AutoStore performance and the influence of context and configurations

Sammanfattning: Problem description Multiple cases show that companies can improve performance after implementing an AutoStore system. However, research on optimizing the design and how to successfully control the AutoStore system is limited. Furthermore, studies focusing on optimizing operations around Robot-based Compact Storage and Retrieval Systems (RCSRS) in general, are important for the development of operation efficiency, but are also currently lacking. Today, Element Logic does not have a clear overview of what, and how, contextual factors and configurations are correlated to performance of the AutoStore system. Purpose Evaluate what, and how, different contextual factors and configurations affect the performance of the AutoStore system, and how they should be handled to increase performance. Research questions RQ1. What contextual factors and configurations are affecting the performance of the AutoStore system? RQ2. How do the contextual factors and configurations affect performance of the AutoStore system? RQ3. How should the contextual factors and configurations be handled in order to improve the performance of the AutoStore system? Methodology Investigation has been using a multiple case study, to locate key differences in ways of working around the AutoStore as well as the influence of different contexts. The multiple case study provided the ability to compare and generalize improvements rather than optimizing one specific case. Findings Seven out of eight investigated contextual factors had an influence on the performance to varying degrees, with their corresponding configurations. This thesis resulted in 14 propositions and recommendations for companies using an AutoStore, and sellers of the AutoStore system. Conclusion Measures to lower complexity and uncertainty, remove time-consuming activities from ports, and align configurations with contextual factors, are advantageous actions when operating an AutoStore.