Simulation Driven Design Of Automated Storage And Retrieval System (ASRS) : A Case Study In Semiconductor Manufacturing - Lim, Wen Chiang (2020)
Industry 4.0 connects digital and physical technologies – artificial intelligence, the Internet of Things, robotics and cloud computing – to drive businesses to be more flexible, responsive, and interconnected in order to have a more informed decision. In a semiconductor case study, the stepping-stone into Industry 4.0 involves a centralised storage and transportation system that begins in test segment. Automation is a broad concept of manufacturing with the purpose of optimizing production and product transactions by making full use of advanced information and manufacturing technologies. In the Backend (BE) sites of the case study manufacturing, CAMSTAR (Manufacturing Execution System) have been enabled as a paperless system used for lot tracking. However, there is no storage location traceability and there are many manual handlings between processes. Automated Storage and Retrieval System (ASRS) opens the doors to data driven backend semiconductor. A system of this expanse naturally involves sequences of revisions and changes along the way that impacts costs, resources and time. This methodology helps realise the best effective method to derive to the design of the ASRS with the help of technology, with theoretical evaluations, simulation and 3D modelling. This project developed a framework for simulating the design of ASRS and other material handling systems and implemented it to the case study in semiconductor manufacturing. It is identified that the critical design parameters of ASRS is the speed and the intelligence of the system. The intelligence of the system here also depends on the relationship or integration of the ASRS with other material handling systems such as conveyors. Based on the findings from the literature reviews, the simulation focused on three main key performance measures which are throughput of the system, resource utilisation or handling capacity of all components and travel time of materials through the system. These performance indicators were simulated against two different designs of automated material handling system for the semiconductor plant. From the analysis of the simulation, specific improvement measures were proposed for the hardware and software that amounts to improvements in the three said performance measures.