000724853 000__ 05307cam\a2200529Ii\4500 000724853 001__ 724853 000724853 005__ 20230306140555.0 000724853 006__ m\\\\\o\\d\\\\\\\\ 000724853 007__ cr\cn\nnnunnun 000724853 008__ 141215t20142015sz\a\\\\ob\\\\000\0\eng\d 000724853 019__ $$a908086205 000724853 020__ $$a9783319133058$$qelectronic book 000724853 020__ $$a3319133055$$qelectronic book 000724853 020__ $$z9783319133041 000724853 035__ $$aSP(OCoLC)ocn898125747 000724853 035__ $$aSP(OCoLC)898125747$$z(OCoLC)908086205 000724853 040__ $$aN$T$$beng$$erda$$epn$$cN$T$$dGW5XE$$dN$T$$dYDXCP$$dOCLCF$$dCDX$$dIDEBK$$dEBLCP$$dOCLCO 000724853 049__ $$aISEA 000724853 050_4 $$aHD38.5 000724853 08204 $$a658.5$$223 000724853 1001_ $$aSachs, Anna-Lena,$$eauthor. 000724853 24510 $$aRetail analytics$$h[electronic resource] :$$bintegrated forecasting and inventory management for perishable products in retailing /$$cAnna-Lena Sachs. 000724853 264_1 $$aCham :$$bSpringer,$$c[2014] 000724853 264_4 $$c©2015 000724853 300__ $$a1 online resource (xvii, 111 pages) :$$billustrations. 000724853 336__ $$atext$$btxt$$2rdacontent 000724853 337__ $$acomputer$$bc$$2rdamedia 000724853 338__ $$aonline resource$$bcr$$2rdacarrier 000724853 4901_ $$aLecture Notes in Economics and Mathematical Systems,$$x0075-8442 ;$$v680 000724853 504__ $$aIncludes bibliographical references. 000724853 5050_ $$aAbstract; Acknowledgements; Contents; List of Tables; List of Figures; Acronyms; 1 Introduction; 1.1 Motivation; 1.2 Problem Statement; 1.3 Outline; 2 Literature Review; 2.1 Unobservable Lost Sales; 2.2 Assortment Planning; 2.3 Assortment Planning with Stockout-Based Substitution; 2.4 Stockout-Based Substitution in a Fixed Assortment; 2.5 Joint Pricing and Inventory Planning with Substitution; 2.6 Behavioral Operations Management; 3 Safety Stock Planning Under Causal Demand Forecasting; 3.1 Introduction; 3.2 Safety Stock Basics and Least Squares Estimation; 3.2.1 The Single-Variable Case 000724853 5058_ $$a3.2.2 The Multi-Variable Case3.2.3 Violations of Ordinary Least Squares Assumptions; 3.3 Data-Driven Linear Programming; 3.3.1 The Cost Model; 3.3.2 The Service Level Model; 3.4 Numerical Examples; 3.4.1 Sample Size Effects; 3.4.2 Violations of OLS Assumptions; 3.4.3 Real Data; 3.5 Conclusions; 4 The Data-Driven Newsvendor with CensoredDemand Observations; 4.1 Introduction; 4.2 Related Work; 4.3 Data-Driven Model with Unobservable Lost Sales Estimation; 4.3.1 Cost Model; 4.3.2 Benchmark Approaches; 4.4 Numerical Examples; 4.4.1 The Normal Distribution; 4.4.2 The Negative Binomial Distribution 000724853 5058_ $$a4.4.3 Sample Size Effects4.4.4 Real Data; 4.5 Conclusions; 5 Data-Driven Order Policies with Censored Demand and Substitution in Retailing; 5.1 Motivation; 5.2 Related Work; 5.3 Model; 5.3.1 Data; 5.3.2 Decisions; 5.3.3 Objective Function; 5.3.4 Known Demand with Stockout Observations of One Product; 5.3.5 Censored Demand; 5.4 Numerical Study and Empirical Analysis; 5.4.1 Benchmark to Estimate Arrival Rates and Substitution Probabilities; 5.4.2 Optimal Solution; 5.4.3 Data Generation; 5.5 Results; 5.5.1 Known Demand with Stockout Observations of One Product 000724853 5058_ $$a5.5.2 Censored Demand with Stockout Observations of One Product5.5.3 Censored Demand with Stockout Observations of Both Products; 5.5.4 Real Data; 5.6 Conclusions; 6 Empirical Newsvendor Decisions Under a Service Level Contract; 6.1 Introduction; 6.2 The Setting; 6.2.1 Data Overview; 6.3 Modeling Demand; 6.4 Normative Decision Model; 6.4.1 Product-Specific Service Level; 6.5 Empirical Analysis; 6.5.1 Expected Profit Maximization; 6.5.2 Alternative Decision Models; 6.5.3 Comparison of Alternative Decision Models with the Empirical Retailer; 6.6 Additional Behavioral Aspects of Decision Making 000724853 5058_ $$a6.6.1 Anchoring and Adjustment6.6.2 Minimizing Ex-Post Inventory Error; 6.6.3 Order Adaptation and Demand Chasing; 6.7 Value of Product Characteristics: Managerial Insights; 6.8 Conclusions; 7 Conclusions; 7.1 Summary; 7.2 Limitations and Future Research Directions; Bibliography 000724853 506__ $$aAccess limited to authorized users. 000724853 520__ $$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 s. 000724853 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed December 31, 2014). 000724853 650_0 $$aBusiness logistics$$xManagement. 000724853 650_0 $$aRetail trade$$xManagement. 000724853 650_0 $$aRetail trade$$xForecasting. 000724853 77608 $$iPrint version:$$aSachs, Anna-Lena$$tRetail Analytics : Integrated Forecasting and Inventory Management for Perishable Products in Retailing$$dCham : Springer International Publishing,c2014$$z9783319133041 000724853 830_0 $$aLecture notes in economics and mathematical systems ;$$v680. 000724853 852__ $$bebk 000724853 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-3-319-13305-8$$zOnline Access$$91397441.1 000724853 909CO $$ooai:library.usi.edu:724853$$pGLOBAL_SET 000724853 980__ $$aEBOOK 000724853 980__ $$aBIB 000724853 982__ $$aEbook 000724853 983__ $$aOnline 000724853 994__ $$a92$$bISE