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The case uses the backdrop of a bitter battle between Nelson Peltz, the activist shareholder and founder of Trian Partners and the management of Procter & Gamble. The battle focused on the future strategic direction of the company. While Nelson Peltz identified a host of concerns ranging from poor brand management and innovation failures to weak corporate governance and P&G's insular culture, the management of P&G countered that it had a sound strategy in place that it was executing and that the initial results were indeed quite positive. The case explores the major strategic questions that P&G confronts at the time of the battle. It offers a wealth of information about trends in the fast moving consumer goods sector globally allowing for a nuanced discussion of strategic options available to competing firms. Combined with a discussion of management imperatives at P&G, the case provides the basis for the key decisions that the reader is expected to make regarding the proxy battle.

IBM needed a win. Facing flattening sales and increasing analyst concerns, the century-old giant that had reinvented itself so often before was in search of market game-changers. The company believed they found such an opportunity in 2014 when employee Jerry Cuomo and other IBM technologists dug deeply into the secure and decentralized ledger that powered cryptocurrency Bitcoin.2 Similar to open-source software, IBM saw widespread potential for blockchain technology outside of the currency it powered. But seeing a technology's promise is different from monetizing it at scale. How could IBM distinguish blockchain technology from the cryptocurrency for which it was known and attract large clients to adopt IBM's approach to the emerging technology? If successful, what could be next for IBM blockchain?

The case provides a discussion of the birth and evolution of Ryanair in Europe. It paints a rich picture of the avenues of competitive advantage that the company had built in its march toward dominance among the low cost carriers segment of the aviation business in Europe. The early segment of its history provides a very useful discussion to explore fundamental concepts in strategy such as competitive positioning, activity maps, low cost strategies, and tradeoffs. The later half of the case describes the transformation of Ryanair from a pure low cost provider to more of a value added company that seeks to enhance the overall customer experience within the constraints of a low cost model. It closes with a set of issues that surface when Ryanair undertakes the massive shift in its strategy to become a more customer friendly airline company. The later half of the case is an ideal vehicle to reinforce the concept of tradeoffs in strategy and the need for strategic clarity around what a company will do and what it will not do.

Swartzberg knew his expansion model was working but wondered if the strategy of partnering with insurance companies was not limiting the potential of South-Africa-based Vitality Group. Swartzberg was concerned about the possible limitations of this model and wondered if the time had come for Vitality to acquire a license and go it alone as a fully-fledged insurance company in a new market. According to Swartzberg, "We are an insurance company; however, we sell our intellectual property internationally. So far it's going well and we are successful. Our dilemma is whether we should have our own license in a country, take the risk ourselves, and get involved in capital investment? Or should we continue with our existing business model of offering intellectual property to partners and expand along with them?"

The push beyond traditional semiconductors signaled that Intel was positioning for new growth. The firm even sponsored an Industry 4.0 (term to highlight trends in automation and data exchange - largely centered on cyber-physical systems, IoT, cloud computer, cognitive computing, and AI - and their related productivity gains) informational series to shape the agenda around next generation technologies - and their implications. Company executives reasoned that the emerging future would be more interconnected and data-driven than ever - requiring a proactive digital convergence strategy. Robust AI capabilities (and related machine learning) were central to this design. But acquiring and developing leading-edge technology is different from winning wide-scale adoption. How could Intel develop compelling use-cases for still emerging technologies? If successful, what could be next for Intel in Industry 4.0?

This case study focuses on the creation of SingularityNET, a blockchain-based marketplace for AI. SingularityNET was conceived by Ben Goertzel, Simone' Giacomelli and David Hanson in a series of brainstorming sessions at Hanson Robotics in Hong Kong in 2017. A key goal for creating this democratized marketplace platform was to accelerate the development of artificial general intelligence, or AGI. Companies that could not afford customizing their own AI could access AI Agents that would subcontract to and collaborate with other AI agents to create customized solutions to meet specific needs. The distributed blockchain-based network allowed for stitching together clusters of narrow AIs that could mimic sections of a brain. Collectively, these clusters could eventually function like a global brain.

In an effort to help Microsoft Brazil achieve financial return and reap strategic benefits, and to help contribute to the Brazilian startup ecosystem, Frankiln Luzes, the chief operating officer, founded Microsoft Participações. Through Microsoft Participações, Luzes established the very first multi-corporate seed capital fund in the world - BR Startup Fund. BR Startup was owned by multinational corporations including Microsoft, government agencies, and financial institutions. It was established to connect innovative startups, especially those in the information technology sector, to financial investors.

The case tells the story of a first generation entrepreneur, Dhirendra Singh, and his struggle for 15 years to establish a mango pulp based beverage marketing company without any external support and funding. The case highlights the travails that Dhirendra goes through in marketing an undifferentiated product, a mango drink. He survives, largely due to his grit and some lucky breaks, until an external funding agency notices him 15 years after he started his journey. With growth and stability (in 2017, it was the fourth largest packaged beverage in India) come new issues for the enterprise to grapple with. The case leaves the reader to reflect upon these challenges that the company encounters. Can the company overcome these hurdles and thrive? How, is the question that remains unanswered.

Mr. Nuno Ávila, General Director at Deimos Engenharia (Deimos), a private Portuguese Aerospace Engineering company and a key player in the European space sector, faced a crucial strategic decision. In the past, Deimos easily secured research grants from their funding sources to conduct path-breaking projects. However, these agencies now require applicants to specify market applications for their projects to qualify. This was an unknown concept for Deimos, a company steeped in cutting-edge scientific research. Ávila's objective was to build a market-driven innovation culture-a difficult prospect in a company that prides itself on its technological advancements. Ávila found himself faced with several options for how to proceed. With the annual board meeting scheduled in six months, he needs to make some decisions soon about what markets to pursue with the company's MyFARM software service. He is unsure about the Board's willingness to adopt a new strategy, so he invites the Value Creation Wheel team to address this challenge. With their help, Ávila prepares a set of recommendations that if successful, will enable Deimos to transform itself from a science- focused company to an international player that makes the perfect bridge between technology and market needs in the space sector.

