Big Data Analytics in Environmental Sustainability Supply Chain Management

This paper examines the integration of Big Data Analytics (BDA) in Environmental Sustainability Supply Chain Management (ESSCM) within the aviation and automotive industries, given their data-intensive nature. Using a multidisciplinary approach, the research employs an analytical framework to assess companies' performance in specific areas, applying the RBV framework to understand how these sectors balance environmental responsibility with BDA implementations. The methodology includes a comprehensive literature review in environmental sustainability and BDA, along with case studies focused on prominent automotive and aviation industries. Incorporating text analytics, the dissertation explores the extent of BDA utilisation in enhancing environmental sustainability within intricate supply chain systems. The study is motivated by the increasing significance of sustainable practises in the modern business landscape, coupled with the growing influence of BDA in shaping decision-making processes. Recognizing the need for a comprehensive understanding of how BDA can contribute to ESSCM within the aviation and automotive industries, this research seeks to bridge the gap in existing literature and offer valuable insights for companies navigating the intersection of technology, environmental responsibility, and supply chain dynamics. By providing a unique approach to advancing the discourse on technology and environmental responsibility, the dissertation underscores its potential to guide these industries in analysing key areas of performance and contributes to the existing literature to provide insights for further in-depth exploration.