What if you knew what your customers were going to purchase before they did?

This is the promise of predictive analytics when applied to demand planning. Many organizations have approached this challenge by creating reports that show past purchases over time. While this can be a useful tool in the hands of an experienced purchaser it is often meaningless to someone new to the role. Understanding seasonality, the impact of holidays, weather and promotions can have a significant impact on a business’ ability to prevent out-of-stock scenarios while not carrying too much inventory that results in waste. Trying to keep track of this at a macro level is one thing, but applying it at a store level can be a daunting task even for the most experienced purchaser—there are just too many variables.

Demand Planning with Machine Learning

Fortunately, the ability to create complex analytic models that can forecast supply chain demand has become more mainstream with the advent of machine learning technologies delivered via cloud platforms. Microsoft’s Cortana Analytics Suite provides a platform that BlumShapiro Consulting has used successfully to solve a variety of predictive analytics challenges. While there are many similarities from one analytics problem to the next, there are just as many differences that require a tailored approach to addressing a specific business problem.

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