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Integrated retail decisions with multiple selling periods and customer segments: Optimization and insights
Affiliation:1. Department of Operations & Information Management, Isenberg School of Management, University of Massachusetts Amherst, 121 Presidents Dr., Amherst, MA 01003, USA;2. Engineering Management Program, American University of Beirut, P.O. Box 11-0236, Riad El Solh, Beirut 1107 2020, Lebanon;1. Schulich School of Business, York University, Toronto, Ontario, Canada M3J 1P3;2. InstitutoTecnológico y de EstudiosSuperiores de Monterrey I.T.E.S.M., Monterrey, Nuevo León 64849, Mexico;1. Molde University College, The Norwegian School of Logistics, 6405 Molde, Norway;2. United Institute of Informatics Problems, National Academy of Sciences of Belarus, Surganova 6, 220012 Minsk, Belarus;1. Department of Logistics Management, National Kaohsiung First University of Science and Technology, No.1, University Road, Yanchao District, Kaohsiung City 824, Taiwan;2. Department of Transportation and Communication Management Science, National Cheng Kung University, No. 1, University Road, Tainan 701, Taiwan;1. Ecole Polytechnique Fédérale de Lausanne (EPFL), College of Management of Technology, Chair of Technology & Operations Management, Station 5, 1015 Lausanne, Switzerland;2. IMD, Chemin de Bellerive 23, P.O. Box 915, 1001 Lausanne, Switzerland;3. Université Catholique de Louvain, Louvain School of Management, 151, Chaussée de Binche, 7000 Mons, Belgium
Abstract:Integrating retail decisions on such aspects as assortment, pricing, and inventory greatly improves profitability. We examine a multi-period selling horizon where a retailer jointly optimizes assortment planning, pricing, and inventory decisions for a product line of substitutable products, in a market with multiple customer segments. Focusing on fast-moving retail products, the problem is modeled as a mixed-integer nonlinear program where demand is driven by exogenous consumer reservation prices and endogenous assortment and pricing decisions. A mixed-integer linear reformulation is developed, which enables an exact solution to large problem instances (with up to a hundred products) in manageable times. Empirical evidence is provided in support of a classical deterministic maximum-surplus consumer choice model. Computational results and managerial insights are discussed. We find that the optimal assortment and pricing decisions do not exhibit a simple, intuitive structure that could be analytically characterized, which reflects the usefulness of optimization approaches to numerically identify attractive trade-offs for the decision-maker. We also observe that suboptimal inventory policies significantly decrease profitability, which highlights the importance of integrated decision-making. Finally, we find that the seasonality of consumer preferences and supply costs present an opportunity for boosting the profit via higher inventory levels and wider assortments.
Keywords:Assortment planning  Pricing and inventory  Dynamic lot-sizing  Optimization modeling  Mathematical programming
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