• 수산경영론집
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• 제39권1호
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• pp.17-42
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• 2008

This study tried to build a structure model for the Korean flatfish aquaculture market by a system dynamics approach. A pool of several factors to influence the market structure was built. In addition, several reasonable factors related to the flatfish aquaculture market were selected to construct the causal loop diagram (CLD). Then the related stock/flow diagrams of the causal loop diagrams were constructed. This study had been forecasting a production price and supply, demand, and consumption volume for the flatfish market by a monthly basis, and then made some validation to the forecasting. Finally, four governmental policies such as import, storage, reduction of input, and demand control were tentatively evaluated by the created model. As a result, the facts that the demand control policy is most effective, and import and storage policies are moderately effective were found.

• Ocean and Polar Research
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• 제34권2호
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• pp.185-195
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• 2012

This paper focused on forecasting a short-term production of oysters, which have been farmed in Korea, with distinct periodicity of production by year, and different production level by month. To forecast a short-term oyster production, this paper uses monthly data (260 observations) from January 1990 to August 2011, and also adopts several econometrics methods, such as Multiple Regression Analysis Model (MRAM), Seasonal Autoregressive Integrated Moving Average (SARIMA) Model, and Vector Error Correction Model (VECM). As a result, first, the amount of short-term oyster production forecasted by the multiple regression analysis model was 1,337 ton with prediction error of 246 ton. Secondly, the amount of oyster production of the SARIMA I and II models was forecasted as 12,423 ton and 12,442 ton with prediction error of 11,404 ton and 11,423 ton, respectively. Thirdly, the amount of oyster production based on the VECM was estimated as 10,425 ton with prediction errors of 9,406 ton. In conclusion, based on Theil inequality coefficient criterion, short-term prediction of oyster by the VECM exhibited a better fit than ones by the SARIMA I and II models and Multiple Regression Analysis Model.

• 대한산업공학회지
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• 제27권3호
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• pp.233-238
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• 2001

This paper deals with demand forecasting of parts in an automobile model which has been extinct. It is important to estimate how much inventory of each part in the extinct model should be stocked because production lines of some parts may be replaced by new ones although there is still demands for the model. Furthermore, in some countries, there is a strong regulation that the automobile manufacturing company should provide customers with auto parts for several years whenever they are requested. The major characteristic of automobile parts demand forecasting is that there exists a close correlation between the number of running cars and the demand of each part. In this sense, the total demand of each part in a year is determined by two factors, the total number of running cars in that year and the failure rate of the part. The total number of running cars in year k can be estimated sequentially by the amount of shipped cars and proportion of discarded cars in years 1, 2,$\cdots$, i. However, it is very difficult to estimate the failure rate of each part because available inter-failure time data is not complete. The failure rate is, therefore, determined so as to minimize the mean squared error between the estimated demand and the observed demand of a part in years 1, 2,$\cdots$, i. In this paper, data obtained from a Korean automobile manufacturing company are used to illustrate our model.

• 한국산학기술학회논문지
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• 제14권10호
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• pp.4812-4818
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• 2013

본 논문은 한국의 식용 천일염 수요 및 공급 규모를 예측한 내용을 담고 있다. 2007년 염관리법 규정에 의해 식용으로 허용된 천일염은 그 이전에는 광물로 분류되어있었기 때문에 식용 천일염 관련 별도의 연도별 통계자료가 정비되어 있지 않은 실정이다. 최근 식용 천일염에 대한 소비자 수요증대와 더불어, 산업계에서 시장규모 파악 및 그 성장가능성에 대한 관심이 높다. 이 연구는 식용 천일염 수급 추정을 위한 관련 자료가 제한적인 상황에서 생산을 위한 기후여건, 생산업체 현황, 소비추세, 수출입 동향 등을 고려하여 식용 천일염 수요 및 공급규모를 예측하였다. 연구결과, 2013-2017년 동안 생산량은 222-384천 톤 수준, 수입량은 498-565천 톤, 수출량은 2.67-3.62천 톤, 소비량은 767-996천 톤 수준에 이를 것으로 예측되었다.

