Identification of critical factors for big data analytics implementation in sustainable supply chain in emerging economies

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.

Enhancing sustainable supply chain performance by adopting sustainable lean six sigma-Industry 4.0 practices

“…Several studies have been conducted on the broad topic of measuring performance in sustainable supply chains. As previously stated, the key issues that have been investigated in this area include improving supply chain performance, critical success factors, Industry4.0 and SSCM, drivers, barriers to SSCM, operational excellence, supply chain leadership and many others (Bag et al ., 2018, 2021a, b; Baliga et al ., 2020; Chen et al ., 2021; Jain et al ., 2022; Mani et al ., 2018; Soni et al ., 2020). There is less research focusing primarily on measuring the performance of sustainable supply chains, but there is an indication of increasing interest in this area.…”

“…In recent papers, the principles of sustainability have emerged as one of the major trends in supply chain management (Panigrahi et al, 2019). Jain et al (2022) quoted that sustainable supply chain management (SSCM) is the management of material, information, capital flows and cooperation between many organizations along the supply chain. Integrating sustainability is a challenging task for supply chain (SC) players such as customers, suppliers, distributors, focal firms, retailers and so on.…”

Research developments in sustainable supply chain management considering optimization and industry 4.0 techniques: a systematic review

“…Frequently occurring themes coming under I4 cluster include Artificial Intelligence (Ethirajan et al, 2020) and Big Data Analytics (Jain et al, 2022), Internet of things (IoT) (Manavalan and Jayakrishna, 2019), Blockchain (Mangla et al, 2022), Digitalization (Belhadi et al, 2022) and Data-driven decision-making (Zhang et al, 2017).…”

Evolving trajectories of circular supply chain domain: a citation path analysis

“…Seuring and Muller (2008, p. 1700) define sustainability as “ the management of material, information and capital flows as well as cooperation among companies along the supply chain while taking goals from all three dimensions of food development, i.e. economic, environmental and social, into account which are derived from customer and stakeholder requirements .” Several concepts, such as SSC (Menon and Ravi, 2022; Jain et al. , 2022), green SC (Gawusu et al.…”

“…economic, environmental and social, into account which are derived from customer and stakeholder requirements." Several concepts, such as SSC (Menon and Ravi, 2022;Jain et al, 2022), green SC (Gawusu et al, 2022), closed-loop SC (Garai and Sarkar, 2022) and other concepts related to sustainability have been discussed in the literature (Horne and Fischter, 2022;Seuring and Mullar, 2008). But a comprehensive definition of CSC has been put forward by Farooque et al (2019a, p. 884), who defined CSCM as the "integration of circular thinking into the management of the supply chain and its surrounding industrial and natural ecosystems.…”