Few theoretical approaches to the problems of Supply Chain Management based on analytics

Abstract

The right strategy about supply chain management is one of the key drivers of any successful business in the manufacturing domain and its importance is growing very fast due to latest trends in the analytical tools and services. The most recent research studies says that the global supply chain analytics value in terms of market size is around USD 3.5 K million in 2018 and is expected to grow up to USD 10K million in 2025 at CAGR around 16% forecasted growth. This is achievable only when we can identify the right opportunities and platforms. The today's supply chain environment presents lot of challenges to the data scientists in terms of data collection, integration, storing and processing because there are huge and unconnected data sources for each of the processing and the speed at which these data getting generated is also very fast. So, the application of data analytics has very immediate benefit as well as many complexities in the implementation In the current paper, we want to focus on the key analytical applications in supply chain domain including procurement, manufacturing, replenishment and customer demand. We want to explain the statistical approach for very highly impactful supply chain problems. The analytical applications discussed in this paper starting from procurement and deals up to warehouse management and also different internal phases in between them.