종합물류기업 인증제도는 제3자 물류시장의 활성화와 글로벌 물류전문기업의 육성을 위한 정부의 대표적인 정책 중 하나이다. 본 논문에서는 모수적 기법인 SFA와 비모수적 기법인 DEA를 이용하여 종합물류인증기업의 기술적 효율성을 분석하였다. 실증분석 결과, SFA와 DEA에 의한 평균 기술적 효율성은 각각 50.8%와 42.2%로 나타나 대부분의 기업들이 비효율적으로 운영되고 있어 개선이 필요한 것으로 드러났다. 이러한 비효율성의 주된 원인은 규모의 비효율성보다는 순수한 기술적 비효율성에 기인한 것으로 분석되었다. 특히 SFA와 DEA에 의한 기술적 효율성의 차이에도 불구하고 두 기법에 의한 추정 결과는 통계적으로 유의한 상관관계를 보이고 있어 양자는 상호보완적으로 활용될 수 있음을 확인하였다. 본 연구 결과는 향후 물류산업 관련 정부 정책 방향에 대한 유용한 정보를 제공할 수 있을 것으로 기대된다.
The certification system of integrated-logistics companies is one of the currentrepresentative logistics policies for promoting global specialized logistics companies inan effort to boost the 3PL market. In this paper, the technical efficiency of certifiedintegrated-logistics companies is analyzed by using parametric Stochastic FrontierAnalysis (SFA) and non-parametric Data Envelopment Analysis (DEA) approaches. The empirical results show that the average technical efficiency scores estimatedfrom SFA and DEA are respectively 50.8% and 42.2%, which indicates that manycompanies are inefficient in operation and hence need to be improved. It is alsofound that most of this inefficiency is due to pure technical inefficiency rather thanscale inefficiency. Especially, despite the differences between the technical efficiencyscores estimated with SFA and DEA, the results from the two methods arestatistically significantly and positively correlated, thus opening the way for bothapproaches to be used together in a complementary manner. It is expected that theresults of this study provide an useful information for government policydevelopment concerning logistics industry in the future.
The certification system of integrated-logistics companies is one of the currentrepresentative logistics policies for promoting global specialized logistics companies inan effort to boost the 3PL market. In this paper, the technical efficiency of certifiedintegrated-logistics companies is analyzed by using parametric Stochastic FrontierAnalysis (SFA) and non-parametric Data Envelopment Analysis (DEA) approaches. The empirical results show that the average technical efficiency scores estimatedfrom SFA and DEA are respectively 50.8% and 42.2%, which indicates that manycompanies are inefficient in operation and hence need to be improved. It is alsofound that most of this inefficiency is due to pure technical inefficiency rather thanscale inefficiency. Especially, despite the differences between the technical efficiencyscores estimated with SFA and DEA, the results from the two methods arestatistically significantly and positively correlated, thus opening the way for bothapproaches to be used together in a complementary manner. It is expected that theresults of this study provide an useful information for government policydevelopment concerning logistics industry in the future.
