How will AI improve demand forecasting accuracy?

Demand forecasting accuracy rests on the multistep process that starts with a mathematical analysis of past demand...

to detect patterns and trends that can be projected into the future.

There are many sophisticated models and methods used in this process, and the typical forecasting software package will apply a number of these methods, compare the results to recent experience and use the one that has proved most accurate. That statistical forecast is only the beginning.

Statistical forecasting is based on the premise that future demand will resemble past demand, but that's only true in the absence of outside influences. Any new effect is not reflected in historical results, so it is important to try to identify those outside influences and anticipate their effect on the established demand pattern. This second forecasting step uses human logic and intuition, as well as a lot of data, so it is fertile ground for AI.

The essence of AI is to teach machines to emulate human thinking processes. While nobody expects computers to really think like people -- at least in the foreseeable future -- limited, human-like logic is possible, and machine learning makes it able to continually improve by building on its own experience.