Availability: Data science for supply chain forecasting

Data science for supply chain forecasting:

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Bibliographic Details
Main Author:	Vandeput, Nicolas (Author)
Format:	Book
Language:	English
Published:	Berlin De Gruyter [2021]
Edition:	2nd edition
Subjects:	Nachfrageermittlung Datenmanagement Supply Chain Management Prognoseverfahren Maschinelles Lernen Künstliche Intelligenz
Links:	http://www.degruyter.com/search?f_0=isbnissn&q_0=9783110671100&searchTitles=true http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032652564&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032652564&sequence=000003&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA
Physical Description:	XXVIII, 282 Seiten Illustrationen, Diagramme 24 cm x 17 cm
ISBN:	9783110671100 3110671107

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Record in the Search Index

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adam_text	CONTENTS ACKNOWLEDGMENTS * VII ABOUT THE AUTHOR * XI FOREWORD - SECOND EDITION * XIII FOREWORD - FIRST EDITION * XV INTRODUCTION * XXI PARTI: STATISTICAL FORECASTING 1 1.1 1.2 1.3 MOVING AVERAGE * 3 MOVING AVERAGE MODEL ----- 3 INSIGHTS ----- 4 DO IT YOURSELF ----- 6 2 2.1 2.2 2.3 2.4 2.5 2.6 FORECAST KPI * 10 FORECAST ERROR * 10 BIAS * 11 MAPE ----- 14 MAE----- 16 RMSE ----- 17 WHICH FORECAST KPI TO CHOOSE? ----- 20 3 3.1 3.2 3.3 3.4 EXPONENTIAL SMOOTHING * 27 THE IDEA BEHIND EXPONENTIAL SMOOTHING ----- 27 MODEL ----- 28 INSIGHTS ----- 31 DO IT YOURSELF ---- 33 4 4.1 4.2 UNDERFITTING ----- 37 CAUSES OF UNDERFITTING -----38 SOLUTIONS -----40 5 5.1 5.2 5.3 5.4 DOUBLE EXPONENTIAL SMOOTHING * 41 THE IDEA BEHIND DOUBLE EXPONENTIAL SMOOTHING ----- 41 DOUBLE EXPONENTIAL SMOOTHING MODEL ----- 41 INSIGHTS ----- 44 DO IT YOURSELF ---- 47 XVIII * * CONTENTS 6 MODEL OPTIMIZATION * 52 6.1 EXCEL ------ 52 6.2 PYTHON ------ 56 7 DOUBLE SMOOTHING WITH DAMPED TREND * 60 7.1 THE IDEA BEHIND DOUBLE SMOOTHING WITH DAMPED TREND 60 7.2 MODEL ----- 60 7.3 INSIGHTS ----- 61 7.4 DO IT YOURSELF -----62 8 OVERFITTING ----- 66 8.1 EXAMPLES ----- 66 8.2 CAUSES AND SOLUTIONS * 68 9 TRIPLE EXPONENTIAL SMOOTHING * 70 9.1 THE IDEA BEHIND TRIPLE EXPONENTIAL SMOOTHING * 70 9.2 MODEL * 70 9.3 INSIGHTS ----- 74 9.4 DOLTYOURSELF ----- 77 10 OUTLIERS * 86 10.1 LDEA#L-WINSORIZATION * 86 10.2 IDEA #2 - STANDARD DEVIATION * 89 10.3 IDEA #3 - ERROR STANDARD DEVIATION -----93 10.4 GO THE EXTRA MILE! ----- 95 11 TRIPLE ADDITIVE EXPONENTIAL SMOOTHING * 96 11.1 THE IDEA BEHIND TRIPLE ADDITIVE EXPONENTIAL SMOOTHING * 96 11.2 MODEL -----96 11.3 INSIGHTS ----- 99 11.4 DOLTYOURSELF * 101 PART II: MACHINE LEARNING 12 MACHINE LEARNING * 109 12.1 MACHINE LEARNING FOR DEMAND FORECASTING * 110 12.2 DATA PREPARATION ----- 111 12.3 DO IT YOURSELF - DATASETS CREATION * 114 12.4 DO IT YOURSELF- LINEAR REGRESSION ----- 118 12.5 DO ITYOURSELF- FUTURE FORECAST * 120 CONTENTS * XIX 13 13.1 13.2 TREE * 122 HOW DOES IT WORK? ----- 123 DO IT YOURSELF ----- 126 14 14.1 14.2 14.3 14.4 PARAMETER OPTIMIZATION * 130 SIMPLE EXPERIMENTS * 130 SMARTER EXPERIMENTS * 131 DO IT YOURSELF ----- 134 RECAP ----- 137 15 15.1 15.2 15.3 15.4 FOREST * 138 THE WISDOM OF THE CROWD AND ENSEMBLE MODELS * 138 BAGGING TREES IN A FOREST ----- 139 DO IT YOURSELF ----- 141 INSIGHTS ----- 144 16 16.1 FEATURE IMPORTANCE * 147 DO IT YOURSELF ----- 148 17 17.1 17.2 EXTREMELY RANDOMIZED TREES * 150 DO IT YOURSELF ----- 150 SPEED ----- 154 18 18.1 18.2 18.3 FEATURE OPTIMIZATION #1 * 155 IDEA #1 - TRAINING SET ----- 156 IDEA #2 - VALIDATION SET ----- 159 IDEA #3 - HOLDOUT DATASET * 161 19 19.1 19.2 19.3 19.4 ADAPTIVE BOOSTING * 167 A SECOND ENSEMBLE: BOOSTING ----- 167 ADABOOST ----- 168 INSIGHTS ----- 169 DO IT YOURSELF ----- 173 20 20.1 20.2 20.3 20.4 DEMAND DRIVERS AND LEADING INDICATORS * 178 LINEAR REGRESSIONS?