## 1 Introduction  
The flourishing development of global technology, the use of artificial intelligence and the Internet of Things, and the integration of data analysis with cloud computing have facilitated great progress in smart manufacturing. This new trend in the technological application and the impact of the COVID-19 pandemic have resulted in a substantial increase in the demand for work from home. Therefore, the market has an urgent demand for high-performance personal computers, data processing servers, 5G smartphones, high-performance logic semiconductors, large-capacity semiconductor memory components, and Wi-Fi modules, which stimulates the development of the electronics industry. Despite the benefits of the increased demand for consumer electronics products, the high frequency of old technologies being replaced with new ones in the electronics industry and the shortness of products' life cycles emphasize the critical importance of supply chain management. Because every link in the supply chain must maintain very high efficiency, warehousing is an indispensable part of the supply chain. Warehousing activities include receiving, put-away, internal replenishment, order picking, sorting, packing, and shipping [1]. Order picking is the process of retrieving and obtaining specific materials from storage locations to complete customer orders and is the most time-consuming and laborious warehousing activity. According to [PERSON] et al. [2], order picking accounts for approximately 55% of overall warehouse operating costs, which indicates the pivotal role of order picking in warehousing activities. Therefore, warehouse managers strive to implement correct warehouse location planning and fast, accurate order picking methods to reduce warehouse operating costs and increase warehousing efficiency. Common methods of optimizing order picking include picking path planning, storage zone planning, optimal storage location planning, and batch picking. These methods are primarily used to reduce picking distance and time. To our knowledge, past studies explored the use of picking path planning and storage location planning separately to reduce overall picking time but have rarely focused on the simultaneous implementation of storage location planning and picking-list clustering to create an optimal picking-list consolidation strategy. Storage location planning is a crucial part of warehouse operations, and an optimal picking operation approach that lacks optimal storage location planning limits the increase in picking efficiency. To overcome this blind spot, this study focuses on improving picking operation performance from the perspective of storage location planning and order clustering. Therefore, this study estimates the demand for component storage space by using order forecast data given by clients to calculate the expected future quantities of required components. Moreover, this study also uses the warehouse data to calculate the average duration of stay (DoS) value of each component in the warehouse. If the component has a less average duration of stay, the component should be placed near the exit of the warehouse to reduce the travel time and distance of picking operation. Accordingly, this study proposes the best suggestion for storage location planning. To increase the picking efficiency, the proposed storage location planning and bill of materials are used as the basis for picking list clustering. Finally, this paper applies the hierarchical clustering method and the K-means clustering method to perform the analysis of picking list consolidation. After the picking capacity of the pickers is considered, a feasible, optimal picking list consolidation strategy is created. The warehouse management personnel can employ the analytical result to consolidate the picking lists so as to significantly reduce the picking time and error rate, thereby reducing manpower and the cost of picking operations. The remaining sections are organized as follows. Related studies on Storage location planning are reviewed in Section 2.