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Demand Modeling for Configurable Products in the Automotive Industry

Demand Modeling for Configurable Products in the Automotive Industry

Petrol or diesel? Automatic or manual? Cloth or leather? Standard or metallic paint? Standard or alloy wheels? Sport suspensions? Engine displacement? Steering technology? Armrest? Cruise control? Parking pilot? Heated seats? Sun protection glass? The options seem endless. 


For most new car customers, far from being a burden, an exhaustive array of choices assures the best value and provides a satisfying level of personalization. More choices can also drive customers to certain brands and model preferences. For example, one German company offers more than 70 configuration options on a single model, theoretically allowing for more than 1024 different production combinations for that car alone. But while providing this abundance, how can a manufacturer efficiently maintain, produce, and deliver such an array of product variants? To succeed in this hyperconfigurable world, manufacturers need to combine smart forecasting of customer demand with careful supply chain planning and execution.

Demand Modeling for Configurable Products

Performance and Insight Optimization Services from SAP offer an intelligent approach called demand modeling for configurable products. SAP automotive and process experts designed a rigorous scientific model for forecasting customer demand, which involves analyzing historical sales data to infer behavioral information about the target customer segment. Each individual customer is viewed as an intelligent agent with a limited budget – one deriving certain utility values from a product and from each of its configurable options. The customer will choose the model and options configuration that maximizes utility while complying with budget constraints. Each customer is then fitted onto a general population that describes decision trends from a macroscopic point of view. The result? A highly accurate tool for prediction and what-if analysis for configurable product production.

For example, an automobile manufacturer can assess the number of new customers attracted from the competition by discounting the climate control system on a certain model of car. SAP’s analytical model also lets the manufacturer evaluate any resulting internal cannibalization of its other options and models. Using such information, an automaker can run multiple pricing scenarios to achieve the optimal price mix for increased revenue and maximum profit. Moreover, it can accurately predict the number of units, broken down by model and option configuration, it should produce – a huge benefit for inventory planning and supply chain efficiency.


A sophisticated mathematical approach to demand modeling based on historical sales data is a critical step, one that allows automobile manufacturers to determine exactly which configurations to offer at what price. In addition to supporting a competitive advantage, this level of demand insight can lead to optimized financial, sales, and operations planning across the supply chain and throughout the product lifecycle.

To find out more about how predictive analytics and mathematical models can help to optimize your business, visit

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