Next-Generation Forecasting: Moving from Traditional to Market-Driven Demand Models

White papers

SCP

Next-Generation Forecasting: Moving from Traditional to Market-Driven Demand Models

Explains four evolutionary stages of forecasting that improve accuracy by incorporating granular data, downstream signals, and machine learning.

Published

June 4, 2026

Read time

3 min read

Source

The white paper outlines why traditional top-down forecasting fails in complex, multi-channel environments and details an incremental path to market-driven forecasting. It covers statistical bottom-up forecasting at the item-location level, outside-in demand sensing using POS and channel data, and machine learning models that incorporate promotions, media, and web signals. Real-world results from Danone and Costa Express demonstrate measurable gains in forecast accuracy, reduced inventory, and lower lost sales.

Key takeaways

Traditional aggregated forecasting loses demand signal detail and increases error at item-location level.

Bottom-up statistical forecasting preserves item-location variability and improves accuracy for intermittent and long-tail SKUs.

Outside-in demand sensing with downstream data reduces forecast error and bullwhip effect by nearly 50 percent in benchmarks.

Machine learning models that incorporate promotions, media, and web metrics can cut forecast error by 20 percent and obsolescence by 30 percent.

Companies already possess most required data; the barrier is integration and advanced analytics rather than data availability.

Market overview

SCR methodology note

Vendor landscape

Leaders

Implementation considerations

Important consideration

Original Source

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