Demand Sensing Solution Utilizes Real-Time Data to Predict Near-Term Demand

This paper details why companies that use demand sensing receive the best forecast for response and execution time horizons, it also provides research from Nucleus about demystifying demand sensing.

Fast-moving trends and changes in consumer behavior make it critical to track demand closely.

Traditional demand planning techniques were developed decades ago when distribution channels were few and history was a relevant predictor of future events. Today this method is insufficient, and there are a number of obstacles to predicting near-term demand:

  • Volatility driven by changes in consumer preferences and social sentiment
  • Growth-through-innovation strategies and rapid stockkeeping unit (SKU) proliferation
  • Promotions and marketing programs designed to shape consumer behavior and change buying patterns
  • Omni-channel and e-commerce strategies that disrupt traditional distribution and make historical orders even less relevant to future demand

Instead of relying on historical information, E2open Demand Sensing uses real-time data across the supply chain to accurately predict near-term demand for make-to-stock products.

The result is a 30 to 40 percent improvement in short-term forecast accuracy compared to traditional time series approaches. Field-proven with more than a decade of use in over 180 countries for $250 billion in annual sales volume, the solution is a core part of the demand-driven digital transformation strategies of leading global companies.


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