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Lead your demand planning process to excellence and deliver real value to your supply chain.In Demand Forecasting Best Practices you’ll learn how to:Lead your team to improve quality while reducing workloadProperly define the objectives and granularity of your demand planningUse intelligent KPIs to track accuracy and biasIdentify areas for process improvementHelp planners and stakeholders add valueDetermine relevant data to collect and how best to collect itUtilize different statistical and machine learning models An expert demand forecaster can help an organization avoid overproduction, reduce waste, and optimize inventory levels for a real competitive advantage. Demand Forecasting Best Practices teaches you how to become that virtuoso demand forecaster.This one-of-a-kind guide reveals forecasting tools, metrics, models, and stakeholder management techniques for delivering more effective supply chains. Everything you learn has been proven and tested in a live business environment. Discover author Nicolas Vandeput’s original five step framework for demand planning excellence and learn how to tailor it to your own company’s needs. Illustrations and real-world examples make each concept easy to understand and easy to follow. You’ll soon be delivering accurate predictions that are driving major business value.Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.About the Technology An expert demand forecaster can help an organization avoid overproduction, reduce waste, and optimize inventory levels for a real competitive advantage. This book teaches you how to become that virtuoso demand forecaster.About the Book Demand Forecasting Best Practices reveals forecasting tools, metrics, models, and stakeholder management techniques for managing your demand planning process efficiently and effectively. Everything you learn has been proven and tested in a live business environment. Discover author Nicolas Vandeput’s original five step framework for demand planning excellence and learn how to tailor it to your own company’s needs. Illustrations and real-world examples make each concept easy to understand and easy to follow. You’ll soon be delivering accurate predictions that are driving major business value.What's InsideEnhance forecasting quality while reducing team workloadUtilize intelligent KPIs to track accuracy and biasIdentify process areas for improvementAssist stakeholders in sales, marketing, and financeOptimize statistical and machine learning modelsAbout the Reader For demand planners, sales and operations managers, supply chain leaders, and data scientists.About the Author Nicolas Vandeput is a supply chain data scientist, the founder of consultancy company SupChains in 2016, and a teacher at CentraleSupélec, France.Table of Contents:Part 1 - Forecasting demand 1 Demand forecasting excellence 2 Introduction to demand forecasting 3 Capturing unconstrained demand (and not sales) 4 Collaboration: data sharing and planning alignment 5 Forecasting hierarchies 6 How long should the forecasting horizon be? 7 Should we reconcile forecasts to align supply chains? Part 2 - Measuring forecasting quality 8 Forecasting metrics 9 Choosing the best forecasting KPI 10 What is a good forecast error? 11 Measuring forecasting accuracy on a product portfolio Part 3 - Data-driven forecasting process 12 Forecast value added 13 What do you review? ABC XYZ segmentations and other methods Part 4 - Forecasting methods 14 Statistical forecasting 15 Machine learning 16 Judgmental forecasting 17 Now it’s your turn! Demand Forecasting Best Practices Full quotes from reviewers of Demand Forecasting Best Practices brief contents contents preface acknowledgments about this book How this book is organized: a roadmap liveBook discussion forum about the author about the cover illustration Part 1—Forecasting demand 1 Demand forecasting excellence 1.1 Why do we forecast demand? 1.2 Five steps to demand planning excellence 1.2.1 Objective. What do you need to forecast? 1.2.2 Data. What data do you need to support your forecasting model and process? 1.2.3 Metrics. How do you evaluate forecasting quality? 1.2.4 Baseline model. How do you create an accurate, automated forecast baseline? 1.2.5 Review Process. How to review the baseline forecast, and who should do it? Summary 2 Introduction to demand forecasting 2.1 Why do we forecast demand? 