2018 Forecasting and Inventory Benchmark Study

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2018 Forecasting and Inventory Benchmark Study

Analysis of forecast accuracy, inventory performance, and item proliferation across $250B in sales from global manufacturers using demand sensing and multi-echelon inventory optimization.

Published

June 4, 2026

Read time

3 min read

Source

This benchmark study aggregates operational data from E2open applications to measure forecasting and inventory outcomes across food, CPG, industrial, chemical, and oil and gas sectors. It quantifies the impact of demand sensing, which reduces forecast error by 36% and doubles forecast value-added, and multi-echelon inventory optimization, which cuts safety stock by 31% when combined with sensing. The study also examines how product proliferation drives complexity, with 94% of new items entering the long tail and the slowest 50% of SKUs generating under 0.5% of sales volume.

Key takeaways

Demand sensing cuts forecast error by 36% and doubles forecast value-added versus traditional demand planning.

Multi-echelon inventory optimization combined with demand sensing reduces safety stock by 31%.

The top 10% of items generate 79% of sales while the bottom 50% generate less than 0.5%.

94% of new products enter the long tail in their first year and rarely move to higher velocity.

Active item count grew 36% since 2010 while sales grew only 15%, reducing sales per item by 17%.

Market overview

SCR methodology note

Vendor landscape

Leaders

Implementation considerations

Important consideration

Original Source

Vendors mentioned in this report

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