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. These statistical forecasts are 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 AI in demand forecasting is a fertile ground.
The essence of artificial intelligence 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.
AI demand forecasting for better accuracy
How does that work to improve demand forecasting accuracy? It enables the process of gathering the outside data, including factors such as demographics; economic data; so-called leading indicators appropriate to the company's markets; known or anticipated competitive actions, such as pricing changes or promotions; and the like. The AI system can, at first, apply these factors as instructed by the users, measure the results and incrementally improve the process as it identifies what works and what doesn't.
Humans could do this, too, of course, but artificial intelligence and machine learning should be able to handle much more data, test far more possibilities and be more sophisticated in its analysis by testing hundreds of models and possibilities and being more precise in its analysis and refinement of the process. Presumably, AI for demand forecasting will be more sensitive to and better able to adapt to new information and emerging changes, such as new product introductions, supply chain disruptions or sudden changes in demand -- in turn, improving the accuracy of demand forecasts.