Revenue Management-Based Pricing And Capacity Allocation Models In BTO Mode

Because products have shorter life cycles and customers pursue variety and personality, successful applications of Build-to-order (BTO) mode have been found in personal computers, farm equipment, furniture and automotive industries. Since manufacturers under BTO mode can not store product inventories to satisfy customizing demands, how to make pricing and capacity allocation policies in order to match demand and supply is a new challenging problem they have to face. Revenue management (RM) has been described as the application of information systems and pricing strategies to allocate the right capacity to the right customer at the right place at the right time. Research on RM theories and methods under BTO mode has important theoretical and practical meanings to the development and richess of RM theory and the assistance for the decision making of manufacturers under BTO mode.Considering the characteristics of BTO mode, pricing and capacity allocation polies based on RM are studied in order to maximize the profit using modeling and simulation. The main contributions are summarized as the following.Firstly, assuming demand is segmented by order lead time, a joint pricing and capacity allocation model is formulated considering scenario-based demand. Due to the stochastic discrete variable in the model, robust optimization is used to solve the model. Numerical examples prove our model and method reflect the stochastic characteristics of demand and provide more pratical pricing and capacity allocation policies than other methods of deterministic and stochastic expectation programming methods especially when the capacity is tight.Secondly, a joint pricing and capacity allocation model is formulated considering bulk demand. Chance constrained programming is used to transform the stochastic model. Numerical examples show our model and method is more near the simulation results than the deterministic programming and bigger learning coefficient leads to higher profit contribution.Thirdly, assuming demand is segmented by product characteristics and different demands occur concurrently, a novel dynamic pricing and capacity allocation policy is developed based on intensity control theory. Numerical simulations show that sophisticated allocation policies are effective only when prices are not optimal.Lastly, a dynamic pricing model for multi products with varying demands is presented based on intensity control theory and the optimal prices are derived from its corresponding deterministic model. Numerical experiments illustrate that a potential benefit is gained from dynamic pricing compared to fixed prices and it tends to increase and level with the number of products increases.Our joint pricing and capacity allocation models could be extended to consider overbooking, cancellation, real demand forecasting, options pricing and etc.