Case studies

In 2007, Best Buy was the leading electronics retailer in the United States with more than 941 stores, revenue totaling $31 billion, and a market cap of $21 billion. In 2005, Best Buy had adopted a new business model, culture, and customer-segmentation template called Customer Centricity. This move created volatility in the price of Best Buy stock because of the higher-than-expected employee costs that went with this new way of doing business and the difficulty of executing the old and the new business models simultaneously while the new model was rolled out. Best Buy responded to Wall Street's short-term focus in a myriad of ways. It first asked for investor patience, and stressed the strong operating results achieved in Best Buy stores operating under the new model. But in June 2007, after the stock dropped again, the CEO knew he had to decide whether to open more Best Buy stores, increase the company's dividend, or increase the stock-repurchase program.

This short case could be handed out at the end of class discussion on “J&L Railroad” [UVA-F-1053] in preparation for the following class, or if students are more experienced with hedging and option pricing, the instructor may choose to cover both cases in a single class period. It is the companion case to “J&L Railroad” [UVA-F-1053], and presents more technical issues regarding the hedging problem by requiring students to understand option-pricing principles. The board likes the CFO's hedging recommendations, but it wants a more careful analysis of the bank's prices for its risk-management products: the caps and floors. Besides demanding an understanding of option pricing, this case puts particular emphasis on the calculation and use of implied volatility.

The Home Depot case is a great story. It's about entrepreneurship, growth, CEO leadership, and the dramatic impact, good and bad, a CEO can have on a company's growth culture, strategy, and performance. Home Depot had faced market growth challenges for the last seven years as it tried in numerous ways to reignite its growth engine. The case explores the growth strategies of CEOs Bernie Marcus, Arthur Blank, and Blank's successor Bob Nardelli, a former GE executive. After examining Home Depot's growth history, the case challenges students to devise a growth strategy for the company under a new CEO.

Tiffany & Company was the leading U.S. luxury jewelry brand, generating more than $2.6 billion in revenue through 167 retail outlets globally and from catalogue and Internet sales. For nearly 170 years, Tiffany had managed its brand. In February 2007, a hedge fund, Trian Fund Management LP, announced that it had bought a 5.5% stake in Tiffany, and became its largest shareholder. Trian believed that Tiffany was undervalued and stated that it wanted to help the company “improve its earnings per share by addressing various operational and strategic issues.” In response, Tiffany began to consider different actions to increase shareholder value.

TourAmerica is negotiating a master contract with Voyager Inn International (Bethesda) for hotel rooms during the 1995 tourist season. Issues under consideration include number of rooms during peak, mid-, and off-periods, room rates, breakfast prices, and the cost of ancillary services. While the hotel manager is evaluated on the basis of several criteria, including adjusted daily rates, occupancy rates, and food and beverage profitability, and is also provided with a utility scheme to facilitate trade-offs among the criteria, TourAmerica uses an effective cost per registrant (adjusted for intangibles). These two approaches provide an opportunity to contrast measurement schemes and to justify the use of utility functions. This case is a role-play exercise and must be used in conjunction with “Voyager Inn International” (UVA-QA-0463).

This case, along with its B case (UVA-QA-0865), is an effective vehicle for introducing students to the use of machine-learning techniques for classification. The specific context is predicting customer retention based on a wide range of customer attributes/features. The specific techniques could include (but are not limited to): regressions (linear and logistic), variable selection (forward/backward and stepwise), regularizations (e.g., LASSO), classification and regression trees (CART), random forests, graduate boosted trees (xgboost), neural networks, and support vector machines (SVM).

The case is suitable for an advanced data analysis (data science, machine learning, and artificial intelligence) class at all levels: upper-level business undergraduate, MBA, EMBA, as well as specialized graduate or undergraduate programs in analytics (e.g., masters of science in business analytics [MSBA] and masters of management analytics [MMA]) and/or in management (e.g., masters of science in management [MScM] and masters in management [MiM, MM]).

The teaching note for the case contains the pedagogy and the analyses, alongside the detailed explanations of the various techniques and their implementations in R (code provided in Exhibits and supplementary files). Python code, as well as the spreadsheet implementation in XLMiner, are available upon request.

The business-travel department of American Express is facing rapid growth in demand but is plagued with overstaffing in some offices because of the broad distribution of client demand. Management's challenge is to reduce costs in local offices while maintaining a high level of service. One alternative under consideration is a centralized regional business-travel center to handle reservation functions for up to 20 other Amexco offices. This case gives students the opportunity to apply queuing theory to a practical situation. Normally, in order to facilitate the numerous calculations required, it is used with the UVA “QUEUE” program.

This case explores the decision of a fast-growing company to expand its distribution network. Financial information is provided in it so students can understand the basic distribution network design covering inbound transportation, outbound transportation, distribution-center operations, and inventory.

This case is used in Darden's Supply-Chain Operations elective. The field-based case gives supply-chain educators the ability to teach the newsvendor model with pricing under a capacity constraint using real-life decisions. By 2005, Eastman Chemical Company, based in Tennessee, had created a new specialty plastic, Tritan, which demonstrated heat resistance and durability properties that might allow Eastman to compete in the lucrative polycarbonate plastics market. Development of this product was a major breakthrough for both Eastman and the broader chemical industry. The Eastman specialty plastics team had to contend with numerous challenges, however, before producing Tritan at full scale. First, Eastman had to commercialize a completely new material that only had been produced in the lab; second, the team had to develop a supply chain to manufacture a new component (monomer) and a new product (polymer) simultaneously; and finally, it had to analyze market entrance options given capacity constraints. Thus, the specialty plastics team faced several dilemmas: who should the initial launch partners be, given Eastman's limited manufacturing capacity, and how aggressively should Eastman price Tritan, given that price would drive demand in the launch markets and in new markets?

This case examines a set of expansion options for a successful Internet luggage retailer, with a particular emphasis on the operational complexities.