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010   $a3-030-85855-3
024 7 $a10.1007/978-3-030-85855-1
035   $a(MiAaPQ)EBC6839019
035   $a(Au-PeEL)EBL6839019
035   $a(CKB)20275220200041
035   $a(OCoLC)1291316403
035   $a(DE-He213)978-3-030-85855-1
035   $a(PPN)259387665
035   $a(EXLCZ)9920275220200041
100   $a20211221d2022      u|         0
101 0 $aeng
135   $aurcnu||||||||
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aDemand Prediction in Retail $eA Practical Guide to Leverage Data and Predictive Analytics /$fby Maxime C. Cohen, Paul-Emile Gras, Arthur Pentecoste, Renyu Zhang
205   $a1st ed. 2022.
210  1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2022.
215   $a1 online resource (166 pages)
225 1 $aSpringer Series in Supply Chain Management,$x2365-6409 ;$v14
311 08$aPrint version: Cohen, Maxime C. Demand Prediction in Retail Cham : Springer International Publishing AG,c2021 9783030858544 
327   $a1. Introduction -- 2. Data Pre-Processing and Modeling Factors -- 3. Common Demand Prediction Methods -- 4. Tree-Based Methods -- 5. Clustering Techniques -- 6. Evaluation and Visualization -- 7. More Advanced Methods -- 8. Conclusion and Advanced Topics.
330   $aFrom data collection to evaluation and visualization of prediction results, this book provides a comprehensive overview of the process of predicting demand for retailers. Each step is illustrated with the relevant code and implementation details to demystify how historical data can be leveraged to predict future demand. The tools and methods presented can be applied to most retail settings, both online and brick-and-mortar, such as fashion, electronics, groceries, and furniture. This book is intended to help students in business analytics and data scientists better master how to leverage data for predicting demand in retail applications. It can also be used as a guide for supply chain practitioners who are interested in predicting demand. It enables readers to understand how to leverage data to predict future demand, how to clean and pre-process the data to make it suitable for predictive analytics, what the common caveats are in terms of implementation and how to assess prediction accuracy.
410  0$aSpringer Series in Supply Chain Management,$x2365-6409 ;$v14
606   $aSales management
606   $aBusiness logistics
606   $aProduction management
606   $aQuantitative research
606   $aRetail trade
606   $aData mining
606   $aSales and Distribution
606   $aSupply Chain Management
606   $aOperations Management
606   $aData Analysis and Big Data
606   $aTrade and Retail
606   $aData Mining and Knowledge Discovery
615  0$aSales management.
615  0$aBusiness logistics.
615  0$aProduction management.
615  0$aQuantitative research.
615  0$aRetail trade.
615  0$aData mining.
615 14$aSales and Distribution.
615 24$aSupply Chain Management.
615 24$aOperations Management.
615 24$aData Analysis and Big Data.
615 24$aTrade and Retail.
615 24$aData Mining and Knowledge Discovery.
676   $a658.7
700   $aCohen$b Maxime C.$01080231
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
906   $aBOOK
912   $a9910523751603321
996   $aDemand Prediction in Retail$92593131
997   $aUNINA