Determined optimal network configuration for unconstrained scenario, plus two more scenarios with network constraints and pressures.
Compared optimal network configuration costs to calculated baseline (current-state) costs.
Our model reduced network costs by 45%, for a savings of $59 million!
Made data-driven business recommendations based on our findings.
Map of customers (blue) and DCs (orange) (More)
By allowing for split quantities, i.e. relaxing binary constraints on Decision Variables, our model allowed all customers to be served and all demand to be met - even when distribution centers had limited capacity, three distribution centers were closed, and total network demand was increased by 15%. (More)
Tools Used: Regex, Python (Pandas, Numpy), Excel, Solver, OpenSolver
Course: DSB 6200 Supply Chain Analytics, Wayne State University
Assignment: Final Term Project (XRetailer Supply Chain Network Design Problem Case Study)
Submitted: May 7, 2018
Last Updated: May 9, 2018