Case studies

This case series describes the startup of Farm to School of Park County, an emerging nonprofit organization in the US state of Montana. Case (A) describes the community, the need, and the origins of Farm to School in Livingston, Montana. The leaders of Farm to School face a budget crisis and need to evaluate four options to decide whether, when, and how it should become an independent organization. As Case (B) begins, Farm to School has decided to enter into a fiscal sponsorship agreement with the local community foundation. The next task for the organization's leaders is recruiting founding board members. They need to decide whom to ask and how to do it. In Case (C), the board develops a strategic plan and establishes committees. However, the board members and leaders start to feel fatigue in the face of the demands of a startup organization, leading to questions about what is truly strategic and how work will get done. The Farm to School organization in Case (D) has just issued its first annual report, filled with meaningful accomplishments. The leaders of the organization begin to plan to build an organization that will outlast them and the founding board members.

The COO for Suape Container Terminal, the largest deep–water port in Brazil's Northeast must consider a proposal presented by the users' council that calls for the establishment of a reservation scheme that minimizes the risk of docking delays. Under this proposal, ocean carriers, on the one hand, agree to pay a reservation fee that significantly increases revenue for Tecon Suape. On the other hand, they expect Tecon Suape to compensate them financially when a berth is not available upon vessel arrival. Tecon Suape's management team must evaluate that suggestion, as the team prepares to enter contractual negotiations with the users.

In 2008, Starbucks was in crisis as a result of undisciplined growth and loss of focus, and its stock declined almost 70%. In August of that year, Howard Schultz, the founder of Starbucks, came out of retirement to take over as the CEO. The company regained its footing by refocusing on its core and driving strong organic growth. By 2014, the stock price had reached $40, an all-time high. To prevent history from repeating itself, Schultz wanted to ensure that Starbucks' growth strategies not only addressed market opportunities, but also were aligned with the company's brand image, assets, and capabilities.

Starbucks announced a five-year growth plan in December 2014 with ambitious goals that included nearly doubling its revenues from $16 billion to $30 billion, doubling operating income, and expanding its footprint to more than 30,000 stores globally by 2019. The growth plan consisted of seven specific growth strategies, one of which was the New Occasions strategy. The objective of New Occasions was to drive growth by diversifying Starbucks' revenues beyond breakfast to the lunch, afternoon, and evening dayparts. Starbucks created specific offerings for each daypart, called the Lunch, Sunset, and Evenings programs. The case focuses on evaluating these three occasions-based growth opportunities and identifying the best path forward.

Polaris Battery Labs was an Oregon-based startup that provided innovation services to companies in the lithium ion battery industry. Its operating philosophy and expertise in this fast-growing industry enabled it to provide great value to its clients, but as a startup that was seeking growth the company was subject to multiple risks.

For Polaris, taking clients, developing new manufacturing capabilities to meet unproven battery technologies, and even extending credit to its clients posed real risk. Many of its clients were startups themselves and had a significant probability of failure. Others were established firms testing new and unproven battery technologies, many of which were unlikely to gain traction in the market.

The case examines how a technology-driven firm managed the risk of working with startups, claiming appropriate intellectual property, and developing a sustainable portfolio of clients.

Dr. Phillip Hoopes, a LASIK surgeon, is considering how to respond to discounting by his competitors. He had built a high-end practice with costly advanced technology, but discounters had recently entered the market and other high-end practices in the area were starting to offer deep promotions in an effort to compete.

In this case, students step into the shoes of a brand manager at UK-based online peer-to-peer lending company Zopa as she decides whether or not to recommend that the company undertake a brand redesign. Students are provided a brand design worksheet to help them understand the component parts of brand strategy and brand design and to help structure their assessment of Zopa's current brand. They are then challenged to consider if, to what extent, and how Zopa's brand design should change as a function of the company's brand strategy. Finally, they will learn a set of best practices, or guidelines, for approaching brand redesigns.

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.