Please cite this article in press as: Murray, Chase C., et al, Joint Optimization of Product Price, Display Orientation and Shelf-Space Allocation in Retail Category Management, Journal of Retailing (xxx, 2010), doi:10.1016/j.jretai.2010.02.008 ARTICLE IN PRESS +Model RETAIL-348; No. of Pages 12 Journal of Retailing xxx (xxx, 2010) xxx–xxx Joint Optimization of Product Price, Display Orientation and Shelf-Space Allocation in Retail Category Management Chase C. Murray a,∗ , Debabrata Talukdar b,1 , Abhijit Gosavi c,2 a Department of Industrial & Systems Engineering, State University of New York at Buffalo, 308A Bell Hall, Buffalo, NY 14260-2050, United States b Department of Marketing, State University of New York at Buffalo, 215 E Jacobs Management Center, Buffalo, NY 14260-4000, United States c Department of Engineering Management & Systems Engineering, Missouri University of Science and Technology, 219 Engineering Management, Rolla, MO 65409-0370, United States Abstract We develop a model that jointly optimizes a retailer’s decisions for product prices, display facing areas, display orientations and shelf-space locations in a product category. Unlike the existing shelf-space allocation models that typically consider only the width of display shelves, our model considers both the width and height of each shelf, allowing products to be stacked. Furthermore, as demand is influenced by each product’s two-dimensional facing area, we consider multiple product orientations that capture three-dimensional product packaging characteristics. That enables our model to not only treat shelf locations as decision variables, but also retailers’ stacking patterns in terms of product display areas and multiple display orientations. Further, unlike the existing studies which consider a retailer’s shelf-space allocation decisions independent of its product pricing decisions, our model allows joint decisions on both and captures cross-product interactions in demand through prices. We show how a branch-and-bound based MINLP algorithm can be used to implement our optimization model in a fast and practical way. © 2010 New York University. Published by Elsevier Inc. All rights reserved. Keywords: Shelf-space allocation; Retail category management; Pricing and revenue management; MINLP model Introduction In this study, we focus on developing marketing decision sup- port systems for strategic category management by consumer packaged goods (CPG) retailers. The CPG retail industry rep- resents an annual market size of about half a trillion dollars in the USA and is a significant part of the household consump- tion expenditure (US Census Bureau 2006). Several mutually reinforcing trends in this industry in recent years have made the issue of efficient shelf-allocation systems as one of the most crit- ical marketing and operational decisions for the CPG retailers (Chen et al. 1999; Hall, Kopalle, and Krishna, this issue; Levy and Weitz 1995; Levy et al. 2004). One such trend is the fact that competition for shelf space in CPG retail stores is at an all time high (Ball 2004). This ∗ Corresponding author. Tel.: +1 716 536 2770. E-mail addresses: cmurray3@buffalo.edu (C.C. Murray), dtalukda@buffalo.edu (D. Talukdar), gosavia@mst.edu (A. Gosavi). 1 Tel.: +1 716 645 3243. 2 Tel.: +1 573 341 4624. tremendous demand for shelf space is driven by the competi- tive need for retailers to introduce new products or categories. Since the 1990s, there has been a significant proliferation in new product items or so-called “store keeping units” (SKUs) in super- markets as both manufacturers and retailers see it as a strategic way for increasing respective market shares (Drèze, Hoch, and Purk 1994; Kurt Salmon Associates 1993). Retailers have also increasingly ventured into new categories (e.g., organic prod- ucts) to satisfy consumer needs (Tarnowski 2007). The average number of different items stocked by a CPG supermarket store had increased by 20 percent between 1970 and 1980, and by 75 percent between 1980 and 1990 (Greenhouse 2005). These new products and categories are putting a huge demand on the available shelf space which is practically fixed for the existing stores. At the same time, the CPG supermarket retailers have seen a steady increase in their operational costs from carrying the aforesaid large assortment of SKUs and as a consequence of competition from lower cost, lower assortment-carrying dis- count retailers (Mullin 2005). According to a report by the Federal Reserve Bank of Philadelphia, conventional supermar- kets accounted for 73 percent of retail grocery sales in 1980. But 0022-4359/$ – see front matter © 2010 New York University. Published by Elsevier Inc. All rights reserved. doi:10.1016/j.jretai.2010.02.008