• 한국건설관리학회논문집
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• 제14권5호
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• pp.103-112
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• 2013

1990년대 이후 국가경제에서 미치는 영향이 감소 추세에 들어선 건설업은 호황과 불황을 넘나들고 있다. 건설업의 경기변동이 심할수록 경기예측은 어려워지며, 불확실한 예측의 피해는 기업과 건설 종사자들이 직접적으로 받게 되므로 건설경기를 예측하는 것은 매우 어려우면서 중요한 일이다. 본 연구에서는 건설경기를 나타내는 지표 중 하나인 건설업생산지수를 GDP와 기온효과를 이용하여 실질소득과 야외활동이 많은 건설업의 특성에 따라 기온효과를 반영한 공급측면에서의 단기 건설 경기예측 모형을 제시하였다. 분석결과, 건설경기는 뚜렷한 기온효과가 있으며 GDP에도 큰 영향을 받는 것으로 나타났다. 이와 같은 과정을 통해 입증된 건설경기 예측모델을 기반으로 GDP예상증가율 3.5%와 2.4%일 때, 두 가지 시나리오로 2013년도 건설업생산지수를 예측하였다. 본 연구결과는 건설업의 경기를 판단하는 지표 중 하나로 활용 가능할 것이며, 향후 기후변화가 건설업에 미치는 영향에 대한 연구의 초석이 될 것이다.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2002년도 학술발표회 논문집(I)
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• pp.43-50
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• 2002

Accurate quantitative forecasting of rainfall for basins with a short response time is essential to predict streamflow and flash floods. Previously, neural networks were used to develop a Quantitative Precipitation Forecasting (QPF) model that highly improved forecasting skill at specific locations in Pennsylvania, using both Numerical Weather Prediction (NWP) output and rainfall and radiosonde data. The objective of this study was to improve an existing artificial neural network model and incorporate the evolving structure and frequency of intense weather systems in the mid-Atlantic region of the United States for improved flood forecasting. Besides using radiosonde and rainfall data, the model also used the satellite-derived characteristics of storm systems such as tropical cyclones, mesoscale convective complex systems and convective cloud clusters as input. The convective classification and tracking system (CCATS) was used to identify and quantify storm properties such as life time, area, eccentricity, and track. As in standard expert prediction systems, the fundamental structure of the neural network model was learned from the hydroclimatology of the relationships between weather system, rainfall production and streamflow response in the study area. The new Quantitative Flood Forecasting (QFF) model was applied to predict streamflow peaks with lead-times of 18 and 24 hours over a five year period in 4 watersheds on the leeward side of the Appalachian mountains in the mid-Atlantic region. Threat scores consistently above .6 and close to 0.8 ∼ 0.9 were obtained fur 18 hour lead-time forecasts, and skill scores of at least 4％ and up to 6％ were attained for the 24 hour lead-time forecasts. This work demonstrates that multisensor data cast into an expert information system such as neural networks, if built upon scientific understanding of regional hydrometeorology, can lead to significant gains in the forecast skill of extreme rainfall and associated floods. In particular, this study validates our hypothesis that accurate and extended flood forecast lead-times can be attained by taking into consideration the synoptic evolution of atmospheric conditions extracted from the analysis of large-area remotely sensed imagery While physically-based numerical weather prediction and river routing models cannot accurately depict complex natural non-linear processes, and thus have difficulty in simulating extreme events such as heavy rainfall and floods, data-driven approaches should be viewed as a strong alternative in operational hydrology. This is especially more pertinent at a time when the diversity of sensors in satellites and ground-based operational weather monitoring systems provide large volumes of data on a real-time basis.

• 한국해양학회지:바다
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• 제22권3호
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• pp.103-117
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• 2017

이 노트는 국립해양조사원이 5년(2012~2016년)간에 걸쳐 지역해(동해, 황 동중국해) 수치예측시스템을 구축하여 자동으로 끊임없이 운영하면서 확보한 기술들 중 다음 3가지를 담고 있다. (1) 끊임없이 3일 해양예측 자료를 생산하기 위한 전략, (2) 매일 특정시각에 외부 해양 기상자료(HYCOM, NOAA/NCEP GFS)를 안정적으로 내려 받는 방법과 (3) 해양예측시스템 운영자들이 휴대전화 단문 메시지 서비스(Short Message Service)를 이용하여 해양예측시스템 수행 시 발생하는 시스템 오류를 신속하게 파악할 수 있는 기능에 관하여 기술하였다. 이들 기본 기술과 운영시스템 구성의 기본 개념은 지역해와 연안 해양 수치예측시스템을 자동으로 운영하는 체계를 구축하는 데 있어서 유용하게 사용될 것이다.

