Conservative profitability estimation using Bayesian approach for a newsboy-type product in supply chain.
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| Title: | Conservative profitability estimation using Bayesian approach for a newsboy-type product in supply chain. |
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| Authors: | Su, Rung-Hung1 (AUTHOR), Wu, Chia-Huang2 (AUTHOR) jacalwu@nctu.edu.tw, Shih, Ting-Hui1 (AUTHOR) |
| Source: | Annals of Operations Research. Jun2026, Vol. 361 Issue 3, p1749-1771. 23p. |
| Subjects: | Bayesian analysis, Profitability, Supply chain management, Sensitivity analysis, Manufacturing industries, Inventory control, Newsvendor model |
| Abstract: | Effective inventory management can strengthen supply chain resilience. One of the main factors affecting the profit of the overall supply chain is the sold products at the retail end. Therefore, optimizing the order/manufacturing quantities of stock throughout the supply chain to minimize operation costs, and selecting highly profitable products to launch the market are crucial. The achievable capacity index (ACI) is a measurement tool to characterize the profitability of a newsboy-type product with normally distributed demand under optimal order/manufacturing conditions. From a frequentist perspective, the lower confidence bound (LCB) on ACI has been derived to express a conservative estimate of profitability. However, the indeterminacy of information pertaining to demand means that Bayesian statistical techniques could also be considered. Our primary objective in this study was to investigate the measurement of LCB on ACI using the Bayesian approach. The results of extensive numerical simulations under various sample sizes, credible levels, and estimates have also been tabulated as a reference for retailers. Besides, ACI can also be applied in the manufacturing industry. The manufacturer can adopt ACI to assess the profitability of a production order, in particular the contract order involving the manufacturing item comprised of single-period material. Therefore, this paper provides an example application involving the manufacture of car body kits to illustrate the practicality of the proposed method. Sensitivity analysis was also performed on various cost and statistical parameters to derive managerial implications and guidance for decision-making. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Effective inventory management can strengthen supply chain resilience. One of the main factors affecting the profit of the overall supply chain is the sold products at the retail end. Therefore, optimizing the order/manufacturing quantities of stock throughout the supply chain to minimize operation costs, and selecting highly profitable products to launch the market are crucial. The achievable capacity index (ACI) is a measurement tool to characterize the profitability of a newsboy-type product with normally distributed demand under optimal order/manufacturing conditions. From a frequentist perspective, the lower confidence bound (LCB) on ACI has been derived to express a conservative estimate of profitability. However, the indeterminacy of information pertaining to demand means that Bayesian statistical techniques could also be considered. Our primary objective in this study was to investigate the measurement of LCB on ACI using the Bayesian approach. The results of extensive numerical simulations under various sample sizes, credible levels, and estimates have also been tabulated as a reference for retailers. Besides, ACI can also be applied in the manufacturing industry. The manufacturer can adopt ACI to assess the profitability of a production order, in particular the contract order involving the manufacturing item comprised of single-period material. Therefore, this paper provides an example application involving the manufacture of car body kits to illustrate the practicality of the proposed method. Sensitivity analysis was also performed on various cost and statistical parameters to derive managerial implications and guidance for decision-making. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 02545330 |
| DOI: | 10.1007/s10479-025-06503-0 |
