Search results

The learning outcomes are as follows: to assess the competitive position of a small business-to-business (B2B) company to remain competitive in a developing country; to identify and shape the business opportunity that represents serving small and medium enterprises that would lead to competitive positioning in the B2B environment; and to compare innovation and commercialization projects through strategic dimensions related to managing a technology company in a way that fosters business profitability and growth.

Margarita Kaplun, founder and CEO of Kapter, a small company providing country-wide technical and consultant services on thermography to industrial hubs in Mexico, had positioned the company as a reputed provider. However, the company had experienced razor-thin margins. Margarita needs to decide whether they pursue the following grant to fund a project on technology development that will source the competitive position or pursue a commercial project that help them achieve a larger customer base to reap higher income.

The case is oriented for an undergraduate audience, attending entrepreneurship, innovation and industrial organization courses. It depicts issues of technology-based entrepreneurship and is suitable for students in business, management and engineering.

Teaching notes are available for educators only.

CSS 11: Strategy.

Uber had pioneered the growth and delivery of modern ridesharing services by leveraging the explosive growth of technology, GPS navigation, and smartphones. Ridesharing services had expanded across the world, growing rapidly in the United States, China, India, Europe, and Southeast Asia. Even as these services expanded and gained popularity, however, the pickup experience for drivers and riders did not always meet the expectations of either party. Pickups were complicated by traffic congestion, faulty GPS signals, and crowded pickup venues. Flawed pickups resulted in rider dissatisfaction and in lost revenues for drivers. Uber had identified the pickup experience as a top strategic priority, and a team at Uber, led by group product manager Birju Shah, was tasked with designing an automated solution to improve the pickup experience. This involved three steps. First, the team needed to analyze the pickup experience for various rider personas to identify problems at different stages in the pickup process. Next, it needed to create a model for predicting the best rider location for a pickup. The team also needed to develop a quantitative metric that would determine the quality of the pickup experience. These models and metrics would be used as inputs for a machine learning.

This case replaces UVA-M-0837. It can be used in a variety of marketing and strategy classes to understand how (1) at a macro level, a shift in consumer and environmental factors can impact firm strategy and (2) at a micro level, an e-mail-based marketing campaign designed to address these changes can impact firm-level performance.

The case puts the students in the position of CEO Robert Huth as he is preparing for a board meeting. He had taken David's Bridal from a loss in 1996 to sales of over $1 billion by 2011, but he was concerned about future growth. People were waiting longer and longer to get married and, once they decided to, were spending much less than in the past, so the industry had seen year-over-year declines since 2007. How would David's Bridal establish its brand in the minds of a new generation of brides who shopped, purchased, and decided differently than had brides in past generations?

Marketing.

The case should ideally be discussed in an advanced second year MBA course or towards the end of a first year course where the students are already exposed to the basics of marketing. The case works well in courses on destination marketing, strategic marketing, campaign management, entrepreneurship. It also works well with executives, the case discussion affording the instructor an opportunity to illuminate the complexity of designing and implementing marketing strategy.

Two young MBAs, each with a marketing specialization working in a bank, left their jobs and started their own company by buying a rubber plantation business from another businessman. The businessman was one of the co-owners of the “Ocean World Water Park” theme park (amusement park) close to Bhubaneswar-Cuttack twin city. The amusement park had good potential as it was located in an area with exponential growth of young executives having high disposable incomes. But the business performed poorly because of management ineffectiveness. The case explores the possibility of a turnaround.

The case: sensitizes students about the commercial implications of their marketing decisions by giving them adequate data to work on evaluating the revenue and profit impact of marketing initiatives on business; and helps them to understand that promotional variables are not independent in nature and hence separating the impact of one promotion when a large number of initiatives are operational, is not very dependable. Though the case provides a particular way of attempting to solve the problem, it does not lead to unique solutions.

Teaching notes are available for educators only. Please contact your library to gain login details or email support@emeraldinsight.com to request teaching notes.

In May 2016, Sarita Digumarti, Chief Operating Officer of Jigsaw Academy in Bengaluru, India, faced a challenging situation. Jigsaw Academy provided online courses in data analytics and Big Data at the beginner, intermediate and advanced levels for students as well as working professionals. It was perceived that plenty of students from premier institutions in India had a high level of theoretical knowledge about the process involved in number crunching and data analysis; however, the hands-on experience on actual business problems or actual data sets was a major limitation with these students. Given the rapid growth of the analytics sector and the limited number of academic institutions offering analytics courses, there was a lack of availability of the right skills in the analytics market. Jigsaw Academy seized this opportunity and started offering relevant courses. All efforts were made to enhance the number of students enrolling for the courses, which in turn resulted in improving its customer base. Realizing the demand of industries for employees skilled in the analytics sector, Jigsaw Academy wanted to grow its brand equity and to achieve this through business to business (B2B) collaborations and/or alliances. However, expansion through B2B has its own challenges. Given the competitive landscape of analytics market, Jigsaw Academy was wondering whether they should opt for B2B channel, and if yes, the question was related to the process of choosing potential B2B partners.

The authors have collected the data from primary sources as well as secondary sources. Primary sources include field visits and audio-recorded interviews conducted with key departmental heads in the organization. Secondary sources include data retrieved from the company website and the relevant information available about the industry with the assistance of the internet. Except the founder’s name, all other names are disguised to protect the individual’s privacy as per instructions from the founders of Jigsaw Academy.

This case can be used at the graduate or MBA level in courses such as entrepreneurship, sales and distribution management, strategic alliances and mergers.