2.2 Definitions 2.2.1 Demand, sales, and supply 2.2.2. Supply plan, financial budget, and sales targets Summary 3 Capturing unconstrained 3.1 Order collection and management 3.2 Shortage-Censoring and Uncollected Orders 3.2.1. Using demand drivers to forecast historical demand 3.3 Substitution and cannibalization Summary 4 Collaboration: data sharing 4.1 How supply chains distort demand information 4.2 Bullwhip effect 4.2.1 Order forecasting 4.2.2 Order batching 4.2.3 Price fluctuation and promotions 4.2.4 Shortage gaming 4.2.5 Lead time variations 4.3 Collaborative planning 4.3.1 Internal collaboration 4.3.2 External collaboration 4.3.3 Collaborating with your suppliers Summary 5 Forecasting hierarchies 5.1 The three forecasting dimensions 5.2 Zooming in or out of forecasts 5.3 How do you select the most appropriate aggregation level? 5.3.1 Which aggregation level should you focus on? 5.3.2 What granularity level should you use to create your forecast? Summary 6 How long should the forecasting horizon be? 6.1 Theory: Inventory optimization, lead times, and review periods 6.2 Reconciling demand forecasting and supply planning 6.3 Looking further ahead 6.3.1 Optimal service level and risks 6.3.2 Collaboration with suppliers 6.4 Going further: Lost sales vs. backorders 6.4.1 Lost sales 6.4.2 Backorders 6.4.3 Hybrid Summary 7 Should we reconcile forecasts to align supply chains? 7.1 Forecasting granularities requirements 7.2 One number forecast 7.3 Different hierarchies . . . different optimal forecasts 7.3.1 Spot sales and stock clearances 7.3.2 Product life-cycles 7.3.3 Example: top-down vs. bottom up 7.4 One number mindset Summary Part 2—Measuringforecasting quality 8 Forecasting metrics 8.1 Accuracy and bias 8.2 Forecast error and bias 8.2.1 Interpreting and scaling the bias 8.2.2 Do it yourself 8.2.3 Insights 8.3 Mean Absolute Error (MAE) 8.3.1 Scaling the Mean Absolute Error 8.3.2 Do it yourself 8.3.3 Insights 8.4 Mean Absolute Percentage Error (MAPE) 8.4.1 Do it yourself 8.4.2 Insights 8.5 Root Mean Square Error (RMSE) 8.5.1 Scaling RMSE 8.5.2 Do it yourself 8.5.3 Insights 8.6 Case study – Part 1 Summary 9 Choosing the bestforecasting KPI 9.1 Extreme demand patterns 9.2 Intermittent demand 9.3 The best forecasting KPI 9.4 Case study – Part 2 Summary 10 What is a good forecast error? 10.1 Benchmarking 10.1.1 Naïve forecasts 10.1.2 Moving average 10.1.3 Seasonal benchmarks 10.2 Why tracking demand coefficient of variation is not recommended 10.2.1 COV and simple demand patterns 10.2.2 COV and realistic demand patterns Summary 11 Measuring forecasting accuracy 11.1 Forecasting metrics and product portfolios 11.2 Value-weighted KPIs Summary Part 3—Data-driven forecasting process 12 Forecast value added 12.1 Comparing your process to a benchmark 12.1.1 Internal benchmarks 12.1.2 Industry (external) benchmarks 12.2 Tracking Forecast Value Added 12.2.1 Process efficacy 12.2.2 Process efficiency 12.2.3 Best practices 12.2.4 How do you get started? Summary 13 What do you review? ABC XYZ segmentations and other methods 13.1 ABC XYZ segmentations 13.1.1 ABC analysis 13.1.2 ABC XYZ analysis 13.2 Using ABC XYZ for demand forecasting 13.2.1 Products’ importance 13.2.2 Products’ forecastability 13.2.3 ABC XYZ limitations 13.3 Beyond ABC XYZ: Smart multi-criteria classification Summary Part 4—Forecasting methods 14 Statistical forecasting 14.1 Time series forecasting 14.1.1 Demand components: Level, trend, and seasonality 14.1.2 Setting up time series models 14.2 Predictive analytics and demand drivers 14.2.1 Demand drivers 14.2.2 Challenges 14.3 Times series forecasting vs. predictive analytics 14.4 How to select a model 14.4.1 The 5-step framework 14.4.2 4-step model creation framework Summary 15 Machine Learning 15.1 What is machine learning? 15.1.1 How does the machine learn? 15.1.2 Black boxes versus whites boxes 15.2 Main types of learning algorithms 15.2.1 Short history of machine-learning models 15.2.2 Tree-based models 15.2.3 Neural networks 15.3 What should you expect from ML-driven demand forecasting? 15.3.1 Forecasting competitions 15.3.2 Improving the baseline 15.4 How to launch a machine-learning initiative Summary 16 Judgmental forecasting 16.1 When to use judgmental forecasts? 