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How can quality control be improved? 


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Quality control can be improved by implementing various methods and strategies. One approach is to analyze data and identify errors, then search for the cause of the error and correct it if possible. If the cause cannot be determined, the data can either be ignored or included in the model with increased uncertainty. This reduces the influence of erroneous data on the overall model . Another method is to use Failure Mode and Effect Analysis (FMEA) to identify critical processes and implement In-Process Quality Control (IPQC) to detect and filter defective products. This helps reduce the proportion of defective finished goods and decreases scrap costs . Technological solutions such as measurement digitization, label scanners, and improved management metrics can also be implemented to increase the accuracy and efficiency of inspectors, resulting in significant time and cost savings . Additionally, proper emphasis on quality planning, design, implementation, and improvement can lead to better quality control systems, reduced costs, improved customer satisfaction, and increased productivity and efficiency .

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The paper provides several technological solutions and process improvements to improve quality control, including measurement digitization, label scanners, and improved management metrics. These solutions resulted in significant time and cost savings.
Open accessJournal ArticleDOI
Siwaporn Krasaephol, Parames Chutima 
01 Feb 2018
1 Citations
The quality control process can be improved by implementing inspection gates and In-Process Quality Control (IPQC) at critical processes to filter out defective products. This is done by using the Failure Mode and Effect Analysis (FMEA) method. (No answer found in the paper)
The paper discusses the weaknesses of control-based quality control and suggests that continuous quality control would be ideal. However, it does not provide specific methods for improving quality control.
The paper provides a method for improving quality control in subsurface modeling by analyzing data, detecting and correcting errors, and reducing the influence of data with errors on the model.

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