Intelligent Rework Process Management System under Smart Factory Environment

Da-Seol Jo, Tae-Woong Kim and Jun-Woo Kim
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Da-Seol Jo: Department of Industrial and Management Systems Engineering, Dong-A University, Busan 49315, Korea
Tae-Woong Kim: Department of Industrial and Management Systems Engineering, Dong-A University, Busan 49315, Korea
Jun-Woo Kim: Department of Industrial and Management Systems Engineering, Dong-A University, Busan 49315, Korea

Sustainability, 2020, vol. 12, issue 23, 1-17

Abstract: Rework for defective items is very common in practical shopfloors; however, it generally causes unnecessary energy consumptions and operational costs. In order to address this problem, we propose a novel approach called the intelligent rework process management (i-RPM) system. The proposed system is based on intelligent rework policy, which provides a preventive rework procedure for items with latent defects. Such items can be detected before quality tests by applying conventional classification techniques. Moreover, training sets for the classification algorithms can be collected by using modern information and communications technology (ICT) infrastructures. Items with latent defects are not allowed to proceed to the following processes under intelligent rework policy. Instead, they are returned to the preceding processes for rework in order to avoid unnecessary losses on the shopfloor. Consequently, the proposed system helps to achieve a sustainable manufacturing system. Nevertheless, misclassification by the classification model can degrade the performance of intelligent rework policy. Therefore, the i-RPM system is designed to compare rework policies based on classification accuracy and choose the best one of them. For illustration, we applied the i-RPM system to the rework procedure of a steel manufacturer located in Busan, South Korea, and our experiment results revealed that the cost reduction effect of the intelligent rework policy is affected by several input parameters.

Keywords: rework process; data mining; smart factory; classification; quality forecasting (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
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