----- 178 DEMAND DRIVERSAND MACHINE LEARNING * 180 ADDING NEW FEATURES TO THE TRAINING SET ----- 182 DO IT YOURSELF ----- 185 21 21.1 EXTREME GRADIENT BOOSTING * 189 FROM GRADIENT BOOSTING TO EXTREME GRADIENT BOOSTING ----- 189 XX CONTENTS 21.2 DOLTYOURSELF ----- 189 21.3 EARLY STOPPING ----- 192 21.4 PARAMETER OPTIMIZATION * 195 22 CATEGORICAL FEATURES * 200 22.1 INTEGER ENCODING ----- 200 22.2 ONE-HOT LABEL ENCODING ----- 203 22.3 DATASET CREATION * 206 23 CLUSTERING * 209 23.1 K-MEANS CLUSTERING * 209 23.2 LOOKING FOR MEANINGFUL CENTERS * 211 23.3 DOLTYOURSELF ----- 214 24 FEATURE OPTIMIZATION #2 * 219 24.1 DATASET CREATION * 219 24.2 FEATURE SELECTION ----- 222 25 NEURAL NETWORKS * 228 25.1 HOW NEURAL NETWORKS WORK ----- 229 25.2 TRAINING A NEURAL NETWORK * 234 25.3 DOLTYOURSELF ----- 241 PART III: DATA-DRIVEN FORECASTING PROCESS MANAGEMENT 26 JUDGMENTAL FORECASTS * 249 26.1 JUDGMENTAL FORECASTS AND THEIR BLIND SPOTS ------ 249 26.2 SOLUTIONS ----- 252 27 FORECAST VALUE ADDED * 254 27.1 PORTFOLIO KPI ----- 254 27.2 WHAT IS A GOOD FORECAST ERROR? * 257 NOW IT * S YOUR TURN! * 263 A PYTHON ----- 265 BIBLIOGRAPHY * 273 GLOSSARY * 277 INDEX * 281 Contents Acknowledgments — VII About the Author----- XI Foreword ֊ Second Edition----- XIII Foreword - First Edition----- XV Introduction----- XXI Part I: Statistical Forecasting 1 1.1 1.2 1.3 2 2.1 2.2 2.3 2.4 2.5 2.6 3 3.1 3.2 3.3 3.4 4 4.1 4.2 5 5.1 5.2 5.3 5.4 Moving Average----- 3 Moving Average Model------3 Insights------ 4 Do It Yourself-----6 Forecast KPI----- 10 Forecast Error-----10 Bias------11 МАРЕ------14 MAE------16 RMSE------17 Which Forecast KPI to Choose?-----20 Exponential Smoothing----- 27 The Idea Behind Exponential Smoothing-----27 Model------28 Insights------ 31 Do It Yourself-----33 Underfitting------ 37 Causes of Underfitting------38 Solutions-----40 Double Exponential Smoothing------ 41 The Idea Behind Double Exponential Smoothing Double Exponential Smoothing Model-----41 Insights------ 44 DoltYourself-----47 XVIII — Contents 6 Model Optimization----- 52 6.1 Excel----- 52 6.2 Python---- 56 7 Double Smoothing with Damped Trend — 60 7.1 The Idea Behind Double Smoothing with Damped Trend 7.2 Model----- 60 7.3 Insights----- 61 7.4 Do It Yourself------ 62 8 Overfitting----- 66 8.1 Examples----- 66 8.2 Causes and Solutions----- 68 9 Triple Exponential Smoothing—70 9.1 The Idea Behind Triple Exponential Smoothing----- 70 9.2 Model----- 70 9.3 Insights----- 74 9.4 Do It Yourself------ 77 10 Outliers----- 86 10.1 Idea #l-Winsorization----- 86 10.2 Idea #2 - Standard Deviation — 89 10.3 Idea #3 - Error Standard Deviation----- 93 10.4 Go the Extra Mile!------95 11 Triple Additive Exponential Smoothing — 96 11.1 The Idea Behind Triple Additive Exponential Smoothing 11.2 Model----- 96 11.3 Insights----- 99 11.4 DoltYourself------101 Partii: Machine Learning 12 Machine Learning —109 12.1 Machine Learning for Demand Forecasting —110 12.2 Data Preparation----- 111 12.3 Do It Yourself - Datasets Creation —114 12.4 Do It Yourself - Linear Regression----- 118 12.5 Do It Yourself- Future Forecast------120 Contents J3 13 ļ շ Tree------ 122 How Does It Work?