Exponential smoothing methods have been around since the 1950s, and are still the most popular forecasting methods used in business and industry. However, a modeling framework incorporating stochastic models, likelihood calculation, prediction intervals and procedures for model selection, was not developed until recently. This book brings together all of the important new results on the state space framework for exponential smoothing. It will be of interest to people wanting to apply the methods in their own area of interest as well as for researchers wanting to take the ideas in new directions. Part 1 provides an introduction to exponential smoothing and the underlying models. The essential details are given in Part 2, which also provide links to the most important papers in the literature. More advanced topics are covered in Part 3, including the mathematical properties of the models and extensions of the models for specific problems. Applications to particular domains are discussed in Part 4.

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.

Bring the latest statistical tools to bear on predicting future variables and outcomes A huge range of fields rely on forecasts of how certain variables and causal factors will affect future outcomes, from product sales to inflation rates to demographic changes. Time series analysis is the branch of applied statistics which generates forecasts, and its sophisticated use of time oriented data can vastly impact the quality of crucial predictions. The latest computing and statistical methodologies are constantly being sought to refine these predictions and increase the confidence with which important actors can rely on future outcomes. Time Series Analysis and Forecasting presents a comprehensive overview of the methodologies required to produce these forecasts with the aid of time-oriented data sets. The potential applications for these techniques are nearly limitless, and this foundational volume has now been updated to reflect the most advanced tools. The result, more than ever, is an essential introduction to a core area of statistical analysis. Readers of the third edition of Time Series Analysis and Forecasting will also find: Updates incorporating JMP, SAS, and R software, with new examples throughout Over 300 exercises and 50 programming algorithms that balance theory and practice Supplementary materials in the e-book including solutions to many problems, data sets, and brand-new explanatory videos covering the key concepts and examples from each chapter. Time Series Analysis and Forecasting is ideal for graduate and advanced undergraduate courses in the areas of data science and analytics and forecasting and time series analysis. It is also an outstanding reference for practicing data scientists.

Accompanying CD-ROM ... "includes spreadsheet models with ready-to-use formulas ..."

Russell and Taylor's Operations and Supply Chain Management, 10th Edition is designed to teach students understand how to create value and competitive advantage along the supply chain in a rapidly changing global environment. Beyond providing a solid foundation, this course covers increasingly important OM topics of sustainability, corporate social responsibility, global trade policies, securing the supply chain, and risk and resilience. Most importantly, Operations Management, Tenth Edition makes the quantitative topics easy for students to understand and the mathematical applications less intimidating. Appropriate for all business students, this course takes a balanced approach to the foundational understanding of both qualitative and quantitative operations management processes.

Forecasting and Analytics with the Augmented Dynamic Adaptive Model (ADAM) focuses on a time series model in Single Source of Error state space form, called “ADAM” (Augmented Dynamic Adaptive Model). The book demonstrates a holistic view to forecasting and time series analysis using dynamic models, explaining how a variety of instruments can be used to solve real life problems. At the moment, there is no other tool in R or Python that would be able to model both intermittent and regular demand, would support both ETS and ARIMA, work with explanatory variables, be able to deal with multiple seasonalities (e.g. for hourly demand data) and have a support for automatic selection of orders, components and variables and provide tools for diagnostics and further improvement of the estimated model. ADAM can do all of that in one and the same framework. Given the rising interest in forecasting, ADAM, being able to do all those things, is a useful tool for data scientists, business analysts and machine learning experts who work with time series, as well as any researchers working in the area of dynamic models. Key Features: • It covers basics of forecasting, • It discusses ETS and ARIMA models, • It has chapters on extensions of ETS and ARIMA, including how to use explanatory variables and how to capture multiple frequencies, • It discusses intermittent demand and scale models for ETS, ARIMA and regression, • It covers diagnostics tools for ADAM and how to produce forecasts with it, • It does all of that with examples in R.

In this edition, efforts have been made to assist readers in converting data into useful information that can be used by decision-makers in making more thoughtful, information-based decisions.

MCDM 2009, the 20th International Conference on Multiple-Criteria Decision M- ing, emerged as a global forum dedicated to the sharing of original research results and practical development experiences among researchers and application developers from different multiple-criteria decision making-related areas such as multiple-criteria decision aiding, multiple criteria classification, ranking, and sorting, multiple obj- tive continuous and combinatorial optimization, multiple objective metaheuristics, multiple-criteria decision making and preference modeling, and fuzzy multiple-criteria decision making. The theme for MCDM 2009 was “New State of MCDM in the 21st Century.” The conference seeks solutions to challenging problems facing the development of multiple-criteria decision making, and shapes future directions of research by prom- ing high-quality, novel and daring research findings. With the MCDM conference, these new challenges and tools can easily be shared with the multiple-criteria decision making community. The workshop program included nine workshops which focused on different topics in new research challenges and initiatives of MCDM. We received more than 350 submissions for all the workshops, out of which 121 were accepted. This includes 72 regular papers and 49 short papers. We would like to thank all workshop organizers and the Program Committee for the excellent work in maintaining the conference’s standing for high-quality papers.