16.2 Judgmental biases 16.2.1 Cognitive biases 16.2.2 Misalignment of incentives (intentional biases) 16.2.3 Biased forecasting process 16.3 Group forecasts 16.3.1 Wisdom of the crowds 16.3.2 Assumption-based discussions Summary 17 Now it’s your turn! Closing words references index Lead your demand planning process to excellence and deliver real value to your supply chain.In Demand Forecasting Best Practices you'll learn how to: Lead your team to improve quality while reducing workload Properly define the objectives and granularity of your demand planning Use intelligent KPIs to track accuracy and bias Identify areas for process improvement Help planners and stakeholders add value Determine relevant data to collect and how best to collect it Utilize different statistical and machine learning models An expert demand forecaster can help an organization avoid overproduction, reduce waste, and optimize inventory levels for a real competitive advantage. Demand Forecasting Best Practices teaches you how to become that virtuoso demand forecaster. This one-of-a-kind guide reveals forecasting tools, metrics, models, and stakeholder management techniques for delivering more effective supply chains. Everything you learn has been proven and tested in a live business environment. Discover author Nicolas Vandeput's original five step framework for demand planning excellence and learn how to tailor it to your own company's needs. Illustrations and real-world examples make each concept easy to understand and easy to follow. You'll soon be delivering accurate predictions that are driving major business value. About the Technology An expert demand forecaster can help an organization avoid overproduction, reduce waste, and optimize inventory levels for a real competitive advantage. This book teaches you how to become that virtuoso demand forecaster. About the Book Demand Forecasting Best Practices reveals forecasting tools, metrics, models, and stakeholder management techniques for managing your demand planning process efficiently and effectively. Everything you learn has been proven and tested in a live business environment. Discover author Nicolas Vandeput's original five step framework for demand planning excellence and learn how to tailor it to your own company's needs. Illustrations and real-world examples make each concept easy to understand and easy to follow. You'll soon be delivering accurate predictions that are driving major business value. What's Inside Enhance forecasting quality while reducing team workload Utilize intelligent KPIs to track accuracy and bias Identify process areas for improvement Assist stakeholders in sales, marketing, and finance Optimize statistical and machine learning models About the Reader For demand planners, sales and operations managers, supply chain leaders, and data scientists. About the Author Nicolas Vandeput is a supply chain data scientist, the founder of consultancy company SupChains in 2016, and a teacher at CentraleSupélec, France. Table of Contents: Part 1 - Forecasting demand 1 Demand forecasting excellence 2 Introduction to demand forecasting 3 Capturing unconstrained demand (and not sales) 4 Collaboration: data sharing and planning alignment 5 Forecasting hierarchies 6 How long should the forecasting horizon be? 7 Should we reconcile forecasts to align supply chains? Part 2 - Measuring forecasting quality 8 Forecasting metrics 9 Choosing the best forecasting KPI 10 What is a good forecast error? 11 Measuring forecasting accuracy on a product portfolio Part 3 - Data-driven forecasting process 12 Forecast value added 13 What do you review? ABC XYZ segmentations and other methods Part 4 - Forecasting methods 14 Statistical forecasting 15 Machine learning 16 Judgmental forecasting 17 Now it's your turn! Lead your demand planning process to excellence and deliver real value to your supply chain. In Demand Forecasting Best Practices youll learn how An expert demand forecaster can help an organization avoid overproduction, reduce waste, and optimize inventory levels for a real competitive advantage. Demand Forecasting Best Practices teaches you how to become that virtuoso demand forecaster. This one-of-a-kind guide reveals forecasting tools, metrics, models, and stakeholder management techniques for delivering more effective supply chains. Everything you learn has been proven and tested in a live business environment. Discover author Nicolas Vandeputs original five step framework for demand planning excellence and learn how to tailor it to your own companys needs. Illustrations and real-world examples make each concept easy to understand and easy to follow. Youll soon be delivering accurate predictions that are driving major business value. