Big Data Challenges in the Supply Chain Management: Perspective from Data Envelopment Analysis

The market demand, consumer preferences, supply chain challenges, unstable geopolitical environment, and other business environmental factors have emerged to be highly dynamic. In this scenario, data-driven business decisions and support have become vital. The constant need for high volume, high variety, accuracy, and reliability of data has made big data imperative for businesses. The data being generated and used in abundance also brings a few challenges. This paper focuses on challenges faced by data scientists and big data analysts in using big data models in effective and efficient supply chain decisions. The data envelopment analysis (DEA) technique is used to analyze the variable and evaluate the performances of various entities engaged in numerous activities. The study’s findings indicate that variables like Sharing and Accessing Data, Privacy, Security, and Fault tolerance have proved efficient during the model deployment. Still, the factors like Analytical challenges, Quality of data, and Scalability of data have proved inefficient during the deployment of the defined big data model in the supply chain.