Optimize distribution center
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This document discusses optimizing the distribution center network for Target. It analyzes the current distribution center and store locations and cargo amounts. It then uses a gravity method to realign the distribution center locations to reduce transportation costs. By applying this approach to all distribution centers, the analysis estimates it could save 32% of transportation costs compared to the original configuration.
Optimize distribution center
- 1. Optimize Targets Distribution Center Gyeoul Jung Find optimal location using python JungWinter
- 2. Visualize stores, RDCs position Calculate transport ation cost Analysis current status Analysis monthly cargo of store Realign RDC using gravity method RDC: Regional Distribution Center Flow
- 3. Environment Data DC network map.pdf 2010 STORE LIST 110309.xls Tool Python 3.5 Jupyter notebook Address to GPS coordinate Library Pandas Numpy Matplotlib Scikit-learn
- 4. Analysis Extract required columns from STORE LIST and import to jupyter stores is DataFrame object of 1752x23 matrix
- 5. Analysis Visualize to scatter plot using X, Y columns
- 7. Analysis Check noise data Alaska & Hawaii
- 8. Analysis Re-visualize except Alaska and Hawaii stores
- 9. Analysis Analysis monthly cargo of store First store
- 13. Analysis Check validation using RDCs in/outbound
- 16. Analysis Visualize first RDC - 551
- 18. Analysis Weighting according to cargo
- 19. Analysis Convert MILES between RDCs and stores to X, Y position
- 20. Analysis Mapping between RDC Store To give different colors according to RDC
- 21. Analysis Convert address to X, Y position Longitude is X Latitude is Y
- 22. Analysis Set position of RDCs
- 23. Analysis Visualize RDCs with stores Store RDC
- 24. Analysis Current Status
- 25. Analysis Find optimal location of RDCs
- 26. Analysis Realign 551 RDC using gravity method
- 27. Analysis Check the new 551 RDCs position
- 28. Analysis Apply to all RDCs
- 29. Analysis Apply to all RDCs
- 30. Analysis Before comparing original and new cost based on location Required calculating distances between new location(X, y) and original stores
- 31. Analysis Mapping stores X, Y with MILES
- 32. Analysis Mapping stores X, Y with MILES
- 33. Analysis Mapping stores X, Y with MILES Different of X, Y between first Store with 551 RDC = 15 miles
- 34. Analysis Mapping stores X, Y with MILES X-Y distance To miles X-Y distance To miles
- 35. Analysis Mapping stores X, Y with MILES Obtaining the conversion formula gps distance and mile
- 36. Analysis Mapping stores X, Y with MILES Correlation coefficient R2 = 0.98
- 37. Analysis Mapping stores X, Y with MILES - 551 RDC Regression using machine learning
- 38. Analysis Mapping stores X, Y with MILES - all Get Mile = W * gps_diff + b function
- 41. Result Comparing Save the 32% cost
- 42. Limitation of this study Consider the cost of the RDC migration Fix RDC-Store mapping Consider second option