Achieving Lower Rejection Rates through Statistical Observation and Recording

Background

In the manufacturing sector, particularly in the production, maintaining high quality while minimizing product rejection rates is crucial for profitability and customer satisfaction. One leading manufacturer faced increasing rejection rates in their line, which led to higher costs and customer dissatisfaction. To address this issue, Organization decided to implement a data-driven approach, using statistical observation and recording to identify and mitigate the root causes of product rejections.

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Problem Identification

M/s Alcom Extrution observed a significant increase in rejection rates over the past six months. Rejections were occurring at various stages of the process, leading to inefficiencies and waste. The company aimed to reduce these rejection rates by identifying patterns and anomalies through rigorous data analysis.

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Data Collection and Analysis

1.Initial Data Gathering:

?? -Metrics Recorded: The company collected data on various parameters including defect types, frequency, time of occurrence, operator shifts, machine performance, and material batches.

?? -Tools Used: Tracking systems were installed on the production line to capture data. Additionally, manual records from quality inspection stations were integrated into the data pool.

2.Statistical Observation:

?? -Descriptive Statistics: Descriptive statistics were employed to summarize data, identifying trends and patterns. For instance, the average rejection rate was calculated, and variations across different shifts and machine setups were observed.

?? -Control Charts: Control charts were used to monitor the stability of the production process over time. These charts highlighted periods where the process deviated from its expected performance, signalling potential issues.

3.Advanced Statistical Analysis:

?? -Regression Analysis: Regression analysis helped in understanding the relationship between different variables and rejection rates. The analysis identified that certain machine settings and material batches had a higher correlation with defects.

?? -Pareto Analysis: Pareto charts were utilized to prioritize defects. It was found that a small number of defect types accounted for the majority of rejections, guiding the focus of corrective actions.

4.Root Cause Analysis:

?? -Fishbone Diagram: A fishbone (Ishikawa) diagram was created to visually map out potential causes of defects. Factors such as machine calibration, operator training, material quality, and environmental conditions were evaluated.

?? -Failure Mode and Effects Analysis (FMEA): FMEA was performed to assess the potential impact of different failure modes on the rejection rates. This analysis prioritized issues based on their severity, occurrence, and detectability.

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Findings

The data analysis revealed several key insights:

-Machine Calibration: Machines used in the production line had frequent Variations, leading to inconsistent product quality.

-Material Quality: Certain batches of materials were more defects, suggesting issues with the supplier or material handling.

-Operator Training: Variability in rejection rates correlated with operators’ experience levels, indicating a need for more comprehensive training.

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Actions Taken

Based on the findings, M/s Alcom extrusion implemented the following corrective actions:

1.Machine Maintenance and Monitoring: A more rigorous and regular calibration schedule was introduced. Additionally, automated systems for real-time monitoring of machine performance were put in place.

2.Strengthening on Supplier Quality Control: The company worked closely with suppliers to ensure material quality and introduced stricter quality checks for incoming materials.

3.Enhanced Training Programs: A revised training program was developed to ensure all operators were equipped with the necessary skills and knowledge. Periodic refresher courses were also implemented.

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Results

Within three months of implementing these changes, M/s Alcom Extrusion observed a significant reduction in rejection rates. The rejection rate decreased by 25%, leading to cost savings and improved customer satisfaction. Furthermore, the improved data collection and analysis methods allowed for ongoing monitoring, ensuring that the processes remained within optimal performance parameters.

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Conclusion

This case study demonstrates the power of statistical observation and recording in identifying and mitigating issues leading to high rejection rates in manufacturing. By leveraging data-driven insights, M/s Alcom Extrusion. not only resolved its immediate quality issues but also established a foundation for continuous improvement in its production processes.