Typology and literature review on multiple supplier inventory control models

• Published in: European journal of operational research Vol. 293; no. 1; pp. 1 - 23
• Main Authors: Svoboda, Josef, Minner, Stefan, Yao, Man
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 16.08.2021
• Subjects: Inventory control; Multiple sourcing; Review; Typology; Inventory control; Multiple sourcing; Typology; Review
• ISSN: 0377-2217; 1872-6860
• DOI: 10.1016/j.ejor.2020.11.023

•We present a typology and classify the literature on multiple supplier inventory models.•The development of modeling assumptions and applied methodologies are discussed.•Existing research gaps and future research opportunities are identified. This paper reviews the literature on inventory models with multiple sourcing options and presents a typology for classification. By means of the classification, the progression of the literature (policies and modeling assumptions) is illustrated, the main decision trade-offs in multiple sourcing are identified and avenues for future research are pointed out. Multiple sourcing decision models trade off the added costs of backup sourcing against higher inventory or shortage costs under single sourcing. The value of multiple over single sourcing is found to increase in the uncertainty to be buffered, in inventory holding and shortage costs, as well as in the constraints of the primary source. The literature evolved from small, restrictive models to larger problems and more realism. Accordingly, replenishment policies progressed from optimal policies to more heuristic decision rules. Three areas for future research are suggested for moving the field forward and towards more practical applicability. (1) Further integration of model aspects such as the extension of replenishment policies to more than two suppliers and to multi-echelon models. (2) Focusing on supply chain resilience with decision making disruption events or demand spikes under consideration of risk preferences. (3) Utilizing industry data in machine learning and data-driven methodologies.