Supply chain risks in Industry 4.0 environment: review and analysis framework

Pandey, Shipra; Singh, Rajesh Kumar; Gunasekaran, Angappa

doi:10.1080/09537287.2021.2005173

Cited by 45 publications

(21 citation statements)

References 120 publications

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“…Several studies have examined the relationship between big data analytics capabilities and green supply chain performance (Benzidia et al, 2021;Lee and Mangalaraj, 2022;Mani et al, 2017;Pandey et al, 2021). These tools related to the collection and analyses of big data provide opportunities to improve sustainable performance through the design and manufacture of environmentally friendly products (Singh et al, 2018).…”

Section: Theoretical Framework and Hypotheses Developmentmentioning

confidence: 99%

Big data analytics capabilities and green supply chain performance: investigating the moderated mediation model for green innovation and technological intensity

Al-Khatib¹

2022

BPMJ

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PurposeThis study investigates the impact of big data analytics capabilities on green supply chain performance. Moreover, it assesses the mediating effect of the green innovation and moderating effect of technological intensity.Design/methodology/approachThis study is based on primary data that were collected from the food and beverages manufacturing sector operating in Jordan. A total of 420 samples were used for the final data analysis. Data analysis was performed via structural equation modeling (SEM) using SmartPLS 3.3.9.FindingsThe results of the data analysis supported a positive relationship between big data analytics capabilities and the green supply chain performance as well as a mediating effect of green innovation. It was confirmed that technological intensity moderated the relationship of green innovation on green supply chain performance.Research limitations/implicationsThe study faced many limitations such as the method of collecting primary data, which relied on a questionnaire only and the use of cross-sectional data, as well as studying one context and in one country.Practical implicationsThe findings can guide managers and policymakers in the Jordanian food and beverage manufacturing sector on how to manage organizational capabilities related to big data analytics to enhance green supply chain performance and improve green innovation in these firms.Originality/valueThis study developed a theoretical and empirical model to investigate the relationship between big data analytics capabilities, green innovation, technological intensity and green supply chain performance. This study offers new theoretical and managerial contributions that add value to the supply chain management and innovation literature by testing the moderated mediation model of these constructs in the food and beverages manufacturing sector in Jordan.

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Section: Theoretical Framework and Hypotheses Developmentmentioning

confidence: 99%

Big data analytics capabilities and green supply chain performance: investigating the moderated mediation model for green innovation and technological intensity

Al-Khatib¹

2022

BPMJ

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“…Slow but steady, few companies equipped with the capability to innovate are seeing this threat as an opportunity to diversify the business (Hanelt et al, 2021 ). Previous studies have shown that researchers either assessed the I4.0 risks in a limited manner or did not use the well-defined and most important KPIs to evaluate these risks to develop stakeholders’ confidence in progressing forward on the road of the I4.0 vision (Birkel et al, 2019 ; Colak et al, 2019 ; Kodym et al, 2020 ; Pandey et al, 2021 ). This encourages the researchers to address this crucial concern of identifying the most significant I4.0 risks by considering the most important KPIs and establishing the relevance of KPIs by focusing on the causal relationship among the KPIs.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Increased consumerism also increases energy consumption, thereby adding toxicity and radioactive substances, and other waste materials to the environment. This waste increases pollution and perturbs the ecological balance Wiengarten et al ( 2016 ), Beier et al ( 2017 ), Stock et al ( 2018 ), Birkel et al ( 2019 ), Habibi Rad et al ( 2021 ), Pandey et al ( 2021 ), Gadekar et al ( 2022 ) R5 Social risks The change is mostly perceived negatively by those directly impacted. It builds massive stress among employees.…”

Section: Literature Reviewmentioning

confidence: 99%

“…It builds massive stress among employees. Even if the change process is carried out professionally, the employees are susceptible to the unknown fear of job loss, excessive workload, loss of health and peace of mind, work-life balance, and future confusion Thus the resistance of employees to change and adapt to the new corporate culture is inevitable which can be resolved by using methods such as upskilling, counseling, and employee training, to align the employee with changing environment Bonekamp and Sure ( 2015 ), Stock and Seliger ( 2016 ), Kazancoglu and Ozkan-Ozen ( 2018 ), Müller and Voigt ( 2018 ), Müller et al ( 2018a , 2018b ), Stock et al ( 2018 ), Birkel et al ( 2019 ), Habibi Rad et al ( 2021 ), Pandey et al ( 2021 ), Gadekar et al ( 2022 ) R6 Technological risks The technological risks are related to the data handling, cybersecurity, cloud computing, and networking ability of a factory through the right skills and knowledge. Strategic technical integration of the technological infrastructure and resources is critical to avert technological risks.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The model is assured to increase the sustainability in the manufacturing industry in the present and post-COVID-19 era as it has carefully taken into account the decision maker’s needs and expectations. It is to the findings that none of the researchers in the past have used these methods to assess and analyze the I4.0 implementation risks considering the multifaceted contribution of significant KPIs (Pandey et al, 2021 ; Žižek et al, 2020 ). Further, the comprehensive identification and evaluation of various KPIs and I4.0 implementation risks are also unattended (Birkel et al, 2019 ; Colak et al, 2019 ; Hermann et al, 2016 ; Wang et al, 2020 ); this ascertained the developed model’s application in real-world scenarios.…”

