Use analytics for supply chain

- Supply chain analytics is a hot topic and one that's full of buzzwords. So it's easy to get confused when people talk about descriptive, predictive, and prescriptive analytics. But understanding the differences between them can help you make better choices about the tools and the skills that you really need to move your supply chain forward. Let's look at three kinds of questions that supply chain managers often need to answer. Then we can see how analytics can help with each of them. The first kind of question we often face is what happened in the past. When we answer questions about the past, we use descriptive analytics. Descriptive analytics involve structuring and filtering data and then looking for patterns. For example, you could use descriptive analytics to look backwards and determine the impact of a promotion or to calculate the savings from a project. The second kind of question we often need to answer is what will happen in the future. To answer these questions, we build on descriptive analytics to create forecasts. This is called predictive analytics. You'd use predictive analytics to do things like estimating the sales of a new product or forecasting the price of transportation. The third kind of question we often have to answer is what should we do and to answer those questions, we need a different approach called prescriptive analytics. Prescriptive analytics basically involves looking at lots of different scenarios and then making a recommendation and prescribing the best course of action. For example, prescriptive analytics could involve monitoring weather forecasts and then telling us which port to route a shipment through in order to minimize the threat of a disruption. Prescriptive analytics often involves creating models and running simulations. It's also one of the common uses for artificial intelligence. Knowing that we have these three different approaches to analytics, the question you should ask is what kind of analytics should I use. The answer depends on the improvements that you're trying to drive in your supply chain. It often helps to create a matrix like this one that maps your technologies to your goals. For example, if you're trying to reduce costs, increase revenues, and lower risk, then you can use this chart to show where each analytics project and each analytics tool fits in. You might have a project to improve forecasting accuracy. That would help you increase revenues and decrease costs. And a new supply chain control tower could be designed to lower your risk using prescriptive analytics. For a deeper dive and some hands on practice with analytics, check out John Johnson's course Data Analytics for Business Professionals. When you structure and filter your data correctly, you'll uncover trends and patterns that lead to important insights about your supply chain. Descriptive, predictive, and prescriptive analytics are three different approaches that can help you answer important business questions and most of all, if you use them correctly, each of these approaches can help you make better supply chain decisions.