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Supplement to case 919002.

Supplement to case 919002.

Supplement to case UV7587.

Supplement to case UV7587.

The A case in this series takes the viewpoint of a senior portfolio manager who has to decide how to vote the shares held by Wells Fargo Asset Management: she can vote for the current board members of Imation Corp. (IMN) or for the nominees put forward by an activist hedge fund. The hedge fund sent an open letter to IMN shareholders seeking support to replace the key members of IMN's board, as the first step in pushing through sweeping changes in compensation and strategy at IMN. The case illustrates the power of an activist investor to change the governance and strategy of a company. The B case reveals that the activist's nominees were elected, and a series of decisions ensued that dismantled IMN. The case presents students with ethical questions regarding the treatment of IMN shareholders; it also highlights the influence of proxy advisers in a proxy vote. The case has been taught in an MBA corporate finance elective as the second class in activism, following another activist case, "Genzyme and Relational Investors: Science and Business Collide?" (UVA-F-1660). It can be taught to all undergraduate and graduate levels of business students, as well as seasoned executives.

Supplement to W18507.

Supplement to W18507

Supplement for product 9B18C026.

Supplement to TVO: Leading Transformation Change (A)

Supplement to TVO: Leading Transformation Change (A)

This case builds directly on the case Predicting Purchasing Behavior at PriceMart (A), in which VP of Marketing, Jill Wehunt, and analyst Mark Morse build a logistic regression model to predict whether a customer household is expecting a baby. In this case, Wehunt and Morse are concerned about the logistic regression model overfitting to the training data, so they explore two methods for reducing the sensitivity of the model to the data by regularizing the coefficients of the logistic regression. Wehunt and Morse then compare the models and select the model most effective at correctly classifying households as expecting. Students explore the relationship between the model's confusion matrix, which organizes the model's correct and incorrect classifications, the cutoff point on the curve that matches true positives and true negatives, and the payoff matrix Wehunt and Morse construct. Students can then follow the link directly from their model to their marketing strategy. Technical topics covered: Ridge logistic regression (or L2 regularization) as a modelling technique; Lasso logistic regression (or L1 regularization) as a modelling technique; Comparing models, thinking about coefficients, and selecting model for deployment; Evaluating model output; ROC curve, cutoff point, confusion matrix; payoff matrix as a framework for utilizing the model to carry out marketing strategy.

Supplement to case W18557 Jaga suffered negative economic effects from the 2008 financial crisis. As the crisis persisted in 2011, management planned to substantially reduce the number of employees, but the founder instead chose to fire half of the management team and reorganize the company. Although this decision was considered counterintuitive, it was perfectly aligned with the philosophy of this creative company.

Supplement to case W18504

Supplement to case W17363. In 2015, the assistant general manager at JSW Steel Ltd. (JSW), one of India's largest steelmakers, faced a dilemma. Should JSW continue to transport the company's end products to clients, or should JSW instead outsource the transportation to a third-party provider? Outsourcing would ensure timely delivery but would increase the cost. Another option was to pay an agency a premium for sharing information on the availability of Class I and Class II barge vendors. Class I barges were more reliable in terms of on-time and damage-free delivery, whereas Class II barges had a smaller capacity, were less reliable, and had a greater risk of goods being damaged, for which JSW could face both monetary and non-monetary losses. Although the Class I barges led to higher payoffs, the outsourcing option offered a fixed and relatively lower payoff. The assistant general manager's objective was twofold: to meet the customer requirements in time and to benefit JSW financially. How should he decide which option to pursue?

Brown Bag Films produced programming for Disney, Amazon, BBC, and Nickelodeon, reaching an audience of more than 100 million pre-school children around the world. This case examines the development of Brown Bag Films, which began in 1994 as a partnership between two art school students in Ireland who had a passion for making animated children's films-but no business plan, and only a few thousand pounds of borrowed funds. Early successes like an Oscar nomination meant name recognition, but the company had a decidedly non-linear track before it became known for producing popular animated series. The company pivoted a number of times, yet still saw itself as a start-up, a technology venture that was also a creative venture, at the cutting edge of 3D animation. Topics discussed include strategic thinking, mission alignment, management training, and corporate culture.

Brown Bag Films produced programming for Disney, Amazon, BBC, and Nickelodeon, reaching an audience of more than 100 million pre-school children around the world. This case examines the development of Brown Bag Films, which began in 1994 as a partnership between two art school students in Ireland who had a passion for making animated children's films-but no business plan, and only a few thousand pounds of borrowed funds. Early successes like an Oscar nomination meant name recognition, but the company had a decidedly non-linear track before it became known for producing popular animated series. The company pivoted a number of times, yet still saw itself as a start-up, a technology venture that was also a creative venture, at the cutting edge of 3D animation. Topics discussed include strategic thinking, mission alignment, management training, and corporate culture.

This case builds directly on the case LendingClub (A). In this case students follow Emily Figel as she builds two tree-based models using historical LendingClub data to predict, with some probability, whether borrower will repay or default on his loan. Technical topics include: (1) Decision trees as a modelling technique, overfitting and induction bias, model validation; (2) Random forest as an ensemble-style modelling technique, bootstrapping, random feature selection; and (3) Log loss as a metric for evaluating and comparing models, feature impact.

This case builds directly on the cases LendingClub (A) and (B). In this case students follow Emily Figel as she builds an even more sophisticated model using the gradient boosted tree method to predict, with some probability, whether a borrower would repay or default on his loan. Having now built three models, Figel compares them to determine which model is most effective at classifying borrowers correctly then uses that model to determine how to invest in a portfolio of loans. Students explore the relationship between her model's confusion matrix, which organizes the model's correct and incorrect classifications, the cutoff point on the curve that matches true positives and true negatives, and the payoff matrix Figel constructs. Students can then follow the link directly from the model Figel builds to her specific investment decisions. Technical topics include: (1) Gradient boosted trees as a modelling technique; hyperparameters and learning rate; model validation; and (2) Evaluating model output; ROC curve, cutoff point, confusion matrix; payoff matrix as a framework for utilizing the model to compare investment opportunities.

This case builds directly on Chateau Winery (A). In this case Bill Booth, marketing manager of a regional wine distributor, shifts to supervised learning techniques to try to predict which deals he should offer to customers based on the purchasing behavior of those customers closest to them. Topics include: Supervised learning; collaborative filtering; K-nearest neighbor as a modeling technique; collaborative filtering with cosine similarity of customers; collaborative filtering with cosine similarity of products; comparison of different prediction models.

In 2013, GE implemented the FastWorks program, an initiative that utilized tools and methods adapted from start-up methodologies to make the company more customer centric, lean and agile. With the spread of FastWorks within GE, a startling conclusion became evident - the existing performance management system might no longer be fit for the company's new direction. This case series follows the journey of GE's new performance management system, Performance Development, and its PD@GE app. By utilizing FastWorks in its development process, the HR team was able to leverage customer feedback and rapid prototyping to build a new system with unique features, such as instantaneous feedback, upward feedback and removal of employee ratings.