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Nick Sugimoto, CEO of Honda Innovations-the open innovation organization of Honda, has to decide how to extend his organization's approach to innovation across the world. Honda Innovations dates back to 2000 when Honda created Honda Research Institute in Silicon Valley. Since then, the organization has evolved through various models from a venture capital fund to its current form as Honda's corporate innovation effort. The case describes the various approaches to innovation between Honda and outside firms and how it fits into an increasingly dynamic car industry.

Supplement to case 118044.

The manager of the catalog division of an art gallery is attempting to prepare an assessment of the divisions financial performance.

Similar to Case TCG121. The department of neurology within a department of medicine is attempting to build its budget. The chair of medicine has asked the division head to give him a business plan, which is presented in the case. The chief must decide how credible the plan is. To fully understand what is going on, the budget-building (and business plan) effort must be taken down to the individual provider level.

Over 1.5 million ISO 9001 certificates are in effect worldwide, 30 years after this quality management standard was launched. As the factory of the world, China is by far the leading country for ISO 9001, in terms of both absolute and relative numbers and growth. Nevertheless, practitioners have cast doubts on the reliability of adopting ISO third-party quality certification in this country. In-depth interviews with 40 senior quality managers, consultants, and auditors with broad field experience and other complementary methods paints a disturbing picture. The widespread prevalence of fake ISO 9001 certificates is indicated, together with an eroded credibility of the process of third-party certification. We discuss the profound implications of the study-including whether or not the phenomenon is restricted to China-and introduce suggestions for managers and other stakeholders, as well as avenues for further research.

Health Services Software (HSS) was a software company specializing in software for hospitals. Founded by a health care team with first-hand knowledge of the issues facing hospitals, HSS had first mover advantage. At the onset of HSS's operations, hospitals had capital funding that allowed them to acquire the software with five-year licensing agreements. Five years later in 2016, as the licences were expiring, the hospitals no longer had the capital funding and could not afford a large cash outlay from their operating budgets for a new five-year licence. In light of the change in funding options for the hospitals, HSS was considering whether to move to an annual-subscription-based software. But before the team could establish the prices for the software, they needed to better understand the company's cost structure. HSS hired a consultant to conduct the costing exercise.

In May 2019, Cheryl Leong, Head of Fraud Analytics and Data Management at Moss & Associates, a mid-sized New York accounting firm, was tasked with fraud detection in annual reports. Besides helping clients present financial information to stakeholders, accounting firms had to ensure there were no misrepresentations. The incidence in companies reporting material falsehoods had risen in recent years and regulators were pushing accounting firms to detect those instances earlier. Companies had been using qualitative text to mislead stakeholders of their financial wellbeing. Leong was working on a data analytics platform that would replace the time consuming task of manually going over executive statements and management discussion & answers (MD&A) sections. Several text mining techniques were available within the system to break text down for classification. With the fraud detection tool ready for launch, she wondered whether she had adequately addressed the challenges of analysing text in annual reports. What steps should she programme the tool to take? How successful it would be?

One of the fastest-growing areas in business is socially responsible investment (SRI), which incorporates environmental, social, and governance concerns. From 2007 to 2017, SRI increased from $2.71 trillion to over $21 trillion, and in 2017, 84% of all millennials were interested in SRI. Given these trends, how should an entrepreneur with a social mission proceed? One of the first decisions is choosing an appropriate legal entity. This technical note presents an array of legal entities that a business with a social mission could consider, with special emphasis on a relatively recent option: the benefit corporation.

Student spreadsheet for case W18857

Student spreadsheet to case W19007

Character House Entertainment was a small entertainment company in the Greater Toronto Area that provided Disney and cartoon-character performances for children's birthday parties and corporate events. The company's customizable services featured musical performances, meet-and-greets, games, dances, stuffed animals, and cotton candy. In April 2017, the company's founder and chief executive officer anticipated an influx in demand for a recently released movie that had achieved worldwide success and critical acclaim. She wanted to offer this new theme in the company's event options in order to contend with competitors and stay current with customers. In addition to customer and competitor indicators, she wanted to assess the return the company could expect if it expanded into offering those characters.

Character House Entertainment was a small entertainment company in the Greater Toronto Area that provided Disney and cartoon-character performances for children’s birthday parties and corporate events. The company's customizable services featured musical performances, meet-and-greets, games, dances, stuffed animals, and cotton candy. In April 2017, the company’s founder and chief executive officer anticipated an influx in demand for a recently released movie that had achieved worldwide success and critical acclaim. She wanted to offer this new theme in the company's event options in order to contend with competitors and stay current with customers. In addition to customer and competitor indicators, she wanted to assess the return the company could expect if it expanded into offering those characters.

As Berkshire Hathaway had reported in its 2018, first quarter, 10-Q, it was "engage[d] in a number of diverse business activities." Maria knew that Berkshire Hathaway bought the stock of numerous other companies. She assumed that the company's holdings in the common stock of other companies represented various ownership percentages, and she wanted to explore that possibility more extensively.

