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010   $a3-319-13305-5
024 7 $a10.1007/978-3-319-13305-8
035   $a(CKB)3710000000315933
035   $a(EBL)1966795
035   $a(SSID)ssj0001407904
035   $a(PQKBManifestationID)11727544
035   $a(PQKBTitleCode)TC0001407904
035   $a(PQKBWorkID)11411410
035   $a(PQKB)10188029
035   $a(DE-He213)978-3-319-13305-8
035   $a(MiAaPQ)EBC1966795
035   $a(PPN)183153073
035   $a(EXLCZ)993710000000315933
100   $a20141210d2015      u|         0
101 0 $aeng
135   $aur|n|---|||||
181   $ctxt
182   $cc
183   $acr
200 10$aRetail Analytics $eIntegrated Forecasting and Inventory Management for Perishable Products in Retailing /$fby Anna-Lena Sachs
205   $a1st ed. 2015.
210  1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2015.
215   $a1 online resource (126 p.)
225 1 $aLecture Notes in Economics and Mathematical Systems,$x0075-8442 ;$v680
300   $aDescription based upon print version of record.
311   $a3-319-13304-7 
320   $aIncludes bibliographical references.
327   $aIntroduction -- Literature Review -- Safety Stock Planning under Causal Demand Forecasting -- The Data-Driven Newsvendor with Censored Demand Observations -- Data-Driven Order Policies with Censored Demand and Substitution -- Empirical Newsvendor Decisions under a Service Contract -- Conclusions.
330   $aThis book addresses the challenging task of demand forecasting and inventory management in retailing. It analyzes how information from point-of-sale scanner systems can be used to improve inventory decisions, and develops a data-driven approach that integrates demand forecasting and inventory management for perishable products, while taking unobservable lost sales and substitution into account in out-of-stock situations. Using linear programming, a new inventory function that reflects the causal relationship between demand and external factors such as price and weather is proposed. The book subsequently demonstrates the benefits of this new approach in numerical studies that utilize real data collected at a large European retail chain. Furthermore, the book derives an optimal inventory policy for a multi-product setting in which the decision-maker faces an aggregated service level target, and analyzes whether the decision-maker is subject to behavioral biases based on real data for bakery products.
410  0$aLecture Notes in Economics and Mathematical Systems,$x0075-8442 ;$v680
606   $aProduction management
606   $aOperations research
606   $aDecision making
606   $aManagement science
606   $aSales management
606   $aOperations Management$3https://scigraph.springernature.com/ontologies/product-market-codes/519000
606   $aOperations Research/Decision Theory$3https://scigraph.springernature.com/ontologies/product-market-codes/521000
606   $aOperations Research, Management Science$3https://scigraph.springernature.com/ontologies/product-market-codes/M26024
606   $aSales/Distribution$3https://scigraph.springernature.com/ontologies/product-market-codes/524000
615  0$aProduction management.
615  0$aOperations research.
615  0$aDecision making.
615  0$aManagement science.
615  0$aSales management.
615 14$aOperations Management.
615 24$aOperations Research/Decision Theory.
615 24$aOperations Research, Management Science.
615 24$aSales/Distribution.
676   $a330
676   $a519.6
676   $a658.40301
676   $a658.5
676   $a658.81
700   $aSachs$b Anna-Lena$4aut$4http://id.loc.gov/vocabulary/relators/aut$01063546
906   $aBOOK
912   $a9910298493203321
996   $aRetail Analytics$92532949
997   $aUNINA