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Management of Innovation and Technology/Management Information Systems.

Information Systems.

SER (Sugar, Energy & Rum) was a company belonging to the Grupo Pellas Corporation. The company operated in four countries, had six subsidiaries, employed more than 25,000 people, had more than 43,500 manzanas of sugarcane crops in Nicaragua alone and had global annual sales of more than US$400m. In 2008, due to the negative effects of the crisis on the company’s business model (increasing costs due to higher prices for fuel and decreasing income because of low international sugar prices), the company decided to implement a business intelligence (BI) system to optimize its processes to reduce costs and increase productivity. At that time, the company had more than 100 years of data, information systems that fed into their main business processes and a culture that appreciated data as the basis for decision-making. However, there were inconsistencies among data systems, users received highly complex reports in Excel or green screens and process monitoring happened long after the tasks had been completed. As a response, SER used extract–transform–load to collect and clean data that would be used in the BI system (the case leaves the questions regarding the systems selection unsolved for discussion). Based on their business model, they selected the most critical processes and defined key performance indicators to measure the impact of changes in those processes. They considered graphic design as a tool to make the system more accepted by users and worked together with users so that reports only offered the most important information. The result was improved costs and productivity. They decreased manual time spent by 14 per cent, automated time spent by 10 per cent, and eliminated 1,556 hours of dead time for equipment in the field, which allowed them to increase productivity by US$1m just in sugar. They saved 20,000 trips from the fields to the factories, which represented more than US$1m in savings by monitoring the weight of wagons loaded with sugarcane in real time. They improved client perceptions about the company both locally and internationally by implementing a sugar traceability system.

The case “Business Intelligence at the Grupo Pellas SER Company” has as its objective to respond to the question: How does a company make its BI system implementation successful? As such, the case: Discusses what a BI system is and what it provides to a business analyses challenges, benefits and context when implementing a BI system; analyses success factors and recommendations in the BI system implementation process; analyses the process of implementing a BI and highlights the importance of the system priority questions and technological alternatives.

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

CSS 11: Strategy

Illinois Superconductor Corp. a technology start-up, came up with an innovative new superconducting filter for use in cellular base stations. It needed to estimate the demand for its filters. The manager came up with a simple chain-ratio-based forecasting model that, while simple and intuitive, was too simplistic. The company had also commissioned a research firm to develop a model-based forecast. The model-based forecast used diffusion modeling, analogy-based forecasting, and conjoint analysis to create a forecast that incorporated customer preferences, diffusion effects, and competitive dynamics.

To use the data to generate a model-based forecast and to reconcile the model-based forecast with the manager's forecast. Requires sophisticated spreadsheet modeling and the application of advanced forecasting techniques.

This case is designed to enable students to understand the role of women in artificial intelligence (AI); understand the importance of ethics and diversity in the AI field; discuss the ethical issues of AI; study the implications of unethical AI; examine the dark side of corporate-backed AI research and the difficult relationship between corporate interests and AI ethics research; understand the role played by Gebru in promoting diversity and ethics in AI; and explore how Gebru can attract more women researchers in AI and lead the movement toward inclusive and equitable technology.

The case discusses how Timnit Gebru (She), a prominent AI researcher and former co-lead of the Ethical AI research team at Google, is leading the way in promoting diversity, inclusion and ethics in AI. Gebru, one of the most high-profile black women researchers, is an influential voice in the emerging field of ethical AI, which identifies issues based on bias, fairness, and responsibility. Gebru was fired from Google in December 2020 after the company asked her to retract a research paper she had co-authored about the pitfalls of large language models and embedded racial and gender bias in AI. While Google maintained that Gebru had resigned, she said she had been fired from her job after she had raised issues of discrimination in the workplace and drawn attention to bias in AI. In early December 2021, a year after being ousted from Google, Gebru launched an independent community-driven AI research organization called Distributed Artificial Intelligence Research (DAIR) to develop ethical AI, counter the influence of Big Tech in research and development of AI and increase the presence and inclusion of black researchers in the field of AI. The case discusses Gebru’s journey in creating DAIR, the goals of the organization and some of the challenges she could face along the way. As Gebru seeks to increase diversity in the field of AI and reduce the negative impacts of bias in the training data used in AI models, the challenges before her would be to develop a sustainable revenue model for DAIR, influence AI policies and practices inside Big Tech companies from the outside, inspire and encourage more women to enter the AI field and build a decentralized base of AI expertise.

This case is meant for MBA students.

Teaching Notes are available for educators only.