Essex Farm, in the town of Essex, New York, employs both traditional and innovative agricultural methods to support the environmentally friendly production of healthy foods, an innovative business model, and a variety of educational initiatives. Its owners, Mark and Kristin Kimball, have become leaders and role models in the movement to revive family farms across the country, revitalize small towns, and reinvent a sustainable food system in the face of climate change and environmental degradation. An experiment in sustainable agriculture, in food entrepreneurship, and in community building, Essex Farm raises important questions about the economic sustainability of the new food movement, and about the future of agriculture in general. The case describes this experiment and addresses key questions that arise when diversified organic farming is proposed as both a lifestyle and an alternative to large-scale industrial farming.

Sunny Bee is a real-world example of a recent business student putting his learning to work to build a successful social venture in his home country of India. The case includes: -A base case that provides background on the material and addresses the entrepreneurial process. This case should be assigned first. -Four follow-on cases that can be taught in any order: -A lean supply chain case -A sustainability case -A business analytics case -A digital innovation case

Supplement to case BAB402.

Supplement to case BAB402.

Supplement to case BAB402.

Supplement to case BAB402.

The first rule of customer service--when something goes wrong, apologize--can backfire. New research based, uniquely, on videos of actual customer interactions shows that after a point, apologies only heighten unhappy customers' frustration. Employees should worry less about displaying empathy and instead focus on creative, energetic attempts to solve the problem at hand.

The idea behind performance feedback is that to help people grow, we need to shine a light on the things they can't see about themselves. But a recent study shows that when employees receive critical appraisals from their peers, it mostly makes them seek out colleagues who will give them positive reviews instead.

This book is about Business Analytics (BA)-an emerging area in modern business decision making. The first part provides an overview of the field of Business Intelligence (BI), which uses historical data to better understand and thereby improve business performance, as well as create new strategic opportunities for growth. Business analytics (BA) is about anticipated future trends of the key performance indicators used to automate and optimize business processes. The three major categories of business analytics-the descriptive, predictive, and prescriptive analytics along with advanced analytics tools are explained. The flow diagrams outlining the tools of each of the descriptive, predictive, and prescriptive analytics are presented. We also describe various terms related to business analytics. The second part of the book is about descriptive analytics and its applications. It discusses Data, Data Types and Descriptive Statistics, Data Visualization, Data Visualization with Big Data, Basic Analytics Tools: Describing Data Numerically-Concepts and Computer Applications. Finally, it presents an overview and a case on descriptive statistics with applications and notes on implementation. Chapter 1 provides an overview of business analytics and why these techniques are important.

This book is about Business Analytics (BA)-an emerging area in modern business decision making. The first part provides an overview of the field of Business Intelligence (BI), which uses historical data to better understand and thereby improve business performance, as well as create new strategic opportunities for growth. Business analytics (BA) is about anticipated future trends of the key performance indicators used to automate and optimize business processes. The three major categories of business analytics-the descriptive, predictive, and prescriptive analytics along with advanced analytics tools are explained. The flow diagrams outlining the tools of each of the descriptive, predictive, and prescriptive analytics are presented. We also describe various terms related to business analytics. The second part of the book is about descriptive analytics and its applications. It discusses Data, Data Types and Descriptive Statistics, Data Visualization, Data Visualization with Big Data, Basic Analytics Tools: Describing Data Numerically-Concepts and Computer Applications. Finally, it presents an overview and a case on descriptive statistics with applications and notes on implementation. Chapter 2 discusses analytics, business intelligence (BI) and business analytics (BA) as decision-making tools in businesses today. Although these terms are used interchangeably, there are slight differences in the terms of the tools they use. The connection between BI and BA tools combined with the applications in the computer science and information technology are critical for the success of BA in a company. This chapter discusses the broad meaning of the terms-BI, BA, data analytics (DA), computer and information technology, and how they are used in business decision making. The chapter also distinguishes between BI and BA, and the specific tools used in each case. provides an overview of business analytics and why these techniques are important.

This book is about Business Analytics (BA)-an emerging area in modern business decision making. The first part provides an overview of the field of Business Intelligence (BI), which uses historical data to better understand and thereby improve business performance, as well as create new strategic opportunities for growth. Business analytics (BA) is about anticipated future trends of the key performance indicators used to automate and optimize business processes. The three major categories of business analytics-the descriptive, predictive, and prescriptive analytics along with advanced analytics tools are explained. The flow diagrams outlining the tools of each of the descriptive, predictive, and prescriptive analytics are presented. We also describe various terms related to business analytics. The second part of the book is about descriptive analytics and its applications. It discusses Data, Data Types and Descriptive Statistics, Data Visualization, Data Visualization with Big Data, Basic Analytics Tools: Describing Data Numerically-Concepts and Computer Applications. Finally, it presents an overview and a case on descriptive statistics with applications and notes on implementation. Statistics is the science and art of making decision using data. Almost every field uses data and statistics to learn about systems and their processes. In fields such as business, research, health care, and engineering, a vast amount of raw data are collected and warehoused rapidly. Data and statistical tools aid in gaining skills such as: (1) Collecting, describing, analyzing, and interpreting data for intel­ligent decision-making (2) realizing that variation is an integral part of data and almost all data show variation (3) understanding the nature and pattern of variability of a phenomenon in the data, and (4) being able to measure reliability of the population parameters from which the sample data are collected to draw valid inferences.