Big data analytics in mitigating challenges of sustainable manufacturing supply chain

Manufacturing Supply Chain (MSC) becomes more complex not only from the business viewpoint but also for environmental care and sustainability. Despite the current progress in realizing how Big Data Analytics (BDA) can considerably enhance the Sustainable Manufacturing Supply Chain (SMSC), there is a major research gap in the storyline relating to factors of Big Data-based operations in managing several forms of SMSC operations. This study attempts to fill this major research gap by studying the key challenges of using Big Data in SMSC operations obtained from IoT devices, group behavior parameters, social networks, and ecosystem frameworks. Big Data Analytics (BDA) is receiving more attention in management, yet there is relatively little empirical research available on the topic. The authors use the multi-criteria strategy employing analytic hierarchy process (AHP) and grey relational analysis (GRA) methodology due to the dearth of comparable information at the junction of BDA and MSC. The presented multi-criteria strategy findings add to the body of understanding by identifying eleven critical criteria and five associated challenges (Financial, Quality, Operation, Technical, and Logistics) related to the emergence of Big Data Analytics from a corporate and supply chain perspective. The findings reveal that product safety barriers (C4) and lack of information sharing (C8) are the critical factor immensely surge and affect the MSC in attaining sustainability. As no empirical study has yet been presented, it is important to examine the challenges at the organizational and MSC levels with a focus on the effects of BDA implementation to achieve sustainability with enhanced customer trust and improved SMSC performance.