Supply Chain Data Analytics – A Prime Focus Area for Organizations

The growing vulnerabilities within supply chain management have underscored the need for supply chain data analytics. Supply chain data analytics plays a pivotal role in shaping the success journeys of organizations. Supply chain management involves myriad challenges, which only accentuates the importance of supply chain data analytics.

Let us dig deep into how supply chain data analytics can benefit organizations.

Mitigate Inventory Costs through Demand Sensing

Supply chain data analytics can help organizations add value by gaining a clearer picture of their day-to-day operations. This is where predictive analytics comes into play for forecasting future demand. For a long time, organizations had to bank on past orders for estimating future demand, which means the decision-making of organizations was not aligned with future demand trends. Today, organizations can combine all their previous order data with real-time market analyses to create dynamic demand forecasts that prove much more successful at predicting future demand. Better demand sensing via Advanced Analytics helps organizations anticipate future demand changes by reducing their production of the part in question, thus freeing up valuable storage space.

Optimizing Production Plans

Unlike Predictive Analytics, which attempts to forecast future events, Prescriptive Analytics analyzes specific processes in order to identify potential areas of waste or inefficiency. For example, you were attempting to schedule production of one or more automotive parts in a non-clocked, job shop environment. Unlike timed production processes, there is no linear order between the different stations or machines that each part takes and so it is almost impossible to create an optimal job shop production schedule by hand. This has caused supply chain and production planners to be stuck with inefficient processes that fail to maximize production capacity and don’t respond well to disruptions. Prescriptive analytics feeds your existing job shop schedule into one of these analytics workflows, optimizes it, and presents back-up plans in response to real, thereby keeping your production plans on track even in adverse unexpected situations. 

Cross-functional Collaboration

Supply chain data analytics also has operational benefits that are slightly harder to quantify. Predictive Analytics can help promote cross-functional collaboration by creating highly-visible, shareable forecasts that can help disparate teams across a given organization to get on the same page and adhere to a common vision for the future. For instance, your inventory planners are expecting higher demand levels than your transport planners, you could easily wind up in a position where there isn’t enough freight capacity to move existing inventory, or costly inventory space is being used unnecessarily. If both teams are working from the same forecasts, this is much less likely to happen.