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๐ Forecast the Future, Own the Supply Chain!
Data Science for Supply Chain Forecasting, 2nd Edition, is a 312-page professional guide that blends scientific methodology with practical tools, offering 45% new content including neural networks and process management. Perfectly bound and packed with Python and Excel tutorials, it empowers supply chain professionals to master demand forecasting with cutting-edge models and expert insights.




| Best Sellers Rank | 313,302 in Books ( See Top 100 in Books ) 121 in Professional Financial Forecasting |
| Customer Reviews | 4.6 out of 5 stars 112 Reviews |
G**N
Great book
Great book, lots of useful data
F**S
Good reference!!
Well Done!!
V**Y
Must read!
A must read for everyone interested in learning machine learning for supply chain
K**N
Practical hands-on book for supply chain professionals and data analysts
Nicolas style to write books displays his strong academic foundation as well as his vast experience in consulting various companies. The synergy of theory and practical application makes the content lively. The language is supply chain and data analysis specific, but yet simple enough and easy to understand. The book is well structured and the chapters build upon each other. I really enjoy the sub-structure of first being presented with the context, then theory and finally the call-to-action to apply the knowledge either in excel or python - this definitely helped to fortify each chapter's content. Nicolas challenges you to not only "having heard about it" but actually "know how it's done" - of course it's up to you what you choose to do. I work in aerospace and used the content of this book to develop a machine learning forecasting model which uses fleet flight hours, meantime between failures, meantime between unscheduled repairs and known maintenance intervals to forecast the predicted influx of returned parts from the field. This helped better prepare any required inventory to ensure short turnaround times and bring parts back into service as quickly as possible. Using this forecasting model and knowing the accuracy/error of your forecast, you can simply take this parameter as "demand deviation" and optimize your inventory to account for the known fluctuations, while achieving a cost optimal inventory level or target service/fill rate level. If you want to learn more about inventory optimization, I strongly recommend to check out Nicolas' book "Inventory Optimization", which is my personal favorite. To summarize, the book "Data Science for Supply Chain Forecasting" is great resource for any data analyst who wants to increase exposure to simple and advanced forecasting methods - in my opinion this book is useful in many other disciplines than only supply chain, e.g. also in planning, sales, operations, etc.
A**K
Good content, low print quality, very expensive
I am following the author on LinkedIn for more than a year now. I like his posts on demand forecasting. I ordered a paperback book. Although book content is very good, print quality is aweful. It should not cost more than 500 for this print quality. Seller is looting. 4k for this print quality is not acceptable. Content quality: 5 star Print quality: 1 star
W**Z
Great tips, good code, excellent book
As a consultant and as a teacher, I have truly enjoyed reading this book. Apart from the Python code it provides, which is easily understandable even for beginners in Python, Nicholas also gives many interesting points of view about commonly confusing concepts in supply chain. I highly recommend it.
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