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Coda Coffee's commitment to ethical coffee came at a high cost. The company paid three times the commodity exchange rate for raw coffee beans, or cherries. By late 2018, their supply chains reached from Denver all across the world. Could AI, machine vision, blockchain, IoT help Coda Coffee, and in turn, their customers, receive assurance that this premium pricing translated into fairer wages for farmers? It is this assurance that Coda continually strived for through the relationships they built and the methods of sourcing they pursued. This case describes a partnership between relatively new Coda Coffee and Bext360, a technology startup, to leverage Industry 4.0 technologies to ensure greater transparency in Coda Coffee's supply chain. Specifically, the case addresses the motivation behind conducting a pilot study in Uganda. The case can be used to stimulate discussion about the strengths and limitations of Fair Trade certification and how companies can go beyond Fair Trade with their own certification programs supplemented by increased supply chain transparency. The case can also be used to generate discussion about how Industry 4.0 technologies can serve both business and the greater good.

Data reveals the C-suite recognizes that technical debt -the "price" companies pay for short-term technological fixes -hinders their ability to innovate and adapt in the digital age. One strategy to combat technical debt? Digital decoupling.

In 2017, a sports journalist based in Houston, Texas was confused about his editor's latest idea. His editor had emailed him the night before proposing an article on the best (and worst) Major League Baseball hitters of all time. The first step was to provide a list of the players that would be profiled. This meant finding the 10 best (and worst) batters. However, after ranking the players, could he quantify the probability that he ranked them incorrectly? Because the article was based on data, he had to justify his choices and he wanted his decisions to be based on sound statistical grounds.

Student spreadsheet for case W18610

invisaWear is a start-up that used Internet of Things (IoT) technology to create a fashionable, single-purpose wearable for young females to provide them with peace of mind when faced with a dangerous situation. The smart product consisted of a charm attached to either a bracelet, necklace, or key chain which, when pressed, sent an SOS to the user's family, friends, and optionally, the police. The case study illustrates the entrepreneurial steps of identifying an opportunity, assembling a team, gathering resources, and then thinking big but starting small. In describing the invisaWear line of smart jewelry, the case provides a primer on IoT and it highlights the steps required to build smart, connected products.

The fear of robots eclipsing human power in society, in particular the workforce, has persisted for decades despite the moderate progress of artificial intelligence systems. For professor and robotics researcher Ken Goldberg, a hybrid human-machine workforce is much more likely to take shape, and in many industries, it has already begun.

SF Express Group Co. Ltd. (SF Express) was a leading express delivery and logistics solutions provider in China. In May 2017, SF Express became involved in a dispute with the logistics-tracking platform Cainiao Network Technology (Cainiao), which was controlled by Alibaba Group Holding Limited. SF Express and Cainiao were the two largest players in China's express delivery market. The dispute, which involved an SF Express-affiliated smart package locker company that declined a data-sharing request from Cainiao, caused SF Express and Cainiao to sever their ties over proprietary data and cybersecurity. This severance caused significant disruption to China's e-commerce sector. After regulatory intervention, both parties agreed to resume sharing data for the time being and had to negotiate a resolution to their data dispute within 30 days. The chief information officer of SF Express needed to come up with an immediate solution to the dispute as well as a long-term data strategy for SF Express.

The purpose of this note is to explain the application areas of the chi-square goodness-of-fit test and how to run the test in Excel.

As the world continues its digital evolution, more aspects of our lives are dependent on data and digital devices. This shift has required technology manufacturers to emphasize encryption and robust security in their evolving designs. While this pursuit of security is necessary to keep users' critical personal information safe, criminals and terrorist organizations are using these same security technologies to make it difficult or even impossible for authorities to access that data. Law enforcement agencies claim that they need the technical ability to lawfully access digital data, while many technology and privacy advocates hold that authorities should never, under any circumstances, force companies to weaken or circumvent security. The conflict between privacy and security is examined in this case, starting with the 2016 legal challenge when, after a mass shooting, the U.S. Federal Bureau of Investigation sought a court order directing Apple, Inc. to create software that would unlock the suspect's iPhone. Although that legal challenge was dismissed, the issues remain live and pressing.

In July 2018, Google's Android software platform was fined $5 billion by the EU's Antitrust Commission. This case follows Hiroshi Lockheimer, SVP at Google responsible for Android, Google Play, Chrome and ChromeOS, as he and his team respond to the fine and the challenges of managing Android's increasing ubiquity and success.

