• Journal of Korean Society of Industrial and Systems Engineering
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• v.39 no.1
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• pp.130-139
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• 2016

The purpose of this paper is to analyze the problems and the sources of defective products and draw improvement plans in a small plastic boat manufacturing process using TOC (Theory Of Constraints) and statistical analysis. TOC is a methodology to present a scheme for optimization of production process by finding the CCR (Capacity Constraints Resource) in the organization or the all production process through the concentration improvement activity. In this paper, we found and reformed constraints and bottlenecks in plastic boat manufacturing process in the target company for less defect ratio and production cost by applying DBR (Drum, Buffer, Rope) scheduling. And we set the threshold values for the critical process variables using statistical analysis. The result can be summarized as follows. First, CCRs in inventory control, material mix, and oven setting were found and solutions were suggested by applying DBR method. Second, the logical thinking process was utilized to find core conflict factors and draw solutions. Third, to specify the solution plan, experiment data were statistically analyzed. Data were collected from the daily journal addressing the details of 96 products such as temperature, humidity, duration and temperature of heating process, rotation speed, duration time of cooling, and the temperature of removal process. Basic statistics and logistic regression analysis were conducted with the defection as the dependent variable. Finally, critical values for major processes were proposed based on the analysis. This paper has a practical importance in contribution to the quality level of the target company through theoretical approach, TOC, and statistical analysis. However, limited number of data might depreciate the significance of the analysis and therefore it will be interesting further research direction to specify the significant manufacturing conditions across different products and processes.

• Journal of Korean Society for Quality Management
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• v.33 no.4
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• pp.88-95
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• 2005

Multistage screening is a common practice when a component has a critical effect on the function of the assembly. A defect in a component might incur malfunction of an electronic device, resulting in a great amount of loss. Multistage screening, including duplicated screening inspections, may provide a good solution for this problem when inspection errors are present. In the company studied here, the manufacturing process of the multiple layer chip capacitor includes two-stage screening. In the first stage, screening inspection is performed repeatedly until no defects are found in the lot. In the second stage, sampling inspection is performed by a group of experts prior to shipment. In this article, we review the procedure used in the field and suggest a revised model of the multiple screening procedure and solution method for this situation. The usefulness of the proposed model is discussed through a practical example.

• International Journal of Quality Innovation
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• v.7 no.3
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• pp.58-69
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• 2006

This paper presents neural network-based recognition system for automatic light emitting diode (LED) inspection. The back-propagation neural network (BPNN) is proposed and tested. The current-voltage (I-V) characteristic data of LED from the inspection process is used for the network training and testing. This study selects 300 random samples as network training and employs 100 samples as network testing. The experimental results show that if the classification work is done well, the accuracy of recognition is 100%, and the testing speed of the proposed recognition system is almost one half faster than the traditional inspection system does. The proposed neural-network approach is successfully demonstrated by real data sets and can be effectively developed as a recognition system for a practical application purpose.

• Information Systems Review
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• v.22 no.1
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• pp.113-124
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• 2020

In the operation of farms, it is very important to evaluate the quality of harvested crops and to classify defective products. However, farmers have difficulty coping with the cost and time required for quality assessment due to insufficient capital and manpower. This study thus aims to detect defects by analyzing the epidermis of fruit using deep learning algorithm. We developed a model that can analyze the epidermis by applying YOLOv3 algorithm based on Region Convolutional Neural Network to video images of peach. A total of four classes were selected and trained. Through 97,600 epochs, a high performance detection model was obtained. The crop failure detection model proposed in this study can be used to automate the process of data collection, quality evaluation through analyzed data, and defect detection. In particular, we have developed an analytical model for peach, which is the most vulnerable to external wounds among crops, so it is expected to be applicable to other crops in farming.

• Journal of the Korea Safety Management & Science
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• v.6 no.3
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• pp.197-209
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• 2004

A company wishes to obtain a competitive edge by adopting an efficient logistics information system to control the logistics management process. The elements of the competitive edges a company targets are to maintain good quality, reduce cost and on-time delivery and those competitive edges are closely related to the cycle time of a product. The logistics information system in a company is also closely tied with other business activities than the logistics itself. Therefore it is essential to choose a system that will help rationalize the logistics but also overall business operations along the supply chain from manufacturing to distribution of the products. This paper aims to research and analyze a problem with a logistics information system through case study and to suggest a logistics information system with RFID in place adopted to eliminate problems such as damages, loss and defect rates.

