Examining the Influence of Big Data Analytics and Additive Manufacturing on Supply Chain Risk Control and Resilience: An Empirical Study

S. Gupta, S. Bag, S. Modgil, Ana Beatriz Lopes de Sousa Jabbour () and A. Kumar
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Ana Beatriz Lopes de Sousa Jabbour: Métis Lab EM Normandie - EM Normandie - École de Management de Normandie = EM Normandie Business School

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Abstract: Drawing upon the contingent resource-based perspective of supply chain resilience, this study tests whether fourth industrial revolution (4IR) technologies such as big data analytics (BDA) and additive manufacturing (AM) control risks and develop supply chain (SC) resilience under flexible orientation and control orientation. Primary data was collected from 190 samples in India and the PLS-SEM technique was then used to perform data analysis. The findings indicate that big data analytics and additive manufacturing can aid in risk control and in turn improve the SC resilience of a firm and further minimize the propagation of the supply chain ripple effect in case of disruption. This study sheds light on firms' 4IR resources (BDA and AM) that can be useful in developing risk control capabilities to deal with disruptions in supply chains. BDA, in particular, impacts risk intelligence, whereas AM impacts both preparedness and intelligence risk control. Distinguishing between BDA and AM is therefore important when firms are considering which technology to adopt. Therefore, for the sample analyzed, BDA has a prominent role in building risk control and resilience capabilities. These findings are an important contribution to SC risk management theory and this study also creates new research opportunities. Firms need to adopt collaborative planning, forecasting, smart manufacturing, and replenishments initiatives for vulnerable supply chain activities to reduce the SC ripple effect. Lastly, flexible, real-time production helps reduce the SC ripple effect. \textcopyright 2022 Elsevier Ltd

Keywords: 3D printers; Additive Manufacturing; Additives; Big data; Big data analytic; Big Data Analytics; Contingent resources; Data analytics; Data Analytics; Empirical studies; Resources based; Ripple effects; Risk Control; Risk management; Risks controls; Supply Chain Resilience; Supply chain resiliences; Supply chain ripple effect; Supply Chain Ripple Effect; Supply chains; Supply-chain risks (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (5)

Published in Computers & Industrial Engineering, 2022, 172, ⟨10.1016/j.cie.2022.108629⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-04276056

DOI: 10.1016/j.cie.2022.108629

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