Bayesian Inventory Management with Potential Change‐Points in Demand

We consider the inventory management problem of a firm reacting to potential change points in demand, which we define as known epochs at which the demand distribution may (or may not) abruptly change. Motivating examples include global news events (e.g., the 9/11 terrorist attacks), local events (e.g., the opening of a nearby attraction), or internal events (e.g., a product redesign). In the periods following such a potential change point in demand, a manager is torn between using a possibly obsolete demand model estimated from a long data history and using a model estimated from a short, recent history. We formulate a Bayesian inventory problem just after a potential change point. We pursue heuristic policies coupled with cost lower bounds, including a new lower bounding approach to non‐perishable Bayesian inventory problems that relaxes the dependence between physical demand and demand signals and that can be applied for a broad set of belief and demand distributions. Our numerical studies reveal small gaps between the costs implied by our heuristic solutions and our lower bounds. We also provide analytical and numerical sensitivity results suggesting that a manager worried about downside profit risk should err on the side of underestimating demand at a potential change point.     

The Impact of Central-Place Theory on Wal-Mart

This study applies central-place theory to the Wal-Martization process. Central-place theory posits that there are laws determining the number, size, and distribution of cities. One of the theory's major premises is its emphasis on the size and spacing of towns that specialize in selling goods and services. The Wal-Mart blueprint has adopted the central-place model in that it follows an ordered region with snowflake-like proportion. For example, the geographic expansion of Wal-Mart stores has been one of diffusing up the urban or metropolitan hierarchy over the years. Overall, the authors of this study conclude that the effectiveness of such spatial/organizational communication has contributed to Wal-Mart's successful expansion worldwide.     

直销渠道开通的情形下制造商与电商平台合约选择的研究

理论界认为代售合约具有明显的消除双重边际效应的作用,许多制造商和电商平台选择签订代售合约,但是也有制造商和电商平台选择了批发合约。本文以在线销售系统为背景,研究了直销渠道开通的情形下制造商和电商平台合约的选择。通过研究发现,制造商和电商平台并不是总偏好批发合约或代售合约,并且存在一个帕累托改进区域;虽然制造商在电商平台上有网络销售渠道,但是制造商会保留原有的直销渠道;对于电商平台来说,收益分配系数并不是越大越好;代售合约虽然能够消除双重边际效应,但在代售合约下,整个在线销售系统的效率并不总是最高的。另外,随着直销渠道的效率增加,制造商会先降低批发价格,然后再增加批发价格;与以往的研究结论不同,代售合约下电商平台的市场销售价格并不总是最小的。以上的结论其实都是批发合约下的直销渠道和代售合约下的电商平台在缓解双重边际效应方面的不同作用带来的。     

西安店名的文化释读

店名作为一种特殊的语言样式，蕴涵着丰富的文化内涵。结合西安的人文地理和历史因素，通过分析，指出西安店名体现出浓厚的民族文化、独特的地域文化、鲜明的行业文化、厚重的历史文化和时尚的现代文化。     

文学与风俗

文学是社会生活的反映,它总是以某个特定区域的特定生活为描写对象,从而体现时代精神与民族特色,而风俗则是遗传性与习惯性的表现.从三个方面探讨文学与风俗的关系问题,并列举古今中外优秀作家的成功范例,以期揭示文学与风俗之间的密切联系.     

发展湛江零售业的问题与对策

本文对湛江市零售业的现状、态势及发展战略选择进行分析研究。提出完善企业的经营体制,强化管理和营销手段,走连锁化道路,发展多种业态,重点讨论了今后发展的对策建议。     

"哈客隆"现象与大型百货商店改革

大型百货商店改革一直是我国零售业改革的难点和重点.从脱胎于大型百货商店的哈客隆连锁超市的崛起入手,探讨在新的市场环境下,大型百货商店如何摆脱困境,参与市场竞争,重振辉煌,从而找到我国零售业中的新型业态--连锁超市的发展之路.     

医疗体制弊端与药品定价扭曲

通过对中国医疗体制和药价管制模式的分析,我们发现公立医疗机构在药品零售环节上的双向垄断地位是导致药价虚高的根本原因,医疗服务价格低估导致的“以药补医”机制赋予了医疗机构抬高药价的合法权力,而收益率管制政策进一步诱导医院进销高价药品。此外,单独定价政策加之宽松的新药审批制度为药厂提高药价、医院购销高价药提供了便利。所有这些问题的出现根源于政府管制措施的失当。因此,解决药价虚高的根本措施是减少政府管制,放开处方药零售权,改革公费医疗和医疗保险报销制度,打破公立医院垄断。     

Traffic‐Based Labor Planning in Retail Stores

Staffing decisions are crucial for retailers since staffing levels affect store performance and labor‐related expenses constitute one of the largest components of retailers’ operating costs. With the goal of improving staffing decisions and store performance, we develop a labor‐planning framework using proprietary data from an apparel retail chain. First, we propose a sales response function based on labor adequacy (the labor to traffic ratio) that exhibits variable elasticity of substitution between traffic and labor. When compared to a frequently used function with constant elasticity of substitution, our proposed function exploits information content from data more effectively and better predicts sales under extreme labor/traffic conditions. We use the validated sales response function to develop a data‐driven staffing heuristic that incorporates the prediction loss function and uses past traffic to predict optimal labor. In counterfactual experimentation, we show that profits achieved by our heuristic are within 0.5% of the optimal (attainable if perfect traffic information was available) under stable traffic conditions, and within 2.5% of the optimal under extreme traffic variability. We conclude by discussing implications of our findings for researchers and practitioners.     

Promotion Planning and Supply Chain Contracting in a High–Low Pricing Environment

Demand forecast errors threaten the profitability of high–low price promotion strategies. This article shows how to match demand and supply effectively by means of two‐segment demand forecasting and supply contracts. We find that demand depends on the path of past retail prices, which leads to only a limited number of reachable demand states. However, forecast errors cannot be entirely eliminated because competitive promotions entail some degree of random (i.e., last‐minute) pricing. A hedging approach can be deployed to distribute demand risk efficiently over multiple promotional campaigns and within the supply chain. A retailer that employs a portfolio of forward, option, and spot contracts can avoid both stockouts and excess inventories while achieving the first‐best solution and Pareto improvements. We provide an improved forecasting method as well as stochastic programs to solve for optimal production and purchasing policies such that the right amount of inventory is available at the right time. By connecting a stockpiling model of demand with the supply side, we derive insights on optimal risk management strategies for both manufacturers and retailers in a market environment characterized by frequent price promotions and multiple discount levels. We employ a data set of the German retail market for a key generator of store traffic—namely, diapers.