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The Benefits of Optimizing Prices to Manage Demand in BTO Manufacturing Revenue Management System

Li Li, Rongqiu Chen, Yan Lei

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Abstract

Revenue management has been described as the application of information systems and pricing strategy to allocate the right capacity to the right customer at the right place at the right time. Traditional revenue management is equated with capacity control but there is a growing consensus that the pricing decisions that induce demand cannot be separated from capacity decisions. A joint pricing and allocation stochastic model is proposed and solved based on the analysis of the characteristics of Build-to-order (BTO) manufacturing revenue management. Then the revenue impacts of Price Optimizing and Capacity Rationing are investigated and compared via computer simulations. The results show that even if order sizes are not unit sized, sophisticated allocation policies are effective only when prices are not optimized and FCFS policy is effective when prices are optimal.

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What this paper is about

Revenue management has been described as the application of information systems and pricing strategy to allocate the right capacity to the right customer at the right place at the right time. Traditional revenue management is equated with capacity control but there is a growing consensus that the pricing decisions that induce demand cannot be separated from capacity decisions. A joint pricing and allocation stochastic model is proposed and solved based on the analysis of the characteristics of Build-to-order (BTO) manufacturing revenue management. Then the revenue impacts of Price Optimizing and Capacity Rationing are investigated and compared via computer simulations. The results show that even if order sizes are not unit sized, sophisticated allocation policies are effective only when prices are not optimized and FCFS policy is effective when prices are optimal.

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Available abstract

Revenue management has been described as the application of information systems and pricing strategy to allocate the right capacity to the right customer at the right place at the right time. Traditional revenue management is equated with capacity control but there is a growing consensus that the pricing decisions that induce demand cannot be separated from capacity decisions. A joint pricing and allocation stochastic model is proposed and solved based on the analysis of the characteristics of Build-to-order (BTO) manufacturing revenue management. Then the revenue impacts of Price Optimizing and Capacity Rationing are investigated and compared via computer simulations. The results show that even if order sizes are not unit sized, sophisticated allocation policies are effective only when prices are not optimized and FCFS policy is effective when prices are optimal.

Key concepts: Revenue management, Revenue, Rationing, Order (exchange), Capacity management, Dynamic pricing, Marginal revenue, Demand management

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