Earticle

초록

한국어

본 연구는 자동차 산업 1차 협력사를 대상으로 DEA-SBM 모델과 EM 군집분석을 활용하여 자동차 공급 사슬의 효율성 분석을 실시하였다. 협력사가 속한 EM 군집의 변동과 효율성의 변동에 대해 시계열 분석을 하였으며, EM 군집 및 업종을 고려한 효율성 분석을 통하여 개선이 필요한 집단을 선별하였다. 효율이 낮은 기업이 효과적으로 개선을 진행할 수 있도록 단계별 벤치마킹 경로를 제시하고 전략적 개선 방향과 함께 최종 개선 목표값을 제시하였다. 기존 선행 연구에서는 협력사가 속한 업종 및 규모에 대한 통합적 고려 없이 전체 협력사를 대상으로 산출된 효율성 점수만으로 개선 대상을 선정하였고, 전체 공급사슬의 경쟁력을 향상하기 위해 개선이 필요한 저 효율 기업에 대해 구체적인 개선 경로 및 방안을 제시하지 못 하였다. 자동차 샤시 부품 군에 속하는 64개 협력사의 3년간 재무 자료와 고객사 평가 점수를 투입변수와 산출변수로 사용하여 실증 연구를 실시한 결과, 규모와 업종에 따른 개선 대상 집단을 효과적으로 선별할 수 있었으며, 단계별 개선 경로를 도식화하여 개선의 방향성에 대해서 명확히 제시할 수 있었다. 협력사의 특성을 통합적으로 고려한 평가 방법과 단계별 구체적인 개선 방안을 제시한 점에서 기존 연구와 차별화되며, 자동차 산업 공급사슬 관리에 실효성 있는 모형으로 공헌할 것으로 판단된다.

영어

The objective of this paper is to present the model for evaluating the supply chain efficiency and benchmarking process of the component suppliers in Korean automotive industry. The proposed method is theoretically based on the combination of the slacks based super efficiency model (DEA-SBM) and the expectation maximization (EM) algorithm clustering analysis. DEA-SBM is one of many data envelopment analysis (DEA) with input and output criteria to evaluate the relative efficiency in order to rank the decision making unit (DMU). EM algorithm makes cluster based on probability considering the maximization of log-likelihood. This method can be differentiated from previous approaches in the literature. This study uses time series data from 2012 to 2014 of 64 tier-one Korean domestic automotive chassis parts suppliers and adopts the DEA-SBM model in order to increase the analysis's accuracy and EM clustering to classify the suppliers by the actual input resources. It also suggests the practical stepwise benchmarking paths to the inefficient firms. In this study, three input factors are selected: number of employees, assets and cost of goods sold (COGS). The output factors include two financial and three non-financial data: sales, operating profit and three kinds of customer’s evaluation scores about suppliers, i.e quality, delivery and technology. Firstly, we conduct the EM clustering to classify the suppliers depending on three input factors, resulting in seven clusters. We analyze the cluster changes of each supplier over time so as to monitor the cause of changes. Then, we calculate the super-efficiency scores of the suppliers using DEA-Solver Pro 9.0 software. We also conduct the analysis about the changes of efficiency scores over time. From this analysis, we apparently recognize which suppliers get better or worse over three years. For the sake of the comprehensive evaluation, we use a hybrid approach, i.e. the efficiency analysis based on the EM cluster as well as on the industrial sectors. Since the value chain is totally different depending on the industrial sectors. The industrial sector is also a key perspective like EM cluster. The best efficient EM cluster was cluster 7 and the worst was cluster 4. On the other hand, the best efficient industrial sector was assembly sector and the worst was fastener sector. From the results of the analysis, we can find out the most inefficient cluster and industrial sectors which need to improve efficiency to increase the overall supply chain performance. In the case study, we selected the fastener sector as the improvement targets. We suggested the stepwise improvement paths. One stage is the efficiency improvement within the same EM cluster, which anticipated fast benchmarking and improvement due to the similarity of the firms' scales. Other stage is the improvement between different EM clusters. To obtain the final improvement targets, we adopt the DEA projection analysis within the selected industrial sector. We illustrate the benchmarking paths as the case of DMU #7 belonging to the fastener sector and EM cluster 5. As we have seen in the case study, this study provides the inefficient firms with a stepwise benchmark approach considering industrial sectors with final improvement target in terms of input and output factors to reach the highest level of efficiency frontier in the same industrial sector. With this proposed method, the buyer, specifically a car manufacturer, can precisely evaluate their suppliers, strategically select the improvement targets, practically determine improving direction and suggest effective benchmarking paths to suppliers.

목차

국문요약
 Ⅰ. 서론
 Ⅱ. 선행 연구
  2.1 효율성 분석 선행 연구
  2.2 DEA-SBM 분석
  2.3 EM 군집분석
 Ⅲ. 자동차 공급사슬 효율성 평가 모형
  3.1 연구 모형
  3.2 투입변수와 산출변수의 선정
 Ⅳ. 실증분석 결과
  4.1 평가 대상 선정 및 자료의 통계적 특성
  4.2 DMU 군집 분류 (EM 군집분석)
  4.3 DMU 군집 변동 분석
  4.4 효율성 점수 산출 (DEA-SBM)
  4.5 효율성 점수 변동 분석
  4.6 군집별 효율성 분석
  4.7 업종별 효율성 분석
  4.8 개선 대상 선정 및 효율성 분석
  4.9 벤치마킹 경로 및 방안
  4.10 최종 개선 지표 제시 (DEA-SBM)
 Ⅴ. 결론
 <참고문헌>
 Abstract

저자

• 박성철 [ Park, Sung-Chul | 한국기술교육대학교 일반대학원 기술경영학과 박사과정 ] 제1저자
• 이장희 [ Lee, Jang-Hee | 한국기술교육대학교 산업경영학부 교수 ] 교신저자

참고문헌