Main Article Content
Purpose: Big data analytics is the analysis of large amount of data, which can be used to identify changing market trends, customer buying behavior, forecasting demand and bring about value addition to all entities in a supply chain network. The purpose of this paper is to understand the implications of Big data analytics in supply chain management and to identify the different techniques used to make data driven effective decision making in supply chain and also to optimize the supply chain framework
Methodology: The research is carried out on the basis of secondary data that was gathered from various articles published in many cited journals and web references.
Practical implications: The supply chain network is global and complex; the organization has to deal with multiple supply chains to provide the best customer experience. This results in a lot of unstructured data, complexity and lack of visibility which leading to various risks and problems in the supply chain. Big data analytics can be used to reduce these bottlenecks and find an optimal solution.
Social Implications: Application of data analytics in supply chain to provide better customer experience and a cost effective and faster response to retain customers.
Future Scope: Bigdata is reshaping the supply chain industry, but these data driven methods have not yet been fully applied. Innovations in supply chain is the next step for exploring the vast opportunities existing and to create a competitive advantage.
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