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.
AI for better demand forecasting 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 AI 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 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 demand forecasting accuracy.
Dig Deeper on Demand management
Related Q&A from Dave Turbide
Some companies are blazing forward in their efforts to implement industrial IoT, while the issue of standards causes some to hold back until there is... Continue Reading
Sophisticated versions of speech recognition technology are coming to the plant floor. Here's what that looks like and why you should be researching ... Continue Reading
Sustainability is composed of social, environmental and economic pillars. That's why technologies focused on this market address different needs. ... Continue Reading
Have a question for an expert?
Please add a title for your question
Get answers from a TechTarget expert on whatever's puzzling you.