Search results

Emerging markets, business models, information technology.

This case is designed for MBA groups or students from MSc in Management, International Business, Logistics, Information Systems or Environmental Management programs. It can be covered in courses on Strategy, Process Management, International Business, Process Management, Supply Chain Management and Managing Information Systems.

Returpack is a Hungarian company dealing with reverse vending machines (RVMs) that collect aluminum beverage cans, even in crushed form, based on a worldwide technology innovation. All RVMs are online and monitored and managed remotely. RVMs are mainly “fed” by the poorest, often homeless people, who are still motivated by the extremely low (less than 1 euro cent for a can) incentive that comes from the selling of the aluminum waste to recycling smelters. Based on the success of the business model in Hungary, projects were planned in the USA, Austria, Romania, and Turkey in 2013. However, beyond economic, legal and cultural challenges, a dramatic decline in the global aluminum waste prices early in 2014 questioned the return on investment at these projects. Advancements in the material-recognition technologies at waste sorting plants raise further questions.

Evaluating the business model innovation in the case by combining the different approaches of the business model concept with the knowledge on the recycling industry, the crowdsourcing method and the Internet of Things. Based upon this, students may identify and evaluate options for implementing the business model in and adapting to new markets, also by simulating these changes in a formal (numerical) business model.

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

Strategy.

CSS 11: Strategy

Greenko, a renewable power generating company investing in biomass, small and medium hydro power and wind power projects, had projected to achieve 1GW (Giga Watt = 1000 Mega Watt) of installed capacity by March 2015. The company had been financing its projects with debt from Indian banks and financial institutions on a project finance basis and it had to now decide whether to refinance the project finance debt with an international bond issue of USD 550 million. The case provides an opportunity to discuss the public policy and financing aspects of renewable energy in India.

This case invites students to assess the impact that Brexit, the withdrawal of the United Kingdom from the European Union, might have on a New York–based hedge fund's portfolio and, specifically, its UK assets. The case is designed to prompt students to make market assumptions and investment hypotheses based on a combination of numerical data and qualitative information. It requires no numerical computations; instead, it asks the student to interpret both markets' short-term reactions to the Brexit vote and strategy shifts from UK and European business leaders in order to evaluate longer-term implications for the economies of the United Kingdom, Europe, and the world.

In August 2005, an investment manager of a hedge fund is considering purchasing an equity interest in a start-up biotechnology firm, Arcadian Microarray Technologies, Inc. The asking price is $40 million for a 60 percent equity interest. Managers of the firm are optimistic about the firm's future performance; the investment manager is more conservative in his expectations. He calls on the help of an analyst with her firm to fashion a counterproposal to Arcadian's management. The tasks for the student are to apply the concept of terminal value, interpret completed analyses and data, and derive implications of different terminal-value assumptions in an effort to recommend a counterproposal. Very little numerical figure-work is required of the student.

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.

Hundreds of thousands of would-be hoteliers have been popping up all around the world, hoping to rent their own homes and apartments to complete strangers through a service called Airbnb. The goal of Airbnb’s aspiring hosts was to use the company’s website to attract guests who were willing to pay the highest rates to stay in their homes for a short time. For Airbnb, the goal was to improve customer review performance so it could, in turn, increase profits. How could the company achieve its goal? Enter text mining, a technique that allowed businesses to scour Internet pages, decipher the meaning of groups of words, and assign the words a sentiment proxy through the use of a software package.

In order for text mining to be useful for Airbnb, its marketing professionals first had to gain access to customer review data on the company’s own website. The team then had to analyze the data to find ways to improve property performance. Was the team going to be able to leverage this large amount of data to determine a strategy going forward?

This case study considers the supply chain redesign of a multinational company, with specific emphasis on production lot sizing, inventory policy and transportation decisions.

The material is intended for senior level students of business administration, logistics and similar departments. An intermediate knowledge of supply chain, purchasing and inventory concepts is required; therefore, the case is better suited for students who have taken one-semester courses on supply chain management and inventory management. This case can be used in graduate courses as part of discussions on physical distribution, supply chain design/redesign, risk pooling through process optimization.

Within the global market, establishing the right business model where cost of operations is optimized has become key for competitiveness. This necessitates the simultaneous consideration and reevaluation of production, inventory and transportation interactivities within the integrated supply chain. We first discuss the business procurement model of a multinational company with emphasis on critical aspects of the current structure. An alternative model brought into consideration by the managers of the company considers consolidation of shipments through supply hubs and distribution to regional manufacturers. We present an analysis based on perspectives of company managers for and against this new business model. We finally provide numeric evidence on relevant costs of both models in order to enhance further discussion on redesign decisions.

The discussion regarding the case will provide a better understanding of key concepts of supply chain integration and coordination as well as the significance of the optimization of underlying processes.

Teaching notes.

Faeyee Electronics was an electronics company that manufactured and sold smartphones including XS and XT types. The management of Faeyee wanted to pursue the maximum contribution margin as much as possible, especially since they were faced with limited resources. It was necessary to apply analytical tools and cost accounting concepts to study this case including cost-volume-profit analysis, learning curve analysis, regression analysis, definitions of competitive products, constrained non-linear optimization, and contribution margin.

The case uses business analytics tools and cost accounting concepts, including regression models and constrained optimization approaches, to study how to maximize business outcomes, such as contribution margin and profits when limited business resources are available. The company and individuals are disguised.

This case can be used in any junior-, senior-, masters- or MBA-level managerial accounting course. Students need to have at least one course of introduction to statistics or instructors review required statistics concepts or techniques before assigning this case. Students are exposed to the challenges of deriving learning curve models, using regression analysis to study collected data and allocating limited resources to maximize contribution margin.

Greg Mills describes his search for the perfect engagement ring which includes an analysis of the prices of 6,000 diamonds. An engineer, Greg hopes to impress Sarah Staggers by using regression to find an underpriced diamond. Students are asked to either select one of the 6,000 diamonds or provide point forecasts for prices of 3,142 diamonds in a hold-out sample. The instructor can use the actual prices of the held-out diamonds to evaluate student pricing models.