B.S. (Bachelor of Science)
Department of Operations Management and Information Systems
This research explores how items are grouped into product families in a manner that reduces travel time (or distance) for operators picking orders in a warehouse. By identifying items commonly ordered together and locating those items together in the warehouse, operators can pick multiple items using a single trip rather than separate trips to different warehouse locations. Using secondary data source provided by our business sponsor, this research uses K-means clustering analysis and Market basket analysis as the primary analytic methodologies to generate SKU families. Other technical tools are MS Excel, MS Access and SAS University Edition. This final report gathers the various deliverables pertaining to the four analytic steps: 1) Getting & Exploring Data, 2) Cleaning Data, 3) Analyzing Data, and 4) Presenting Results. Final results reveal fifty items grouped into 6 families can reduce warehouse picking expenses of $420,000 by $40,134.85. This reflects a 9.6% reduction in total warehouse labor expenses.
Shan, Xiaoyue, "Improving warehouse picking efficiency using product families" (2017). Honors Capstones. 659.
Appendix B_K-Means Clustering-results_6.rtf (719 kB)
Appendix B_K-Means Clustering-results_6
Appendix C_K-Means ClusteringCl2-results.rtf (276 kB)
Appendix C_K-Means ClusteringCl2-results
Appendix D_Highlighted Summary.xlsx (21 kB)
Appendix D_Highlighted Summary
analysis.xlsx (68 kB)
Analysis process excel file
financial.xlsx (28 kB)
Financial excel file
HonorCapstoneData_XiaoyueShan.accdb (138008 kB)
MS Access file
Northern Illinois University
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