A Random Model of Supply Chain Networks

59 Pages Posted: 8 Nov 2024

See all articles by Philippe Blaettchen

Philippe Blaettchen

Bayes Business School (formerly Cass)

Andre Calmon

Georgia Institute of Technology - Operations Management Area; INSEAD - Technology and Operations Management

Georgina Hall

INSEAD - Decision Sciences

Date Written: September 30, 2024

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

Suggested Citation

Blaettchen, Philippe and Calmon, Andre and Hall, Georgina, A Random Model of Supply Chain Networks (September 30, 2024). Available at SSRN: https://ssrn.com/abstract=4971934 or http://dx.doi.org/10.2139/ssrn.4971934

Philippe Blaettchen (Contact Author)

Bayes Business School (formerly Cass) ( email )

United Kingdom

Andre Calmon

Georgia Institute of Technology - Operations Management Area ( email )

800 West Peachtree St.
Atlanta, GA 30308
United States

INSEAD - Technology and Operations Management ( email )

Boulevard de Constance
77 305 Fontainebleau Cedex
France

Georgina Hall

INSEAD - Decision Sciences ( email )

United States

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