The post-pandemic scenario is making businesses sit up, take notice and deploy supply chain analytics. The supply chain is an essential element of business success today. An optimized supply chain can enhance the cost-efficiency and customer satisfaction of a company. With vast amounts of data generated at various supply chain touchpoints, managing the data for efficient business practices becomes challenging. Supply chain analytics helps to streamline the data and enables data-driven decision-making.

The ever-increasing demand clusters for a given geography and supplying them with the optimal resources in the current fast-paced retail and service industry leads these industries to rework their supply chain channels with the application of advanced Machine Learning and Neural Network algorithms. Using supply chain analytics, a business can make accurate forecasts of what the overall demand will be. Apart from this,it is also important for them to successfully fulfill the demand across different segments and timely supply those desired signals. Demand fulfilment from limited number of Big Demand Centers is not enough; the business needs to improve their localized fulfillment centers or warehouses across geography to improve their overall fulfillment rate and customer satisfaction. Due to increase in the market challenges and competition, the traditional business models can no longer keep up with supply interruptions.

What if you could apply machine learning and other types of AI to the terabytes of transactional and sensor data being collected from the supply chain? The result could be a much more autonomous and effective form of supply chain analytics and, ultimately, a more responsive supply chain. In fact, there is a lot of interest in using AI and machine learning to enhance supply chain analytics, according to David Simchi-Levi, professor of engineering systems at MIT. Much of the focus in the supply chain is driven by the ability to integrate predictive and prescriptive analytics to, in essence, blend AI and optimization technologies. Specifically, organizations are using AI in two areas of the supply chain: to enhance predictive analytics to understand behaviors and for prescriptive analytics, where optimization technologies take input from machine learning and try to help decision-makers make better decisions, Simchi-Levi said.