AHP 분석을 활용한 수입 자동차 리콜부품의 수요 예측 요인에 관한 연구

초록

This study analyzes the priority among factors to consider in predicting the demand for parts for automobile recalls in Korea. The pattern of demand for recall parts depends on the type of recalls. Therefore, this study attempts to analyze the various factors behind recall as the basis of classification of recall types, and their relative priority. Researchers reviewed the prior study and used the Delphi method to seek industry experts’ opinions, and classified the factors for predicting demand for automobile parts into four main categories: properties of target vehicle models, size of recalls, cause of recalls, and external factors. We used the Delphi technique to identify the categories of factors, and applied AHP to analyze the relative priorities of identified factors. The process and the result of the AHP for this study are as follows: among the four main categories, the cause of recalls was found the most important, followed by the size of recalls, external factors, and properties of target vehicle models. Analysis of sub-categories showed the three items under the main category “cause of recalls” ranked the 1st,2nd, and 3rd in terms of importance, on the list of all factors. Fatality is found to be the most critical factor among all sub-category items. Except for the strength of the government’s recall policy, external factors are found less important, along with the sales price of target vehicles, in determining recall demand. Different perception of priorities between dealers and non-dealers was also analyzed. The analysis showed that both dealers and non-dealers find the cause of recalls as the most important, and properties of target models as the least important, which is in line with the overall survey result. However, the two groups apply different weights to the size of recalls, probably because of the difference in what they do. This study concluded that the determinants of recall demand include major reasons for recall, whether the recalled vehicle is a flagship model, the number of recall vehicles, type of recall (voluntary or mandatory), whether competitors are doing a recall, the rate of recall by period, and economic situation. These factors will provide important information on estimating the prediction model for part demand by type of recalls. If recall types are classified and the demand pattern per type is estimated based on the various factors identified through this research, a more accurate model of recall part demand will become possible.

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