Consumer-driven operations management /

Consumer-driven operations management /

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Bibliographic Details
Author / Creator:	Wu, Xiao, author.
Imprint:	2015. Ann Arbor : ProQuest Dissertations & Theses, 2015
Description:	1 electronic resource (176 pages)
Language:	English
Format:	E-Resource Dissertations
Local Note:	School code: 0330
URL for this record:	http://pi.lib.uchicago.edu/1001/cat/bib/10773168

Hidden Bibliographic Details
Other authors / contributors:	University of Chicago. degree granting institution.
ISBN:	9781321910650
Notes:	Advisors: John R. Birge Committee members: Donald D. Eisenstein; Rodney P. Parker; Christopher T. Ryan; Song A. Yang. This item must not be sold to any third party vendors. This item must not be added to any third party search indexes. Dissertation Abstracts International, Volume: 76-12(E), Section: A. English
Summary:	For Chapter 1, I investigate the financially distressed retailers facing a vicious feedback loop between their financial health and customer demand: when customers anticipate the possibility of bankruptcy, they may strategically wait for deep discounts in liquidation sales. Such waiting in turn lowers the retailer's profitability and aggravates the retailer's financial trouble. Using a parsimonious model, I characterize such strategic waiting behavior and the retailer's optimal operational response. I find that customers' anticipation of bankruptcy can be a self-fulfilling prophecy: when consumers predict the probability of bankruptcy to be low, they prefer to purchase early, and when they anticipate a high bankruptcy probability, they prefer to delay their purchase, making the retailer more likely to go bankrupt. In the presence of multiple rational expectations equilibria, strategic consumers prefer the one that hurts the retailer and social welfare the most. Facing such behavior, the retailer should behave conservatively during the regular sales period. With both inventory and pricing levers on hand, the retailer should lower only inventory when financial distress is mild and only a small fraction of customers are strategic, and both inventory and price when financial distress is severe and the fraction of strategic consumer is high. Under optimal price and inventory decisions, strategic waiting may account for a majority of the retailer's total cost of financial distress. In addition to inventory reduction and immediate price discount, I propose deferred discounts, in the form of a rebate or store credit, as an innovative mechanism to mitigate strategic waiting. As a contingent price discount, deferred discount aligns the interests of consumers and the retailer, and it is most effective to alter consumer behavior when the fraction of strategic consumers is high and the level of financial distress is moderate. In Chapter 2, I will focus on a new practice that becomes dominant in the app industry due to the power of word-of-mouth. This practice is to launch with a simple and intuitive design and release premium features and content over time in the form of in-app purchases (IAPs) to monetize users. I propose an optimal control model that yields insights into the optimality and economic justification of such an approach. Through analytically characterizing the product life cycle and optimal release pattern of an app for certain structured instances, I characterize when Apps launch with an initial period free of IAPs in order to maximize the growth of its user-base and later exploit this user-base for generating revenue by releasing IAP. This strategy balances the trade-off between the effects of IAP release on acquiring and retaining users with the revenue-generating potential of selling multiple IAPs. This model also admits comparative dynamics results on how exogenous factors, such as the social nature of the App, influence the optimal time of initial IAP release. In the numerical study, I show that Apps can be permanently free if they generate revenues directly from the user base, for example, with advertising revenue. By incorporating per-user operating cost, I also show that the length of the loss period at the beginning of the product life cycle and how deep is the loss depends on exogenous factors, including the strength of word-of-mouth. In Chapter 3, I investigate a supplier's dynamic pricing and capacity allocation decisions in the presence of heterogeneous customers with memory of past purchase experiences: prices and fill-rates provided in the past. On the one hand, there is a short-term opportunity for profit-taking by supplying only the most profitable customers today. On the other hand, this is potentially damaging to the firms' relationships with the less profitable customers. A customer's decision is correlated to not only the current price but also the past prices and fill-rates. Customers are heterogeneous in terms of the following basic characteristics: profit margin, sensitivity to the past, demand volatility and price elasticity. The supplier needs to trade off these basic characteristics when making price and capacity decisions to maximize his expected long run average profit. I model the problem as a Markov decision process whose state is a vector of customer goodwill representing exponentially smoothed summaries of price and past fill-rates specific to individual customers. To characterize the optimal decisions for the deterministic demand case, I introduce a static problem in which demand stays the same for each period and show that the static problem shares an optimal solution with the basic problem. Under deterministic demand, the supplier's optimal strategy is to leverage pricing and capacity to make every customer's order fulfilled and not to hold back any excess capacity. I find through numerical experiments that similar properties hold in the stochastic model. (Abstract shortened by UMI.).