A Model for Determining Retail Product Category Assortment and Shelf Space Allocation

Abstract

We develop a category management model to aid retailers in the space constrained decisions of which products to stock (assortment) and how much shelf space to allocate to those products. The model is formulated as a constrained optimization problem with two basic decision variables: assortment and allocation of space to the items in the assortment. The non-linearities in the objective function and the zero-one decision variables disallow a closed form solution. We develop a heuristic solution procedure based on simulated annealing and test it on a problem with a known optimum. We also apply the technique to a larger problem without a known optimum. Finally, the solution found by simulated annealing is compared against a solution produced using a shelf allocation rule based on share of sales.

Interaction Between Shelf Layout and Marketing Effectiveness and Its Impact on Optimizing Shelf Arrangements

“…For example, in most categories, products perform best when placed at eye level. Borin et al (1994) develop a category management model formulated as a constrained optimization problem, with assortment and allocation of space as the decision variables. The model parameters are based on judgmental estimates, that is, they are not based on an econometric model.…”

Heuristic, meta-heuristic and hyper-heuristic approaches for fresh produce inventory control and shelf space allocation

“…Furthermore, when we consider a range of goods rather than single items, the shelf space allocation among different items is especially important. The importance of shelf space allocation for non-perishable merchandise is underlined in several previous studies (Kotzan and Evanson, 1969;Curhan, 1972;Borin et al, 1994;Urban, 1998;Yang andChen, 1999, Bai and. This research is supported by the UK Engineering and Physical Sciences Research Council (EPSRC) and a leading UK retailer (Tesco).…”

Private labels and retail assortment planning: a differential evolution approach

“…Shelf space planning considers facing and replenishment decisions (see e.g., Corstjens and Doyle, 1981), while assortment planning considers the question of which and how many different products to offer (Mantrala et al, 2009). In the past two decades, numerous models and analytical solutions have been proposed to deal with both areas of research (e.g., Anderson and Amato, 1974;Borin and Farris, 1995;Borin et al, 1994;Brijs et al, 2000;Brijs et al, 1999;Bultez and Naert, 1988;Bultez et al, 1989;Corstjens and Doyle, 1981;Corstjens and Doyle, 1983;Fadılog lu et al, 2010;Hansen and Heinsbroek, 1979;Russell and Urban, 2010;Urban, 1998;Yang, 2001). In the shelf space planning literature, researchers traditionally apply the individual space elasticity and crosselasticity between products to determine which products to stock and how much shelf space to display these products, whereas, the main body of literature on assortment planning models is based on the estimation of substitution effects and develops optimization algorithms to define inventory levels by stochastic demand.…”

“…As a result, retail assortment studies have so far introduced various heuristic techniques for consumer-driven substitution, which have been employed to solve such complex formulations. For example, Urban (1998) introduced Genetic Algorithms, Borin et al (1994) and Bai and Kendall (2005) implemented Simulated Annealing, while Smith and Agrawal (2000) addressed the problem using Lagrange relaxation. In these studies, the assortment planning problem is usually formulated with the use of mixed integer nonlinear objective functions in an attempt to maximize the expected product profit.…”