Study On Revenue Management Of Manufacturing Enterprises Based On Customers' Behaviors In Disruption

Disruption usually causes the imbalance between market demand and enterprisecapacity supply. Therefore, how to deal with this imbalance in disruption to acquire abetter profit becomes an important issue of revenue management.As solving the problem of the imbalance between market demand and enterprisecapacity, we employed manufacturing enterprises as research subject, as well as qualitativeand quantitative methods to study the revenue management based on consumers'behaviorsin disruption.Firstly, taking outer disruption, such as SARS and H1N1, for example, we conducteda theoretic and empirical investigation into consumers'behaviors and found that it was thesafety requirement that causing the consumers'changing behaviors. On the basis of thestudy on consumers'behaviors, we constructed a revenue management model using theincreasing capacity as decision variable to address the problem of supply shortage causedby sharp increasing market demand. This model can help manufacturing enterprises todecide the optimization increasing capacity in the condition of outer disruption. Also, theresults from a numerical example showed that the accurate forecast for market demandwas very important to the optimization decision.Secondly, taking the situation of impaired capacity for example, we carried out a theoretic and empirical investigation into consumers'behaviors under the situation ofstock out, finding that the brand recognition and time-sensitivity of consumers were thekey factors influencing the consumers'choices. Based on the study of consumers'behaviors, we constructed the other revenue management model to deal with the problemof supply shortage caused by insufficient capacity. This model can be applied formanufacturing enterprises to decide the best recovery time in the condition of innerdisruption. Besides, the results from a numerical example revealed that the brandrecognition and time-sensitivity of consumers were also very important to the optimizationdecision.