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The president and chief executive officer of Romet Limited, was preparing for a project team meeting that would start at 1:00 p.m. that afternoon. It was Friday April 27, 2018, and he had just finalized the architectural design and layout for the company’s new plant. The lease for the current plant would expire at the end of that year, and he was reviewing the activities required to complete the facility relocation project. The plant relocation was named “Project Destiny.” Recognizing that extending the completion beyond the end of the lease was impossible, he was concerned about being able to complete the project on schedule without increasing costs.

This fictional, international case explores online delivery models through the voices of two female protagonists: Melis Aydin, COO, who is from Turkey; and Athena Galini, CEO, who is from Greece. They have recently begun an online grocery-delivery (OGD) service called GidaExpress (i.e., Express Food) by closing on a TRY300,000 low-interest loan and entering a start-up incubator in Istanbul, Turkey. Aydin and Galani are evaluating three models for hiring drivers-all full-time employees who can be trained (and perhaps provide more consistent and higher-quality customer service); all independent workers who are paid a wage rate that escalates in order to meet demand; or a hybrid of the two. This story allows for a discussion of the strategic trade-offs between investing in a full-time workforce and sourcing independent workers of unknown quality using a fluctuating wage rate, and how these decisions impact the ability of the business to deliver on the value proposition.

Student spreadsheet for case UV7907.

This case describes the efforts of the Bill & Melinda Gates Foundation to lower the cost of production of vaccines to prevent polio infections. It is an abridged version of Case No. 620-021 with less emphasis on comparison between traditional and the new compact modular process developed with the Foundation's funding. It focuses more on the lowering of entry barriers to vaccine manufacture and some of the perceived risks associated with that.

The case on Chow Tai Fook (CTF), the world's largest jewelry company, introduces an important challenge for data analytics in the jewelry or luxury product industry. It is difficult to provide an accurate demand prediction for a product at CTF. The reasons are mainly three-fold. First, the jewelry products are of high value and slow-moving, with mostly single digit sales numbers over a year. Second, the demand of a product partly depends on the availability of other products, due to customers' substitution behavior. There are 16 product categories with more than 15,000 distinct products offered by CTF, and how the sales of different products are related is not clearly understood. In this case, traditional statistical methods such as linear regression is not useful.

Indigo Agriculture used a digital-enabled research and development (R&D) process to launch its initial product, microbial coatings for agricultural seeds, which increase crop yields while reducing the need for fertilizers. In doing so, the company developed direct relationships with farmers, in contrast to typical agricultural supply chains that use intermediaries. The company then launched a marketplace platform to link growers directly to crop buyers, again disintermediating the market. Indigo Agriculture is now considering launching an initiative to incentivize farmers to engage in regenerative agricultural practices by setting up a carbon market that could pay them for sequestering carbon into their soils. If scaled globally, the idea could sequester as much as one trillion tons (a teraton) of carbon dioxide from the earth's atmosphere into agricultural soils, but pursing the idea has many risks.

For 160 years, Singapore Post had been one of Singapore’s main postal service providers, delivering trusted and reliable postal services to homes and businesses. However, in 2019, Singapore Post was plagued by recent service lapses and operational problems, which had elicited customer complaints and concern from various stakeholders. Singapore Post was also facing increasing pressure from rising customer expectations, surging mail volumes, and the growing popularity of e-commerce. In response, Singapore Post pursued several initiatives to improve service operations and maintain its competitiveness in the postal industry. However, the company needed to devise a long-term plan to address recent problems, market changes, and deeply-rooted operational issues—and to regain consumer confidence over the long term.

For 160 years, Singapore Post had been one of Singapore's main postal service providers, delivering trusted and reliable postal services to homes and businesses. However, in 2019, Singapore Post was plagued by recent service lapses and operational problems, which had elicited customer complaints and concern from various stakeholders. Singapore Post was also facing increasing pressure from rising customer expectations, surging mail volumes, and the growing popularity of e-commerce. In response, Singapore Post pursued several initiatives to improve service operations and maintain its competitiveness in the postal industry. However, the company needed to devise a long-term plan to address recent problems, market changes, and deeply-rooted operational issues-and to regain consumer confidence over the long term.

