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)
Our model reduced network costs by 45%, for a savings of $59 million!
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