Enhancing Order-Picking Efficiency through Data Mining and Assignment Approaches

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Abstract: Using data mining techniques, this study attempts to explore how a proper layout zoning following class-based storage enhances order picking efficiency over randomized storage. Association web statistics and association rule mining examines the relative intensity levels among various product categories. These findings serve as the layout for zoning of the storage area of company, a pharmaceutical industry master distributor. A class-based assignment policy is then proposed. Finally, by transforming accumulated orders into Pallet-Case-Broken case (PCB) data and adopting batch picking, this study compares random assignment policy with zoning and class-based assignment policy. The results conclude that zoning and class-based assignment policy decreases travel distances by 24% and improves picking times by 21%.

WSEAS Transactions on Business and Economics, ISSN / E-ISSN: 1109-9526 / 2224-2899, Volume 11, 2014, Art. #5

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Yi-Fei Chuang, Shui-Hui Chia, Jehn-Yih Wong, "Enhancing Order-Picking Efficiency through Data Mining and Assignment Approaches," WSEAS Transactions on Business and Economics, vol. 11, pp. 52-64, 2014, DOI:
Yi-Fei Chuang, Shui-Hui Chia, Jehn-Yih Wong. Enhancing Order-Picking Efficiency through Data Mining and Assignment Approaches. WSEAS Transactions on Business and Economics. 2014;11:52-64.
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