Demand Planning and Forecasting Transformation in Supply Chains

White papers

SCP

Demand Planning and Forecasting Transformation in Supply Chains

Expert discussion on demand sensing, data integration, machine learning, and S&OP process gaps in modern supply chain planning.

Published

June 4, 2026

Read time

3 min read

Source

This vendor white paper captures conversations with supply chain executives and analysts on demand planning challenges. It covers structured and unstructured data access, demand sensing limitations, automation of routine planning tasks, and the role of AI and machine learning in improving forecast accuracy. The document also addresses cross-functional S&OP execution, data quality issues, and future technology directions including blockchain for supply chain visibility.

Key takeaways

Demand sensing and demand management remain major gaps due to high uncertainty and changing data inputs.

Companies struggle with integrating structured and unstructured data from internal systems, customers, and suppliers.

Machine learning improves data quality by identifying error patterns and automating cleansing beyond manual or rigid tools.

S&OP processes have not evolved in 30+ years; automation is needed so planners focus on exception handling and scenario analysis.

Future demand planning will blend structured and unstructured data, use predictive models that learn over time, and expand visibility beyond the four walls.

Market overview

SCR methodology note

Vendor landscape

Leaders

Implementation considerations

Important consideration

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