Efficient inventory management is critical for automotive recall campaigns. Toyota Canada needed a way to accurately forecast demand for recall parts, but faced challenges such as:

• Limited historical data for new recall events
• Seasonal and regional variation in parts demand
• External factors (e.g., weather, supply chain disruptions) impacting parts consumption
• High costs associated with either overstocking or understocking parts

Without accurate forecasts, Toyota risked customer dissatisfaction, increased storage costs, and logistical inefficiencies.

Toyota engaged CVM to develop an AI-powered forecasting model capable of predicting recall parts demand with greater accuracy.

Key elements of the solution included:

• Data Collection and Preparation: Aggregated historical demand data, campaign activity logs, and external factors such as seasonal patterns and regional trends.
• Predictive Modeling: Built a machine learning model using Python to forecast future parts demand at both national and regional levels.
• Scenario Analysis: Enabled "what-if" analyses to simulate different market conditions and supply chain scenarios.
• Integration into Operations: Provided Toyota’s team with actionable forecasts through dashboards and automated reporting pipelines.

The model was designed for continuous learning — automatically improving its predictions as new data became available.

• Optimized Inventory Levels: Toyota was able to better align parts supply with anticipated demand, reducing excess stock and minimizing shortages.
• Cost Savings: Improved inventory management led to significant cost reductions in storage, shipping, and emergency parts sourcing.
• Improved Customer Satisfaction: Ensured faster availability of recall parts, enhancing customer trust and brand reputation.
• Scalable Solution: The forecasting framework can be adapted to future recall campaigns and other parts-related planning initiatives.

• Predictive AI Can Reduce Operational Risk: Forecasting models help companies better manage uncertainty and volatility.
• Continuous Learning Improves Accuracy: Feeding real-time data back into models ensures that forecasts become even more reliable over time.
• Cross-Functional Collaboration Drives Success: Close engagement with Toyota’s technology, service, and marketing teams was crucial for operational adoption.