With the flood of data available to businesses regarding their supply chain these days, companies are turning to analytics solutions to extract meaning from the huge volumes of data to help improve decision making. Huge ROI’s can be enjoyed as evidenced by companies that have optimized their supply chain, lowered operating costs, increased revenues, or improved their customer service and product mix. However, analyzing all the analytics options can be a daunting task, and hence they are best addressed into three distinct types- descriptive, predictive and prescriptive.
Descriptive Analytics, which use data aggregation and data mining techniques to provide insight into the past and answer: “What has happened?”
Predictive Analytics, which use statistical models and forecasts techniques to understand the future and answer: “What could happen?”
Prescriptive Analytics, which use optimization and simulation algorithms to advice on possible outcomes and answer: “What should we do?”
No one type of analytic is better than another, and in fact, they co-exist with, and complement each other.
Predictive analytics has its roots in the ability to “Predict” what might happen. These analytics are about understanding the future. Predictive analytics provides companies with actionable insights based on data. Predictive analytics provide estimates about the likelihood of a future outcome. It is important to remember that no statistical algorithm can “predict” the future with 100% certainty. Companies use these statistics to forecast what might happen in the future. This is because the foundation of predictive analytics is based on probabilities.
Have you developed predictive analytics solutions? If yes, do share learnings from the same!