Through a special arrangement, presented here for discussion is a summary of a current article from the blog of Nikki Baird, VP of retail innovation at Aptos. The article first appeared on Forbes.com.
The forecast error in retail is as high as 32 percent, according to some estimates. Will artificial intelligence (AI) technology do any better?
AI promises to change the way demand forecasting works in retail in six key ways, but those promises include a bit of hype:
- AI can examine a nearly unlimited number of causal factors simultaneously. AI’s accuracy requires the right kind of data.
- AI can apply analysis to every granular SKU, across every granular location where it will be sold. Problems arise when you start getting into very sparse or highly intermittent demand history.
- AI can apply advanced algorithms, like neural nets, to create new methods of forecasting. A wide enough variety of use-cases and data sets have not been tested to say that a neural net approach to forecasting should outright replace traditional models or even AI models that take a different approach.
- AI can select the right forecasting model to use for each specific circumstance. Where things get interesting is when you start looking at the next place where AI can be applied to demand forecasting, in the attributes that are used to identify when different products should use similar models.
- AI can identify when specific causal factors deteriorate in their contribution to the forecast and replace them with new, more important causal factors. Some AI-assigned attributes are not easily expressed in language that humans can understand. If misunderstood, AI’s recommendations can drive a company in the wrong direction.
- AI can react to changes in demand assumptions much more quickly than a human can. My sense is that it’s still an open question as to whether AI is really good at this or even if it’s needed.