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 demand planning 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:
- Volatility driven by fast-moving changes in consumer preferences and social sentiment
- Increased pace of new product introductions and the resulting item proliferation
- More varied marketing programs that alter consumer behavior
- Omni-channel strategies that disrupt traditional distribution and make order history of limited use