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology An expert demand forecaster can help an organization avoid overproduction, reduce waste, and optimize inventory levels for a real competitive advantage. This book teaches you how to become that virtuoso demand forecaster. About the Book Demand Forecasting Best Practices reveals forecasting tools, metrics, models, and stakeholder management techniques for managing your demand planning process efficiently and effectively. Everything you learn has been proven and tested in a live business environment. Discover author Nicolas Vandeputs original five step framework for demand planning excellence and learn how to tailor it to your own companys needs. Illustrations and real-world examples make each concept easy to understand and easy to follow. Youll soon be delivering accurate predictions that are driving major business value. What's Inside About the Reader For demand planners, sales and operations managers, supply chain leaders, and data scientists. About the Author Nicolas Vandeput is a supply chain data scientist, the founder of consultancy company SupChains in 2016, and a teacher at CentraleSuplec, France. Table of Part 1 - Forecasting demand 1 Demand forecasting excellence 2 Introduction to demand forecasting 3 Capturing unconstrained demand (and not sales) 4 data sharing and planning alignment 5 Forecasting hierarchies 6 How long should the forecasting horizon be? 7 Should we reconcile forecasts to align supply chains? Part 2 - Measuring forecasting quality 8 Forecasting metrics 9 Choosing the best forecasting KPI 10 What is a good forecast error? 11 Measuring forecasting accuracy on a product portfolio Part 3 - Data-driven forecasting process 12 Forecast value added 13 What do you review? ABC XYZ segmentations and other methods Part 4 - Forecasting methods 14 Statistical forecasting 15 Machine learning 16 Judgmental forecasting 17 Now its your turn! Master the demand forecasting skills you need to decide what resources to acquire, what products to produce, and where and how to distribute them. In Demand Forecasting Best Practices you’ll learn how to: • Lead a demand planning team to improve forecasting quality while reducing workload • Properly define the objectives, granularity, and horizon of your demand planning process • Use smart, value-weighted KPIs to track accuracy and bias • Spot areas of your process where there is room for improvement • Help planners and stakeholders (sales, marketing, finances) add value in your process • Identify what kind of data you should be collecting, and how • Utilize different types of statistical and machine learning models Demand Forecasting Best Practices teaches you to optimize demand planning to deliver a more effective supply chain. In this unique step-by-step guide, you’ll learn forecasting tools, metrics, and models alongside stakeholder management techniques that work in a live business environment. Follow author Nicolas Vandeput’s original five step framework for demand planning excellence and learn how to tailor it to your own company’s needs. You’ll soon be delivering accurate predictions that are driving major business value. About the technology Demand forecasting is vital for the success of any product supply chain. It allows companies to make better decisions about what resources to acquire, what products to produce, and where and how to distribute them. As an effective demand forecaster, you can help your organization avoid overproduction, reduce waste, and optimize inventory levels for a real competitive advantage. About the book Demand Forecasting Best Practices is a handbook of techniques for effective demand planning for products of all types. You’ll learn how to optimize your data, metrics, processes, models, and even people to make better decisions and deliver value to your supply chains. Discover pro tips from author Nicolas Vandeput’s global career in supply chain consultancy, and dodge the common mistakes you might not know you’re making. Illustrations, clear explanations, and relevant real-world examples make each concept easy to understand and easy to follow. About the reader For anyone who wants to improve their demand planning process, including demand planners, S&OP managers, supply chain leaders, and data scientists. About the author Nicolas Vandeput is a supply chain data scientist specializing in demand forecasting and inventory optimization. He founded his consultancy company SupChains in 2016, delivering models and training courses worldwide. He co-founded SKU Science—a demand forecasting platform—in 2018. Passionate about education, Nicolas is an avid learner enjoying teaching at universities. He currently teaches forecasting and inventory optimization to master students in CentraleSupélec, Paris, France.