초록·키워드 목차 오류제보하기 Lead time at the manufacturing site is one of the most important performance indicators to meet the customer"s delivery date or to establish a production schedule plan. Lead time prediction is a very important role in establishing production schedule plans in most manufacturing sites. However, there are many uncertainties in the actual manufacturing system, such as machine failure, product reprocessing, and fluctuations in preparation time and processing time disrupt the prediction of accurate lead time. Therefore, this paper aims to predict and analyze the lead time of hybrid flow-shop with machine failure and reprocessing through a step-by-step experiment. In addition, in order to generate training data in predictive analysis, we intend to build hybrid flow-shop using manufacturing modeling simulation and apply lead time predictive analysis using data generated through it. In addition, we intend to show a predictive model with high accuracy in each situation by comparing and analyzing the variables through a step-by-step experiment in consideration of whether they are selected or reprocessed within the data generated in the simulation. #Artificial Neural Network(ANN) #Hybrid flow shop #Lasso regression #Lead-time prediction #Multiple regression #Regression analysis #Simulation ABSTRACT1. 서론2. 연구 배경3. 문제 정의4. 실험 결과5. 결론 및 추후 연구 과제References

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