ISCAP Proceedings: Abstract Presentation
Prescriptive Analytics for Reverse Logistics: A Contemporary Case Study
Nazli Turken
Johns Hopkins University
Avinash Geda
University of North Carolina Wilmington
Jeff Cummings
University of North Carolina Wilmington
Abstract
Prescriptive analytics, although existing for several years, has recently gained increasing prominence as a powerful yet underutilized approach for data-driven decisions. This case study presents a timely scenario where students apply prescriptive techniques to optimize a fictional company’s (i.e., HCell) reverse logistics network for e-waste collection in Europe. The challenge involves a strategic decision for the cellphone manufacturer to comply with Europe’s Waste Electrical and Electronic Equipment (WEEE) regulation. Students develop a mixed-integer programming model to decide between retrofitting warehouses or establishing new collection centers for optimal facility locations and material flows.
This case not only introduces students to real-world sustainability and regulatory challenges but also underscores the pivotal role of prescriptive analytics in enabling data-driven decisions for contemporary issues. Students can use various tools like Excel Solver, GAMS, or Python to implement and solve the optimization model, enhancing their skills in algebraic modeling and coding. By connecting theoretical learning with practical application, this case equips students to use prescriptive analytics for complex supply chain decisions under environmental regulations, preparing them for roles in businesses that increasingly rely on advanced analytics for decision support.