Initial Shipment Decisions for New Products at Zara

Given uncertain popularity of new products by location, fast fashion retailer Zara faces a trade-off. Large initial shipments to stores reduce lost sales in the critical first days of the product life cycle, but maintaining stock at the warehouse allows restocking flexibility once initial sales are observed. In collaboration with Zara, we develop and test a decision support system featuring a data-driven model of forecast updating and a dynamic optimization formulation for allocating limited stock by location over time. A controlled field experiment run worldwide with 34 articles during the 2012 season showed an increase in total average season sales by approximately 2% and a reduction in the number of unsold units at the end of the regular selling season by approximately 4%.

[1]  Felipe Caro,et al.  Clearance Pricing Optimization for a Fast-Fashion Retailer , 2010, Oper. Res..

[2]  Ananth V. Iyer,et al.  Improved Fashion Buying with Bayesian Updates , 1997, Oper. Res..

[3]  Jayashankar M. Swaminathan,et al.  Utilizing Forecast Band Refinement for Capacitated Production Planning , 2000, Manuf. Serv. Oper. Manag..

[4]  Marshall L. Fisher,et al.  Reducing the Cost of Demand Uncertainty Through Accurate Response to Early Sales , 1996, Oper. Res..

[5]  Tong Wang,et al.  A Multiordering Newsvendor Model with Dynamic Forecast Evolution , 2012, Manuf. Serv. Oper. Manag..

[6]  Kumar Rajaram,et al.  Accurate Retail Testing of Fashion Merchandise: Methodology and Application , 2000 .

[7]  P. Jackson,et al.  Risk pooling in a two‐period, two‐echelon inventory stocking and allocation problem , 1989 .

[8]  Kumar Rajaram,et al.  Optimizing Inventory Replenishment of Retail Fashion Products , 2001, Manuf. Serv. Oper. Manag..

[9]  Daniel Adelman,et al.  Relaxations of Weakly Coupled Stochastic Dynamic Programs , 2008, Oper. Res..

[10]  Yale T. Herer,et al.  Matching Supply and Demand: Delayed Two-Phase Distribution at Yedioth Group - Models, Algorithms, and Information Technology , 2014, Interfaces.

[11]  Felipe Caro,et al.  Inventory Management of a Fast-Fashion Retail Network , 2007, Oper. Res..

[12]  Stephen A. Smith,et al.  Retail Supply Chain Management , 2009 .

[13]  W. H. Hausman,et al.  Optimal centralized ordering policies in multi-echelon inventory systems with correlated demands , 1990 .

[14]  Andres Garro,et al.  New product demand forecasting and distribution optimization : a case study at Zara , 2011 .

[15]  Stephen A. Smith,et al.  Clearance Pricing and Inventory Policies for Retail Chains , 1998 .

[16]  Gabriel R. Bitran,et al.  Production Planning of Style Goods with High Setup Costs and Forecast Revisions , 1986, Oper. Res..

[17]  Bala Shetty,et al.  The nonlinear knapsack problem - algorithms and applications , 2002, Eur. J. Oper. Res..

[18]  James E. Ward,et al.  Two-Interval Inventory-Allocation Policies in a One-Warehouse N-Identical-Retailer Distribution System , 1993 .

[19]  Paolo Toth,et al.  Knapsack Problems: Algorithms and Computer Implementations , 1990 .

[20]  M. Fisher,et al.  In-Store Experiments to Determine the Impact of Price on Sales , 2009 .

[21]  Hidetoshi Shimodaira,et al.  Pvclust: an R package for assessing the uncertainty in hierarchical clustering , 2006, Bioinform..

[22]  Stephen A. Smith,et al.  Multi-location Inventory Models for Retail Supply Chain Management , 2008 .

[23]  Ōzalp Ōzer,et al.  Replenishment Strategies for Distribution Systems Under Advance Demand Information , 2003 .

[24]  Silvano Martello,et al.  Heuristic algorithms for the general nonlinear separable knapsack problem , 2011, Comput. Oper. Res..

[25]  James B. Ayers,et al.  Retail Supply Chain Management , 2007 .

[26]  Özalp Özer,et al.  Integrating Replenishment Decisions with Advance Demand Information , 2001, Manag. Sci..

[27]  Narendra Agrawal,et al.  Optimal inventory management for a retail chain with diverse store demands , 2013, Eur. J. Oper. Res..