Journal of the Korea Society for Simulation (한국시뮬레이션학회논문지)

The Korea Society for Simulation (한국시뮬레이션학회)
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A Study on ROK Military PBL Using Simulation and Meta Model

시뮬레이션과 메타 모델을 이용한 한국군 성과기반군수 연구

  • Received : 2018.12.26
  • Accepted : 2019.03.25
  • Published : 2019.03.31
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Abstract

The ROK military uses Performance Based Logistics(PBL) as one of the ways to utilize civilian resources and advanced techniques. However, the Korean PBL is mainly focused on purchasing and repairing parts, which is not contributing to the improvement of the availability of overall system. The objective of this study is to suggest the methodology to evaluate the PBL metrics using the simulation and meta model. A meta model is a regression model to analyze the effect of the PBL through simulating various scenarios with performance metrics. As a result, if the PBL is limited to the part level, the performance has little influence on the availability of overall system. In addition, analysis using the meta model shows that it cannot achieve the performance targets when the same metrics are applied to various items without considering the characteristics of the applied items. Therefore, in order to improve availability, PBL coverage should be extended to a system level that includes key components that have a large impact on availability. If multiple items are included in the PBL coverage, the metrics should be applied differently, taking into account the characteristics of each item.

한국군은 민간 자원과 선진 기법을 도입하는 방안의 하나로 PBL을 적용하고 있다. 그러나 한국군의 PBL은 수리부속 구매와 정비를 중심으로 시행하고 있어 PBL 적용 목적인 장비가동률 향상에 기여하지 못하고 있다. 본 연구는 한국군의 PBL이 장비가동률에 미치는 영향 요소를 분석하는 방법론을 제시하고자 한다. 이를 위해 한국군의 PBL 상황을 시뮬레이션 모델로 설계하고, 시뮬레이션 결과를 회귀분석하여 PBL 성과가 장비가동률에 미치는 영향을 분석할 수 있는 메타 모델을 제안한다. 연구 결과 수리부속 수준에만 제한적으로 PBL을 적용하면 성과가 장비가동률에 미치는 영향력은 크지 않았다. 또한, 메타 모델을 이용해 분석해 보면 적용품목 특성을 고려하지 않고 여러 품목에 동일한 성과지표를 설정할 경우 성과목표를 달성할 수 없었다. 따라서 장비가동률을 향상시키기 위해서는 장비가동률에 영향력이 큰 핵심구성품을 포함하는 체계 수준으로 PBL 적용범위를 확대해야 하며, PBL 적용범위에 다수 품목이 포함될 경우 품목별 특성을 고려해 성과지표를 차등 적용해야 한다.

Keywords

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Fig. 1. Spectrum of PBL Strategies

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Fig. 2. Simulation Process of PBL Model

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Fig. 3. Overall Architecture of Simulation Model

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Fig. 4. Data Conversion Process

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Fig. 5. Result of Simulation Tendency Analysis

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Fig. 6. Engine Availability by Metric Decrease

Table 1. PBL Support Integration

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Table 2. Item and Definition of Simulation Model Design

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Table 3. Number of Occurrences by Type of Maintenance

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Table 4. Classification of Maintenance type

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Table 5. Probability Distribution of Maintenance Period

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Table 6. Probability Distribution of Other Time Required

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Table 7. Number of Spare

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Table 8. Maintenance Period for Meta Model Design

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Table 9. Simulation Result of Maintenance Period Scenario

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Table 10. Result of Regression Analysis I

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Table 11. Result of Regression Analysis II

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Table 12. Result of Regression Analysis III

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