Is it beneficial to invest in flexible capacity for hybrid remanufacturing?
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| Title: | Is it beneficial to invest in flexible capacity for hybrid remanufacturing? |
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| Authors: | Francas, David1 (AUTHOR) francas@hs-worms.de, Öner-Közen, Miray2 (AUTHOR), Minner, Stefan2 (AUTHOR) |
| Source: | International Journal of Production Research. Nov2024, Vol. 62 Issue 21, p7836-7859. 24p. |
| Subjects: | Supply & demand, Stochastic programming, Circular economy, Investment analysis, Process capability |
| Abstract: | Firms that aim to close the loop via remanufacturing returned products face uncertainties on both the demand and supply side. Inspired by industrial circular business models, we study the capacity investment problem of a manufacturer. The manufacturer can invest in a flexible (shared) resource to share capacity across processes and/or less costly dedicated manufacturing and remanufacturing resources. We model the capacity investment problem as a two-stage stochastic programme and provide structural results. Our analysis shows how the optimal resource selection depends on margin (price) and cost differentials and highlights the focal role of capacity coefficients. We identify conditions under which an investment in flexibility is beneficial even if remanufacturing is a lower-margin process. Moreover, an investment in flexible capacity can be optimal if demand and returns are perfectly positively correlated, and thus, return risk is eliminated. Contrary to intuition, optimal profits may decrease in demand-return correlation if the optimal investment includes a flexible resource. The analysis of investment thresholds shows two benefits of resource flexibility: (a) mitigation of demand and return mismatches and (b) an ex-post revenue maximisation option to allocate capacity to the more profitable process. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Production Research is the property of Taylor & Francis Ltd 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.) | |
| Database: | Engineering Source |
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