Experiences during extraction of variability models for warehouse management systems

Warehouse management systems (WMS) play a critical role in supply chains and large production processes. WMS pose two crucial challenges for variability modeling and management: Firstly, the physical configuration of each warehouse differs significantly. Numerous different electronic devices like controllers, sensors, and motors are used to automate warehouses. Secondly, the processes running in a warehouse demand control and coordination of these various software and hardware components. These processes have different configurations according to customer requirements. This paper reports on our experiences when applying variability modeling techniques in WMS, in order to improve the degree of reuse and shorten the delivery time to customers. Feature modeling is used to extract commonalities and variability in WMS. More than 200 features were identified, and categorized into three hierarchical layers. Furthermore, we linked the feature models to assets, and utilize feature models to support product derivation. Then, the lessons learned from the experiences are discussed. Based on these experiences, we conclude that feature modeling can be applied nicely for scoping features and tracing features to assets, but is not comprehensive enough to support automated configuration during product derivation in WMS.