This study provides a novel way to utilise big data analytics and blockchain technology in minimising supply chain risks.
Authors
Sachin Kumar Mangla, Assistant Professor, Jindal Global Business School, O.P. Jindal Global University, Sonipat, Haryana, India.
Vaibhav S. Narwane, Department of Mechanical Engineering, K. J. Somaiya College of Engineering, Vidyanagar, Vidya Vihar East, Ghatkopar East, Mumbai, Maharashtra, India.
Rakesh D. Raut, Department of Operations and Supply Chain Management, National Institute of Industrial Engineering (NITIE), Vihar Lake, NITIE, Powai, Mumbai, Maharashtra, India.
Manoj Dora, Brunel Business School, Brunel University London, London, UK.
Balkrishna E. Narkhede, Department of Industrial Engineering and Management Systems, National Institute of Industrial Engineering (NITIE), Vihar Lake, NITIE, Powai, Mumbai, Maharashtra, India.
Summary
Supply chains (SCs) are susceptible to risks because of their dynamic and complex nature. Big data analytics (BDA) through blockchain technology (BCT) can significantly contribute to managing SC risks. However, to date, the combined effect of BDA-BCT for SC risks has not been investigated extensively in the literature.
This paper aims to identify the risk factors of the BDA-BCT initiative for Indian manufacturing organisations. Through the literature and experts’ judgments, sixteen risk factors were identified. Data was collected from machine tool, automobile component, and electrical manufacturing organisations. Further interrelations between risk factors were evaluated using the grey DEMATEL approach.
The results show that ‘supply chain visibility risks’, ‘infrastructure and development costs’, ‘demand forecasting and sensing risks’, ‘data privacy and security risks’, ‘policy and legality related risks’, and ‘supply chain resilience’ were identified as common factors in the adoption of BDA-BCT practices by the three organisations.
The cause-effect relationship between risk factors can assist managers, suppliers, service providers, and policymakers in the significant adoption of BDA-BCT in the context of manufacturing organisations. The study provides a novel way to utilise BDA-BCT in minimising supply chain risks. Limitations of the study are that it was conducted only for Indian organizations. In the future, the findings of the study can be validated through empirical analysis.
Published in: Annals of Operations Research
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