This exercise includes 10 well-known US-based companies, all but one with international operations, from a variety of industries, and 10 financial profiles that must be matched. The companies included are Alaska Airlines Inc., Amazon.com Inc., The Cheesecake Factory Incorporated, Merck & Co. Inc., Netflix Inc., Pacific Gas & Electric, Ralph Lauren Corporation, Starbucks Corporation, Tesla Inc., and Uber Technologies Inc.
This exercise immerses participants in the innovative intersection of artificial intelligence (AI) and luxury branding. Set in the competitive perfume industry, participants assume the role of entrepreneurs tasked with launching a new luxury perfume brand. They must establish a brand identity, analyze market data, design a product, and develop a marketing campaign, all while leveraging generative AI tools. The case challenges participants to think creatively and strategically, confronting the complexities of combining luxury with sustainability and innovation. Through this scenario, they will explore how AI-driven insights can shape decision-making in an evolving marketplace.
Two expert woodworkers at Craft Wood Furniture, a mid-sized production house that employs approximately 50 people, work on the company’s furniture products in the finishing stages of the production cycles: sanding, primer polishing, and applying final coats of paint. Their expertise is critical to ensure that each furniture item meets the company’s high standards of durability and aesthetics. However, each furniture item differed in terms of size, type of material used, and level of complexity, which means that the total completion time also differs for each item. Craft Wood Furniture wants to determine the optimal scheduling sequence for the most time-effective and smoothest workflow. How should the various tasks be arranged for a seamless flow between the two workers without unnecessary delays?
In October 2021, Ashish Agarwal, vice-president of the solar business vertical at Blupine Energy Pvt. Ltd., was preparing for an upcoming tender for a 300 megawatt solar project. Relying on his industry experience, Agarwal noted all the assumptions related to the capital and operating expenses for setting up this plant. He wanted to compute a tariff that he could bid that would be competitive while generating a 16 per cent equity internal rate of return—the minimum Blupine required for greenfield projects. Agarwal knew the most critical project risks were a delay in arranging the required land and module price fluctuations, so he wanted to also consider their effect on the rate of return.
This exercise provides a structured framework for introducing business students to supervised machine learning (SML), unsupervised machine learning (UML), and reinforcement machine learning (RML). The exercise revolves around applying these techniques to evaluate harassment policies, offering a hands-on and practical approach to understanding how machine learning (ML) works in real-world scenarios. The exercise is suitable for undergraduate classes and graduate business classes, particularly in core information technology (IT) management, innovation management, fundamentals of AI, and governance courses.
The exercise offers students an intriguing learning scenario, involving analysis of the financial statements of 13 anonymized but well-known companies from diverse industries and sectors using financial ratios to eventually identify them. All 13 companies in the exercise are listed on the National Stock Exchange of India and were selected using National Industrial Classification codes. Moreover, the selected companies are taken from the top three firms in terms of market capitalization within each industry sector. The exercise provides financial data from the balance sheets and income statements of the 13 anonymized companies. Students are required to first categorize the anonymized companies into broad sectors and then identify their specific industry. Last, students are asked to identify the names of the anonymized companies. The sample has been divided into two categories: a training set and a testing set. Students should use the training set to develop a blueprint (a set of procedures) to identify the companies based on the sectoral and industrial characteristics reflected in their financial statements. They should then validate the blueprint by applying it to determine the identity of the companies in the testing set.
This exercise explores the operational and strategic challenges faced by Philanthropy Insight (PI), a Canadian non-profit organization founded by Emily Hayes, which aims to provide donors with information to make informed giving decisions. Despite significant growth in website traffic, PI struggles with chronic underfunding, leading Hayes to continually finance the deficit from her personal savings. The case delves into PI’s data management issues, including its use of disorganized and error-prone Excel spreadsheets, and looks at the potential value of structuring its data into a centralized database to better analyze donor behaviour and content effectiveness. Students are tasked with analyzing PI’s current data practices, understanding the implications of data structuring, and recommending strategies to improve operational efficiency and financial sustainability through better data management and analytics.
This exercise details a significant service failure in 2021 at the in-house bakery of Elite Hypermarket, based in Goregaon, Mumbai, India, involving the accidental sale of puff pastries containing non-vegetarian filling to two strict vegetarian customers. The incident may provoke a social-media backlash and hence threatens to damage the hypermarket's reputation, prompting the store manager to quickly investigate and find solutions that will address the crisis and prevent similar situations in the future. Students will be challenged to apply service-management concepts to address customer grievances, improve operational processes, and communicate effectively with internal and external stakeholders.
Management of the project included determining capacity requirements, construction management, and records management. During its planning and construction, the nature of the Gautrain Project generated significant stakeholder interest from both supporters and those in opposition. For this reason, a holistic approach towards communication and stakeholder management was established. Given the ever-changing environment in which Gautrain operates, the purpose of Gautrain’s integrated communication strategies was always to be proactive in managing and engaging its multiple—and highly complex—stakeholder groups.
