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.
The relatively new field of prescriptive analytics allows users to “prescribe” a number of different possible actions to and guide them towards a solution. In a nut-shell, these analytics are all about providing advice. Prescriptive analytics attempt to quantify the effect of future decisions in order to advise on possible outcomes before the decisions are actually made. At their best, prescriptive analytics predicts not only what will happen, but also why it will happen providing recommendations regarding actions that will take advantage of the predictions.
Have you developed predictive analytics solutions? If yes, do share learnings from the same!