Alberto Fabregat - Presales Director EMEA, o9 Solutions
Two Case Studies on AI Powered Forecasting and Realized Improvements:
Case Study 1: Large CPG company (25b+ in revenues): problem: low forecast accuracy and not fast enough to respond to events in the market. We implemented AI Powered Forecasting including drivers like promotions, weather, channel inventory, point of sales, price, discount, etc. to drive significant forecast improvement, reduction of the bias and a faster response to the market.
Case Study 2: Large Automotive supplier (12b+ in revenues): problem: heavily relying on an inaccurate forecast from the OEMs and no ability to triangulate various forecasts: We implemented AI Powered Forecasting including drivers like IHS data, customer forecast, value of exports, steel output, etc. to drive significant forecast improvement. In addition, we built a collaborative model to triangulate various forecasts (e.g. OEM, EDI, ML-Forecast, Sales Account FCST, etc.).
In addition, if ML is perceived to be a black box, users will NOT adopt the solution. In this session you will learn how we make ML explainable and transparent so it does not only boost user adoption but also provides insights for sales to act on!