Success Story

Standardized Demand Forecasting for a Supply Chain distributor, enabling data-driven insights to support planning and adapt to market fluctuations

About the Client

Supply Chain Distributor

Challenges

  • Absence of a standardized and repeatable process for generating data-driven demand forecasts.
  • Inability to measure the predictive accuracy of demand at the required granularity, hindering effective inventory planning.
  • Increasing market volatility impacting both demand volumes and price stability, making traditional forecasting methods obsolete.

Approach

  • Needs Assessment: Analyzed existing forecasting methods and identified gaps in accuracy, scalability, and adaptability to market shifts.
  • Model Development: Designed and trained a machine learning (ML)-enabled demand forecasting model tailored to the client’s industry and operational needs.
  • Control Tower Deployment: Implemented an analytics dashboard to provide real-time insights into demand patterns, forecast accuracy, and inventory optimization.
  • Performance Evaluation: Established a continuous feedback loop to monitor model performance and refine predictions as market conditions evolved.

Solutions

1. Built an ML-powered demand forecasting system capable of delivering high-accuracy predictions at the necessary granularity to inform planning decisions.

2. Deployed a Control Tower solution, empowering the client with on-demand analytics and actionable insights into demand shifts and forecast performance.

3. Enabled scenario analysis to help the client adjust to price volatility and changing market conditions dynamically.

Impact Delivered

10

Percentage points higher forecasting accuracy compared to incumbent methods.

Of branches reduced underage and overage deviations, optimizing inventory levels

Enhanced operational agility, enabling proactive planning and improved response to market fluctuations.