Demand Forecasting Best Practices: Stories Beyond Boundaries

Nicholas has a great impact in my career, his insights on forecasting and demand planning is helpful. His books and wisdom have guided me a lot and expertise continues to inspire me.

Esta me ajudando muito, estou começando na área e cobre muito do que um iniciante precisa. Dando clareza ao que significa previsão de demanda

I can highly recommend this book if you want to review your demand planning process and bring it to the next level. It is full of so many practical advises and 100% to the point. Furthermore Nicolas outlines risks & opportunities and gives great advises how to handle objectives and expectations regarding forecast accuracy.

In my time I have read a few S&OP books. This book definitely has the most practical advice and has been very thought provoking and reflective on what changes we can make to our S&OP process and how it is working. My only criticism would be that even though this book is very long I would have been happy for it to go into even more detail with some of the topics.

highly recommended this book as well as the other two: inventory optimization and data science for supply chain forecasting.

The author does a fantastic job of introducing all of the relevant aspects of forecasting demand. There's not an immense amount of technical depth, but that makes the high level insights very accessible for the layman. After reading it, I feel that the way forward (at least as far as the business logic) for building a forecasting system at my company seems much more clear. He wrote some other books that get more into the technical details of modeling demand and optimizing inventory. This book serves as a solid introduction to that content or as a companion book for non-technical co-workers to read for context. If you're technical and intend to build your own data infrastructure to do all of the work mentioned in this book, then you should try to find a good book on data engineering to pair with this author's work. I picked up Fundamentals of Data Engineering by Reis and Housley, and that seemed to compliment it well in my case.