A New Decision Making Tool for Automated Industrial Storage Systems Selection

Rising customer expectations asking for highly customized products and short delivery lead times is putting under great pressure production and logistic systems, which are forced to become more flexible and reactive to demand requirements. On the market a wide range of options is present and new solutions are continuously introduced. This work thesis is divided in two main steps. First, after a general academic introduction on warehousing and storage systems, a thorough analysis of all the existing storage technologies for both pallet unit loads and small unit loads is conducted, in order to gain insights into the alternatives currently available and to highlight their strengths and weaknesses. Especially as far as light loads are concerned a big variety of possibilities are present, but, despite the key role that the appropriate storage and retrieval system plays on warehouse performance, academic research on storage system selection is scarce and a large gap exists between academic literature and the practice of warehouse design. This thesis aims at filling this gap by providing warehouse and distribution center managers a practical tool assisting them in the decision making process of selecting the most appropriate small loads automated storage and retrieval system through the Analytic Hierarchy Process (AHP). Seven evaluation criteria (throughput, picking accuracy, scalability, storage and retrieval interference, flexibility in product dimensions, space utilization, picking ergonomics) are used to prioritize eight different automated storage systems for small-sized unit loads (miniload AS/RS, horizontal carousel, vertical carousel, vertical lift module, automated vehicle S/RS, robot-based compact S/RS, robotic mobile fulfillment system and A-frame). Providing that the classification of alternatives with respect to each evaluation criterion is conclusive, decision makers only attribute the weight to each criterion according to their specific requirements. The developed multi-criterion decision aiding (MCDA) tool returns a ranking of the analyzed systems indicating their degree of appreciation. Such result can be used by the decision-maker as a starting point in the best storage system selection process.