Optimal pricing for build-to-order supply chain design under price-dependent stochastic demand |
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Institution: | 1. Division of Transplant Surgery, Weill Cornell Medicine, New York, New York;3. Division of Cardiology, Weill Cornell Medicine, New York, New York;5. Division of Nephrology and Hypertension, Weill Cornell Medicine, New York, New York;2. Division of Cardiology, Albany Medical Center, Albany, New York;4. Division of Cardiology, Duke University School of Medicine, Durham, North Carolina;6. The Rogosin Institute, New York, New York;1. Eindhoven University of Technology, P.O. Box 513, 5600 MB, Eindhoven, The Netherlands;2. Department of Management Science, Lancaster University Management School, Lancaster, LA1 4YX, UK;3. BCAM — Basque Center for Applied Mathematics, Mazarredo 14, 48009, Bilbao (Basque Country), Spain;1. College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China;2. Departamento de Sistemas Informáticos y Computación, Universitat Politecnica de Valencia, Camino de Vera s/n, 46071 Valencia, Spain |
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Abstract: | Build to order (BTO) is a supply chain disruption mitigation strategy. Whereas cost minimization is an operational objective, the goal of the BTO manufacturer is to maximize its profit by using pricing as its competitive decision-making strategy. In this paper, we study a BTO manufacturer who simultaneously determines its product prices and designs its supply chain network to maximize its expected profit under price-dependent stochastic demand. We propose an L-shaped decomposition with complete enumeration to solve for optimality and show that the expanded master problem remains convex programming, although the optimality cuts are quadratic inequalities. The computational results demonstrate that stocking up on differentiated components and allocating modules appropriately to meet realized demand is a resilient policy that sustains variations in demand. Furthermore, the pricing decision balances the expected revenue and expected operating cost with an increase in expected profit. The integration of pricing and operational planning results in a higher expected profit than by individual decisions. We also demonstrate that cost minimization may not provide the same level of profit if the manufacturer overestimates or underestimates its most profitable demand. |
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Keywords: | Pricing Supply chain network design Build-to-order Price-dependent stochastic demand |
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