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.

Solution

• Built an ML-powered demand forecasting system capable of delivering high-accuracy predictions at the necessary granularity to inform planning decisions.
• Deployed a Control Tower solution, empowering the client with on-demand analytics and actionable insights into demand shifts and forecast performance.
• Enabled scenario analysis to help the client adjust to price volatility and changing market conditions dynamically.