Demand Forecasting for Executives and Professionals 1st Edition by Stephan Kolassa, Bahman Rostami-Tabar, Enno Siemsen ISBN 9781032507729 1032507721

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Product details:

ISBN 10: 1032507721

ISBN 13: 9781032507729 

Author: Stephan Kolassa, Bahman Rostami-Tabar, Enno Siemsen

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.

Table of contents: 

1  Introduction

1.1 The value of a good forecasting process

1.2 Software

Key takeaways

2 The forecasting workflow

2.1 Identify a decision requiring a forecast

2.2 Determine requirements

2.3 Gather data and information

2.4 Prepare data.

2.5 Visualize data

2.6 Choose and train models

2.7 Produce forecast.

2.8 Evaluate quality.

2.9 Communicate forecast

2.10 Possibly adjust forecast

Key takeaways

3 Choice under uncertainty

3.1 Forecasting methods

3.2 Forecasts and service levels

Key takeaways

4 A simple example

4.1 A point forecast

4.2 Prediction intervals.

4.3 Predictive distributions

4.4 Decision-making.

Key takeaways

Forecasting basics

5 Know your time series

5.1 Data availability.

5.2 How much data is necessary?

5.3 Data quality

5.4 Stationarity

5.5 Forecastability and scale

5.6 Using summary statistics to understand your data

Key takeaways

6 Time series components

6.1 Level

6.2 Trend

6.3 Seasonality

6.4 Cyclical patterns.

6.5 Other characteristics of time series

6.6 Visualizing time series features.

Key takeaways

7 Time series decomposition

7.1 The purpose of decomposition

7.2 Decomposition methods

7.3 Stability of components

Key takeaways

Forecasting models

8 Low-hanging fruit: simple forecasts

8.1 The historical mean

8.2 The naive forecast

8.3 The seasonal naive forecast

8.4 Other simple methods

8.5 Non-expectation forecasts

8.6 Ensemble forecasting

8.7 When are our forecasts too simple?

Key takeaways

9 Exponential smoothing

9.1 Change and noise

9.2 Optimal smoothing parameters

9.3 Extensions

Key takeaways

10 ARIMA models

10.1 Autoregression

10.2 Integration.

10.3 Moving averages

10.4 Autocorrelation and partial autocorrelation

10.5 Information criteria

10.6 Discussion

Key takeaways

11 Causal models and predictors

11.1 Association and correlation

11.2 Predictors in forecasting

11.3 Leading and lagging predictors

11.4 An example: advertising and sales

11.5 Correlation, causation and forecasting

11.6 Combination with time series

11.7 Model complexity and overfitting

Key takeaways

12 Count data and intermittent demand

12.1 Definitions

12.2 Traditional forecasting methods.

12.3 Croston’s method

Key takeaways

13 Forecasting hierarchies

13.1 Structural hierarchies

13.2 Forecasting hierarchical time series

13.3 Temporal hierarchies and aggregation

13.4 When do we not want coherent forecasts?

Key takeaway

14 Artificial intelligence and machine learning

14.1 Neural networks and deep learning

14.2 Tree-based methods and random forests

14.3 Boosting and variants

14.4 Point, interval and density AI/ML. forecasts

14.5 Al and ML versus conventional approaches

Key takeaways

15 Long, multiple and non-periodic seasonal cycles

15.1 Introduction..

15.2 Long seasonality.

15.3 Multiple seasonalities

15.4 An example: ambulance demand

15.5 Modeling complicated seasonality

15.6 Forecasting models for complicated seasonality

Key takeaways

16 Human judgment

16.1 Cognitive biases

16.2 Domain-specific knowledge

16.3 Political and incentive aspects

16.4 Correction and combination.

16.5 Forecasting in groups

Key takeaways

Forecasting quality

17 Error measures

17.1 Bias and accuracy

17.2 Percentage, scaled and relative errors

17.3 Assessing prediction intervals and predictive distributions

17.4 Accuгаcy measures for count data forecasts.

17.5 Forecast accuracy and business value

Key takeaways

18 Forecasting competitions

18.1 Planning

18.2 Data

18.3 Procedure

18.4 Additional aspects.

Key takeaways

Forecasting organization

19 Leading forecasters and forecasting teams

19.1 The ideal forecaster.

19.2 How do forecasters tick?

19.3 Making your forecaster happy.

19.4 What can you expect from your forecaster?

Key takeaways

20 Sales and operations planning

20.1 Forecasting organization

20.2 Organizational barriers

Key takeaways

21 Why does forecasting fail?

21.1 There is no alternative to forecasting

21.2 What we can forecast

21.3 We can’t achieve unlimited accuracy

21.4 Confusing forecasts, targets, decisions and plans

21.5 No systematic tracking of forecast quality

21.6 Inappropriate error measures

21.7 Data availability and quality

21.8 Too much judgmental intervention

21.9 Follow-on processes do not leverage forecasts

21.10 Feedback to the process being forecasted

Key takeaways

Learning more

22 Resources

22.1 Non-technical material

22.2 Somewhat technical material.

22.3 Technical material

22.4 Non-profit organizations

22.5 Events.

22.6 Datasets

22.7 Online resources

Key takeaways

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Tags: Stephan Kolassa, Bahman Rostami Tabar, Enno Siemsen, Demand Forecasting, Executives, Professionals