電子商務的快速發展及技術的更新，產品開發週期越來越短。產品的生命周期不斷縮短，市場的快速變化迫使供應鏈需要更快速的了解需求並做出決策，當新產品推出，缺乏確切的需求資訊會顯著提高供應鏈的不確定性，因而影響決策的複雜度。過多的庫存可能導致存貨壓力，而庫存不足則可能導致缺貨和失去盈利的機會。在供應鏈決策中可透過需求學習取得資訊來更新決策，對於具有短生命週期的產品，如何有效地進行需求學習並實現利潤最大化成為一個重要議題。
本研究採用了兩期的報童模型來分析具有短生命週期的產品在供應鏈中的決策問題。此報童模型包括了一個製造商和一個零售商。製造商負責生產和確定產品批發價格，然後將產品供應給零售商。而零售商則根據製造商確定的批發價格來決定自己的訂貨量，目的是滿足市場需求，同時雙方都追求利益最大化，其中考慮了預售期間的需求學習效果，除此之外我們還考量了需求審查對學習需求所帶來的影響。我們考量了不同的定價策略對供應鏈與其個別成員所帶來的影響，分別對靜態與動態批發價格策略進行了模擬，以比較在需求不確定性環境下的效能及決策的不同。
在數值分析中我們發現了需求學習在供應鏈具有雙向學習的特性，在未來產品預期價值較高、景氣較好時，零售商較有利可圖，否則，在資訊共享的前提下，製造商制定出的最佳決策將使得零售商利益受損。研究建立的模型和分析旨在為供應鏈管理提供新的見解，尤其是在新產品推出的關鍵時期。藉此，我們期望能夠幫助企業在需求不確定性及短生命週期產品條件下，制定出更有效的營運策略。

This study adopts a two-period newsvendor model to analyze the decision-making problems in the supply chain for products with short life cycles. The model involves a manufacturer and a retailer. The manufacturer is responsible for production and determining the wholesale price, subsequently supplying products to the retailer. The retailer, in turn, decides on the order quantity based on the manufacturer's determined wholesale price, aiming to meet market demand while both parties seek to maximize their profits. The model considers the effects of demand learning during the pre-sales period, as well as the impact of censored on demand learning. We examine the effects of different pricing strategies on the supply chain and its individual members, simulating both static and dynamic wholesale pricing strategies to compare performance and decision-making under demand uncertainty.
In our numerical analysis, we discovered that demand learning exhibits a bidirectional learning characteristic within the supply chain. When the expected future product value is high and the economic conditions are favorable, retailers tend to be more profitable. Conversely, under the premise of information sharing, the optimal decisions made by manufacturers may result in reduced profits for retailers.
The model and analysis established in this study aim to provide new insights for supply chain management, especially during the critical period of new product launches. Through this, we hope to assist businesses in formulating more effective operational strategies under conditions of demand uncertainty and short product life cycles.

Adida, E., & DeMiguel, V. (2011). Supply chain competition with multiple manufacturers and retailers. Operations Research, 59(1), 156–172.
Azoury, K. S. (1985). Bayes solution to dynamic inventory models under unknown demand distribution. Management Science, 31(9), 1150–1160.
Cachon, G. P., & Fisher, M. (2000). Supply chain inventory management and the value of shared information. Management Science, 46(8), 1032–1048.
Cachon, G. P., & Lariviere, M. A. (2005). Supply chain coordination with revenue-sharing contracts: strengths and limitations. Management Science, 51(1), 30–44.
Caro, F., & Gallien, J. (2012). Clearance pricing optimization for a fast-fashion retailer. Operations Research, 60(6), 1404–1422.
Chen, B., & Chao, X. (2019). Parametric demand learning with limited price explorations in a backlog stochastic inventory system. IISE Transactions, 51(6), 605–613.
Chen, Y. (2022). Pricing strategy in a supply chain system with demand learning. Master’s thesis, National Cheng Kung University.
Ding, X., Puterman, M. L., & Bisi, A. (2002). The censored newsvendor and the optimal acquisition of information. Operations Research, 50(3), 517–527.