CCS 11: Strategy

In 2013, ‘Fashion Destination’, a well-established clothing retailer considered setting up a clothing and accessories rental service. They hired a market research agency ‘Wright & Company’ to conduct a research on the sustainability and profitability of such a business model. The consultants collected primary data and did an extensive analysis for Fashion Destination. Based on the secondary research, expert interviews, extensive qualitative and quantitative research the consultants recommended the management to start a clothes and accessories rental service but suggested that the product offering be limited to formal clothes only and offer accessories. Vishal had doubts despite of the go-ahead signal from consultancy. He wondered what recommendations should he accept and which needed further verification.

This case is based on a real-life consulting engagement with a major Fortune 100 telecommunications company. The name of the firm has been disguised for confidentiality reasons. Completing the case teaches students how to develop a cost-containment ROI analysis and develop a business case for a large enterprise technology project. The class discussion focuses on strategies to understand and manage the risks of the project and organizational issues. In addition, the case teaches students good questions to ask when reviewing a complex project business case, and how to present a project for funding approval. This case is the second in a series of three cases designed to teach students ROI analysis for technology projects; the first is “B&K Distributors: Calculating Return on Investment for a Web-Based Customer Portal” and the third is the case “ROI for a Customer Relationship Management Initiative at GST.”

The case objective is for students to learn how to compute a return on investment (ROI) analysis for a large cost-containment technology project. Students learn the best practice of computing the range of possible outcomes (the best, worst, and expected case), and how to present the results to senior management. In addition, students learn how to incorporate important management issues of personnel reduction and technology project risk into an ROI analysis.

A consulting team to an international food packaging company (SDYesBox) is attempting to decide which algorithm is the most useful for selecting two national markets in Central America and the Caribbean. SDYesBox wants to work closely with its immediate customers – manufacturers in the dairy and food industry and their customers (retailers) – to develop and market innovative products to low-income consumers in emerging markets; the “next big opportunity for the dairy industry” according to SDYesBox.

New product development and market selection in emerging markets in Latin America.

Five algorithms are “on the table” for assessing 14 countries by 12 performance indicators: weighted-benchmarking each country by the country leader's indicator scores; tallying by ignoring indicator weights and selecting the countries having the greatest number of positive standardized scores; applying a conjunctive and lexicographic combination algorithm; and using a “fluency metric” of how quickly consumers can say each country aloud. At least one member of the consulting team is championing one of these five algorithms. Which algorithm do you recommend? Why?

Learners gain skills, insights, and experience in alternative decision tools for evaluating and selecting choices among emerging markets to enter with new products for low-income (bottom of the pyramid) products ands services.

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

On completion of this case study, students will be able assess new venture opportunities by properly allocating expansion fund in growing the business; analyzing various scaling-up options; applying the Ansoff matrix for growth and expansion; designing a framework for scaling up; and using the business model canvas.

Mr Sreeram established Eruvaka Technologies in Vijayawada, Andhra Pradesh (India), in 2015 to provide products and services related to aquaculture. The company was founded with the goal of assisting prawn farmers who had trouble keeping up with the demands of the industry. Eruvaka Technologies created risk-reducing and productivity-boosting on-farm diagnostic devices for aquaculture growers. The company developed low-cost monitoring and automation solutions for aquaculture by merging sensors, mobile connection and decision tools. Eruvaka’s primary objective was to offer reasonably priced, technologically advanced goods and services to farmers. Eruvaka matured into a promising startup over time, attracting $5m in funding. Sreeram and his team had to detail their plan to their investors about how they intended to use the money from each funding rounds toward growing the business, how the company planned to achieve sustainable and competitive advantage while providing value to its consumers and how they would address critical issues including product acquisition cost, supply chain problem and customer anxiety.

This case study can be taught as part of undergraduate- and postgraduate-level courses and Master of Business Administration courses.

Teaching notes are available for educators only.

CSS 11: Strategy.

A year into the launch of TiVo—the “revolutionary new personal TV service that lets you watch what you want, when you want”—John Tebona, VP of business development, was faced with important decisions about TiVo's revenue model and strategic alliances. With television's move from a network-based model to an interactive one, he had to decide what role TiVo would play in the emerging industry landscape. Would TiVo be just a set-top box or would it live up to the vision of revolutionizing the television viewing experience? What revenue streams should it emphasize to capture the most value? What strategic relationships must TiVo form in an environment where companies were cross-investing in multiple technologies across different industry segments? How could it expand its customer base and accelerate its revenues before competitors like Microsoft's WebTV became the default standard?

To understand that disruptive innovation from a value creation standpoint may not mean a profitable or viable business from a value capture standpoint; products are far easier to create than robust business architectures with solid profit engines; the future of interactivity is clouded by the conflicting visions of the varied players; and control over standards is a valuable choke point.

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

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.