Supply chain analytics are everywhere. Consider the similarities between a grocery list and a demand forecast: Before going to the store, you note which groceries you already have in your home. Next, you think about how much of each item you used in the past. Based on this information, you can predict how much of each item you need to purchase. In this micro example, you are acting as a supply chain analyst.

As you look at the implications of a larger-scale supply chain analysis, you'll grasp the complexity that organizations face in making accurate demand forecasts. When grocery shopping, if you make mistakes, you can just go on another trip and correct the purchase. In business situations, however, a mistake could mean a significant loss. In this case, you want to make decisions in a scientific and proven way.

In this course, you will measure performance based on an existing dataset. You will then determine the best forecasting method based on the given data. Finally, you will expand the application of this data by calculating a forecast for future demand and considering holistic approaches for mitigating risk, applying practical skills to incorporate into your future work with supply chain analytics.