Dynamic Operational Policies and Performance Evaluation in Hybrid Warehouse Optimization A Case Study on Iterative Optimization in a Multi-Purpose Hybrid Warehouse

Abstract

Since warehousing contributes to more than 25% of the supply chain’s total cost and largely impacts its efficiency, the importance of optimized and efficient warehouse operations are evident. Growing demand, without increasing operational costs, requires dynamically integrated optimizations operations which can be used iteratively to counter fluctuations in order frequencies. While these challenges are investigated in existing literature, there remains a gap on how to perform this in a multiple purpose warehouse that is using a conventional warehouse complemented by automated storage solutions. Additionally, existing cases mostly discuss one-time solutions, while this thesis aims to address how to successfully iterate the optimization process. This master’s thesis combines quantitative data analysis with qualitative interviews and document analyses to illustrate the current challenges and objectives companies face in their warehouses. Different operational policies and performance evaluations methods and metrics are discussed to decrease the risk of re-shuffling warehouses without increasing their effectiveness. Similarly, different levels of decision making were involved in exploring divergences in challenges and objectives. The study’s findings suggest a class-based approach separating the conventional and automated parts of the warehouse as well as the different product purposes. The automated systems are found to be most effective, an approach utilizing volume and order appearance frequency is therefore used to assign the right products to the automated system. Additionally, by combining slotting, routing and picking policies the rest of the conventional warehouse’s locations are classified. Order appearance frequency was then used to assign the products to the different classes, ensuring minimal travel time and increased efficiency. The performance evaluation metrics and methods provide a foundation for decision makers to know when to iterate the warehouse optimization. By integrating data instead of using an ad hoc strategy it is calculated that the warehouse could save up to 39% on their order picking.