This case follows Silicon Valley software veteran Thomas Siebel as he launches a new company, C3, in 2009, and steers it through two major pivots - transforming the business from a firm focused on energy conservation to a data management, machine learning, and artificial intelligence platform for large enterprises. The case looks at the multiple business transformations and re-brandings C3 went through and examines the strategic challenges the company was facing in 2018 as it began to compete head-to-head with companies such as IBM and SAP. In 2009, three years after selling his eponymous enterprise software company Siebel Systems to Oracle for nearly $6 billion, Siebel launched a new business, C3. "C" stood for carbon, and the "3" was shorthand for three "M" words: measure, mitigate and monetize. The idea was to help large companies navigate the new world of carbon taxes and reduce their carbon footprints. But after just two years, C3 was in trouble. It had found clients for its software product, C3 Energy Management, yet oil prices had stumbled, and in wake of the financial crisis, companies pulled back on spending. Siebel was no going to give up. He saw great potential in the data flowing from sensors in smart meters, turbines, transformers and other infrastructure in power grids. In 2012, he laid off about 100 of C3's 150 employees, retaining the core engineering team. The company renamed itself C3 Energy and helped grid operators aggregate and analyze data from various sensor devices and enterprise software systems. C3 Energy's engine processed data at very high rates, then applied machine learning to do useful things, such as predictive maintenance, detecting theft, and monitoring sensor network health.

Many people take identity for granted, confident that they can prove they are who they say they are by using a driver's license or other ID card. For many millions of people, this is not the case. They possess not only no driver's license, but also no birth certificate which could be used to gain a driver's license and prove they are who they claim to be. As a result, they can sometimes appear invisible to the state, unable to receive aid or participate in critical programs. Indeed, as Nandan Nilekani argues, "unless a person can identify himself or herself and have some sort of proof of existence, you can't even talk about him owning property." This case explores the development and implementation of Aadhaar, an ambitious biometric identity system implemented in India. The system requires every Indian citizen to undergo a fingerprint and retinal scan to create a massive central database that can uniquely identify every person in the country. Supporters say such an identity management system has enormous potential to facilitate government services, reduce corruption, and enable hundreds of millions of Indians to enter the formal economy. To its critics, Aadhaar represents either an estate project that consumes enormous resources that will never reach its potential, or worse, the core infrastructure for a surveillance state that can better monitor and track many things its citizens do.

Many people take identity for granted, confident that they can prove they are who they say they are by using a driver's license or other ID card. For many millions of people, this is not the case. They possess not only no driver's license, but also no birth certificate which could be used to gain a driver's license and prove they are who they claim to be. As a result, they can sometimes appear invisible to the state, unable to receive aid or participate in critical programs. Indeed, as Nandan Nilekani argues, "unless a person can identify himself or herself and have some sort of proof of existence, you can't even talk about him owning property." This case explores the development and implementation of Aadhaar, an ambitious biometric identity system implemented in India. The system requires every Indian citizen to undergo a fingerprint and retinal scan to create a massive central database that can uniquely identify every person in the country. Supporters say such an identity management system has enormous potential to facilitate government services, reduce corruption, and enable hundreds of millions of Indians to enter the formal economy. To its critics, Aadhaar represents either an estate project that consumes enormous resources that will never reach its potential, or worse, the core infrastructure for a surveillance state that can better monitor and track many things its citizens do.

Xbed Technology Group (Xbed), a start-up that branded itself as an online hotel platform, was founded in 2015 with the aim of addressing the changing landscape of China's hospitality industry while coping with issues related to a customer-to-customer sharing economy model. By employing a dual-sharing model and utilizing state-of-the-art digital technologies, the company emerged as a disruptor in the market and expanded to 44 cities in China in just two years of development. As Xbed grew, it faced many challenges, including intense competition from both sharing economy players and traditional hotels. In 2017, as the company looked toward the future, its founder wondered about the future of hospitality services in China and what the company's strategic focus should be as it pursued its next phase of growth.

