## 5 Conclusions  
Within the supply chain process, picking operation is the most time-consuming and labor-intensive activity. Warehouse managers need to employ the smart manufacturing method to boost the picking efficiency to reduce warehouse operating costs. Due to storage location planning as a key factor affecting picking efficiency, this research firstly applies warehouse data to calculate the average DoS values and estimate the storage space demands by the customer orders to develop an optimal storage location planning to reduce the time for the pickers to search and retrieve the components from the shelves. Based on the macro-level optimal result, this research utilizes the hierarchical clustering method and the K-means clustering method to find the picking lists with high similarity to help warehouse managers create the best picking-list consolidation strategy for decreasing the distances and trips traveled by pickers and increasing the picking efficiency. To validate the feasibility and effectiveness of the proposed approach, a case study of new storage location planning and consolidated picking process was illustrated.  
To sum up, this study makes several significant contributions explained as follows. To our knowledge, many studies have investigated picking route planning, storage location planning, and batch picking separately and have rarely considered combining two or more above-mentioned factors. Because storage location planning affects picking performance, this research uses the storage space demands of the components and their DoS values in the warehouse to optimize the storage locations of the components. In addition, this study develops a method of calculating DoS values to ensure that they reflected the warehouse operations of company H. Moreover, the hierarchical clustering method and the K-means clustering method were used to effectively consolidate multiple picking lists and reduce the number of picking trips. According to the study result, the performance of the K-means clustering method is more effective than that of the hierarchical clustering method. Furthermore, the proposed method used the class-based storage strategy to improve on company H's haphazard storage method, which was based on rules of thumb and reduced the time required by pickers to locate components. Therefore, the optimal picking list consolidation strategy proposed by this study enabled pickers to pick components from multiple picking lists and decreased the number of picking trips. This study increased pickers' work efficiency and streamlined the picking process, and the results can provide a practical reference for the warehouse managers in company H and other companies. For the generalization of the proposed method, it can be generally applied to other manufacturers with the same enterprise context with the intelligent machinery under Industry 4.0 to enhance the efficiency and effectiveness of storage location planning and consolidated picking. It is hoped that this work provides practical guidance for achieving a more advanced storage location planning and consolidated picking in intelligent machinery and Industry 4.0.  
Conceptualization, T.-A.C. and Z.-H.C.; methodology, T.-A.C. and Z.-H.C.; software, T.-A.C. and W.-C.L.; validation, T.-A.C., Z.-H.C. and W.-C.L.; formal analysis, T.-A.C., Z.-H.C. and W.-C.L.; investigation, T.-A.C. and Z.-H.C.; resources, T.-A.C.; data curation, T.-A.C.; writing--original draft preparation, T.-A.C., Z.-H.C., C.-H.L. and W.-C.L.; writing--review and editing, T.-A.C., Z.-H.C. and C.-H.L.; visualization, T.-A.C. and Z.-H.C.; supervision, T.-A.C.; project administration, T.-A.C.; funding acquisition, T.-A.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Ministry of Science and Technology, Taiwan, ROC, grant number MOST 110-2221-E-141-010.  
Not applicable.  
The authors declare no conflict of interest.