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).

The premise of the case is how to make best use of customer shopping time while staying competitive and profitable: The increase in the number of ecommerce-based channels and the growth of Amazon and Wal^*Mart have forced brick-and-mortar retailers to seek alternative ways to reach potential customers in a cost- and time-efficient manner. In the U.S., an average of 0.74 hours per day is spent purchasing goods and services, while an average of 1.77 hours per day is spent doing household activities. Regardless of location, customers all have the same 24 hours in a day and only so much of it can be spent shopping.

One of the benefits of ecommerce has been an increase in product variety offered to customers. The online marketplace has enabled consumers in many industries to locate, evaluate and purchase a far wider variety of products than they can with traditional brick-and mortar channels. 30% to 40% of Amazon book sales are titles that wouldn't normally be found in brick-and-mortar stores.

EC-Creamery is an Eau Claire, WI based dairy processor selling yogurt products throughout the Midwest region of the U.S. Inaccurate forecasts have wreaked havoc in the company, leading to frequent stockouts and creating operational challenges. Your task is to draw on the information you obtain from your colleagues to develop sales forecasts, document your choices and procedures and share your thoughts and insights on issues related to forecasting and sales & operations planning.

This case was developed solely for the purpose of classroom discussion. Some details of the case, including names of the companies, have been disguised. This case is not intended to serve as endorsements, sources of academic or business data, or illustrations of effective or ineffective management of the personnel or company.

Only when the custodial staff showed up at his office door did Vinod Mehra realize that it was already 3 am. Vinod is the VP of Supply Chain for Dockomo Heavy Machinery Equipment Limited. He had spent the entire night analyzing the data from the spare parts division in Pune, India. It was April 15 and he had just two weeks to go before the annual review of the company.

The spare parts division's growth at Dockomo has slowed down to about 10 percent annually when compared to the growth rate of 20 percent an nually over the previous years. Their cancelled orders stood at a staggering 8 percent due to parts unavailability, but at the same time the inventory in the system was $6 million higher than the previous year. Vinod was unsure of the response he would receive from the board of directors, since the inventory level increased along with the number of cancelled orders.

At the meeting, the board was considerate, but Vinod was asked to conduct an analysis of the shortcomings and prepare a report on the leading causes for the unavailability of parts to the customers. He was also asked to prepare a report on the approach to be followed to fix these problems by the next quarterly meeting. Vinod was already aware of many issues which existed in the supply chain, but he had to go through a complete analysis to gain a clearer understanding of the shortcomings in their distribution processes.

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.