Environmental impact assessment is a legal requirement for all large infrastructure projects. The environmental impact assessment process for the Gautrain Project was governed by the 1997 EIA regulations based on the Environment Conservation Act 73 of 1989. Developing environmental impact assessment is a complex process that covers a wide variety of impact assessments, requiring skills from various experts and input from interested and affected parties, including large-scale public participation and adherence to a prescribed administrative regime. As such, environmental impact assessment poses a management challenge.
The Gautrain Rapid Rail Link Project was announced as a Blue IQ project in 2000 and officially approved by the Gauteng Provincial Government in 2001. As a Blue IQ project, the primary aim of the Gautrain Project was to stimulate economic growth in Gauteng through enhancing infrastructure development and creating employment. The further aim was to alleviate the traffic congestion on roads between Johannesburg and Tshwane by promoting public transport as an alternative to private vehicle usage. A highly efficient, as well as user-friendly, automatic fare collection system had to be developed in order to promote Gautrain as a viable alternative to private modes of transport.
A key example of strategic partnerships is the Gautrain Management Agency public transport integration model with the taxi industry, referred to as the Midibus Feeder Distribution Service. The partnership model was developed in 2011 with the objective of providing a service from the Gautrain rail system’s Marlboro station to Linbro Business Park. Through an innovative contracting model, a partnership was formed with the taxi association operating in these service areas.
Assuming the roles of a first-time property owner and a university student, students will play out a simple two-person apartment rental negotiation. While the end goal of the negotiation—what rent will be paid—seems simple, each role has sufficient background to allow for creative tactics and solutions by savvy participants. <br><br>This negotiation takes place in a situation familiar to students and can be used as an introductory exercise in a negotiation course or as a one-off negotiation exercise in other courses such as organizational behaviour.
This case analyses CY Company’s rationale for strengthening its investments in marketing and research and development (R&D) in an increasingly competitive market environment. It comprehensively evaluates the company’s financial performance to help students understand the underlying reasons for the company’s changing financial performance and learn methods of systematically assessing corporate financial performance. Furthermore, the case emphasizes evaluating the reasonableness of executive compensation policies through financial performance assessment and discusses how to formulate appropriate financial strategies based on a company’s operational performance.
Queenie Stone started her career as an asset manager for a large investment bank in New York City. Her manager tasked her with creating a new three-stock portfolio and investigating how changes in her inputs of expected returns, standard deviations, and correlations would impact her portfolio asset weights. The stocks included the Procter and & Gamble Company (PG), JP Morgan & Chase & Co. (JPM), and Advanced Micro Devices, Inc. (AMD). Queenie created an equally weighted portfolio as a base case to compare with Markowitz’s optimal portfolio construction methodology, based on modern portfolio theory. Using Queenie’s equally weighted model with the necessary formulas, students use Excel’s Solver tool to find the optimal portfolio allocations among these stocks.
In October 2022, Mr. Kumar was looking at adding Page Industries to his equity portfolio. He approached Mr. Paibir of P3 Investment Solutions in Goa, India, for advice. Mr. Kumar wanted to know the fair value of Page Industries’ equity shares. An equity analyst was tasked with analyzing Page Industries and suggesting a price level for long-term investment. This exercise addresses several key issues in estimating a firm’s fair value that makes it unique. First, a comparative analysis of Page and its peers on key financial factors is used. The next issue is modelling for future growth to estimate revenue, profits, cash flows, and computation of intrinsic value. Finally, the exercise addresses the challenges in estimating fair multiples to value a firm using the relative valuation method.
A recent MBA graduate from a premier business school in India successfully secured a position with a major investment firm. After starting his job, he was asked by his supervisor to evaluate the performance of three mutual funds in which he had invested as a student. He completed a primary evaluation of those three funds, which were all large-cap funds. The recent graduate was then asked by his supervisor to conduct a new evaluation of the same three mutual funds to assess their risk adjustment. His new objective was to better understand the performance of the three mutual funds and determine which was the best investment choice based on risk adjustment.
This exercise introduces the L.E.A.R.N. dialogue process, a structured method for creating a psychologically safe space in which people with diverse experiences, knowledge, skills, and perspectives can discuss the complex challenges facing organizations today. During the session, students are introduced to the method, learn how to work through the five L.E.A.R.N. stages—Launch, Explore, Acknowledge, Reflect, and Notice—and are given the tools to craft and facilitate their own L.E.A.R.N. dialogues. The exercise includes an example of the L.E.A.R.N. dialogue process in which participants explore the question “How much access should employers have to monitor employees working remotely?” The L.E.A.R.N. process allows the company executives in the example to hold dialogues with employees to further understand the challenges and, over time, find solutions. Having conversations about difficult topics with people with diverse lived experiences is challenging, so this exercise provides a set of tools to guide these conversations.