Impact of customer behavior on price and inventory management

定价决策是最常见的营销实践形式之一。它需要彻底了解影响客户最终购买决策的所有因素。对于大多数公司来说，定价不再是一个静态的决定。在数据处理技术和电子商务的帮助下，动态定价已成为帮助企业更好地匹配供需、提高竞争优势的必要能力。为了实现这一目标，一家公司现在必须不断决定何时、多频繁和多大程度地改变其产品的价格。然而，有两个与动态定价相关的问题，如果没有适当考虑，可能弊大于利。第一个问题是定价和运营规划决策之间的协调日益重要。第二个问题是关于定价决策和客户行为之间的互动。在这个研究方案中，我们解决了这两个问题，并计划建立分析性和大规模的优化模型，以研究客户行为在协调价格和库存管理中的作用。我们提出的研究计划有三个主要组成部分。首先，我们计划开发一个框架来建模客户行为，并在确定性的生产/库存订购环境下对其进行分析。接下来，我们打算通过纳入运营和营销环境中的不确定性来扩展分析。最后，我们打算开发复杂供应链中库存控制和定价问题的分解方法。这项研究将是第一批开发新模型的人之一，这些模型考虑了与动态定价相关的问题的整个范围，从供应管理--在正确的时间采购材料--到需求管理--理解客户行为并相应地设定价格。此外，我们将修改和应用各种相关领域的现有技术，特别是营销和库存中的客户行为建模以及运营中的价格协调方面的最新进展，以深入了解这些模型。

Pricing decision is one of the most common forms of marketing practices. It requires thorough understanding of all the factors that affect customers' ultimate purchase decisions. For most of the firms, pricing is not a static decision anymore. With the help of data processing technology and e-commerce, dynamic pricing has become more of a necessary capability that helps firms better match their supply with demand and increase their competitive advantage. In order to achieve this goal, a firm now has to continuously decide on when, how frequently and how much to change prices of its offerings. However, there are two issues related to dynamic pricing which, if not appropriately taken into account, can cause more damage than good. First issue is about the increased importance of coordination between pricing and operational planning decisions. Second issue is about the interaction between pricing decisions and customer behavior. In this research proposal, we address both issues and plan to build analytical and large scale optimization models in order to study the roles of customer behavior in coordinated price and inventory management. Our proposed research plan has three primary components. First, we plan to develop a framework to model customer behavior and analyze it in deterministic production/inventory ordering setting. Next, we intend to extend the analysis by incorporating uncertainties in operational and marketing environment. Finally, we intend to develop decomposition approaches for inventory control and pricing problems in complex supply chains. This research will be among the first to develop novel models that consider the entire scope of the problem associated with dynamic pricing, from supply management -- procuring materials at the right time -- to demand management - understanding customer behavior and setting prices accordingly. In addition, we will modify and apply existing techniques from a wide variety of related fields, and particularly the latest advances in customer behavior modeling in marketing and inventory and price coordination in operations in order to develop a thorough understanding of these models.