As consumer goods companies pursue growth, direct-to-consumer programs and multi-channel marketing, their demand forecasting has suffered. New product configurations, product portfolios and long tail demand have grown. Add the increased influence of demand shaping (media, promotions, NPI) and internet buying behavior, and planners are experiencing a lot of extreme forecast error.
Unfortunately many companies’ supply chain systems and demand analytics can’t handle it. Most are still using outdated forecasting approaches based on cumbersome algorithms and time series of aggregated sales history. The inability to analyze and take advantage of new data is causing forecast accuracy to get worse when it could be getting better.
The good news is that most companies already have the data they need to achieve big improvements in forecasting and in their supply chain. We help consumer goods companies generate more signal with less noise to improve forecast, inventory and service levels. Demand modeling can vastly improve long tail forecasting. Demand sensing can use fresh daily POS or channel data to sense short-term changes. Machine learning can reliably model marketing data and measure customer sentiment and behavior, even including social media. Demand analytics and supply chain optimization can help companies move towards a market-driven forecast.