Is the Implementation of Big Data Analytics in Sustainable Supply Chain Really a Challenge? The Context of the Indian Manufacturing Sector

Prashant Jain, Dhanraj P. Tambuskar and Vaibhav S. Narwane
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Prashant Jain: Department of Mechanical Engineering, Pillai College of Engineering, Navi Mumbai, India
Dhanraj P. Tambuskar: ��Department of Mechanical Engineering, K.J Somaiya College of Engineering, Mumbai, India
Vaibhav S. Narwane: ��Department of Mechanical Engineering, K.J Somaiya College of Engineering, Mumbai, India

International Journal of Innovation and Technology Management (IJITM), 2023, vol. 20, issue 05, 1-39

Abstract: Purpose: In this age, characterized by the incessant generation of a huge amount of data in social and economic life due to the widespread use of digital devices, it has been well established that big data (BD) technologies can bring about a dramatic change in managerial decision-making. This work addresses the challenges of implementation of big data analytics (BDA) in sustainable supply chain management (SSCM).Design/methodology: The barriers to the implementation of BDA in SSCM are identified through an extensive literature survey as per PESTEL framework which covers political, economic, social, technological, environmental and legal barriers. These barriers are then finalized through experts’ opinion and analyzed using DEMATEL and AHP methods for their relative importance and cause-and-effect relationships.Findings: A total of 13 barriers are identified out of which the lack of policy support regarding IT, lack of data-driven decision-making culture, compliance with laws related to data security and privacy, inappropriate selection and adoption of BDA technologies, and cost of implementation of BDA are found to be the key barriers that have a causative effect on most of the other barriers.Research limitations: This work is focused on the Indian manufacturing supply chain (MSC). It may be diversified to other sectors and geographical areas. The addition of missed-out barriers, if any, might enrich the findings. Also, the fuzzy or grey versions of MCDM methods may be used for further fine-tuning of the results.Practical implications: The analysis presented in this work gives hierarchy of the barriers as per their strength and their cause-and-effect relationships. This information may be useful for decision makers to assess their organizational strengths and weaknesses in the context of the barriers and fix their priorities regarding investment in the BDA project.Social implications: The research establishes that the successful implementation of BDA through minimizing the effect of critical causative barriers would enhance the environmental performance of the supply chain (SC) which in turn would benefit society.Originality/value: This is one of the first studies of BDA in SSCM in the Indian manufacturing sector using PESTEL framework.

Keywords: Big data analytics (BDA); sustainable supply chain management (SSCM); PESTEL analysis; Indian manufacturing sector (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1142/S0219877023500335

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