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Data Science for Supply Chain Forecasting : Vandeput, Nicolas: desertcart.ae: Books Review: Great book, lots of useful data Review: Well Done!!
| Best Sellers Rank | #135,512 in Books ( See Top 100 in Books ) #74 in Business Planning & Forecasting #81 in Business Information Management #143 in Business Production & Operations |
| Customer reviews | 4.5 4.5 out of 5 stars (94) |
| Dimensions | 24.13 x 1.78 x 16.76 cm |
| Edition | 2nd ed. |
| ISBN-10 | 3110671107 |
| ISBN-13 | 978-3110671100 |
| Item weight | 522 g |
| Language | English |
| Print length | 310 pages |
| Publication date | 22 March 2021 |
| Publisher | De Gruyter |
G**N
Great book, lots of useful data
F**S
Well Done!!
V**Y
A must read for everyone interested in learning machine learning for supply chain
K**N
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
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
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