Abstract
Retailers must define their assortments and assign shelf space to the items included in these assortments. These two planning problems are mutually dependent if space is scarce. We formulate a model that maximizes a retailer's profit by selecting the optimal assortment and assigning limited shelf space to items. This model is the first decision model to integrate assortment and shelf-space planning by considering stochastic and space-elastic demand, out-of-assortment and out-of-stock substitution effects. To solve the model, we develop a specialized heuristic that efficiently yields near-optimal results, even for large-scale problems. We show that our approach outperforms alternative approaches, e.g. a sequential planning approach that first picks assortments and then assigns shelf space by up to 18%, and a greedy algorithm by up to 16% in terms of profit. We test our model on two real data sets for perishable and non-perishable items and show how it can support retailers in increasing their profits by up to 25%. We then use the model to generalize these results and find that space elasticity and substitution effects have a significant impact on profits, assortment size as well as facing decisions, and that both effects reinforce each other. Using our model, we finally derive rules-of-thumb for planners in practice.
Originalsprache | Englisch |
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Seiten (von - bis) | 302-316 |
Seitenumfang | 15 |
Fachzeitschrift | European Journal of Operational Research |
Jahrgang | 261 |
Ausgabenummer | 1 |
DOIs | |
Publikationsstatus | Veröffentlicht - 16 Aug. 2017 |
Extern publiziert | Ja |