個案總覽

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.

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 the text as a whole, describing business analytics and how it relates to business intelligence and statistical analysis. Data analytics, its applications, and business analytics models are all reviewed as are artificial intelligence, machine learning, and deep learning. Business analytics can be divided into descriptive analytics, predictive analytics, prescriptive analytics, and advanced analytics models. This book focuses on predictive analytics, the techniques of which use mostly statistical models and algorithms to predict future business trends. The ultimate goal of business analytics is to improve business performance by making data-driven decisions through helping businesses increase revenues and profitability, improve operational efficiency, optimize marketing campaigns and respond quickly changing market trends, improve customer service efforts, address the needs and requirements of customers, gain a competitive edge over the competition, and increase market share.

In early 2019, arc Thrift Stores was supporting 14 Colorado chapters of the Arc, an organization that advocated for children with intellectual and developmental disabilities (I/DD) in schools, helped individuals with I/DD live more independently, and supported legislation regarding their rights all across the state. The chief executive officer and marketing director of arc Thrift Stores were reviewing a survey they hoped would help them understand arc Thrift Stores' customers and broaden the interest in arc Thrift Stores among younger generations. By all measures, arc Thrift Stores was very profitable, but its core audience was aging, and the team wanted to ensure that it continued the successes of its past 50 years into the next 50 years. The company wanted people to understand the value of shopping their stores and the quality of items they could expect to find there. How could the company make inroads with a younger demographic? Should it focus more on sustainability? Should it consider moving into the e-commerce space?

Market lore recounts a host of fabled asset price bubbles: the Dutch tulip bubble of the 1630s, the South Sea and Mississippi bubbles of 1720, the dot-com bubble of the late 1990s, and the global real estate bubble of the mid-2000s. One survey reported the occurrence of some 60 bubbles since the 17th century. Despite their apparent prevalence, asset price bubbles are a challenging theoretical and empirical concept. The aims of this note are to: explore the concept of an asset price bubble; consider some tell-tale signs of a bubble from the standpoints of economic theory, empirical research, and market lore; survey evidence about the existence of bubbles; summarize theories about the causes of bubbles-especially the apparent irrationality of bubbles and the behavior of rational sophisticated investors in buying into them; and finally, highlight some of the regulatory dilemmas that bubbles present. Although the existence of the occasional asset price bubble may seem obvious to the casual observer, this note explains why bubbles are hard to identify and challenging to trade upon.

Debates on issues about the economy, growth, inequality, fiscal responsibility, and financial crises frequently reduce to debates about money. Moreover, debates about money often reduce to the soundness of money, meaning the ability of a unit of currency to retain its value. Eventually, discussions about soundness touch on the classic era of the gold standard. The aims of this note are to (1) describe the mechanism of the international monetary system known as the gold standard during the time that it had the greatest influence on the global economy; (2) place the gold standard in context with other systems that preceded and followed it; and (3) dispel some enduring misconceptions.

Basler is one of those lesser-known midsize companies (the so-called hidden champions), which, despite their moderate scale of operation, enjoy worldwide leadership in several niche markets. Basler primarily comprises two business segments: one, quality control systems for industrial goods production; and, two, cameras for diverse industries. The 2008/2009 global financial crisis, however, spelled trouble for Basler. With the company's very survival at stake, Basler's CEO had to make a strategic decision, namely, cut back on critical resources (human, financial, and material); this would include slashing as many as 50 jobs, which represented a fifth of its workforce. While so doing, some key questions remained unanswered in his mind: • In which of the company's two business segments should he reduce resources and cut those 50 jobs? • At the same time, regardless of the financial crisis, in which area, if any, should he consider investing resources in order to drive the company's growth in the next 5- 10 years? The case study provides the student with the crucial inputs required to answer the above questions convincingly. To arrive at an optimal solution, the student would need to think through the various options before the CEO during one of the worst financial crises in global history and weigh up their pros and cons.

In 2018, Lindsay Kaplan is preparing to meet with Carolyn Childers about the possibility of co-founding Chief, a New York-based peer network for women executives. Kaplan is currently the vice president of communications and brand engagement at a successful mattress company, and she is happy in her current role. However, she is excited by Childers's vision for a community of women executives and believes she could bring valuable expertise as Chief builds its brand experience. Should Kaplan join Chief as a co-founder? What do they need to discuss before teaming up?

Chief is a New York-based peer network that provides mentorship, support, networking opportunities, and a sense of community to women executives. Co-founders Carolyn Childers and Lindsay Kaplan launched the company in January 2019, and just two months later, Chief has 400 members and a growing waiting list. Now, Childers and Kaplan must determine how aggressively to scale Chief's model to other cities. How much money should they raise in Chief's Series A, and when? Which cities should they expand to first? What are the potential risks of moving too fast, and how can they best mitigate them? For more background on Childers and Kaplan's decision to found Chief, please see "Chief: Role for Carolyn Childers" (HBS No. 920-019) and "Chief: Role for Lindsay Kaplan" (HBS No. 920-020).

Supplement to case 319030.

For more than two decades, scientific advances have been driving profound changes in drug discovery and the drug industry itself. This case provides an overview and description of these technical and scientific advances. Written for the nonscientific reader, it may be used as companion reading for other case materials that require basic knowledge of the tools, techniques, and approaches used in the pharmaceutical and biotechnology industries.

This was largely achieved through alliances and collaborations with public and private organizations. The case ends when Ascardio's Administrative Technical Council was discussing the organization's options to face the progressive loss of support from key actors, in an increasing complicated economic and political environment. On one hand, Ascardio had the option of modifying its fee system. Crosssubsidies were becoming increasingly difficult to manage and the remuneration of residents required urgent updating. Also, public and private contributions to patients in need of financial support to access Ascardio's cardiological services were declining. On the other hand, Ascardio had the option to use the social capital of Dr. Finizola and his family, as well as the one accumulated by Ascardio, to launch an aggressive fundraising campaign, and seek contributions in volunteering hours from the citizens of the region-well known for the mutual trust and solidarity of its citizens and organizations. Having analyzed both options, students will decide if Ascardio: 1) Should sacrifice universality to ensure financial sustainability and market positioning, while running the risk of losing legitimacy among its stakeholders. 2) Should seek alternatives for mobilizing the region's civil society and different forms of volunteering to secure an inclusive health service, available for all public regardless of their ability to pay, but risking to lose quality of service.