Estimating aggregated demand months in advance is an established practice, but accurately predicting customer orders over a time horizon of several weeks is next to impossible without advanced data science. E2open Demand Sensing uses automation and machine learning technology to analyze real-time supply chain data, determine the influence of multiple demand signals and produce an accurate daily forecast for every item at every location.
Traditional techniques were developed decades ago when distribution channels were few and history was the best predictor of the future demand. Such techniques are inadequate for near-term forecasting in today’s more complex environment, which presents a number of obstacles:
- Volatilité induite par l'évolution rapide des préférences des consommateurs et du climat socialÂ
- Accélération du rythme d'introduction de nouveaux produits et prolifération des articles qui en résulte
- Programmes de marketing plus variés qui influencent le comportement des consommateursÂ
- Stratégies de type omnicanal qui bousculent la distribution traditionnelle et limitent l'utilité de l'historique des commandes
