Discover the role of machine learning and artificial intelligence in business forecasting from some of the brightest minds in the field In Business Forecasting: The Emerging Role of Artificial Intelligence and Machine Learning accomplished authors Michael Gilliland, Len Tashman, and Udo Sglavo deliver relevant and timely insights from some of the most important and influential authors in the field of forecasting. You'll learn about the role played by machine learning and AI in the forecasting process and discover brand-new research, case studies, and thoughtful discussions covering an array of practical topics. The book offers multiple perspectives on issues like monitoring forecast performance, forecasting process, communication and accountability for forecasts, and the use of big data in forecasting. You will find: Discussions on deep learning in forecasting, including current trends and challenges Explorations of neural network-based forecasting strategies A treatment of the future of artificial intelligence in business forecasting Analyses of forecasting methods, including modeling, selection, and monitoring In addition to the Foreword by renowned researchers Spyros Makridakis and Fotios Petropoulos, the book also includes 16 "opinion/editorial" Afterwords by a diverse range of top academics, consultants, vendors, and industry practitioners, each providing their own unique vision of the issues, current state, and future direction of business forecasting. Perfect for financial controllers, chief financial officers, business analysts, forecast analysts, and demand planners, Business Forecasting will also earn a place in the libraries of other executives and managers who seek a one-stop resource to help them critically assess and improve their own organization's forecasting efforts.

Forecasting is required in many situations. Stocking an inventory may require forecasts of demand months in advance. Telecommunication routing requires traffic forecasts a few minutes ahead. Whatever the circumstances or time horizons involved, forecasting is an important aid in effective and efficient planning. This textbook provides a comprehensive introduction to forecasting methods and presents enough information about each method for readers to use them sensibly.

The recent crisis in the financial markets has exposed serious flaws in management methods. The failure to anticipate and deal with the consequences of the unfolding collapse has starkly illustrated what many leaders and managers in business have known for years; in most organizations, the process of forecasting is badly broken. For that reason, forecasting business performance tops the list of concerns for CFO's across the globe. It is time to rethink the way businesses organize and run forecasting processes and how they use the insights that they provide to navigate through these turbulent times. This book synthesizes and structures findings from a range of disciplines and over 60 years of the authors combined practical experience. This is presented in the form of a set of simple strategies that any organization can use to master the process of forecasting. The key message of this book is that while no mortal can predict the future, you can take the steps to be ready for it. ’Good enough’ forecasts, wise preparation and the capability to take timely action, will help your organization to create its own future. Written in an engaging and thought provoking style, Future Ready leads the reader to answers to questions such as: What makes a good forecast? What period should a forecast cover? How frequently should it be updated? What information should it contain? What is the best way to produce a forecast? How can you avoid gaming and other forms of data manipulation? How should a forecast be used? How do you ensure that your forecast is reliable? How accurate does it need to be? How should you deal with risk and uncertainty What is the best way to organize a forecast process? Do you need multiple forecasts? What changes should be made to other performance management processes to facilitate good forecasting? Future Ready is an invaluable guide for practicing managers and a source of insight and inspiration to leaders looking for better ways of doing things and to students of the science and craft of management. Praise for Future Ready "Will make a difference to the way you think about forecasting going forward" —Howard Green, Group Controller Unilever PLC "Great analogies and stories are combined with rock solid theory in a language that even the most reading-averse manager will love from page one" —Bjarte Bogsnes, Vice President Performance Management Development at StatoilHydro "A timely addition to the growing research on management planning and performance measurement." —Dr. Charles T. Horngren, Edmund G. Littlefield Professor of Accounting Emeritus Stanford University and author of many standard texts including Cost Accounting: A Managerial Emphasis, Introduction to Management Accounting, and Financial Accounting "In the area of Forecasting, it is the best book in the market." —Fritz Roemer. Leader of Enterprise Performance Executive Advisory Program, the Hackett Group

From data collection to evaluation and visualization of prediction results, this book provides a comprehensive overview of the process of predicting demand for retailers. Each step is illustrated with the relevant code and implementation details to demystify how historical data can be leveraged to predict future demand. The tools and methods presented can be applied to most retail settings, both online and brick-and-mortar, such as fashion, electronics, groceries, and furniture. This book is intended to help students in business analytics and data scientists better master how to leverage data for predicting demand in retail applications. It can also be used as a guide for supply chain practitioners who are interested in predicting demand. It enables readers to understand how to leverage data to predict future demand, how to clean and pre-process the data to make it suitable for predictive analytics, what the common caveats are in terms of implementation and how to assess prediction accuracy.