Eyeing an opportunity to establish a dominant presence in the gigantic China market, one of Japan’s largest supermarket operators is rapidly opening new stores. However, the geography is huge and dense, with various hyper-local influences, their own buying patterns, and preferences. In addition, other operators race to capture market share, creating fierce competition.
The challenge is to run fast while exercising precise control to avoid multiplying errors. For example, accurate revenue and traffic forecasts are needed to determine where, when, and how much to set up and invest in a particular store location.
The sheer volume of analyses to support decision-making is overwhelming. The hyper-locality has made it challenging, even impossible, to collect sufficient stratified data to build accurate ML models.
The management team’s data-only ML approach automated a significant forecasting workload but with low accuracy. Naturally, no one wants to build an automated system to make erroneous decisions faster.