Big data analytics adaptive prospects in sustainable manufacturing supply chain

What is it about?

Manufacturing Supply Chain (MSC) becomes more complex not only from the business viewpoint but also for environmental care and sustainability. To handle operations efficiently a 360-degree view of suppliers, distributors, and logistics providers’ information and trust are essential. In the Post-Covid scenario, the supply chain partner’s data has become more important to develop resiliency in the supply chain. Despite the current progress in realizing how Big Data Analytics can considerably enhance the Sustainable Manufacturing Supply Chain (SMSC), there is a major gap in the storyline relating to factors of Big Data operations in managing several forms of SMSC operations. This study attempts to fill this gap by studying the key enablers of using Big Data in SMSC operations obtained from IoT devices, group behavior parameters, social networks and ecosystem framework. Adaptive Prospects (Improving SC performance, combating counterfeits, Productivity, Transparency, Security and Safety, Asset Management, and Communication) are the constructs that this research first conceptualizes, defines, and then evaluates in studying Big Data Analytics-based operations in SMSC. To begin, two situations are explored one with Big Data Analytics and the other without are addressed using empirical studies. Second, Big Data deployment in addressing MSC barriers and synergistic role in achieving the goals of SMSC is analyzed. The study identifies lesser encounters of barriers and higher benefits of big data analytics in the SMSC scenario.

Featured Image