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.

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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.

PurposeWith big data (BD), traditional supply chain is shifting to digital supply chain. This study aims to address the issues and challenges in the way toward the implementation of big data analytics (BDA) in sustainable supply chain management (SSCM).Design/methodology/approachThe factors that affect the implementation of BDA in SSCM are identified through a widespread literature review. The PESTEL framework is used for this purpose as it covers all the political, economic, social, technological, environmental and legal factors. These factors are then finalized by means of experts' opinion and analyzed using structural equation modeling (SEM).FindingsA total of 10 factors are finalized with 31 sub-factors, of which sustainable performance, competitive advantage, stakeholders' involvement and capabilities, lean and green practices and improvement in environmental performance are found to be the critical factors for the implementation of BDA in SSCM.Research limitations/implicationsThis research has taken up the case of Indian manufacturing industry. It can be diversified to other geographical areas and industry sectors. Further, the quantitative analysis may be undertaken with structured or semi-structured interviews for validation of the proposed model.Practical implicationsThis research provides an insight to managers regarding the implementation of BDA in SSCM by identifying and examining the influencing factors. The results may be useful for managers for the implementation of BDA and budget allocation for BDA project.Social implicationsThe result includes green practices and environmental performance as critical factors for the implementation of BDA in SSCM. Thus the research establishes a positive relationship between BDA and sustainable manufacturing that ultimately benefits the environment and society.Originality/valueThis research addresses the challenges in the implementation of BDA in SSCM in Indian manufacturing sector, where such application is at its nascent stage. The use of PESTEL framework for identifying and categorizing the factors makes the study more worthwhile, as it covers full spectrum of the various factors that affect the strategic business decisions.

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