Using data science in order to solve a problem requires a scientific mindset more than coding skills. Data Science for Supply Chain Forecasting, Second Edition contends that a true scientific method which includes experimentation, observation, and constant questioning must be applied to supply chains to achieve excellence in demand forecasting.
This second edition adds more than 45 percent extra content with four new chapters including an introduction to neural networks and the forecast value added framework. Part I focuses on statistical “traditional” models, Part II, on machine learning, and the all-new Part III discusses demand forecasting process management. The various chapters focus on both forecast models and new concepts such as metrics, underfitting, overfitting, outliers, feature optimization, and external demand drivers. The book is replete with do-it-yourself sections with implementations provided in Python (and Excel for the statistical models) to show the readers how to apply these models themselves.
This hands-on book, covering the entire range of forecasting–from the basics all the way to leading-edge models–will benefit supply chain practitioners, forecasters, and analysts looking to go the extra mile with demand forecasting.
Title Page Copyright Contents Acknowledgments Second Edition First Edition About the Author Foreword – Second Edition Foreword – First Edition Introduction Supply Chain Forecasting What Is Tomorrow Going to Be Like? How to Read This Book Old-school Statistics and Machine Learning Concepts and Models Do It Yourself Can I Do This? Is This Book for Me? The Data Scientist’s Mindset The Data Scientist’s Toolkit Excel Python Python Code Extracts Other Resources Part I Statistical Forecasting 1 Moving Average 1.1 Moving Average Model 1.2 Insights 1.3 Do It Yourself 2 Forecast KPI 2.1 Forecast Error 2.2 Bias 2.3 MAPE 2.4 MAE 2.5 RMSE 2.6 Which Forecast KPI to Choose? 3 Exponential Smoothing 3.1 The Idea Behind Exponential Smoothing 3.2 Model 3.3 Insights 3.4 Do It Yourself 4 Underfitting 4.1 Causes of Underfitting 4.2 Solutions 5 Double Exponential Smoothing 5.1 The Idea Behind Double Exponential Smoothing 5.2 Double Exponential Smoothing Model 5.3 Insights 5.4 Do It Yourself 6 Model Optimization 6.1 Excel 6.2 Python 7 Double Smoothing with Damped Trend 7.1 The Idea Behind Double Smoothing with Damped Trend 7.2 Model 7.3 Insights 7.4 Do It Yourself 8 Overfitting 8.1 Examples 8.2 Causes and Solutions 9 Triple Exponential Smoothing 9.1 The Idea Behind Triple Exponential Smoothing 9.2 Model 9.3 Insights 9.4 Do It Yourself 10 Outliers 10.1 Idea #1 – Winsorization 10.2 Idea #2 – Standard Deviation 10.3 Idea #3 – Error Standard Deviation 10.4 Go the Extra Mile! 11 Triple Additive Exponential Smoothing 11.1 The Idea Behind Triple Additive Exponential Smoothing 11.2 Model 11.3 Insights 11.4 Do It Yourself Part II Machine Learning 12 Machine Learning What Is Machine Learning? 12.1 Machine Learning for Demand Forecasting 12.2 Data Preparation 12.3 Do It Yourself – Datasets Creation 12.4 Do It Yourself – Linear Regression 12.5 Do It Yourself – Future Forecast 13 Tree 13.1 How Does It Work? 13.2 Do It Yourself 14 Parameter Optimization 14.1 Simple Experiments 14.2 Smarter Experiments 14.3 Do It Yourself 14.4 Recap 15 Forest 15.1 The Wisdom of the Crowd and Ensemble Models 15.2 Bagging Trees in a Forest 15.3 Do It Yourself 15.4 Insights 16 Feature Importance 16.1 Do It Yourself 17 Extremely Randomized Trees 17.1 Do It Yourself 17.2 Speed 18 Feature Optimization #1 18.1 Idea #1 – Training Set 18.2 Idea #2 – Validation Set 18.3 Idea #3 – Holdout Dataset 19 Adaptive Boosting 19.1 A Second Ensemble: Boosting 19.2 AdaBoost 19.3 Insights 19.4 Do It Yourself 20 Demand Drivers and Leading Indicators 20.1 Linear Regressions? 20.2 Demand Drivers and Machine Learning 20.3 Adding New Features to the Training Set 20.4 Do It Yourself 21 Extreme Gradient Boosting 21.1 From Gradient Boosting to Extreme Gradient Boosting 21.2 Do It Yourself 21.3 Early Stopping 21.4 Parameter Optimization 22 Categorical Features 22.1 Integer Encoding 22.2 One-Hot Label Encoding 22.3 Dataset Creation 23 Clustering 23.1 K-means Clustering 23.2 Looking for Meaningful Centers 23.3 Do It Yourself 24 Feature Optimization #2 24.1 Dataset Creation 24.2 Feature Selection 25 Neural Networks 25.1 How Neural Networks Work 25.2 Training a Neural Network 25.3 Do It Yourself Part III Data-Driven Forecasting Process Management 26 Judgmental Forecasts 26.1 Judgmental Forecasts and Their Blind Spots 26.2 Solutions 27 Forecast Value Added 27.1 Portfolio KPI 27.2 What Is a Good Forecast Error? Now It’s Your Turn! A Python How to Install Python Lists NumPy Arrays Slicing Arrays Pandas DataFrames Creating a DataFrame from a Dictionary Slicing DataFrames Exporting DataFrames Other Libraries Bibliography Glossary Subject Index Notes
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