Consumption-Based Forecasting and Planning

Preface

Retail and consumer goods executives know that when shaping business plans forecasts serve to temper and balance gut feelings and judgmental bias. Yet, most will admit that their forecasts are still disgracefully inaccurate. There are signs, however, based on early adoption of applying intelligent automation supported by machine learning and traditional predictive analytics that are changing the playing field, particularly for demand forecasting and planning. For example, a large global consumer goods company reduced its global days of finished goods inventory by 1.2 days after improving their overall forecast accuracy from 70% to 81% on average across their product portfolio. That corresponded to a 50 basis points improvement in overall customer service levels. So, you don't need to move the needle that much to gain significant improvements in overall supply chain performance.

The past year of the pandemic has highlighted that companies don't respond quickly to shifting consumer demand patterns, as well as other market disruptions. Companies were already facing many new challenges because of the new digital economy. The unforeseen disruption of COVID-19 worsened the economic uncertainty and market volatility. This perfect supply chain storm has become even more important for commercial teams to explore predictive analytics and automation. Those teams will need new systems to turbocharge their demand forecasting and planning capabilities to capture those shifting consumer ...