Bayesian strategies for dynamic pricing in e‐commerce

E‐commerce platforms afford retailers unprecedented visibility into customer purchase behavior and provide an environment in which prices can be updated quickly and cheaply in response to changing market conditions. This study investigates dynamic pricing strategies for maximizing revenue in an Internet retail channel by actively learning customers' demand response to price. A general methodology is proposed for dynamically pricing information goods, as well as other nonperishable products for which inventory levels are not an essential consideration in pricing. A Bayesian model of demand uncertainty involving the Dirichlet distribution or a mixture of such distributions as a prior captures a wide range of beliefs about customer demand. We provide both analytic formulas and efficient approximation methods for updating these prior distributions after sales data have been observed. We then investigate several strategies for sequential pricing based on index functions that consider both the potential revenue and the information value of selecting prices. These strategies require a manageable amount of computation, are robust to many types of prior misspecification, and yield high revenues compared to static pricing and passive learning approaches. © 2006 Wiley Periodicals, Inc. Naval Research Logistics, 2007     

The impact of demand uncertainty on product line design under endogenous substitution

The existing product line design literature devotes little attention to the effect of demand uncertainty. Due to demand uncertainty, the supply‐demand mismatch is inevitable which leads to different degrees of lost sales depending on the configuration of product lines. In this article, we adopt a stylized two‐segment setup with uncertain market sizes and illustrate the interplay between two effects: risk pooling that mitigates the impact of demand uncertainty and market segmentation that facilitates consumer differentiation. Compared to downward substitution, inducing bidirectional substitution through product line decisions including quality levels and prices can yield greater risk pooling effects. However, we show that the additional benefit from the risk pooling effect cannot compensate for the reduced market segmentation effect. We demonstrate that the presence of demand uncertainty can reduce the benefit of market segmentation and therefore the length of product lines in terms of the difference between products. We also propose three heuristics that separate product line and production decisions; each of these heuristics corresponds to one particular form of demand substitution. Our numerical studies indicate that the best of the three heuristics yields performance that is close to optimality. © 2015 Wiley Periodicals, Inc. Naval Research Logistics 62: 143–157, 2015     

震灾应急物资动态需求预测模型研究

为提高应急物资需求预测方法的实用性,同时考虑提前期和需求的不确定性,采用安全库存理论构建震灾应急物资动态需求预测模型,以观测得到的实际到货量、实际消耗量和实时剩余库存量等信息来动态预测当前时刻应急物资的需求量。应用算例表明,所建模型不仅能够准确反映需求量与被保障人员总数的正相关关系,而且还能实时体现被保障人员总数的变化情况。因此,该模型能够为震灾过程中应急物资需求量的确定提供科学决策依据,具有一定的实用价值和应用前景。     

日本防卫产业发展潜力问题分析

日本自卫队追求能力提升和活动范围扩大的努力受到其防卫产业发展的局限。防卫预算的有限直接导致防卫产业的市场规模狭小。部分核心技术自主研发的缺失限制了防卫产业独立发展的能力。融合式发展模式在成本核算、劳动生产率提升和产业发展稳定性等方面限制了防卫产业发展的潜力。     

A note on the forecast performance of temporal aggregation

Earlier research on the effects of nonoverlapping temporal aggregation on demand forecasting showed the benefits associated with such an approach under a stationary AR(1) or MA(1) processes for decision making conducted at the disaggregate level. The first objective of this note is to extend those important results by considering a more general underlying demand process. The second objective is to assess the conditions under which aggregation may be a preferable approach for improving decision making at the aggregate level as well. We confirm the validity of previous results under more general conditions, and we show the increased benefit resulting from forecasting by temporal aggregation at lower frequency time units. © 2014 Wiley Periodicals, Inc. Naval Research Logistics 61: 489–500, 2014     

On the (S − 1, S) lost sales inventory model with priority demand classes

In this paper an inventory model with several demand classes, prioritised according to importance, is analysed. We consider a lot‐for‐lot or (S ? 1, S) inventory model with lost sales. For each demand class there is a critical stock level at and below which demand from that class is not satisfied from stock on hand. In this way stock is retained to meet demand from higher priority demand classes. A set of such critical levels determines the stocking policy. For Poisson demand and a generally distributed lead time, we derive expressions for the service levels for each demand class and the average total cost per unit time. Efficient solution methods for obtaining optimal policies, with and without service level constraints, are presented. Numerical experiments in which the solution methods are tested demonstrate that significant cost reductions can be achieved by distinguishing between demand classes. © 2002 Wiley Periodicals, Inc. Naval Research Logistics 49: 593–610, 2002; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/nav.10032     

Stochastic production capacity: A bane or a boon for quick response supply chains?

The quick response (QR) system that can cope with demand volatility by shortening lead time has been well studied in the literature. Much of the existing literature assumes implicitly or explicitly that the manufacturers under QR can always meet the demand because the production capacity is always sufficient. However, when the order comes with a short lead time under QR, availability of the manufacturer's production capacity is not guaranteed. This motivates us to explore QR in supply chains with stochastic production capacity. Specifically, we study QR in a two-echelon supply chain with Bayesian demand information updating. We consider the situation where the manufacturer's production capacity under QR is uncertain. We first explore how stochastic production capacity affects supply chain decisions and QR implementation. We then incorporate the manufacturer's ability to expand capacity into the model. We explore how the manufacturer determines the optimal capacity expansion decision, and the value of such an ability to the supply chain and its agents. Finally, we extend the model to the two-stage two-ordering case and derive the optimal ordering policy by dynamic programming. We compare the single-ordering and two-ordering cases to generate additional managerial insights about how ordering flexibility affects QR when production capacity is stochastic. We also explore the transparent supply chain and find that our main results still hold.     

面向仿真的C4ISR系统领域分析与需求复用研究

面向C4ISR系统需求开发中的仿真支持问题,将复用作为一种的C4ISR系统需求模型的构建方法,可以快速形成一个有效的领域需求模型支持系统仿真验证。本文研究了C4ISR系统需求的领域性与领域需求共性问题,在此基础上研究了C4ISR系统需求工程中的需求框架、需求本体以及需求组件等不同粒度的复用方法,分别对上述三种方法在领域应用中的不同作用进行了对比分析。