Multi-period Newsvendor Model

The newsvendor model is a well-known stochastic model for inventory management; however, it was originally developed for a single-period context and focuses on trading companies. This paper proposes an extension of the newsvendor model into a mutli-period setting, aiming to develop a decision-making tool for manufacturing firms to determine the optimal production batch size. The objective function is to maximize operating profit in accordance with generally accepted accounting principles. The model can also incorporate overhead costs, such as warehousing, shrinkage, cost of capital, and lead time between the production decision and output. Monte Carlo simulations demonstrate that the proposed model results in higher profitability compared to other newsvendor models used in our analysis, as well as the safety stock buffer approach. The key feature explaining its outperformance is better adaptability of the production batch size, that leads to fewer stock-outs relative to other newsvendor models and lower inventory levels compared to the safety stock buffer approach. The robustness analysis shows that the proposed model is quite tolerant of mismatches between the “model” and the “true” demand distributions. Finally, we provide some recommendations on selecting the appropriate “model” distribution for different SKUs.

💡 Research Summary

The paper addresses a fundamental limitation of the classic newsvendor model – its single‑period assumption – and extends the framework to a multi‑period setting suitable for manufacturing firms. The authors begin by reviewing the origins of the newsvendor problem (Arrow et al., 1951) and subsequent extensions that have added back‑ordering, price‑dependent demand, and other complexities. However, they note that very few studies have tackled the multi‑period context, and those that do (e.g., Ullah et al., 2019) focus on trading companies and often violate generally accepted accounting principles (GAAP) by mismatching costs and revenues.

The paper first restates the classic single‑period model (Model 1) with expected profit
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