생존분석을 이용한 디스플레이 FAB의 반송시간 예측모형Prediction Model on Delivery Time in Display FAB Using Survival Analysis
- Other Titles
- Prediction Model on Delivery Time in Display FAB Using Survival Analysis
- Authors
- 한바울; 백준걸
- Issue Date
- 2014
- Publisher
- 대한산업공학회
- Keywords
- Delivery Time; Survival Analysis; Cox PH Model; Accelerated Failure Time Model
- Citation
- 대한산업공학회지, v.40, no.3, pp.283 - 290
- Indexed
- KCI
- Journal Title
- 대한산업공학회지
- Volume
- 40
- Number
- 3
- Start Page
- 283
- End Page
- 290
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/99769
- DOI
- 10.7232/JKIIE.2014.40.3.283
- ISSN
- 1225-0988
- Abstract
- 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.
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Collections - College of Engineering > School of Industrial and Management Engineering > 1. Journal Articles
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