Unlocking Agility and Precision Through Demand Planning Innovation

Unlocking Agility and Precision Through Demand Planning Innovation Case Study Thumbnail
Solution Demand Planning
Solution Demand Planning
Industry Manufacturing
Region North America
Technology AWS
Context
A leading North American tire manufacturer sought to modernize its demand planning operations amid rising forecast volatility, long runtimes, and limited scalability. Partnering with MathCo, the company implemented a cloud-based, AI-powered forecasting solution to streamline processes and improve performance. The initiative led to a 5% increase in forecast accuracy, a 7% reduction in volatility, and a 60% decrease in model runtime, enabling faster planning cycles and more agile, data-driven decisions.
Problem Statement

Our client struggled with slow and inaccurate demand forecasts due to manual processes, fragmented systems, and a lack of standardization. Forecasts took over 12 hours to run, lacked seasonality, and achieved only 40-50% accuracy for key products, leaving planners unable to trust projections for inventory and production decisions. High material-level volatility, limited scalability, and inconsistent feature selection further hindered performance. They needed a scalable, data-driven forecasting solution to boost reliability, reduce complexity, and support agile planning across commercial and consumer segments.

Impact

  • Achieved ~5% improvement in accuracy for top product lines through model tuning and feature optimization.
  • Realized a ~7% reduction in volatility by refining model logic and introducing differentiated outlier handling.
  • Cut forecasting pipeline runtime drastically from over 12 hours to a more agile and manageable cycle.
  • Established a flexible and transparent architecture that supports ongoing evolution and integration with business processes.

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