• Journal of Construction Engineering and Project Management
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• v.4 no.2
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• pp.12-22
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• 2014

Over the last decade, Saudi Arabia has experienced significant economic increases as evidenced by the 30% growth in its gross domestic product; furthermore, the construction industry has increased 10% in the same time period. Due to this significant growth, the construction industry is encountering issues related to construction quality resulting in significant waste and associated environmental impacts. In this research, we applied our previously developed framework that integrates three different methods-Lean, Green, and Six-sigma - to a residential construction complex in Saudi Arabia. Our aim of this case study was to explore the application of the framework in practice to glean quantitative results and further validate the framework. In the case study, we used the developed framework to identify a significant issue related to quality and delays - final completion of 53 residential units was delayed because of failed exterior buildings surfaces. We then used the framework to define the causes behind the defects via a field investigation of the 53 units. Of the defect, construction execution was responsible for 43% of the defect, 31% untrained workers, 19% unfavorable construction weather condition, with 7% accounting for other issues. A procedure was developed in concert with the construction manager and overall developer reducing the amount of rework and waste causes revealed by the field examination and the framework. Although two steps were added to the original construction process to overcome waste causes, still save a lot of resources and reduces environmental impact. In summary, we found that the Lean, Green, Six-sigma framework increased productivity, quality, and reduced waste.

• Korean Journal of Construction Engineering and Management
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• v.21 no.1
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• pp.40-49
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• 2020

This study analyzed the factors affecting the ruling of litigation on the housing reconstruction and housing redevelopment project based on the cross analysis and logistic regression analysis. According to cross analysis result, the defensive process prerequisite group was showed significant in many variables which were past legal relation, no ownership or association member status, double lawsuit, abuse of lawsuit right·litigation trust, existence of claim-preclusion, no standing to sue·no standing to be sued, lapse of litigation period, no legal interest, no own defect of approval. On the other hand, the offensive cause of action group was found to have no significant variable. According to logistic regression result, the defensive process prerequisite group was showed significant in many variables which were past legal relation, no standing to sue·no standing to be sued, no ownership or association member status, double lawsuit, no own defect of approval, abuse of lawsuit right·litigation trust, existence of claim-preclusion. Meanwhile, the offensive cause of action group was showed significant in only one variable that was defect in relation with articles of association. Overall, it is noteworthy that the offensive cause group showed very low significant results compared with the defensive process prerequisite group.

• Journal of the Korea Society of Computer and Information
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• v.17 no.6
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• pp.101-109
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• 2012

To develop high quality software, quality control plan is required about fault correction that is latent within software. We should describe fault correction profile properly for this. The tank and pipe model performs complex processes to calculate fault that is remove and escapes. Also, we have to know in which phase the faults were inserted, removed and escaped and know the fault detection rate at any phases. To simplify such complex process, this paper presented model to fault filtering concept. Presented model has advantage that can describe fault more shortly because need not to consider whether was involved in fault that escaped fault is inserted at any step at free step. Also, presented effort estimating model that do fetters in function of fault removal quality and productivity measure and is required in fault detection.

• Information Systems Review
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• v.6 no.2
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• pp.195-208
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• 2004

In this paper we propose a new software project development management model incorporating quality management to schedule management. Though many efficient techniques such as code review and inspection are used to remove defects, the effect of defect removal time on project schedule hasn't been studied much. However, poor quality management has an important effect upon overall schedule and sometimes software projects fails due to it. Thus, quality management and schedule management should be considered together and we need to reflect the time to maintain software quality into the schedule management. For the proposed model we introduced "Quality Value" representing the needed time to remove software defects. We assume PSP/TSP to gather the needed data for quality value. The proposed model can be used to predict the effect of software defects on schedule in advance and to prevent schedule lag.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• 2004.11a
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• pp.366-369
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• 2004

Construction demands will become high. Defects liability is treating with a consumer on a defect. Maintenance bond is overlooking excessive responsibility of construction industry participants. This study classified the problem that are divided to systematic and internal matter. And this study presented an improvement proposal. An improvement proposal of a process may reduce a financial burden of contractors and sub-contractors. And improved rate decision will do additional consideration of the study result, so that it is appropriated a rate rationally.