Forecasting requirements for production parts can be a relatively simple task. The need for these components, which are used to manufacture new vehicles and equipment, can be driven by production schedules and purchased in planned, economic quantities.
By contrast, service parts forecasting has varied and unique challenges. These parts are needed decades after the original production, yet, when planned well they contribute profit margins that far exceed that of the original vehicle. Leading OEMs have hundreds of thousands of parts supporting thousands of vehicle models in service. This is not a task that can be done by hand.
What does it take to successfully manage the demand for service parts? It requires a balance between profitability, investments in inventory and customer service. Sounds easy, but it means making hundreds of detailed decisions each week: what to buy, how many to buy and when to make a final buy of a part.
At NovoDynamics, we have spent over a decade developing and operating service parts forecasting systems for some of the worlds largest automotive OEMs. Our ForeCee solutions develop a deep understanding of the history of each part and the relationships between the vehicles and equipment that use them. Simple forecasting models can handle short-term forecasts when demand is steady from month to month. When long-term forecasts are needed our solutions really shine. We use state-of-the-art techniques in data analytics and machine learning to continuously evaluate and select the best model for each part.
The result? One large automotive customer has experienced a consistent reduction of 20 – 30% in their scrap and obsolescence while maintaining high service levels to its dealers and customers.