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제조업체에 대한 정부의 환경세 하에서 공급사슬 계약과 공급자 환경혁신 장애요소의 영향
박건수(Kun Soo Park) 한국생산관리학회 2019 韓國生産管理學會誌 Vol.30 No.2
Taxation on pollutants emitted from manufacturers’ production process has been increasingly popular in many governments around the world. Under such an emission tax, manufacturers are motivated to seek emission reduction technology (i.e. environmental innovation) to reduce pollutant emissions from their production processes. Although environmental innovation may be conducted by a manufacturer, in a supply chain, a large portion of such innovation can be conducted by an upstream supplier who provides a key input material to a downstream manufacturer. To stimulate suppliers’ innovation efforts, manufacturers can adopt revenue sharing or quality-dependent contracts with suppliers. Nonetheless, suppliers’ innovation can be discouraged by several obstacles: (i) the innovation project may incur a large fixed cost to initiate or (ii) the outcome of the innovation project is uncertain. In this study, we investigate how obstacles in suppliers’ environmental innovation affect environmental innovation decisions in a supply chain. While these obstacles in innovation in general negatively affect a manufacturers’ profits, we found that the revenue sharing contracts are relatively less sensitive to manufacturers’ profits and suppliers’ environmental innovation compared to the quality-dependent contract. 제조업체가 배출하는 오염물질을 규제하기 위해 많은 국가에서는 배출세를 채택하고 있다. 배출세 규제하에서 기업들은 오염물질의 배출을 감소시키는 신기술 개발(환경혁신)에 투자하여 배출세를 줄이려고 한다. 공급사슬에서 환경혁신은 제 조업체뿐 아니라 원재료나 제조에 활용되는 물질을 공급하는 공급자들 쪽에서도 다수 수행될 수 있다. 공급자의 환경혁신 을 활성화하기 위해 제조업체들은 품질기반계약이나 매출공유계약 등을 고려할 수 있다. 하지만, 공급자의 환경혁신은 높 은 초기투자비와 혁신 결과의 불확실성 등의 장애 요소들 때문에 제한될 수 있다. 본 연구에서는 공급사슬 계약이 공급자 의 환경혁신 투자의 장애 요소들에 미치는 영향을 분석하였다. 연구결과 제조업체가 매출공유계약을 사용하는 경우 품질 기반계약에 비하여 상대적으로 공급자의 환경혁신 투자와 제조업체의 이익이 덜 영향을 받을 수 있는 것으로 나타났다.
데이터기반 재고관리를 위한 DDPG기반 심층강화학습 알고리즘 연구
이병권(Byeongkwon Lee),박건수(Kun Soo Park),정세윤(Se-Youn Jung) 한국SCM학회 2021 한국SCM학회지 Vol.21 No.3
We develop a modified version of deep deterministic policy gradient(DDPG) algorithm, which is one of the most popular deep reinforcement learning algorithms dealing with continuous action spaces, and apply it to the infinite-horizon newsvendor problems with constant lead time. Reflecting the key features of the inventory management problems, our algorithm, named as Inventory based DDPG (IDDPG), is differentiated in the state variables, action spaces, and cost functions compared to the DDPG. By conducting numerical experiments and comparing the performances, we found that when applied to the inventory management problems, IDDPG is able to find the near-optimal solutions and outperforms the DDPG in most cases. We also found that our IDDPG can be applied to a inventory management problem whose optimal solution is not known.
남진모(Jin Mo Nam),박건수(Kun Soo Park) 한국경영과학회 2014 한국경영과학회지 Vol.39 No.2
As productions and deliveries of multiple products to multiple marketplaces have been increasingly popular, supply chain flexibility, which refers to an ability to deal with demand and capacity uncertainty, becomes an important issue in supply chain management. However, logistics costs have been largely neglected in the literature on supply chain flexibility structure. In this paper, we propose mathematical models to investigate the impact of the logistics costs on the optimal flexibility structure. We also conduct a simulation study and observe that logistics costs have a significant impact on the decision on supply chain flexibility structure. Such conclusion is also supported by the case study of a global car manufacturer, Honda Motors.
서용원(Yong Won Seo),이덕희(Duck Hee Lee),정승호(Seung Ho Jung),박건수(Kun Soo Park) 한국IT서비스학회 2015 한국IT서비스학회지 Vol.14 No.2
As interests in the quality of data in database systems are growing recently, analysis and improvement of data quality in databases have been an important issue. However, there has yet to be a clear agreement on how to reasonably calculate the total cost of such project. In this paper, based on real project data and budget statistics, we develop a model to estimate the cost for quality analysis and improvement project of a database. We first conduct statistical analysis to build our basic model. Throughout this analysis, we have identified factors that determine the scale of works required to conduct the project and eventually determine the cost. In addition, we have identified factors that determine the complexity of the project. These factors can adjusts the cost determined by the scale of works. Our model is verified and improved by surveys on experts. We apply our model to newly conducted projects and observe that our model estimates the cost of each project reasonably well.