| dc.description.abstract | Macroeconomic events are putting unprecedented pressure on the warehouse industry. Among these are labor shortages, increased operating costs, and the desire for greater customization and higher throughput from these facilities. Focused on these challenges and strategic issues for warehouse applications, this thesis investigates the obstacles to implementing robotic automation in supply chains. The thesis explores this environment and the lens of using three common integration methods. These are the traditional purchase, lease, and emerging robotic-as-a-service (RaaS) model. With these methods in scope, the study incorporates a multicriteria decision-making framework (MCDM) that is built based on an analytical hierarchy process (AHP) and combined with the technique for order of preference by similarity to the ideal solution (TOPSIS). From this framework, the research identifies key decision criteria and their impact on selecting the most suitable integration strategy for automation.
Through a literature review, the study identified the essential criteria for the project design decision. These include infrastructure requirements, system capabilities, usability, provider reputation, project duration, and the total cost of ownership. We then gained insight from industry professionals familiar with automation integration using a focused field study. Furthermore, we underlined practical issues and general opinions on the criteria and how well they correspond to their integration plans. The results highlight notable trade-offs in the decision criteria, emphasizing the need for a more tailored strategy to make automation adoption more efficient.
This thesis provides an effective decision support system to guide the choice of appropriate automation solutions. It helps clarify how decision makers give the most importance to different criteria when implementing robotic automation. The research findings offer helpful details for practitioners navigating the challenging warehouse automation environment. This, therefore, encourages better informed and more efficient decision-making procedures. | |
