Thesis Archive
Strategic Dynamic Pricing In The Presence of Duopolistic Competition (2008)
Guico
Ngan
Roque
Abstract:
-There are numerous researches conducted in the application of price-based revenue management, more specifically dynamic pricing, for different types of industries. Examples of these industries are the airlines, hotels and retail industries. As opposed to the abovementioned, this paper presents a new, uncovered application of dynamic pricing for a given service system. Consider a general leisure service company with a fixed amount of capacity allocated over a finite selling horizon, subject to seasonality or the peaking of demand with respect to a general time period. Prices are set on a per unit bases as with a movie screening in a cinema, hours in an internet café, and so on. Included in the system being considered is the presence of the firm’s competitor, whose service is similar in every aspect. In this light, the market considered is that of a duopoly. Customers in a population pool have three choices during each time period: they may buy from the firm, buy from the competitor, or they may refuse to buy from either of the establishments. Those who opt for the last choice may transfer from one establishment to the other or they may balk from the market. However, once a customer has availed of the service, there exists a period of time wherein he exhausts this and eventually returns to the customer pool. Given these characteristics, the applications of this particular service system being described includes movie theaters, internet cafes, spa and parlor services, sports centers, equipment rentals, and so on. The main objective of this study is to develop a simulation model which considers an application of the concept of revenue management dynamic pricing by comparing the revenue to be generated across different pricing scenarios, at the same time considering the presence of a competitor. This can be achieved through the use of agent-based simulation to model the system. The validation of the model was carried out in four stages. The developed model was validated through the detailed evaluation of the ff: model structure, model logic, model design or input data and the model response. The model also was assessed through the use of the sensitivity analysis. All of which proved that the model was indeed valid and was able to simulate the service system being studied. In order to assess how different conditions and pricing strategies affect the firm and competitor revenue and several other response variables, the Design of Experiments was utilized. Different scenarios were employed to be able to study the various interactions and effect of parameters of the model with each other and to the responses generated by the model. These responses refer to the revenue of both firms and the number of buyers, transfers and balkers for both firms. The ANOVA Analysis showed that interactions significant to the responses are that of capacity and the scenarios, and price deviation and the scenarios. In addition, the results also show that the factors which are significant to the mean revenue for both establishments, the difference between the two values, and the standard deviation for the firm revenue are capacity, price deviation, and the different scenarios. Lastly, the optimal strategies were classified into two: optimal strategy when there is little freedom over price changes and when there is more freedom to change the price. The optimal strategies established by the simulation suggests the firm to price (1) less than the competitor during peak and off-peak periods, (2) equal to the competitor during peak and off-peak periods, (3) greater during peak and equal to the competitor during off-peak, (4) equal to the competitor during peak and less during off-peak, or (5) greater during peak and less during off-peak. In conclusion, the study showed which pricing policies the firm must set, depending on its own company objectives – whether it be to maximize its own revenue, to maximize its revenue over its competitor’s, to maximize its number of buyer, or to minimize the number of customers who transfer or balk. In addition, the study also shows which pricing policies it must not implement under any condition.
Adviser:
Mr. Bryan Gobaco
[email protected]