统计估计与决策优化在库存和定价管理中的集成研究

The demand depends on the pricing decision, at the same time, the firm makes inventory decision based on the demand, thus, the joint inventory and pricing control has become a common managemnt strategy. But, the increased computing power and the explosion of data are changing the way firms make decisions, i.e., they make more use of statistical analysis of data. However, the traditional inventory and pricing decision models lack support for data analysis. This research project introduces new statistical methods, i.e., regression spline, Cox survival model and quantile regression model, into classical joint inventory and pricing decision models. To the best of our knowledge, we are the first to adopt these ststistical tools in joint inventory and pricing management area. These tools are used to study the complex nonlinear relationship between price and demand and solve the decision making problems with censored demand data and heterogeneous market. By combining these statistical theories and methods with classical inventory-pricing decision models,we creatively integrate the statistical estimation of price- dependent demand with inventory and pricing decision optimization. Our goal is to reduce the mistakes of decision and the loss profit because of the error in statistical estimation. With analytical proof and empirical validation, we will find the optimal data driven inventory and pricing decisions, explore the relationship between the optimal decisions and the history data，and provide effective decision models, tools and management insights from data analysis, which will help the firms to enhance their overall competency.

市场需求受定价影响，同时企业根据需求确定库存，因此，库存与定价联合管理成为一种普遍的管理策略。但不断增长的计算能力以及爆发式增长的数据，使企业更多地采用基于数据统计分析的决策方式，而传统的库存与定价决策模型缺少对数据分析的支持。本项目在经典的库存与定价管理理论模型中，创新性地引入统计学中的样条函数，Cox生存分析模型以及分位数回归等统计方法，研究需求与定价的复杂非线性关系，解决存在删失数据以及市场异质性条件下的库存与定价决策优化问题。通过统计方法与决策优化理论的交叉融合，我们将需求的统计估计与库存定价的决策优化加以集成创新研究,以减少因统计估计不准导致的决策失误与利润损失。通过理论推导与实证分析，寻找数据驱动型的最优库存与定价决策，揭示最优库存定价决策与历史数据的相互关系，为库存与定价管理实践提供基于数据分析的决策模型、工具和管理智慧，提高我国企业的综合竞争力。

库存与定价联合决策优化已经成为商业实践中一种普遍应用的管理策略，根据产品的库存及服务能力，采用复杂的定价策略，动态调整产品及服务的价格，可以获得市场上的竞争优势和更高的运营利润。另一方面，随着新兴信息技术在社会经济领域不断渗透, 企业开始应用数据挖掘机器学习等技术，通过分析数据来了解细分市场对产品与服务的不同需求，管理和优化其库存与定价策略，促进业务创新与利润增长。本项目正是从大数据发展的趋势出发，在经典的库存与定价管理理论模型中，创新性地引入统计学中的样条函数,分位数回归以及运营统计等半参数与非参数的统计方法，研究库存定价管理中的需求与定价的复杂非线性关系及其库存与定价决策优化问题。通过统计理论方法与决策最优化的交叉融合以及集成创新研究,以减少因统计估计不准导致的决策失误与利润损失。该项目获得了丰富的基础理论研究成果，并在国际顶尖学术期刊上发表，同时研究成果为库存定价管理实践提供有效了的决策模型，工具和管理智慧，有助于提高我国现代制造业与服务业的综合竞争力。

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