個案資料

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.

• Predictive analytics
• Management performance
• Technology and analytics
• Decision making
• Business analysis