A Data-Driven Approach to Generate Planned Order Book Scenarios in Multi-Variant Production

Customer satisfaction is an essential goal for long-term business success. Besides offering various possibilities to individualize a product, the implementation of a stable order-to-delivery (OTD) process is indispensable to fulfill customer wishes. However, manufacturers are facing challenges such as dynamic markets and global supply chain networks. Innovative data-driven methods like the concept of planned orders can contribute to increase the stability of production while guaranteeing flexibility and transparency over the entire supply chain. The concept of planned orders enables anticipating customer demands by generating fully defined product specifications–so-called configuration suggestions–, which are then scheduled in the order book and assigned to real customer and stock orders. In doing so, sales and production planning as well as the derivation of material requirements are integrated in the OTD process. This paper introduces a data-driven approach that uses a rating concept and optimization methods to generate upcoming planned order book scenarios in a multi-variant order management process. The configuration suggestions are evaluated taking into account various criteria like flexibility, stability, or sales probability. Coupled with the consideration of given market demands, current trends, and suppliers and factory restrictions, the approach offers the ability to generate valid planned order book scenarios for the upcoming months using Linear Programming (LP) and Integer Programming (IP). This allows the derivation of material requirements and the comparison between different scenarios to strengthen strategic decisions. Besides generating order book scenarios, a concept to support the weighting of the dimensions is applied, which takes into account the strategic orientation as well as operational principles. The presented approach has a high potential to be applied in a practical usage and is validated by a real-world use case of the Dr. Ing. h.c. F. Porsche AG.