There is already a rich literature on the planning of regular shelves. For example, two different products can be positioned next to and behind one another on two-dimensional shelves.Įxamples of Categories Stored on Two-Dimensional Shelves With these shelves, items can be arranged more flexibly in the two-dimensional space, whereas with regular shelves the options are restricted by the shelf levels and their height. Many other retail formats fit into these settings, e.g., products and magazines in kiosks, snacks or electronics in vending machines and display ads (see also Geismar et al. These two-dimensional shelves types are, for example, used for the presentation of fresh food like bakery products and sausages, frozen products or in fashion retailing and consumer electronics. Examples of these shelf types are to be found in Figure 1. Some products are presented on “tilted shelves” (like counters, fridges or tables) onto which customers have a total perspective. Not all retail categories are kept on regular shelves. For example, candies, coffee and tea, canned goods, cleaners, and personal care products are presented on regular shelves. We refer to this shelf type as a “regular shelf” herein. This firstly implies that a customer can only see the facing, and secondly, that two different products can only be positioned next to but not behind one another. Common characteristics of these models are that demand depends on the number of facings (= the foremost unit of an item in the front row of the shelf), and that retail shelves are observed by customers from a frontal direction. This is referred to as “space-elastic demand.” This topic has gained a lot of research attention over recent years (see e.g., Bianchi-Aguiar et al. The more shelf space is allocated to an item, the more it attracts customers and the higher its demand. These decisions are not only based on the margins of the products but also on associated demand and customer preferences. Consequently, planning retail shelves involves the tasks of specifying the product assortments as well determining the space and quantities for selected items. The space available per product is less if broader assortments are offered and vice versa. The selection of items and space allocation of the items to the shelf are interdependent planning problems when shelf space is restricted. In order to achieve superior performance, retailers have to recognize customers’ needs and identify these as key drivers (Eltze et al. Furthermore, customer satisfaction is mostly driven by availability of the right products. The increasing number of items to allocate, the shortage of shelf space, narrow margins in retail and the intensity of competition have greatly magnified the importance of retail assortment and shelf-space planning (cf. Up to 30% more products compete for the limited space than was the case ten years ago (EHI Retail Institute 2014, Hübner et al. e.g., Brown and Tucker 1961, Geismar et al. Shelf space has been referred to as one of the retailer’s scarcest resources (cf. This study considers the problem of selecting, allocating and arranging products on retail shelves. We demonstrate with further simulated data that integration of stochastic demand, substitution, and space elasticity results in up to 80% higher profits. Based on a numerical study with data from one of Germany’s largest retailers, we were able to show within the scope of a case study that our approach can lead to an increase in profits of up to 15%. By comparing it to an exact approach and other benchmarks, we prove its efficiency and demonstrate that results are near-optimal with an average solution quality of above 99% in terms of profit. To solve the model, we implement a specialized heuristic that is based on a genetic algorithm (GA). We contribute to current literature by integrating the assortment decision and accounting for stochastic demand, space elasticity and substitution effects in the setting of such self types. We develop a decision model that optimizes the two-dimensional shelf-space assignment of items to a restricted, tilted shelf. Customers have a total perspective on these shelves and can observe units of one particular item horizontally and vertically instead of just seeing the foremost unit of an item, as is the case of regular shelves. These are categories that are kept on tilted shelves. This study provides a modeling and solution approach for two-dimensional shelves, e.g., for meat, bread, fish, cheese, or clothes. The existing shelf-space literature mainly deals with regular retail shelves onto which customers only have a frontal perspective. Product proliferation and changes in demand require that retailers regularly determine how items should be allocated to retail shelves.
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