Practical-nontechnical-solutions to the problems of business forecasting Written in a nontechnical style, this book provides practical solutions to common business forecasting problems, showing you how to think about business forecasting in the context of uncertainty, randomness and process performance. * Addresses the philosophical foundations of forecasting * Raises awareness of fundamental issues usually overlooked in pursuit of the perfect forecast * Introduces a new way to think about business forecasting, focusing on process efficiency and the elimination of worst practices * Provides…mehr
Practical-nontechnical-solutions to the problems of business forecasting Written in a nontechnical style, this book provides practical solutions to common business forecasting problems, showing you how to think about business forecasting in the context of uncertainty, randomness and process performance. * Addresses the philosophical foundations of forecasting * Raises awareness of fundamental issues usually overlooked in pursuit of the perfect forecast * Introduces a new way to think about business forecasting, focusing on process efficiency and the elimination of worst practices * Provides practical approaches for the non-statistical problems forecasters face * Illustrates Forecast Value Added (FVA) Analysis for identifying waste in the forecasting process Couched in the context of uncertainty, randomness, and process performance, this book offers new, innovative ideas for resolving your business forecasting problems.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
MICHAEL GILLILAND is Product Marketing Manager at SAS Institute and has worked in consu-mer products forecasting for more than twenty years. Prior to joining SAS in 2004, Mike held forecasting management positions in the food, electronics, and apparel industries and served as a consultant. He is a frequent speaker at industry events, has published articles in Supply Chain Management Review, Journal of Business Forecasting, Foresight, and APICS magazine, and was a columnist on "Worst Practices in Business Forecasting" for Supply Chain Forecasting Digest. Mike holds a BA in philosophy from Michigan State University, and master's degrees in philosophy and mathematical sciences from Johns Hopkins University. Follow his blog, The Business Forecasting Deal, at blogs.sas.com/forecasting.
Inhaltsangabe
Foreword-Tom Wallace xiii Foreword-Anne G. Robinson xv Acknowledgments xvii Prologue 1 Chapter 1 Fundamental Issues in Business Forecasting 5 The Problem of Induction 5 The Realities of Business Forecasting 6 The Contest 7 What Is Demand? 10 Constrained Forecast 13 Demand Volatility 15 Inherent Volatility and Artificial Volatility 17 Evils of Volatility 19 Evaluating Forecast Performance 22 Embarking on Improvement 24 Notes 26 Chapter 2 Worst Practices in Business Forecasting: Part 1 29 Worst Practices in the Mechanics of Forecasting 30 Model "Overfitting" and "Pick-Best" Selection 32 Confusing Model Fit with Forecast Accuracy 41 Accuracy Expectations and Performance Goals 43 Failure to Use a Naïve Model or Assess Forecast Value Added 47 Forecasting Hierarchies 48 Outlier Handling 50 Notes 54 Chapter 3 Worst Practices in Business Forecasting: Part 2 55 Worst Practices in the Process and Practices of Forecasting 55 Politics of Forecasting 57 Blaming the Forecast 60 Adding Variation to Demand 61 Evangelical Forecasting 64 Overinvesting in the Forecasting Function 66 Forecasting Performance Measurement and Reporting 69 Forecasting Software Selection 74 Editorial Comment on Forecasting Practices 76 Notes 78 Chapter 4 Forecast Value Added Analysis 81 What Is Forecast Value Added? 82 The Naïve Forecast 83 Why Is FVA Important? 90 FVA Analysis: Step-by-Step 92 Further Application of FVA Analysis 101 Case Studies 102 Summary: The Lean Approach to Forecasting 107 Notes 108 Chapter 5 Forecasting without History.111 Typical New Product Forecasting Situations 111 New Product Forecasting by Structured Analogy 114 Organizational Realignment 120 Summary 131 Notes 132 Chapter 6 Alternative Approaches to the Problems of Business Forecasting.133 Statistical Approach 134 Collaborative Approach 136 Supply Chain Engineering Approach 142 Pruning Approach 145 Summary 149 Notes 150 Chapter 7 Implementing a Forecasting Solution 151 Why Do Forecasting Implementations Fail? 151 Preproject Assessment 153 Requesting Information or Proposals 154 Evaluating Software Vendors 155 Warning Signs of Failure 157 Notes 159 Chapter 8 Practical First Steps 161 Step 1: Recognize the Volatility versus Accuracy Relationship 161 Step 2: Determine Inherent and Artifi cial Volatility 165 Step 3: Understand What Accuracy Is Reasonable to Expect 166 Step 4: Use Forecast Value Added Analysis to Eliminate Wasted Efforts 167 Step 5: Utilize Meaningful Performance Metrics and Reporting 168 Step 6: Eliminate Worst Practices 169 Step 7: Consult Forecasting Resources 170 Notes 173 Chapter 9 What Management Must Know About Forecasting 175 