Product proliferation and changes in demand require that retailers regularly determine how items should be allocated to retail shelves. The existing shelf‐space literature mainly deals with regular retail shelves onto which customers only have a frontal perspective. This study provides a modeling and solution approach for two‐dimensional shelves, e.g., for meat, bread, fish, cheese, or clothes. These are categories that are kept on tilted shelves. 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. We develop a decision model that optimizes the two‐dimensional shelf‐space assignment of items to a restricted, tilted shelf. 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. To solve the model, we implement a specialized heuristic that is based on a genetic algorithm (GA). 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. 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%. We demonstrate with further simulated data that integration of stochastic demand, substitution, and space elasticity results in up to 80% higher profits.
Shelf-space optimization models support retailers in making optimal shelf-space decisions. They determine the number of facings for each item included in an assortment. One common characteristic of these models is that they do not account for in-store replenishment processes. However, the two areas of shelf-space planning and in-store replenishment are strongly interrelated. Keeping more shelf stock of an item increases the demand for it due to higher visibility, permits decreased replenishment frequencies and increases inventory holding costs. However, because space is limited, it also requires the reduction of shelf space for other items, which then deplete faster and must be reordered and replenished more often. Furthermore, the possibility of keeping stock of certain items in the backroom instead of the showroom allows for more showroom shelf space for other items, but also generates additional replenishment costs for the items kept in the backroom. The joint optimization of both shelf-space decisions and replenishment processes has not been sufficiently addressed in the existing literature. To quantify the cost associated with the relevant in-store replenishment processes, we conducted a time and motion study for a German grocery retailer. Based on these insights, we propose an optimization model that addresses the mutual dependence of shelf-space decisions and replenishment processes. The model optimizes retail profits by determining the optimum number of facings, the optimum display orientation of items, and the optimum order frequencies, while accounting for space-elasticity effects as well as limited shelf and backroom space. Applying our model to the grocery retailer's canned foods category, we found a profit potential of about 29%. We further apply our model to randomly generated data and show that it can be solved to optimality within very short run times, even for large-scale problem instances.
123Business Research (2017) 10:123-156 DOI 10.1007 replenishment cost on retail profits and solution structures. Based on the insights gained from the application of our model, the grocery retailer has decided to change its current approach to shelf-space decisions and in-store replenishment planning.
Zunehmende Sortimentsbreiten sowie sinkende Flächenproduktivität zwingen Einzelhändler dazu, den verfügbaren Regalplatz optimal zu nutzen. In diese Planung des Regalplatzes für jedes Produkt geht eine Vielzahl von Faktoren ein. Neben Produktmargen ist dies vor allem die Kundennachfrage, deren Kenntnis essentiell für ein optimal gestaltetes Regal ist. Dieser Beitrag führt in das Thema der Regalplatzplanung ein und stellt ein Optimierungsmodell vor, das Einzelhändler bei der Regalplanung unterstützt.
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