives, considering regional variations in supply chain practices and the adoption of i4.0 technologies. By examining how DscR strategies differ between regions, researchers can help multinational organizations tailor their approaches to regional nuances. 7.6. Organizational maturity models The development of organizational maturity models could help businesses assess their readiness for DscR. such models would consider factors like technological infrastructure, organizational culture, process optimization, and workforce competencies. By providing a structured framework for self-assessment, these models can guide organizations in their journey towards digital supply chain resilience. in summary, this research serves as a valuable foundation for future explorations in the field of DscR. By addressing the limitations and charting new research directions, scholars and practitioners can collaboratively contribute to a more comprehensive understanding of DscR. The ongoing evolution of i4.0 and the dynamic nature of scR necessitate continuous research efforts to support businesses and policymakers in adapting to an ever-changing digital landscape. These research endeavors are essential to ensuring the continued relevance and applicability of findings in the face of an evolving and dynamic business environment. 8. Data availability statement (DAS) This paper employs an empirical investigation analysis based on a comprehensive survey that received the necessary recognition and approvals from institutional Review Board (iRB) under the number hBKU-iRB-2024-10, with the objective of evaluating perspectives from supply chain (sc) professionals in cOGENT BUsiNEss & MANAGEMENT 33 industrial (market, business) and academic environments about the impact of a wide range of i4.0 technologies on scR. in order to comply with, and respect the participants desire to maintain their identities confidential, and not to disclose their organizations identities, the actual data are not available, however the aggregate data that does not expose the participants privacy are available upon request. Acknowledgements Open Access funding provided by the Qatar National library. Disclosure statement No potential conflict of interest was reported by the authors. About the authors Adnan Al-Banna, a seasoned executive with more than 25 years of international experience in digital transformation, strategic planning, and supply chain across diverse industries including aviation, oil & gas, maritime and logistics. in addition to managing commercial, business and operations units, Dr. AlBanna’s expertise includes managing corporates support services, including hR, iT, hsE, cybersecurity and Procurement, where he spearheaded multi-billion-dollar projects, negotiated for, and purchased aircraft, ships, engines, etc. Dr. AlBanna led multiple successful digital transformation and A.i. projects from strategies to implementation, hence, this research summarizes a wide range of his findings and recommendations for organizations marching towards their digital transformations projects. Dr. AlBanna encapsulates a harmonious fusion of academic distinction and unwavering vocational commitment, holding a PhD in logistics and supply chain Management, an MBA, and a Bachelor’s in Mechanical Engineering. he held leadership positions in blue-chip organizations, including Qatar Airways, Gulf Air, AsRY and Milaha. Dr. Mohamed Yaqot is a Postdoctoral Fellow at hamad Bin Khalifa University (hBKU) specializing in Engineering Management and Decision sciences. he earned his Ph.D. in logistics and supply chain Management from hBKU, where his research focused on digital transformation in manufacturing systems and supply chains. With over 10 years of experience in the field of Engineering Management, Dr. Yaqot has contributed to numerous scientific publications. his current research involves exploring novel approaches in process systems in different manufacturing and service industries. Driven by a passion for advancing scientific knowledge, he is dedicated to translating research findings into real-world applications. Dr. Yaqot with his team achieved the firstplace position in the prestigious Qatar science & Technology Park QsTP-XlR8-2023 Program to transform tech-based ideas into commercially viable businesses. Brenno C. Menezes received the B.sc.,M.sc. and D.sc. degrees in process engineering from the Federal University of Rio de Janeiro, Brazil. he is currently an Assistant Professor with the college of science and Engineering, Division of Engineering Management and Decision sciences, hamad Bin Khalifa University, Qatar. he has more than 15 years of international experience as a process engineer and a researcher in smart manufacturing and engineering management. he has published many papers in top academic international journals, such as computers and chemical Engineering, industrial Engineering chemistry Research, computer Aided Process Engineering, and among many others. his current research interests include simulation and optimization, lNG value chain, process industry, and machine learning. ORCID Adnan Al-Banna http://orcid.org/0000-0002-5523-3405 References Al-Banna, A., Yaqot, M., & Menezes, B. (2023). Roadmap to digital supply chain resilience under investment constraints. 