Technological progress-including the development of online channels and universal access to the internet via mobile devices-has advanced both the quantity and the quality of data that companies can acquire. Private information such as this may be considered a type of fuel to be processed through the use of technologies, and represents a competitive market advantage. This article describes situations in which consumers tend to disclose personal information to companies and explores factors that encourage them to do so. The empirical studies and examples of market activities described herein illustrate to managers just how rewards work and how important contextual integrity is to customer digital privacy expectations. Companies' success in obtaining client data depends largely on three Ts: transparency, type of data, and trust. These three Ts-which, combined, constitute a main T (i.e., the transfer of personal data)-deserve attention when seeking customer information that can be converted to competitive advantage and market success.

Digital transformation recently converged on organizations as a new paradigm-a must-have exemplar-to enable competitive advantage. While the effects of digital transformation and their analytics, along with platform technologies, are becoming pronounced in companies, there is still a need to examine their implications on higher education. In light of the dynamics of digital transformation, how can higher education better manage the shift toward newer competencies and the need for innovation presented by the emergence of digital technologies? In this article, I examine the issues around the need for this balance-often defined in strategy as ambidexterity, or the need to address both competency with innovation-by outlining the historical trajectories that led to this problem in higher education, identifying three common pitfalls that higher education programs and administrators face, and tying these issues to higher education's absorptive capacity. To resolve these pitfalls, this article builds upon absorptive capacity frameworks for education practitioners and strategies as a prospective change management tool.

In October 2014, Carestream Health Inc. (Xiamen) (CHX) was faced with a supply chain disruption caused by a labour dispute involving most of the ports on the west coast of the United States. The work-to-rule strike was having a considerable impact on the operations of the ports and was putting pressure on CHX’s lean supply chain. In particular, the supply of wide rolls of films, the key material to CHX operations, was compromised. In an emergency meeting, CHX’s management team discussed using other shipping options. One of these options, (airfreighting) appeared to be feasible but costly. The meeting’s leader who was the director of the Asia Pacific Lean Six Sigma asked the management team to determine when CHX should use airfreight for its supply of wide rolls, how many rolls should be airfreighted each time, and what the best mechanism would be to govern the use of airfreight.

Business analytics is an emerging area in modern business decision-making, gaining interest and popularity. This book discusses the models based on fact-based data to measure past business performance in order to guide organizations in visualizing and predicting future business performance and outcomes. Bringing terms, tools, and methods of analytics together, a comprehensive overview of analytics is given with an emphasis on predictive analytics. The book provides an introduction to and types of business analytics, the importance of analytics, and business intelligence. It also discusses the most widely used predictive models, including regression analysis, forecasting, data mining, and an introduction to recent applications of predictive analytics - machine learning, neural networks, and artificial intelligence. The current state, job outlook, and certifications in analytics are also examined. Chapter 1 gives an overview of the field of business analytics, which uses data, statistical analysis, mathematical and statistical modeling, data mining, and advanced analytics tools to explore, investigate, and understand business performance. The tools of business analytics give insight to and drive business planning and decisions. Descriptive analytics tools use statistical, graphical, and numerical methods to understand the occurrence of certain business phenomena; predictive analytics tools are used to predict future business phenomena; prescriptive analytics tools have applications in optimizing and automating business processes. Business analytics helps with informed decision-making in business and can be used to automate and optimize business processes. Data is an asset and can be leveraged for competitive advantage. Successful business analytics depends on data quality and skilled analysts. Business analytics is an organizational commitment to data-driven decision-making.

Business analytics is an emerging area in modern business decision-making, gaining interest and popularity. This book discusses the models based on fact-based data to measure past business performance in order to guide organizations in visualizing and predicting future business performance and outcomes. Bringing terms, tools, and methods of analytics together, a comprehensive overview of analytics is given with an emphasis on predictive analytics. The book provides an introduction to and types of business analytics, the importance of analytics, and business intelligence. It also discusses the most widely used predictive models, including regression analysis, forecasting, data mining, and an introduction to recent applications of predictive analytics - machine learning, neural networks, and artificial intelligence. The current state, job outlook, and certifications in analytics are also examined. This chapter offers an overview of business analytics and business intelligence and outlines the similarities and differences between the two. Business analytics, types of analytics, and the analytics process are all explained. Different tools used in each type of analytics are discussed as well as their relationship and interconnection. These tools overlap in applications and a combination of these tools can be used in many situations. In comparing business analytics and business intelligence, business analytics, data, analytics, and advanced analytics all fall under the wide scope of business intelligence. Business intelligence tools include the business analytics tools but also include data mining, online analytical processing, and business performance management among other things.