Stephen Pratt and Raj Joshi - two veterans of Infosys Consulting - decided in 2016 to launch Noodle Analytics (Noodle.ai) to provide AI capabilities to Fortune 1000 type companies under a SaaS-type business model. The case traces Noodle Analytics from conception through funding, finding product-market fit, and its Series B raise in 2018, looking at issues of strategy, recruiting, business model, developing the product, corporate culture and the larger industry context. As they launched their business in 2016, Artificial Intelligence had moved out of the realm of sci-fi movies and into the mainstream: Technologies like Apple's Siri and Amazon's Alexa brought speech-recognition to mainstream consumers. Driverless cars were being tested on roads in California and elsewhere. Customer-service chatbots - computer programs designed to simulate conversations with human users - regularly interacted with humans on the web, and Google's AlphaGo computer had bested a human world champion in the ancient strategy game of Go. Increasingly, executives were under pressure to incorporate AI into their businesses. An article in the Harvard Business Review warned ominously: "Unlike with the internet, where latecomers often bested those who were first to market, the companies that get started immediately with machine intelligence could enjoy a lasting advantage." A shortage of AI experts and expertise, though, left many enterprises wondering how. Noodle aimed to serve them.

The seductive clamor of social media is a workplace reality from which there's no retreat. Those who'll succeed in this distraction-filled world as managers and innovators must combine two seemingly opposing traits: They must to be able to absorb information from many sources and to focus intensely. Together, these apparently contradictory qualities comprise the skill set for managing your most valuable personal resource -- your attention -- in a hyper-connected age.

It used to be an article of faith that technology-intensive product manufacturers, automakers, or white goods makers could capitalize on their long-standing engineering and design leadership to cement their position worldwide. But that's no longer the case. Today, young upstarts in many product segments, especially from China, can develop world-class design and production capabilities in short periods of time. "In some cases, they are closing gaps with long-established incumbents and becoming market leaders within a decade,"writes Harvard Business School professor Willy Shih. Three main factors drive this: (1) blatant copying of intellectual property, (2) governments pressuring companies to share technology in exchange for rights to do business, and (3) normal knowledge spillover as workers move from multinationals to local companies. But other forces, the author argues, are accelerating commoditization and making product differentiation increasingly difficult to sustain.Knowledge, particularly the tacit know-how that takes years to develop, now flows through pathways that we take for granted. It is embedded in the tools used to design and manufacture products, or it is incor-porated into the building blocks that are used to build more-complex systems. Young competitors, armed with this knowledge, can now skip years of practice and become competitive threats almost instantly.The amount of commoditization pressure a company faces, the author writes, depends on the number of complementary assets it needs to operate successfully and how difficult they are to replicate. Such assets could be other tools or specific operating capabilities. If a producer needs to use a tool in conjunction with other tools or recipes, it's easier to protect the product space. However, if the tool is the keystone and complementary assets are easy to acquire, commoditization is the likely outcome.

The debate about how many and what kinds of jobs smart machines will leave for humans to do misses a salient point, argues author Thomas W. Malone. Just as the automation of human work allowed people and machines to do many things that couldn't be done before, groups of people and computers working together will be able to do many things that neither can do alone now.To think about how this will happen, Malone says, it helps to recognize that virtually all human achievements -"from developing written language to making a turkey sandwich"-require the work of groups of people, not just individuals working on their own. As Malone explains it, "superminds"-groups of individuals acting together in ways that seem intelli-gent -take many forms, including the hierarchies in most organizations; the markets that help create and exchange many kinds of goods and services; the communities that use norms and reputations to guide behavior in many professional, social, and geographical groups; and the democracies that are com-mon in governments and some other organizations. Increasingly, machines can take part in the activities of these groups. "That means we will be able to combine people and machines to create superminds that are smarter than any groups or individuals our planet has ever known,"Malone says. Malone describes a future where humans and computers play different roles, with computers begin-ning as tools, then moving progressively into becoming assistants, peers, and then managers. Before we have general AI, he writes, "we can create more and more collectively intelligent systems by building societies of mind that include both humans and machines, each doing part of the overall task."

When pundits talk about the impact that artificial intelligence will have on the labor market, the outlook is usually bleak, with the loss of many jobs to machines as the dominant theme. But that's just part of the story -a probable outcome for companies that use AI only to increase efficiency. As it turns out, companies using AI to also drive innovation are more likely to increase headcount than reduce it.

While the threat of national champions is nothing new, their essential character has substantially changed, and the competitive advantage of national champions in the global marketplace has become more pronounced. Today's national champions are much more sophisticated, competing in more industries, and harder to spot than ever before. As a result, Western companies need a new strategic guide for competing against them.