This paper studies dynamic wholesale pricing and ordering in a two-tier supply chain where firms share POS data and learn about demand from censored demand data. When stockouts occur, unmet demand is unobserved, so the retailer's order quantity affects not only current profits but also the informativeness of future demand signals. This creates a strategic interaction between pricing, ordering, and learning: the manufacturer can influence the pace of learning through wholesale prices, whereas the retailer internalizes the effect of inventory decisions on future information. We analyze a finite-horizon dynamic game in which a manufacturer sets a wholesale price, the retailer then chooses an order quantity, demand is realized, and both firms observe sales. For Weibull demand with a conjugate prior, we extend a dimensionality-reduction approach from single-agent inventory learning models to a strategic supply-chain setting and use it to establish the existence of a Markov perfect equilibrium. For exponential demand, we further show that the equilibrium is unique and admits a recursive characterization. Our numerical analysis shows that public learning can create conflicting incentives in the supply chain: In order to induce larger orders and reduce future censoring, the manufacturer chooses a wholesale price that is lower than a myopic benchmark. By contrast, because of its forward-looking ordering incentive, the retailer may prefer slower learning to avoid strengthening the manufacturer's future wholesale-pricing position.

翻译：本文研究双层供应链中的动态批发定价与订货问题，其中企业共享销售终端数据并通过删失需求数据进行需求学习。当库存不足时，未满足的需求无法被观测，因此零售商的订货量不仅影响当前利润，还影响未来需求信号的信息含量。这形成了定价、订货与学习之间的策略互动：制造商可通过批发价格影响学习进程，而零售商则内化了库存决策对未来信息的效应。我们分析了一个有限期动态博弈：制造商设定批发价格，零售商据此选择订货量，需求实现后双方观察到实际销售额。针对具有共轭先验分布的威布尔需求，我们将单智能体库存学习模型中的降维方法拓展至策略性供应链场景，并以此证明马尔可夫完美均衡的存在性。对于指数需求，我们进一步证明该均衡具有唯一性，并可递归刻画。数值分析表明，公开学习可能在供应链中产生相互冲突的激励：为诱导更大订单并减少未来删失，制造商设定的批发价格低于短视基准值；相反，由于前瞻性订货激励，零售商可能偏好更慢的学习速度，以避免增强制造商未来批发定价的议价地位。