Applying Clustering Methods to Develop an Optimal Storage Location Planning-Based Consolidated Picking Methodology for Driving the Smart Manufacturing of Wireless Modules

Abstract

Picking operations is the most time-consuming and laborious warehousing activity. Managers have been seeking smart manufacturing methods to increase picking efficiency. Because storage location planning profoundly affects the efficiency of picking operations, this study uses clustering methods to propose an optimal storage location planning-based consolidated picking methodology for driving the smart manufacturing of wireless modules. Firstly, based on the requirements of components derived by the customer orders, this research analyzes the storage space demands for these components. Next, this research uses the data of the received dates and the pick-up dates for these components to calculate the average duration of stay (DoS) values. Using the DoS values and the storage space demands, this paper executes the analysis of optimal storage location planning to decide the optimal storage location of each component. In accordance with the optimal storage location, this research can evaluate the similarity among the picking lists and then separately applies hierarchical clustering and K-means clustering to formulate the optimal consolidated picking strategy. Finally, the proposed method was verified by using the real case of company H. The result shows that the travel time and the distance for the picking operation can be diminished drastically.