Spyros Makridakis
Founder of the Makridakis Open Forecasting Center (MOFC)
Book Recommendations:
Recommended by Spyros Makridakis
“Nicolas did it again! Demand Forecasting Best Practices provides practical and actionable advice for improving the demand planning process.” (from Amazon)
by Nicolas Vandeput·You?
by Nicolas Vandeput·You?
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 Inside Enhance 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 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!
Recommended by Spyros Makridakis
“The objective of Data Science for Supply Chain Forecasting is to show practitioners how to apply the statistical and ML models described in the book in simple and actionable 'do-it-yourself' ways by showing, first, how powerful the ML methods are, and second, how to implement them with minimal outside help, beyond the 'do-it-yourself' descriptions provided in the book.” (from Amazon)
by Nicolas Vandeput·You?
by Nicolas Vandeput·You?
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 traditional statistical forecasting models, Part II on machine learning, and the all-new Part III discusses demand forecasting process management. The various chapters focus on both (demand) forecasting 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, demand planners, forecasters, and analysts looking to go the extra mile with demand forecasting.