Making Use of Data Mining for Presenting Behavior Model of Airline Agencies Ticketing in the supply chain of airline companies. Mohammad Reza Davari, Fariborz Ghahremani, Bahram Kazempour, Behrooz Minaei

Abstract. Every strategic planning requires enough information regarding the subject and the appropriate knowledge of making use of that information. Nowadays the technique of data mining is recognized in the highly developed countries as a principle way for knowledge discovering on the basis of the data accumulated through several years. The need for applying this technique is more remarkable in such companies as airline agencies (because of having low interest and need to be subsidized by the government) than the other ones. The aim of this paper is to extract the existing knowledge as well as the behavior model between an Iranian airline company and the ticketing agencies as its obverse of contracts, using methods of business intelligence, in order to optimizing the relationship between them and producing further and stronger motivations among personals of ticket seller agencies and encourage them to operate tours. The common way of paying commission to the ticketing agencies by the airline companies is to pay a fix percent of the price of the ticket, irrespective of how many tickets each agency has sold. The authors of the present article suggest a new method of paying commission: dispensing a variable coefficient according to the amount of selling the tickets. Interesting to note, in this new method, the sum of commission paid would be finally equal with the whole amount paid in the current method, but the further attempt an agency makes to sell the tickets, the further percent of the price of the ticket it would takes for itself. Considering the variable nature of aforementioned coefficient, this method of paying can be called the dynamic method. Being different in population, number of flight, number of ticketing offices and the sum of tickets sold, cities are primarily divided into four groups by applying the clustering method with the data existing in the system of ticket selling through five recent years. Then, in every group including cities with similar conditions, we will separately obtain to the knowledge of the behavior patterns of agencies in selling tickets according to the index of each agency, five years of selling, twelve months in every solar year, the percent of selling tickets by each agency per month in proportion to whole selling of the same group and the maximum of ticket reservation at every time of selling by each agency per month. Having studied these behavior patterns, we will finally suggest a new dynamic model in the way of paying the commission to the agencies. By comparing the obtained consequences of the knowledge acquired in every different kind of clustering and classification methods 5 , it is possible to prepare an optimum model of knowledge discovering from every sort of these data (which is the same in all countries because of using a same standard frame) for the system of selling ticket of airline companies for countries all over the world.

Keywords. Data Mining, Business Intelligence (BI), Supply Chain Management (SCM), Clustering, Classification.

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