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Appendix Investment in Industry 4.0 (I4.0) technologies to enhance digital supply chain resilience (DSCR) This survey aims to identify supply chain expert’s perspectives on the optimum investment in industry 4.0 (i4.0) technologies to enhance digital supply chain resilience (DscR). Below are some definitions for ease of reference: a. DscR: the ability of an organization to avoid, contain and recover from risks and disruptions through the use of digital technologies, artificial and data-driven intelligence. b. industry 4.0 refers to the fourth industrial revolution, which involves the integration of advanced technologies, such as artificial intelligence, the internet of Things, big data analytics, and others, into manufacturing and other industrial processes. Below are some key i4.0 technologies for ease of reference: 1. Artificial intelligence (Ai): refers to the simulation of human intelligence in machines that are programmed to perform tasks that would typically require human intelligence, such as learning, problemsolving, decision-making, and language processing. 2. internet of Things (ioT): refers to the network of physical objects or “things” that are connected to the internet and can communicate with each other, often via sensors and other data-gathering devices. 3. Big Data Analytics (BDA): refers to the process of analyzing large and complex data sets to extract valuable insights and knowledge. it involves advanced data processing techniques and technologies that can handle vast amounts of data, identify patterns, and make predictions. 4. cloud computing (cc): refers to the delivery of computing services, including servers, storage, software, ΟΟand databases, over the internet. it provides on-demand access to computing resources, enabling businesses to scale up or down quickly, depending on their needs. 5. cyber-Physical systems (cPs): refers to a type of technology that combines physical components with digital components, creating systems that can interact with the physical world through sensors and other devices. cPs is often used in applications like smart homes, self-driving cars, and industrial automation. 6. Additive Manufacturing (AM): refers to a process of creating objects by adding successive layers of material, typically using 3D printers. 7. Blockchain (Bc): refers to a decentralized and distributed digital ledger that records transactions in a secure and transparent way. it provides a way to securely store and transfer information, making it useful in applications like cryptocurrency, supply chain management, and digital identity verification. 1: Which of the following best describes you? (select one) ○supply chain and logistics professional and academic ○supply chain and logistics professional ○supply chain and logistics academic ○none of the above 2: Please describe your experience with DscR and industry 4.0 (select one) ○i have knowledge about DsCR and industry 4.0 ○i have knowledge about DsCR ○i have knowledge about i4.0 ○none of the above 3: in which region is your organization’s headquarters? (select one) ○north america ○east asia ○south america ○south asia ○eastern europe ○southeast asia ○Western europe ○northast asia ○africa ○oceania ○gulf Cooperating Council 4: how many employees work for your organization? (select one) ○<999 ○5,000-9,999 ○1,000-4,999 ○10,000-99,999 5: What is the highest level of education you have completed? (select one) ○Diploma ○Master’s degree ○Bachelor’s degree ○PhD or higher cOGENT BUsiNEss & MANAGEMENT 37 6: how many years of work experience do you have? (select one) ○Less than five ○11 to 20 ○6 to 10 ○More than 20 7: To what extent does the DscR and industry 4.0 represent strategic priorities for your organization? (select one) ○to a great extent ○to minor extent ○to a moderate extent ○i don’t know 8: introduction for Q9-Q15: in the next questions, the survey addresses the interconnectedness between a) sc Resilience drivers, and b) sc Vulnerabilities drivers, where the sc resilience drivers are; 1) sC agility, 5) sC Revenue & Risk sharing, 2) sC structure, 6) sC geographical Distribution, and 3) sC Visibility, 7) Collaboration with sC Partners. 