Josh Williams is a Student at the NAU who has driven buses on campus and wants to improve the transportation on campus. He is convinced that purchasing a new type of bus that is more fuel efficient, has larger capacity, better designed for boarding, and has a longer life is worth the higher purchase cost. He sets out to prove it by creating a discounted cash flow (DCF) analysis. Since many of the estimates for the DCF analysis are uncertain, he decides to perform a Monte Carlo simulation (MCS) analysis. Students are asked to step into Josh’s role and perform the analysis.

Josh Williams was a Student in the authors’ MBA program. Both authors teach in this program and one author was the Advisor for Net Impact and worked with Josh to present his idea to the university administration. The authors have changed a name or two but otherwise, the case describes a real situation in a real organization without disguise.

The authors have used this case in a first semester MBA-Applied Management course, Decision Modeling and Simulation. Students already have experience with DCF analysis and have been introduced to MCS. With this case, students apply MCS at the conclusion of a three-week module on predictive analytics. Students have run at least two MCS models and have become comfortable with the software. The case would also be appropriate for a senior-level undergraduate course such as business analytics or management science. It might also be useful for other courses that include the MCS modeling technique learning objectives such as project management.

This case provides an opportunity for students to perform an MCS analysis. MCS is useful when many of the inputs to a DCF analysis (or any model) have been estimated and the modeler is concerned that the estimates are uncertain and could perhaps be a range of values. MCS can be used to understand the effect of this uncertainty on NPV which in turn may affect the decision. The case could also be used without MCS focusing just on the DCF analysis with deterministic sensitivity analysis.

The case is designed to be used with the digital business model framework developed by Peter Weill and Stephanie Woerner of Massachusetts Institute of Technology (MIT) (Weill and Woerner, 2015) and is referred to as the W & W framework. This approach provides a useful structure for thinking through the strategic options facing environments ripe for digital transformation.

Research for this case was conducted through face-to-face interviews with the protagonist, as well as through a review of their business planning documents and other data and documentation provided by the founder. Some of the market and industry data were obtained using secondary research and industry reports. Interviews were digitally recorded and transcribed to ensure accuracy.

The case follows the story of Kurt Waltenbaugh, a Minnesota entrepreneur who shared the dream of using data analytics to reduce costs within the US health-care system. In early 2014, Waltenbaugh and a physician colleague founded Carrot Health to bring together their personal experience and expertise in both consumer data analytics and health care. From the beginning, they focused on how to use data analytics to help identify high-risk/high-cost patients who had not yet sought medical treatment. They believed that they could use these insights to encourage early medical interventions and, as a result, lower the long-term cost of care.

Carrot’s initial success found them in a consultative role, working on behalf of insurance companies. Through this work, they honed their capabilities by helping their clients combine existing claims data with external consumer behavioral data to identify new potential customers. These initial consulting contracts gave Carrot the opportunity to develop its analytic tools, business model and, importantly, to earn some much-needed cash flow during the start-up phase. However, they also learned that, while insurance companies were willing to purchase data insights for one-off market expansion projects, it was much more difficult to motivate them to use data proactively to eliminate costs on an ongoing basis. Waltenbaugh believed that Carrot’s greatest potential lay in their ability to develop predictive models of health outcomes, and this case explores Carrot’s journey through strategic decisions and company transformation.

This case is intended for either an undergraduate or graduate course on entrepreneurial strategy. It provides an effective introduction to the unique structure and constraints which apply to an innovative start-up within the health-care industry. The case also serves as a platform to explore the critical criteria to be considered when developing a digital transformation strategy and exposing students to the digital business model developed by Weill and Woerner (2015) at MIT (referred to in this instructor’s manual as the W&W framework). The case was written to be used in an advanced strategy Master of Business Administration (MBA) class, an undergraduate specialty health-care course or as part of a health-care concentration in a regular MBA, Master of Health Care Administration (MHA) or Master of Public Health (MPH). It may be taught toward the end of a course on business strategy when students are building on generic strategy frameworks and adapting their strategic thinking to the characteristics of specific industries or sectors. However, the case can also be taught as part of a course on health-care innovation in which case it also serves well as an introduction to the health-care payments and insurance system in the USA. Finally, the case can be used in a specialized course on digital transformation strategy in which case it serves as an introduction to the MIT W&W framework.

The case is particularly well-suited to students who are familiar with traditional frameworks for business strategy and business models. The analysis builds on this knowledge and introduces students interested in learning about the opportunities and challenges of digital strategy. Equally, the case works well for students with clinical backgrounds, who are interested in how business strategy can influence changes within the health-care sphere. Finally, an important aspect of the case design was to develop students’ analytical confidence by encouraging them to “get their hands dirty” and to carry out some basic exploratory data analytics themselves. As such, the case requires students to combine and correlate data and to experience the potentially powerful combination of clinical and consumer data. Instructors should find that the insights from these activities give students unique insights into the potential for of data analytics to move health care from a reactive/treatment ethos to a proactive/intervention ethos. This experience can be particularly revealing for students with clinical backgrounds who may initially be resistant to the use of clinical data by commercial organizations.

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.

Indian Railways (IR) has been slow in innovation. The competition from other modes of transport has posed new challenges to IR. Railways worldwide have taken help from startups to develop innovative solutions to improve railway operations. Such collaborations have helped in leveraging the technical expertise of startups in domains which are non-conventional for railways to develop in-house. These collaborations have been made possible by funding startups through various investment channels.

Risk managers have more tools than ever to help protect their companies from risk. Complex financial instruments, intricate mathematical models, and access to massive amounts of data can help the risk manager structure a multifaceted strategy to decrease volatility and protect the company from a catastrophic event. However, these tools have their own risks that can complicate a risk manager's job.

Analyzing corn price volatility helps students understand four best practices for risk managers, regardless of the specific risks they face or the strategies they employ: quantify the company's exposure; understand the nature of the risk; understand how the hedge works in practice; and separate hedging and speculation.