Business analytics is an emerging area in modern business decision-making, gaining interest and popularity. This book discusses the models based on fact-based data to measure past business performance in order to guide organizations in visualizing and predicting future business performance and outcomes. Bringing terms, tools, and methods of analytics together, a comprehensive overview of analytics is given with an emphasis on predictive analytics. The book provides an introduction to and types of business analytics, the importance of analytics, and business intelligence. It also discusses the most widely used predictive models, including regression analysis, forecasting, data mining, and an introduction to recent applications of predictive analytics - machine learning, neural networks, and artificial intelligence. The current state, job outlook, and certifications in analytics are also examined. Chapter 3 builds on chapter 2, discussing how analytics, business analytics, and data analytics all fit into business intelligence and going further into the comparison between business analytics and business intelligence. Business intelligence is a technology-driven process for processing and analyzing data to make sense from huge quantities of data that businesses collect and obtain from various sources. It is both visualization and analytics. Various functions and applications of business intelligence are described, including but not limited to reporting, online analytical processing, and business process management. Business intelligence can combine external and internal data in order to create an "intelligence" that cannot be derived by any one set of data. Business intelligence along with business analytics can empower organizations and give them a better understanding of the existing markets and customer behavior.

Business analytics is an emerging area in modern business decision-making, gaining interest and popularity. This book discusses the models based on fact-based data to measure past business performance in order to guide organizations in visualizing and predicting future business performance and outcomes. Bringing terms, tools, and methods of analytics together, a comprehensive overview of analytics is given with an emphasis on predictive analytics. The book provides an introduction to and types of business analytics, the importance of analytics, and business intelligence. It also discusses the most widely used predictive models, including regression analysis, forecasting, data mining, and an introduction to recent applications of predictive analytics - machine learning, neural networks, and artificial intelligence. The current state, job outlook, and certifications in analytics are also examined. This chapter provides a more in-depth look at descriptive analytics. Descriptive analytics tools are used to understand the occurrence of certain business phenomena or outcomes and explaining these outcomes through graphical, quantitative, and numerical analyses. A case is given to demonstrate the tools and applications of visual techniques used in descriptive analytics. Descriptive analytics is critical in studying the current state of a business and in learning what has happened in the past using company data. Knowledge derived from descriptive analytics lays a foundation for further analysis and leads to predictive analytics.

Business analytics is an emerging area in modern business decision-making, gaining interest and popularity. This book discusses the models based on fact-based data to measure past business performance in order to guide organizations in visualizing and predicting future business performance and outcomes. Bringing terms, tools, and methods of analytics together, a comprehensive overview of analytics is given with an emphasis on predictive analytics. The book provides an introduction to and types of business analytics, the importance of analytics, and business intelligence. It also discusses the most widely used predictive models, including regression analysis, forecasting, data mining, and an introduction to recent applications of predictive analytics - machine learning, neural networks, and artificial intelligence. The current state, job outlook, and certifications in analytics are also examined. This chapter starts with a discussion about what predictive analytics is and how it differs from descriptive analytics. While descriptive analytics is the process of exploring and explaining data to reveal trends, predictive analytics is about predicting future business outcomes. It uses several models that can be categorized into logic-driven models and data-driven models. A number of tools and statistical models are introduced, which are considered prerequisites to predictive analytics. Descriptions and application areas of these prerequisite tools - including probability concepts, probability distributions, and correlation analysis - are given. The understanding of these prerequisite tools is critical in understanding and applying predictive analytics models.

