000			01894nam a22002177a 4500
999			_c3897 _d3897
005			20221122154727.0
008			221122b ||||| |||| 00| 0 eng d
020			_a9783110671100
082			_a658.70282 _bVAN
100			_aVandeput, Nicolas _99117
245			_aData science for supply chain forecasting
250			_a2nd
260			_bDe Gruyter _aBerlin _c2021
300			_axxviii, 282 p.
365			_aEURO _b44.95
520			_aUsing 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 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.
650			_aPython (Computer program language) _910244
650			_aData mining--Statistical methods _910245
650			_aBusiness forecasting--Data processing _910246
942			_2ddc _cBK