Virtual Build to Order:
Having Cars in Stock That Customers Will Buy Tomorrow

The automotive industry faces a major challenges in ensuring customer satisfaction. Car dealers must reliably predict which variants and configurations of vehicles they need to stock to meet customer demand.

The variety of configuration options and consumer preferences—based on region, culture, age, and individual taste—make this a daunting challenge. They had to analyze data from different production series and order types, several planning units, more than 140 markets, over 3000 feature options and 15 million planning elements.

Previously, this task had to be done by 14 people manually managing various spreadsheets on a daily basis, trying to understand what features will be needed in the future. Car dealers mostly base their decisions on what models to order on gut feeling. As a result, potential sales are lost if customers cannot be supplied with their desired car. Also, vehicles have long downtimes if they do not have the required equipment.

The customer first gained full visibility of their data landscape through the interactive data map to make best use of their data. Then, One Data automatically collected and connected meaningful data and metadata across the customer’s entire ecosystem, including external and 3rd party data, e.g., from end-customers, partners, and market data.

Out of that, a high-quality data product was created. This makes it possible to indicate customer preferences in advance based on, for example, a customer’s internet configuration data and historical sales data from dealers.

The client uses the data product to predict customer preferences and future orders in different markets. It automatically analyzes various company and customer data, as well as Internet configurations and retailer sales histories while ensuring data quality and reliability. This automatically generates preconfigured orders. The Virtual Build to Order data product eliminates the need for manual forecasting and allows dealers to reliably pre-order vehicles based on historical sales data.

By automating their forecasting, our customer is saving 14 people a lot of time compared to previously manually analyzing spreadsheets. Now, they can shift their focus to more value-added tasks. In the future, the company can make use of a repeatable, reliable process that does not require constant oversight. In addition, the customer has significantly improves its data governance by using One Data to gain insight into, understand, and ensure the quality of data products.

With the data product, the market potential can be exploited much better. In addition, reduced inventory and optimized pre-configurations lead to increased sales. Planning accuracy has improved by 3%, and customer satisfaction has increased because vehicles are more readily available.