LEADER 03772nam 2200481 450 001 9910554221303321 005 20230630000512.0 010 $a3-11-067112-3 024 7 $a10.1515/9783110671124 035 $a(CKB)4100000011775379 035 $a(DE-B1597)534678 035 $a(OCoLC)1243310393 035 $a(DE-B1597)9783110671124 035 $a(MiAaPQ)EBC6526649 035 $a(Au-PeEL)EBL6526649 035 $a(OCoLC)1242873230 035 $a(EXLCZ)994100000011775379 100 $a20211010d2021 uy 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aData science for supply chain forecasting /$fNicolas Vandeput 205 $a2nd ed. 210 1$aBerlin, Germany :$cWalter de Gruyter GmbH,$d[2021] 210 4$dİ2021 215 $a1 online resource (XXVIII, 282 p.) 311 $a3-11-067110-7 320 $aIncludes bibliographical references and index. 327 $tFrontmatter -- $tAcknowledgments -- $tAbout the Author -- $tForeword ? Second Edition -- $tForeword ? First Edition -- $tContents -- $tIntroduction -- $tPart I: Statistical Forecasting -- $t1 Moving Average -- $t2 Forecast KPI -- $t3 Exponential Smoothing -- $t4 Underfitting -- $t5 Double Exponential Smoothing -- $t6 Model Optimization -- $t7 Double Smoothing with Damped Trend -- $t8 Overfitting -- $t9 Triple Exponential Smoothing -- $t10 Outliers -- $t11 Triple Additive Exponential Smoothing -- $tPart II: Machine Learning -- $t12 Machine Learning -- $t13 Tree -- $t14 Parameter Optimization -- $t15 Forest -- $t16 Feature Importance -- $t17 Extremely Randomized Trees -- $t18 Feature Optimization #1 -- $t19 Adaptive Boosting -- $t20 Demand Drivers and Leading Indicators -- $t21 Extreme Gradient Boosting -- $t22 Categorical Features -- $t23 Clustering -- $t24 Feature Optimization #2 -- $t25 Neural Networks -- $tPart III: Data-Driven Forecasting Process Management -- $t26 Judgmental Forecasts -- $t27 Forecast Value Added -- $tNow It?s Your Turn! -- $tA Python -- $tBibliography -- $tGlossary -- $tIndex 330 $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 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. 606 $aPython (Computer program language) 615 0$aPython (Computer program language) 676 $a330 700 $aVandeput$b Nicolas$01218937 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910554221303321 996 $aData science for supply chain forecasting$92818771 997 $aUNINA