When a consumer purchases an item, the presumption is that the benefit obtained from it exceeds the price paid-we can think of this difference as the consumer profit. How do consumers calculate this profit, especially when they do not immediately consume the purchased item? This technical note presents a model of mental accounting that gives insights into the process that consumers follow to calculate value. It compares rational logic, accounting logic, and mental accounting logic, incorporating the phenomena of loss aversion, transaction utility, and consumer anomalies. This note is used at Darden in the second-year "Behavioral Decision Making" course. It would also be suitable in courses covering rational decision making in business.

A bank manager is thinking about how to manage the new private banking advisers (PBA) practice in her region. For that she needs to determine how many PBAs she needs, where they should be located, and to which branches each of them should be assigned. This three-case series exposes students to the underlying sequence of analytical tasks, which culminate in solving an integer-programming optimization model-a main tool of prescriptive analytics. The assignment part of the problem (e.g., given the home-base locations of advisers, how should branches be assigned) is solvable in Excel, but the higher-level problem of where the PBAs should be located is beyond the scale of the built-in Excel solver. The case's teaching note recommends the Gurobi solver (which is available to students for free), that is controlled through a Python interface (which is on track to becoming world's most popular programming language). The (A) case presents the problem and data ""as they are."" The (B) case focuses on the descriptive analytics task of visualizing the branch locations to gain intuition. Tableau software is used with default and advanced mapping functionality. The (C) case focuses on the predictive analytics task of estimating travel times between branches. Python code for that is provided, which repeatedly calls Google Maps to obtain the travel times-a task known as the application programming interface (API). By following the A-B-C sequence, the students will have all they need to build the optimization model. With A-B, they have a lot of intuition but still need to estimate travel times. With just the A case, the situation is mostly open ended: it is up to the students to decide what to do, in addition to how to do it. The case is suitable for advanced undergraduate or MBA electives on analytics, or for fast-growing Masters in Analytics programs.

Spreadsheet supplement for case UV7686.

A bank manager is thinking about how to manage the new private banking advisers (PBA) practice in her region. For that she needs to determine how many PBAs she needs, where they should be located, and to which branches each of them should be assigned. This three-case series exposes students to the underlying sequence of analytical tasks, which culminate in solving an integer-programming optimization model-a main tool of prescriptive analytics. The assignment part of the problem (e.g., given the home-base locations of advisers, how should branches be assigned) is solvable in Excel, but the higher-level problem of where the PBAs should be located is beyond the scale of the built-in Excel solver. The case's teaching note recommends the Gurobi solver (which is available to students for free), that is controlled through a Python interface (which is on track to becoming world's most popular programming language). The (A) case presents the problem and data ""as they are."" The (B) case focuses on the descriptive analytics task of visualizing the branch locations to gain intuition. Tableau software is used with default and advanced mapping functionality. The (C) case focuses on the predictive analytics task of estimating travel times between branches. Python code for that is provided, which repeatedly calls Google Maps to obtain the travel times-a task known as the application programming interface (API). By following the A-B-C sequence, the students will have all they need to build the optimization model. With A-B, they have a lot of intuition but still need to estimate travel times. With just the A case, the situation is mostly open ended: it is up to the students to decide what to do, in addition to how to do it. The case is suitable for advanced undergraduate or MBA electives on analytics, or for fast-growing Masters in Analytics programs.

Spreadsheet supplement for case UV7690.

A bank manager is thinking about how to manage the new private banking advisers (PBA) practice in her region. For that she needs to determine how many PBAs she needs, where they should be located, and to which branches each of them should be assigned. This three-case series exposes students to the underlying sequence of analytical tasks, which culminate in solving an integer-programming optimization model-a main tool of prescriptive analytics. The assignment part of the problem (e.g., given the home-base locations of advisers, how should branches be assigned) is solvable in Excel, but the higher-level problem of where the PBAs should be located is beyond the scale of the built-in Excel solver. The case's teaching note recommends the Gurobi solver (which is available to students for free), that is controlled through a Python interface (which is on track to becoming world's most popular programming language). The (A) case presents the problem and data ""as they are."" The (B) case focuses on the descriptive analytics task of visualizing the branch locations to gain intuition. Tableau software is used with default and advanced mapping functionality. The (C) case focuses on the predictive analytics task of estimating travel times between branches. Python code for that is provided, which repeatedly calls Google Maps to obtain the travel times-a task known as the application programming interface (API). By following the A-B-C sequence, the students will have all they need to build the optimization model. With A-B, they have a lot of intuition but still need to estimate travel times. With just the A case, the situation is mostly open ended: it is up to the students to decide what to do, in addition to how to do it. The case is suitable for advanced undergraduate or MBA electives on analytics, or for fast-growing Masters in Analytics programs.