Multi-Echelon Inventory Optimization for Practitioners: a Predictive Global Sensitivity Analysis Approach

Gihan S. Edirisinghe () and Thamer Almutairi
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Gihan S. Edirisinghe: Gordon Ford College of Business, Western Kentucky University
Thamer Almutairi: Carson College of Business, Washington State University

SN Operations Research Forum, 2023, vol. 4, issue 2, 1-20

Abstract: Abstract Intense competition in e-commerce has forced firms to provide highly precise delivery time guarantees such as ‘same-day’ and ‘2-day’ shipping. To meet these stringent standards, it is imperative that companies avoid stock-outs across multi-echelon supply chain networks. Due to their demanding computational requirements, current approaches for optimizing network-wide safety stock levels are challenging tools to use for day-to-day managerial decision-making. We propose a methodology that creates structural equations using regression techniques based on known optimal results. To achieve this, we employ predictive global sensitivity analysis (PGSA), which allows us to create structural equations based on known optimal results using regression techniques. However, these equations do not necessarily require the conventional inputs of models like the guaranteed-service model. Instead, we regress more intelligible independent variables for near-optimal solutions, which suffices in many initial decision-making situations faced by practitioners, such as evaluating different options, what-if-analysis, and scenario planning.

Keywords: Multi-echelon supply chains; Safety stock; Supply chain management; Guaranteed-service model (GSM); Predictive global sensitivity analysis (PGSA) (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s43069-023-00222-7

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