Aphorism 1: Forecasting Is a Huge Waste of Management Time 175 Aphorism 2: Accuracy Is Determined More by the Nature of the Behavior Being Forecast than by the Specific Method Being Used to Forecast It 177 Aphorism 3: Organizational Policies and Politics Can Have a Significant Impact on Forecasting Effectiveness 179 Aphorism 4: You May Not Control the Accuracy Achieved, But You Can Control the Process Used and the Resources You Invest 180 Aphorism 5: The Surest Way to Get a Better Forecast Is to Make the Demand Forecastable 182 Aphorism 6: Minimize the Organization's Reliance on Forecasting 183 Aphorism 7: Before Investing in a New System or Process, Put It to the Test 184 Notes 185 Epilogue 187 Glossary 189 Appendix Forecasting FAQs 193 Accuracy Expectations 193 Performance Benchmarks 196 Performance Measurement and Reporting 198 The Naïve Forecast 208 Forecast Value Added Analysis 211 Forecast Modeling 220 Politics and Practices of Forecasting 224 Demand Volatility 227 Forecasting Process 230 Judgment 237 Forecasting Organization 238 Low Volume/Intermittent Demand 239 New Product Forecasting 241 Forecasting Hierarchy 242 Software Selection 245 Index 247
Foreword-Tom Wallace xiii Foreword-Anne G. Robinson xv Acknowledgments xvii Prologue 1 Chapter 1 Fundamental Issues in Business Forecasting 5 The Problem of Induction 5 The Realities of Business Forecasting 6 The Contest 7 What Is Demand? 10 Constrained Forecast 13 Demand Volatility 15 Inherent Volatility and Artificial Volatility 17 Evils of Volatility 19 Evaluating Forecast Performance 22 Embarking on Improvement 24 Notes 26 Chapter 2 Worst Practices in Business Forecasting: Part 1 29 Worst Practices in the Mechanics of Forecasting 30 Model "Overfitting" and "Pick-Best" Selection 32 Confusing Model Fit with Forecast Accuracy 41 Accuracy Expectations and Performance Goals 43 Failure to Use a Naïve Model or Assess Forecast Value Added 47 Forecasting Hierarchies 48 Outlier Handling 50 Notes 54 Chapter 3 Worst Practices in Business Forecasting: Part 2 55 Worst Practices in the Process and Practices of Forecasting 55 Politics of Forecasting 57 Blaming the Forecast 60 Adding Variation to Demand 61 Evangelical Forecasting 64 Overinvesting in the Forecasting Function 66 Forecasting Performance Measurement and Reporting 69 Forecasting Software Selection 74 Editorial Comment on Forecasting Practices 76 Notes 78 Chapter 4 Forecast Value Added Analysis 81 What Is Forecast Value Added? 82 The Naïve Forecast 83 Why Is FVA Important? 90 FVA Analysis: Step-by-Step 92 Further Application of FVA Analysis 101 Case Studies 102 Summary: The Lean Approach to Forecasting 107 Notes 108 Chapter 5 Forecasting without History.111 Typical New Product Forecasting Situations 111 New Product Forecasting by Structured Analogy 114 Organizational Realignment 120 Summary 131 Notes 132 Chapter 6 Alternative Approaches to the Problems of Business Forecasting.133 Statistical Approach 134 Collaborative Approach 136 Supply Chain Engineering Approach 142 Pruning Approach 145 Summary 149 Notes 150 Chapter 7 Implementing a Forecasting Solution 151 Why Do Forecasting Implementations Fail? 151 Preproject Assessment 153 Requesting Information or Proposals 154 Evaluating Software Vendors 155 Warning Signs of Failure 157 Notes 159 Chapter 8 Practical First Steps 161 Step 1: Recognize the Volatility versus Accuracy Relationship 161 Step 2: Determine Inherent and Artifi cial Volatility 165 Step 3: Understand What Accuracy Is Reasonable to Expect 166 Step 4: Use Forecast Value Added Analysis to Eliminate Wasted Efforts 167 Step 5: Utilize Meaningful Performance Metrics and Reporting 168 Step 6: Eliminate Worst Practices 169 Step 7: Consult Forecasting Resources 170 Notes 173 Chapter 9 What Management Must Know About Forecasting 175 Aphorism 1: Forecasting Is a Huge Waste of Management Time 175 Aphorism 2: Accuracy Is Determined More by the Nature of the Behavior Being Forecast than by the Specific Method Being Used to Forecast It 177 Aphorism 3: Organizational Policies and Politics Can Have a Significant Impact on Forecasting Effectiveness 179 Aphorism 4: You May Not Control the Accuracy Achieved, But You Can Control the Process Used and the Resources You Invest 180 Aphorism 5: The Surest Way to Get a Better Forecast Is to Make the Demand Forecastable 182 Aphorism 6: Minimize the Organization's Reliance on Forecasting 183 Aphorism 7: Before Investing in a New System or Process, Put It to the Test 184 Notes 185 Epilogue 187 Glossary 189 Appendix Forecasting FAQs 193 Accuracy Expectations 193 Performance Benchmarks 196 Performance Measurement and Reporting 198 The Naïve Forecast 208 Forecast Value Added Analysis 211 Forecast Modeling 220 Politics and Practices of Forecasting 224 Demand Volatility 227 Forecasting Process 230 Judgment 237 Forecasting Organization 238 Low Volume/Intermittent Demand 239 New Product Forecasting 241 Forecasting Hierarchy 242 Software Selection 245 Index 247
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