Big data analytics in supply chain decarbonisation: a systematic literature review and future research directions

• Published in: International journal of production research Vol. 62; no. 4; pp. 1489 - 1509
• Main Authors: Kumar, Devinder, Singh, Rajesh Kr, Mishra, Ruchi, Vlachos, Ilias
• Format: Journal Article
• Language: English
• Published: London Taylor & Francis 16.02.2024; Taylor & Francis LLC
• Subjects: antecedent-decision-outcomes; Big Data; Big data analytics; Data analysis; decarbonisation; Energy transition; Literature reviews; Mathematical analysis; net zero economy; Supply chains; System theory; systematic literature review; Systematic review
• ISSN: 0020-7543; 1366-588X
• DOI: 10.1080/00207543.2023.2179346

Supply chain decarbonisation has become a strategic requirement in the era of a net-zero economy. Despite the significant role of Big Data Analytics (BDA) in decarbonising the supply chain (SC), no prior study has evaluated it systematically. The present study aims to provide a systematic literature review on the applications and outcomes of big data analytics in SC decarbonisation. A total of 69 papers on applying BDA technology for supply chain decarbonisation published between 2016 and 2021 have been selected following the PRISMA protocol. The findings show that the topic is evolving. Studies employed methods such as surveys (30), case studies (11), and conceptual research designs (8). Thematic analysis reveals that 65% of the studies are grounded in resource-advantage theories, organisational theories, and system theories. Studies from India and China (35%) dominate the topic, while most studies have been conducted on the food and manufacturing industries. Further, this study applied the Antecedent-Decision-Outcomes (ADO) framework in BDA-based SC decarbonisation. Antecedents include BDA resources and capabilities, workforce skills, and supplier capabilities. Decisions refer to improving decision-making across the supply chain. Outcomes refer to improving decarbonisation, sustainable growth, and sustainable innovativeness. Future research directions and questions are provided using the Theory-Context-Methodology (TCM) framework.