• 한국산학기술학회논문지
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• 제14권11호
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• pp.5417-5422
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• 2013

본 논문은 MODIS 위성 영상을 이용하여 논벼 생산량을 추정하는 모형의 적합도 개선 및 추정모형내 적절한 설명변수를 탐색하고자 수행되었다. 또한 이 연구는 한국에서 논벼 생산량 조사를 위해 위성 영상을 사용하는 방안을 검토하기 위해 수행되었다. 미국, 호주, 일본 등 많은 선진국들은 재배면적 및 생산량 조사와 같은 농업통계를 산출하기 위해 위성 영상을 이용하고 있다. 그러나 위성 영상을 이용한 작물 생산량 조사의 정확성은 아직 충분치 않은 수준이다. 본 연구는 위성 영상을 이용한 논벼 생산량 조사의 정확도를 증대시키기 위한 몇 가지 방법을 검토하고 있다. 많은 작물 중 논벼를 연구대상으로 선정한 이유는 논벼가 다른 작물 보다 재배면적과 작황의 영상 분석이 용이하였기 때문이고, 다양한 위성 영상 중 MODIS 영상을 이용한 것은 한국 논벼 생산량 조사 연구를 위해 보다 적절한 영상을 다수 포함하고 있었기 때문이다. 이 연구에서 등온선에 의해 구분된 논벼로부터 도출된 NDVI지수, 논벼 등숙기의 일조시간, 강우량, 온도 등 기상변수를 이용하여 단수함수가 추정되었다. 단수함수 추정결과, 모형의 적합도(R-squared)는 0.768-0.891를 보였다. 이 연구는 연평균 등온선에 의해 구분된 NDVI지수와 (등숙기) 기상변수가 단수함수 추정에 매우 유용하게 이용될 수 있음을 보이고 있다.

• 산업경영시스템학회지
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• 제42권1호
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• pp.64-73
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• 2019

The manufacturing companies under Make-To-Order (MTO) production environment face highly variable requirements of the customers. It makes them difficult to establish preemptive production strategy through inventory management and demand forecasting. Therefore, the ability to establish an optimal production schedule that incorporates the various requirements of the customers is emphasized as the key success factor. In this study, we suggest a process of designing the simulation model for establishing production schedule and apply this model to the case of a flat glass processing company. The flat glass manufacturing industry is under MTO production environment. Academic research of flat glass industry is focused on minimizing the waste in the cutting process. In addition, in the practical view, the flat glass manufacturing companies tend to establish the production schedule based on the intuition of production manager and it results in failure of meeting the due date. Based on these findings, the case study aims to present the process of drawing up a production schedule through simulation modeling. The actual data of Korean flat glass processing company were used to make a monthly production schedule. To do this, five scenarios based on dispatching rules are considered and each scenario is evaluated by three key performance indicators for delivery compliance. We used B2MML (Business To Manufacturing Markup Language) schema for integrating manufacturing systems and simulations are carried out by using SIMIO simulation software. The results provide the basis for determining a suitable production schedule from the production manager's perspective.

• 대한산업공학회지
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• 제40권3호
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• pp.283-290
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• 2014

In the flat panel display industry, to meet production target quantities and the deadline of production, the scheduler and dispatching systems are major production management systems which control the order of facility production and the distribution of WIP (Work In Process). Especially the delivery time is a key factor of the dispatching system for the time when a lot can be supplied to the facility. In this paper, we use survival analysis methods to identify main factors of the delivery time and to build the delivery time forecasting model. To select important explanatory variables, the cox proportional hazard model is used to. To make a prediction model, the accelerated failure time (AFT) model was used. Performance comparisons were conducted with two other models, which are the technical statistics model based on transfer history and the linear regression model using same explanatory variables with AFT model. As a result, the mean square error (MSE) criteria, the AFT model decreased by 33.8% compared to the statistics prediction model, decreased by 5.3% compared to the linear regression model. This survival analysis approach is applicable to implementing the delivery time estimator in display manufacturing. And it can contribute to improve the productivity and reliability of production management system.