竞争零售服务设施的最佳选址

K. Talluri, Müge Tekin
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引用次数: 1

摘要

我们考虑零售服务设施的选址问题,这些面向消费者的店面提供服务,并在某种程度上与其他零售商竞争。选址是零售企业最重要的战略决策之一。这是一个有风险的,而且往往是不可撤销的决定,因为它涉及大量投资,很难纠正,并影响未来多年的利润和运营。由于以下原因,这个问题尤其具有挑战性:(i)定位模型需要估计当我们定位新设施时需求将如何扩展和变化,但由于公司尚未开始运营,因此没有历史需求数据来校准模型;(ii)竞争对手的未来进入和退出会影响公司的收入和盈利能力,但预测这种未来的战略发展是相当复杂的。在本文中,我们使用一个简单的均衡框架来考虑前瞻性的竞争进入和退出决策,这个框架可以用整数规划来求解,并且可以从公共数据中估计。为了捕捉当地人口的口味,我们基于现有机构的在线评论建立了一个模型,这些机构的设施具有潜在的特征,顾客对这些潜在的特征有偏好。这可以作为预测客户需求的输入,从而驱动我们的最佳定位解决方案,并为企业提供一个简单易用的决策工具包。我们将该模型应用于服务行业,特别是餐饮业,以说明如何使其可操作。我们的估计结果表明,不同类型的餐厅,顾客的旅行意愿和评级敏感性存在显著差异。除了一个易于处理的工具包,以帮助他们的决策过程,我们表明,通过反事实,优化的位置决策可以提高生存机会高达37.5%。本文还提供了对竞争区位分散性质的管理见解。
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Optimal Location for Competing Retail Service Facilities
We consider the location problem for retail service facilities, consumer-facing storefronts that provide a service and compete with other retailers to some degree or the other. Location is one of the most important strategic decisions for a retail firm. It is a risky and often an irrevocable decision, in the sense that it involves a large investment, is very difficult to rectify, and affects profits and operations for many years in the future. This problem is especially challenging for the following reasons: (i) Location models require estimates of how demand will expand and shift when we locate a new facility, but the firm, since it has not yet started operations, has no historical demand data to calibrate the models; (ii) Future entry as well as exits of competitors affect the firm’s revenues and profitability, but predicting such future strategic developments is rather complicated. In this paper, we consider forward-looking competitive entry and exit decisions using a simple equilibrium framework, solvable by integer programming and estimable from public data. To capture the taste of local demographics, we build a model based on online reviews of the incumbent establishments where facilities have latent characteristics and customers have preference for these latent characteristics. This serves as an input to predict customer demand which drives our optimal location solution and gives firms an easy and tractable toolkit for their decision-making. We apply the model to a service industry, specifically the restaurant industry, to illustrate how it can be made operational. Our estimation results show that customers differ significantly in their willingness to travel and rating sensitivities across restaurant types. Apart from a tractable toolkit to help their decision process, we show, via counterfactuals, that optimized location decision-making can increase chances of survival by up to 37.5%. Managerial insight into the nature of competitive location dispersion is also provided.
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