A Random Model of Supply Chain Networks

Abstract

Supply chain problems are frequently formulated as optimization problems over graphs representing complex networks of interlinked input-output relationships. Frequently, these problems are hard, so researchers rely on analyzing stylized structures or developing heuristic solutions. Yet, a scarcity of real-world data has hindered our understanding of how these exact and heuristic solutions perform in practice and whether managerial insights carry over from these simplified settings. We address this critical gap by introducing RG4SC, a versatile random graph model for creating "test tracks" for supply chain management research. RG4SC's simple micro-foundations and interpretable input parameters allow for systematically generating diverse and realistic network structures. We demonstrate its empirical validity and that it more adequately represents real-world supply chain networks than existing random models. We then showcase RG4SC's utility for research through a case study on the Guaranteed Service Model, a widely used framework for safety stock optimization. Our analysis shows how RG4SC can be central to uncovering novel managerial insights, analyzing the computational complexity of algorithms, benchmarking heuristics, and training machine learning models. RG4SC is accessible through a user-friendly web interface at https://scngenerator.pythonanywhere.com/.

Keywords: supply chain networks, random generative models, supply chain management, inventory management, data sets, algorithms, computational complexity