-----123 DoltYourself------126 14 14.1 14 2 14.3 14.4 Parameter Optimization —130 Simple Experiments-----130 Smarter Experiments------ 131 DoltYourself---- 134 Recap-----137 15 15.1 15.2 15.3 15.4 Forest-----138 The Wisdom of the Crowd and Ensemble Models-----138 Bagging Trees in a Forest-----139 DoltYourself-----141 Insights-----144 16 16.1 Feature Importance-----147 DoltYourself-----148 17 17.1 17.2 Extremely Randomized Trees------150 DoltYourself-----150 Speed-----154 18 18.1 18.2 18.3 Feature Optimization #1-----155 Idea #1-Training Set-----156 Idea #2-Validation Set-----159 Idea #3 - Holdout Dataset-----161 19 19.1 19.2 19.3 19.4 Adaptive Boosting —167 A Second Ensemble: Boosting------167 AdaBoost-----168 Insights-----169 DoltYourself-----173 20 20.1 20.2 20.3 20.4 Demand Drivers and Leading Indicators —178 Linear Regressions? —178 Demand Drivers and Machine Learning-----180 Adding New Features to the Training Set-----182 Do It Yourself-----185 21 21.1 Extreme Gradient Boosting----- 189 From Gradient Boosting to Extreme Gradient Boosting-----189 — XIX XX —— Contents 21.2 21.3 Do It Yourself---- 189 Early Stopping----- 192 21.4 Parameter Optimization----- 195 22 22.1 Categorical Features — 200 Integer Encoding----- 200 22.2 One-Hot Label Encoding----- 203 22.3 Dataset Creation — 206 23 Clustering—209 23.1 К-means Clustering — 209 23.2 Looking for Meaningful Centers----- 211 23.3 Do It Yourself---- 214 24 Feature Optimization #2 — 219 24.1 Dataset Creation----- 219 24.2 Feature Selection — 222 25 Neural Networks — 228 25.1 How Neural Networks Work----- 229 25.2 Training a Neural Network----- 234 25.3 Do It Yourself-----241 Part ill: Data-Driven Forecasting Process Management 26 Judgmental Forecasts — 249 26.1 Judgmental Forecasts and Their Blind Spots — 249 26.2 Solutions----- 252 27 Forecast Value Added----- 254 27.1 Portfolio KPI----- 254 27.2 What Is a Good Forecast Error?----- 257 Now It’s Your Turn!----- 263 A Python------265 Bibliography — 273 Glossary----- 277 Index — 281
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spellingShingle	Vandeput, Nicolas Data science for supply chain forecasting Nachfrageermittlung (DE-588)4134942-8 gnd Datenmanagement (DE-588)4213132-7 gnd Supply Chain Management (DE-588)4684051-5 gnd Prognoseverfahren (DE-588)4358095-6 gnd Maschinelles Lernen (DE-588)4193754-5 gnd Künstliche Intelligenz (DE-588)4033447-8 gnd
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title	Data science for supply chain forecasting
title_auth	Data science for supply chain forecasting
title_exact_search	Data science for supply chain forecasting
title_full	Data science for supply chain forecasting Nicolas Vandeput
title_fullStr	Data science for supply chain forecasting Nicolas Vandeput
title_full_unstemmed	Data science for supply chain forecasting Nicolas Vandeput
title_short	Data science for supply chain forecasting
title_sort	data science for supply chain forecasting
topic	Nachfrageermittlung (DE-588)4134942-8 gnd Datenmanagement (DE-588)4213132-7 gnd Supply Chain Management (DE-588)4684051-5 gnd Prognoseverfahren (DE-588)4358095-6 gnd Maschinelles Lernen (DE-588)4193754-5 gnd Künstliche Intelligenz (DE-588)4033447-8 gnd
topic_facet	Nachfrageermittlung Datenmanagement Supply Chain Management Prognoseverfahren Maschinelles Lernen Künstliche Intelligenz
url	http://www.degruyter.com/search?f_0=isbnissn&q_0=9783110671100&searchTitles=true http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032652564&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032652564&sequence=000003&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA
work_keys_str_mv	AT vandeputnicolas datascienceforsupplychainforecasting AT walterdegruytergmbhcokg datascienceforsupplychainforecasting

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