Jain, P; Tambuskar, D P and Narwane, V (2024) Identification of critical factors for big data analytics implementation in sustainable supply chain in emerging economies. Journal of Engineering, Design and Technology, 22(3), pp. 926-968. ISSN 1726-0531
Abstract
Purpose: The advancements in internet technologies and the use of sophisticated digital devices in supply chain operations incessantly generate enormous amounts of data, which is termed as big data (BD). The BD technologies have brought about a paradigm shift in the supply chain decision-making towards profitability and sustainability. The aim of this work is to address the issue of implementation of the big data analytics (BDA) in sustainable supply chain management (SSCM) by identifying the relevant factors and developing a structural model for this purpose. Design/methodology/approach: Through a comprehensive literature review and experts' opinion, the crucial factors are found using the PESTEL framework, which covers political, economic, social, technological, environmental and legal factors. The structural model is developed based on the results of the total interpretive structural modelling (TISM) procedure and MICMAC analysis. Findings: The policy support regarding IT, culture of data-based decision-making, inappropriate selection of BDA technologies and the laws related to data security and privacy are found to affect most of the other factors. Also, the company’s vision towards environmental performance and willingness for material and energy optimization are found to be crucial for the environmental and social sustainability of the supply chain. Research limitations/implications: The study is focused on the manufacturing supply chain in emerging economies. It may be extended to other industry sectors and geographical areas. Also, additional factors may be included to make the model more robust. Practical implications: The proposed model imparts an understanding of the relative importance and interrelationship of factors. This may be useful to managers to assess their strengths and weaknesses and ascertain their priorities in the context of their organization for developing a suitable investment plan. Social implications: The study establishes the importance of BDA for conservation and management of energy and material. This is crucial to develop strategies for enhancing eco-efficiency of the supply chain, which in turn enhances the economic returns for the society. Originality/value: This study addresses the implementation of BDA in SSCM in the context of emerging economies. It uses the PESTEL framework for identifying the factors, which is a comprehensive framework for strategic planning and decision-making. This study makes use of the TISM methodology for model development and deliberates on the social and environmental implications too, apart from theoretical and managerial implications.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | big data analytics; emerging economies; pestel analysis; sustainable supply chain management |
| Index terms: | profitability, implementation, methodology, industry sector, eco-efficiency, emerging economy, decision-making, manager, social sustainability, strategic planning, model development, privacy, environmental performance, society, interpretive structural modelling, relative importance, big data, internet, critical factor, conservation, strategy, paradigm, literature review |
| Subjects: | industry analysis, economic analysis, sustainability assessment, decision analysis, communities and social development, computing systems, contractual arrangements, data analysis and analytics, professional ethics, research methods, information systems, risk assessment, analytical methods, environmental policy, education and knowledge transfer, economic development, practitioner, management |
| Topics: | Digital Applications, Research Practice, Roles and Professions, Sustainability, Legal Issues, Engineering Principles, Stakeholder Management, International Construction, Risk Management, Business Strategy, Procurement |
| Descriptive scope: | 4 PCTA |
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