Section: Introductionmentioning

confidence: 99%

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Key performance indicator based dynamic decision-making framework for sustainable Industry 4.0 implementation risks evaluation: reference to the Indian manufacturing industries

Gadekar¹,

Sarkar

Gadekar

2022

Ann Oper Res

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Global corporate giants are keen to adopt Industry 4.0 (I4.0) owing to its continuous, impactful, and evident benefits. However, implementing I4.0 remains a significant challenge for many organizations, mainly due to the absence of a systematic and comprehensive framework. The risk assessment study is key to the flawless execution of any project is a proven fact. This paper aims to develop a KPIs-based sustainable integrated model to assess and evaluate risks associated with the I4.0 implementation. This research paper has developed the I4.0 risks evaluation model through fifteen expert interventions and an extensive systematic literature review. This research, based on sixteen KPIs evaluates six risks impacting the organization’s decision to adopt I4.0. Initially, the Fuzzy Decision-Making Trial and Evaluation Laboratory method is used to map the causal relationship among the KPIs. Further, the additive ratio assessment with interval triangular fuzzy numbers method is used to rank the risks. The study revealed that information technology infrastructure and prediction capabilities are the most crucial prominence and receiver KPIs. Simultaneously, technological and social risks are found to be highly significant in the I4.0 implementation decision-making process. The developed model meticulously supports the manufacturer’s, policymaker, and researchers’ viewpoint toward I4.0 implementation in the present and post COVID-19 pandemic phases in manufacturing companies. The comprehensive yet simple model developed in this study contributes to the larger ambit of new knowledge and extant literature. The integrated model is exceptionally based on the most prominent risks and a wider range of KPIs that are further analyzed by aptly fitting two fuzzy MCDM techniques, which makes the study special as it perfectly takes care of the uncertainties and vagueness in the decision-making process. Hence, this study is pioneering and unique in context to I4.0 risks prioritization aiming to accelerate I4.0 adoption.

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Role of Industry 4.0 in agile manufacturing to achieve sustainable development

et al. 2022

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The business world is becoming increasingly complex, disrupted, and uncertain. As Industry 4.0 (I4.0) takes shape, to endure this change and focus on growth, industries must learn to embrace change and move quickly; this is particularly so for the automotive industry, which is a major sector of any economy. Agility is needed to embrace this change and promote sustainability across every industry. To face these contingencies, it is essential to identify and understand the strategies that can aid industries in sustaining real‐time scenarios. In this paper, we aim to assess the agility indicators that bring resilience to the inclusion of I4.0 and sustainability in the automotive sector. The unexpected changes in the environment related to sustainability and technological disruptions of I4.0 require the identification of sustainable agile mitigation strategies to manage such situations. The fuzzy Delphi methodology assesses agility indicators based on a questionnaire for prioritization. Further, the fuzzy technique for order performance by similarity to ideal solution (TOPSIS) is utilized to prioritize these agility indicators with the agility strategies that manufacturing industries can deploy to enhance the sustainability of their operations. The fuzzy extension to the methodologies helps in incorporating vagueness and ambiguity in the decision‐making process. A fuzzy approach to prioritizing agile strategies as the industries are changing is helpful. The decision makers have only a few clear ideas about how best to integrate them. Among the agile strategies, “resilience,” “technological capabilities,” and “customer‐centric innovation” are the top strategies. By executing the strategies suggested in the study and understanding the agility indicators, practitioners can ensure resilience in the work systems and minimize the impact of the disruptions caused by the industrial revolution. Thus, this research has been underpinned by the organizational change theory and discusses all mitigation strategies concerning the rapid changes organizations are facing with the fourth industrial revolution.

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Supply chain risks in Industry 4.0 environment: review and analysis framework

Cited by 45 publications

References 120 publications

Big data analytics capabilities and green supply chain performance: investigating the moderated mediation model for green innovation and technological intensity

Big data analytics capabilities and green supply chain performance: investigating the moderated mediation model for green innovation and technological intensity

Key performance indicator based dynamic decision-making framework for sustainable Industry 4.0 implementation risks evaluation: reference to the Indian manufacturing industries

Role of Industry 4.0 in agile manufacturing to achieve sustainable development

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