Business analytics is an emerging area in modern business decision-making, gaining interest and popularity. This book discusses the models based on fact-based data to measure past business performance in order to guide organizations in visualizing and predicting future business performance and outcomes. Bringing terms, tools, and methods of analytics together, a comprehensive overview of analytics is given with an emphasis on predictive analytics. The book provides an introduction to and types of business analytics, the importance of analytics, and business intelligence. It also discusses the most widely used predictive models, including regression analysis, forecasting, data mining, and an introduction to recent applications of predictive analytics - machine learning, neural networks, and artificial intelligence. The current state, job outlook, and certifications in analytics are also examined. Chapter 6 explains key models used in predictive modeling. These models are the core of predictive analytics and are used to predict future business outcomes. Explanations and application areas of each tool are given. The tools described include various types of regression models, forecasting models, ANOVA, data mining, machine learning, artificial intelligence, neural networks, and deep learning.

Business analytics is an emerging area in modern business decision-making, gaining interest and popularity. This book discusses the models based on fact-based data to measure past business performance in order to guide organizations in visualizing and predicting future business performance and outcomes. Bringing terms, tools, and methods of analytics together, a comprehensive overview of analytics is given with an emphasis on predictive analytics. The book provides an introduction to and types of business analytics, the importance of analytics, and business intelligence. It also discusses the most widely used predictive models, including regression analysis, forecasting, data mining, and an introduction to recent applications of predictive analytics - machine learning, neural networks, and artificial intelligence. The current state, job outlook, and certifications in analytics are also examined. Chapter 7 gives an introduction to regression and correlation analysis. Regression techniques allow us to explore the relationship between different variables. Essentials to developing regression models are explained, including dependent and independent variables, the least squares method, determining the best-fitting line through the data points, calculating the slope and y-intercept of the best fitting regression line, interpreting the meaning of the regression line, and measures of association between the covariance and the correlation coefficient. Developing regression models to predict one variable using the other variable or multiple variables is also discussed. Successful implementation of regression models requires an understanding of different types of models. A lack of understanding may lead to drawing wrong conclusions and selecting alternative courses of action.

Business analytics is an emerging area in modern business decision-making, gaining interest and popularity. This book discusses the models based on fact-based data to measure past business performance in order to guide organizations in visualizing and predicting future business performance and outcomes. Bringing terms, tools, and methods of analytics together, a comprehensive overview of analytics is given with an emphasis on predictive analytics. The book provides an introduction to and types of business analytics, the importance of analytics, and business intelligence. It also discusses the most widely used predictive models, including regression analysis, forecasting, data mining, and an introduction to recent applications of predictive analytics - machine learning, neural networks, and artificial intelligence. The current state, job outlook, and certifications in analytics are also examined. This chapter discusses forecasting and time series analysis, both major tools of predictive analytics. Forecasting deals with predicting future business outcomes using quantitative and qualitative methods and is used to make informed decisions. Many business planning production, operations, sales, demand, and inventory decisions are based on forecasting. Produce-to-order and produce-to-stock companies rely on forecasting for production and operations planning. Companies with good forecasting are able to balance demand and supply. The general meaning of forecasting applications and several models-including the simple moving average, weighted moving average, and exponential smoothing-are discussed as well as measuring forecast accuracy.

Business analytics is an emerging area in modern business decision-making, gaining interest and popularity. This book discusses the models based on fact-based data to measure past business performance in order to guide organizations in visualizing and predicting future business performance and outcomes. Bringing terms, tools, and methods of analytics together, a comprehensive overview of analytics is given with an emphasis on predictive analytics. The book provides an introduction to and types of business analytics, the importance of analytics, and business intelligence. It also discusses the most widely used predictive models, including regression analysis, forecasting, data mining, and an introduction to recent applications of predictive analytics - machine learning, neural networks, and artificial intelligence. The current state, job outlook, and certifications in analytics are also examined. Chapter 9 provides an introduction to data mining and its applications to predictive analysis. Data mining explores patterns and relationships in the vast amounts of data that businesses collect. The information that is extracted is used to predict future business outcomes. Several data mining methodologies and tasks are described, including data preparation, knowledge representation, pattern evaluation, and descriptive and predictive data mining. Data mining techniques can also be categorized into supervised and unsupervised learning. Key methodologies of data mining include anomalies detection, association learning, classification, clustering, sequence, prediction, and time series and forecasting. The growing application areas of data mining are discussed, including applications in machine learning. The relationship between data mining and machine learning as well as different types of machine learning are also introduced.