딥러닝을 이용한 한국의 물류경쟁력에 관한 연구
A Study on the Korea’s Logistics Competitiveness using Deep Learning
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초록
연구목적: 세계은행(WB)에서 발표한 국가별 물류성과지수(LPI)에서 우리나라는 160개국 가운데 2014년 21위, 2016년 24위, 2018년 25위로 지속해서 하락하고 있다. 따라서 본 연구의 목적은 다양한 물류 정책을 추진하고 있음에도 불구하고 LPI 지수가 낮아지는 이유를 국가경쟁력 지수(GCI) 측면에서 분석하여 이를 극복하고 국가 물류경쟁력을 높이는 방안을 모색하고자 하였다. 논문구성/논리: LPI와 GCI를 연계하여 구조화한 후 GCI 측면에서 LPI에 영향을 미치는 가장 중요한 요소를 식별하고자 딥러닝을 이용하여 분석하였다. 또한, 딥러닝 기법은 입력값이 출력값에 미치는 상대적 중요성을 정확하게 해석하기에는 한계가 있다. 이러한 한계를 극복하고자 시나리오 분석을 적용하였다. 결과: 우리나라의 경우 LPI 성과향상을 위해서는 정부규제 부담, 다수 이해당사자 간 협력, 급여와 생산성, 통관절차의 효율성, 초고속 인터넷 가입자 수, 부패지수, 도로연결, 노동세율, 권한위임 의지, 관세의 복잡성, 관세율의 개선이 특히 중요한 것으로 분석되었다. 독창성/가치: 국가적 차원에서 물류경쟁력을 분석하고자 경제 지표와 연계하고, 이러한 상호 연계성을 딥러닝 알고리즘을 이용하여 분석하여 핵심적으로 영향을 미치는 요인을 파악하였다는 데 그 의의가 있다. 특히 우리나라의 경우 경제적 요인뿐만 아니라 부패지수, 사법부의 독립 등의 사회적 요인의 중요성도 국가의 물류성과를 개선하는데 중요한 요소임을 확인할 수 있었다.
Abstract
Purpose: According to the World Logistics Performance Index (LPI) released by the World Bank, Korea ranks 21st in 2014, 24th in 2016 and 25th in 2018 among 160 countries. Therefore, the purpose of this study is to analyze the reason why the LPI index is lowered even though various logistics policies are being pursued. Composition/Logic: In order to identify the most important factors affecting LPI in terms of Global Competitive Index(GCI), we analyzed it by using deep learning algorithm. In fact, the deep learning technique has a limitation in accurately interpreting the relative importance of the input value to the output value. Thus, scenario analysis was applied to overcome these limitations. Findings: For improving LPI performance, it was found that the burden of government regulations, cooperation among multiple stakeholders, salary and productivity, efficiency of customs procedures, high-speed internet subscribers, corruption index, road connection, labor tax rate, willingness of delegation, tariff complexity, tariff rate were the most influential factors in Korea. Originality/Value: It is meaningful that we analyzed logistics competitiveness at national level by linking with economic indicators and analyzing such interlinkages using deep learning algorithm. Especially, in Korea, the importance of social factors such as corruption index and independence of the judiciary as well as economic factors were also found to be important factors for improvement of national logistics performance.
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