Fang, Y., & Shou, B. (2015). Managing supply uncertainty under supply chain cournot competition. European Journal of Operational Research, 243(1), 156–176.
Fang, Y., Wang, X., & Yan, J. (2020). Green product pricing and order strategies in a supply chain under demand forecasting. Sustainability, 12(2), 713.
Ferreira, K. J., Simchi-Levi, D., &Wang, H. (2018). Online network revenue management using thompson sampling. Operations Research, 66(6), 1586–1602.
Fu, D., Ionescu, C. M., Aghezzaf, E.-H., & De Keyser, R. (2014). Decentralized and centralized model predictive control to reduce the bullwhip effect in supply chain management.Computers & Industrial Engineering, 73, 21–31.
Giri, B. (2011). Managing inventory with two suppliers under yield uncertainty and risk aversion. International Journal of Production Economics, 133(1), 80–85.
Katok, E., Olsen, T., & Pavlov, V. (2014). Wholesale pricing under mild and privately known concerns for fairness. Production and Operations Management, 23(2), 285–302.
Keskin, N. B., Li, Y., & Song, J.-S. (2022). Data-driven dynamic pricing and ordering with perishable inventory in a changing environment. Management Science, 68(3), 1938–1958.
Lariviere, M. A., & Porteus, E. L. (2001). Selling to the newsvendor: An analysis of price-only contracts. Manufacturing & Service Operations Management, 3(4), 293–305.
Lee, H. L., & Billington, C. (1993). Material management in decentralized supply chains. Operations Research, 41(5), 835–847.
Lin, K. Y. (2006). Dynamic pricing with real-time demand learning. European Journal of Operational Research, 174(1), 522–538.
Linh, C. T., & Hong, Y. (2009). Channel coordination through a revenue sharing contract in a two-period newsboy problem. European Journal of Operational Research, 198(3), 822–829.
Liu, Z. L., Anderson, T. D., &Cruz, J. M. (2012). Consumer environmental awareness and competition in two-stage supply chains. European Journal of Operational Research, 218(3), 602–613.
Lovejoy,W. S. (1990). Myopic policies for some inventory models with uncertain demand distributions. Management Science, 36(6), 724–738.
Nahmias, S. (1994). Demand estimation in lost sales inventory systems. Naval Research Logistics (NRL), 41(6), 739–757.
Olivares, M., Terwiesch, C., & Cassorla, L. (2008). Structural estimation of the newsvendor model: an application to reserving operating room time. Management Science, 54(1), 41–55.
Papanastasiou, Y. (2020). Newsvendor decisions with two-sided learning. Management Science, 66(11), 5408–5426.
Prastacos, G. P. (1984). Blood inventory management: an overview of theory and practice. Management Science, 30(7), 777–800.
Smirnov, D., Herer, Y. T., & Avrahami, A. (2021). Two-phase newsvendor with optimally timed additional replenishment: Model, algorithm, case study. Production and Operations Management, 30(9), 2871–2889.
Taylor, T. A. (2002). Supply chain coordination under channel rebates with sales effort effects. Management Science, 48(8), 992–1007.
Thomas, D. J., & Griffin, P. M. (1996). Coordinated supply chain management. European Journal of Operational Research, 94(1), 1–15.
Ullah, M., Khan, I., & Sarkar, B. (2019). Dynamic pricing in a multi-period newsvendor under stochastic price-dependent demand. Mathematics, 7(6), 520.
Wang, X., Wang, X., & Su, Y. (2013). Wholesale-price contract of supply chain with information gathering. Applied Mathematical Modelling, 37(6), 3848–3860.
Wang, Y., & Sun, X. (2019). Dynamic vs. static wholesale pricing strategies in a dualchannel green supply chain. Complexity, 2019, 1–14.
Yao, Z., Leung, S. C., & Lai, K. K. (2008). Manufacturer’s revenue-sharing contract and retail competition. European Journal of Operational Research, 186(2), 637–651.