4) sC information sharing, While the sc vulnerabilities drivers are: 1) supply side risk, 3) Demand side risk 2) operation process risk, 4) environment risk in reference to the earlier discussed definitions of scR and scV drivers, please select the industry 4.0 technologies that you perceive to be optimum solutions to invest into for the scenario portrayed in each question. 9: Within the domain of supply chain agility, which i4.0 technologies are perceived optimum to address the below supply chain vulnerabilities? supply side Risk operation Process Risk Demand side Risk environment Risk additive Manufacturing○ ○ ○ ○ artificial intellegence○ ○ ○ ○ Cloud Computing○ ○ ○ ○ Blockchain ○ ○ ○ ○ internet of things ○ ○ ○ ○ Big Data analytics ○ ○ ○ ○ Cyber Physical systems ○ ○ ○ ○ 10: Within the domain of supply chain structure, which i4.0 technologies are perceived optimum to address the below supply chain vulnerabilities? supply side Risk operation Process Risk Demand side Risk environment Risk additive Manufacturing○ ○ ○ ○ artificial intellegence○ ○ ○ ○ Cloud Computing○ ○ ○ ○ Blockchain ○ ○ ○ ○ internet of things ○ ○ ○ ○ Big Data analytics ○ ○ ○ ○ Cyber Physical systems ○ ○ ○ ○ 11: Within the domain of supply chain visibility, which i4.0 technologies are perceived optimum to address the below supply chain vulnerabilities? supply side Risk operation Process Risk Demand side Risk environment Risk additive Manufacturing○ ○ ○ ○ artificial intellegence○ ○ ○ ○ Cloud Computing○ ○ ○ ○ Blockchain ○ ○ ○ ○ internet of things ○ ○ ○ ○ Big Data analytics ○ ○ ○ ○ Cyber Physical systems ○ ○ ○ ○ 12: Within the domain of supply chain information sharing, which i4.0 technologies are perceived optimum to address the below supply chain vulnerabilities? supply side Risk operation Process Risk Demand side Risk environment Risk additive Manufacturing○ ○ ○ ○ artificial intellegence○ ○ ○ ○ Cloud Computing○ ○ ○ ○ Blockchain ○ ○ ○ ○ internet of things ○ ○ ○ ○ Big Data analytics ○ ○ ○ ○ Cyber Physical systems ○ ○ ○ ○ 38 A. Al-BANNA ETAl. 13: Within the domain of supply chain risk & revenue sharing, which i4.0 technologies are perceived optimum to address the below supply chain vulnerabilities? supply side Risk operation Process Risk Demand side Risk environment Risk additive Manufacturing○ ○ ○ ○ artificial intellegence○ ○ ○ ○ Cloud Computing○ ○ ○ ○ Blockchain ○ ○ ○ ○ internet of things ○ ○ ○ ○ Big Data analytics ○ ○ ○ ○ Cyber Physical systems ○ ○ ○ ○ 14: Within the domain of supply chain geographical distribution, which i4.0 technologies are perceived optimum to address the below supply chain vulnerabilities? supply side Risk operation Process Risk Demand side Risk environment Risk additive Manufacturing○ ○ ○ ○ artificial intellegence○ ○ ○ ○ Cloud Computing○ ○ ○ ○ Blockchain ○ ○ ○ ○ internet of things ○ ○ ○ ○ Big Data analytics ○ ○ ○ ○ Cyber Physical systems ○ ○ ○ ○ 15: Within the domain of collaboration with supply chain partners, which i4.0 technologies are perceived optimum to address the below supply chain vulnerabilities? supply side Risk operation Process Risk Demand side Risk environment Risk additive Manufacturing○ ○ ○ ○ artificial intellegence○ ○ ○ ○ Cloud Computing○ ○ ○ ○ Blockchain ○ ○ ○ ○ internet of things ○ ○ ○ ○ Big Data analytics ○ ○ ○ ○ Cyber Physical systems ○ ○ ○ ○ 16: From your perspective, what key factors determine the success of investment and implementation of i4.0 technologies? (7 for most important, 1 for least important). g R a C i a s additive Manufacturing [1….7] [1….7] [1….7] [1….7] [1….7] [1….7] [1….7] artificial intellegence [1….7] [1….7] [1….7] [1….7] [1….7] [1….7] [1….7] Cloud Computing [1….7] [1….7] [1….7] [1….7] [1….7] [1….7] [1….7] Blockchain [1….7] [1….7] [1….7] [1….7] [1….7] [1….7] [1….7] internet of things [1….7] [1….7] [1….7] [1….7] [1….7] [1….7] [1….7] Big Data analytics [1….7] [1….7] [1….7] [1….7] [1….7] [1….7] [1….7] Cyber Physical systems [1….7] [1….7] [1….7] [1….7] [1….7] [1….7] [1….7] Where, the acronym (GRAciAs) encompasses the following: • G: Golden Triangle; refers to having the right people, the right Process, and the right technologies to guarantee investment and implementation success. • R: Regulatory Environment: refers to the organization’s operating environment, governing laws, regulations, and tax structure, among others • A: Age of the asset: refers to the point at which investment is considered with respect to the overall asset life. • c: cybersecurity: refers to the security of data creation, sharing, and storing in the digital cyberspace, in relation to isO 27001. • i: investment: refers to the expected return on investment, payback period, and other financial aspects of the considered technology. • A: Agnosticism: refers to the solution’s ability to integrate, interact, exchange data and information and operate seamlessly with the organization enterprise resources planning (ERP) system. s: scalability: refers to the importance of building future expansion capability in the soon-to-be-acquired digital technologies and/or eco-system.