This book surveys what executives who make decisions based on forecasts and professionals responsible for forecasts should know about forecasting. It discusses how individuals and firms should think about forecasting and guidelines for good practices. It introduces readers to the subject of time series, presents basic and advanced forecasting models, from exponential smoothing across ARIMA to modern Machine Learning methods, and examines human judgment's role in interpreting numbers and identifying forecasting errors and how it should be integrated into organizations. This is a great book to start learning about forecasting if you are new to the area or have some preliminary exposure to forecasting. Whether you are a practitioner, either in a role managing a forecasting team or at operationally involved in demand planning, a software designer, a student or an academic teaching business analytics, operational research, or operations management courses, the book can inspire you to rethink demand forecasting. No prior knowledge of higher mathematics, statistics, operations research, or forecasting is assumed in this book. It is designed to serve as a first introduction to the non-expert who needs to be familiar with the broad outlines of forecasting without specializing in it. This may include a manager overseeing a forecasting group, or a student enrolled in an MBA program, an executive education course, or programs not specialising in analytics. Worked examples accompany the key formulae to show how they can be implemented. Key Features: While there are many books about forecasting technique, very few are published targeting managers. This book fills that gap. It provides the right balance between explaining the importance of demand forecasting and providing enough information to allow a busy manager to read a book and learn something that can be directly used in practice. It provides key takeaways that will help managers to make difference in their companies.

This book surveys what executives who make decisions based on forecasts and professionals responsible for forecasts should know about forecasting. It discusses how individuals and firms should think about forecasting and guidelines for good practices. It introduces readers to the subject of time series, presents basic and advanced forecasting models, from exponential smoothing across ARIMA to modern Machine Learning methods, and examines human judgment's role in interpreting numbers and identifying forecasting errors and how it should be integrated into organizations. This is a great book to start learning about forecasting if you are new to the area or have some preliminary exposure to forecasting. Whether you are a practitioner, either in a role managing a forecasting team or at operationally involved in demand planning, a software designer, a student or an academic teaching business analytics, operational research, or operations management courses, the book can inspire you to rethink demand forecasting. No prior knowledge of higher mathematics, statistics, operations research, or forecasting is assumed in this book. It is designed to serve as a first introduction to the non-expert who needs to be familiar with the broad outlines of forecasting without specializing in it. This may include a manager overseeing a forecasting group, or a student enrolled in an MBA program, an executive education course, or programs not specialising in analytics. Worked examples accompany the key formulae to show how they can be implemented. Key Features: While there are many books about forecasting technique, very few are published targeting managers. This book fills that gap. It provides the right balance between explaining the importance of demand forecasting and providing enough information to allow a busy manager to read a book and learn something that can be directly used in practice. It provides key takeaways that will help managers to make difference in their companies.

Every plan needs a forecast - a reasonable prediction of the future. No business plan can be implemented without one. But the academic literature on forecasting is vast and spans disciplines such as statistics, economics, operations management and informed judgment and decision making. Recommendations from this literature have been implemented in a vast array of commercial software, and almost all modern companies have access to some decision support models that provide demand forecasts. In the long run, the demand forecast shapes decisions to build or close down plants, add or remove products from a portfolio, and bolster or challenge investor confidence in the stock price. In the short run forecasting software greatly aids managers in making functional decisions (how much are we going to sell next month, next year, or 5 years from now?) but without a proper understanding of the basics of forecasting, such software appears as a black-box, and the output from this software garners little trust within an organization. The intention of this book is to underscore the importance of demand forecasting and to demonstrate what an executive should know about it. It discusses the value of forecasting, presents both basic and advanced forecasting models, introduces the subject of time series and the technique of exponential smoothing (critical for accurate forecasts), examines the role that human judgment plays in interpreting the numbers and identifying forecasting errors. Finally, the book offers an organizational context by creating a rational framework that shows how forecasting is an integral part of business planning and demonstrates how to use forecasts within an organization.