Optimizing supply chain logistics has become crucial in an era where
sustainability is of paramount importance. The confluence of environmental
consciousness, economic viability, and technological advancements has paved the way
for innovative solutions. At the forefront of this revolution is the integration of Data
Science and Big Data Analytics (BDA), offering unprecedented opportunities to
enhance efficiency, reduce environmental impact, and forge a more sustainable future
for Supply Chain Management (SCM). This chapter delves into the intricate
intersection of supply chain logistics, data science, and big data analytics, exploring the
transformative potential of these technologies in optimizing sustainable practices. This
study aimed to shed light on how businesses can utilize data-driven insights to
transform their supply chain operations and strike a sustainable balance between
economic growth and environmental responsibility by thoroughly examining key
concepts, approaches, and practical applications. Furthermore, attempts are made to
examine the barriers and challenges, highlighting its future potential, and inspire more
research and pedagogical advancements in this domain.
Keywords: Big data analytics, Data science, Machine-learning, Predictive analytics, Supply chain management.
