Description
| Product ID: | 9783110671100 |
| Product Form: | Paperback / softback |
| Country of Manufacture: | DE |
| Title: | Data Science for Supply Chain Forecasting |
| Authors: | Author: Nicolas Vandeput |
| Page Count: | 310 |
| Subjects: | Economic forecasting, Economic forecasting, Management: leadership and motivation, Quality Assurance (QA) and Total Quality Management (TQM), Knowledge management, Production and quality control management, Sales and marketing management, Management: leadership & motivation, Quality Assurance (QA) & Total Quality Management (TQM), Knowledge management, Production & quality control management, Sales & marketing management |
| Description: | Select Guide Rating Data Science for Supply Chain Forecasting, Second Edition, focuses on data science and machine learning and demonstrates how both are closely interlinked. It contends that a true scientific method that includes experimentation, observation and const 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 statistical "traditional" models, Part II, on machine learning, and the all-new Part III discusses demand forecasting process management. The various chapters focus on both forecast 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, forecasters, and analysts looking to go the extra mile with demand forecasting. |
| Imprint Name: | De Gruyter |
| Publisher Name: | De Gruyter |
| Country of Publication: | GB |
| Publishing Date: | 2021-03-22 |
