JOURNAL ARTICLE
Enhancing Customer–Supplier Coordination Through Customer-Managed Inventory.
Published In: Management Science (INFORMS), 2024, v. 70, n. 12. P. 9073 1 of 3
Database: Business Source Ultimate 2 of 3
Authored By: Chen, Shi; Cohen, Morris A.; Lee, Hau 3 of 3
Abstract
This article focuses on analyzing customer-managed inventory (CMI) as a supply chain strategy where the customer controls the supplier's inventory decisions to improve responsiveness and overall supply chain performance. It develops a two-echelon inventory model capturing the interaction between a supplier and a customer, showing that successful CMI implementation requires appropriate incentive mechanisms, including cost-sharing and wholesale price negotiation, to achieve mutually beneficial outcomes. Due to the complexity of the coupled model, the authors propose a decoupled approximate model with pseudo cost parameters that yields near-optimal, closed-form solutions facilitating practical policy design. Using a case study based on Hewlett Packard's network printer division, the study demonstrates that properly designed CMI contracts can reduce total inventory-related costs and highlights operational conditions—such as long supplier lead times and high supplier holding costs—that favor CMI adoption. The article concludes that while CMI offers significant benefits, its success depends on carefully structured incentives and acknowledges that additional factors like multisourcing and product design coordination warrant further research.
Additional Information
- Source:Management Science (INFORMS). 2024/12, Vol. 70, Issue 12, p9073
- Document Type:Article
- Subject Area:Engineering
- Publication Date:2024
- ISSN:0025-1909
- DOI:10.1287/mnsc.2021.03658
- Accession Number:181483490
- Copyright Statement:Copyright of Management Science (INFORMS) is the property of INFORMS: Institute for Operations Research & the Management Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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