Quality control (QC) is a critical part of any manufacturing or production process. By implementing robust quality control methods, companies can dramatically reduce defects, lower costs, improve customer satisfaction, and boost profits. In this article, we will explore how the seven QC tools can be leveraged to make measurable improvements in product quality.
The seven QC tools were developed in Japan beginning in the 1960s and have since become ubiquitous in quality management programs around the world. The tools provide a structured methodology for analyzing quality issues, identifying root causes, and implementing effective solutions. While advanced statistical methods have their place, the seven QC tools remain some of the most accessible and impactful weapons in the quality professional’s arsenal.
In this article, we will walk through a hypothetical scenario where the seven QC tools are used to address quality issues with a high-volume toaster production line. The seven tools we will cover are:
- Flow Chart
- Check Sheet
- Pareto Chart
- Cause and Effect Diagram
- Scatter Diagram
- Histogram
- Control Chart
By properly utilizing these seven tools, our team was able to reduce the toaster defect rate by 25% in just a few weeks. Let’s examine how each tool contributed.
Creating a Flow Chart to Map the Process
The first tool we employed was the flow chart. A flow chart provides a visual representation of the steps in a process from start to finish. Mapping out the exact sequence of steps is crucial for identifying problem areas.
To create the flow chart, our team held a brainstorming session to outline each activity from the arrival of a new work order to the completion of the order. We used Post-It notes to capture the steps before organizing them into a logical flow.
The flow chart gave us an invaluable high-level view of the end-to-end toaster production process. We made an initial goal of reducing defects by 25%. The flow chart also revealed that most defects occurred during final product testing. This critical insight informed our data collection efforts.
Using a Check Sheet to Gather Data
The check sheet is a simple yet powerful tool for gathering data. After mapping the production process, we created a check sheet to quantify the defects occurring during final testing.
Over the course of a week, we collected data on the type and frequency of each defect identified. In total, there were 145 defects that week spread across 8 categories. This vital data exposed the biggest quality challenges and helped us prioritize our improvement efforts.
Creating an effective check sheet requires paying close attention to metadata like dates, times, operators, and machines. Accurate metadata is crucial for drawing meaningful conclusions from the data.
Focusing on Vital Factors with the Pareto Chart
While we identified 8 distinct defect categories on our check sheet, we knew from the Pareto principle that 20% of the causes often create 80% of the problems. The Pareto chart is designed to visually separate the “vital few” causes from the “trivial many”.
When we plotted the defect data into a Pareto chart, it was clear that issues with the control PCB constituted a staggering 40% of all defects. No other category even came close. This allowed us to definitively identify control PCB failure as the root cause to prioritize.
The Pareto chart revealed that by solving the control PCB defect, we could potentially eliminate 40% of total issues, putting our 25% reduction goal within reach.
Performing Root Cause Analysis with a Cause and Effect Diagram
The Pareto chart pointed us to control PCB failure, but we still lacked an understanding of what was causing the PCB issues. This required a deeper root cause analysis using the Cause and Effect Diagram.
Also known as a “fishbone diagram”, this tool provides a structured brainstorming approach to identify potential causes of a problem. We started with control PCB failure as the effect, and then branched out possible causes into major categories like Machine, Methods, Materials, Environment, etc.
This exercise produced a hypothesis that high humidity during the assembly process might be contributing to PCB failures. The next step was to validate this theory.
Using a Scatter Diagram to Assess Cause and Effect
To examine the correlation between humidity and defects, we used a scatter diagram with humidity % on the X-axis and PCB failures on the Y-axis. Each dot on the chart represents humidity and defect data from a specific day.
The scatter diagram revealed a pattern of higher PCB failure rates on high-humidity days, indicating a strong positive correlation. Additional DOE would be required to prove true causation, but for the purposes of this improvement project we decided to move forward with humidity control.
Based on the scatter diagram, we set an upper control limit of 20% humidity to keep defects under our goal of 5 per day.
Understanding Humidity Patterns with a Histogram
Next we needed to characterize the typical humidity levels in the production environment over time. For this we used a histogram to plot the distribution of humidity measurements taken at 6 hour intervals over 6 months.
The histogram allowed us to visualize the full range of humidity levels and frequencies. Most measurements fell between 15-30% humidity, with a mean around 20%. This reinforced that keeping humidity under 20% would represent a significant change.
Understanding the humidity patterns enabled us to quantify the scope of the intervention required.
Monitoring Improvements with a Control Chart
In the final phase of the project, we implemented humidity control measures and utilized a control chart to assess their impact. The control chart tracked defects per day before and after the change was introduced.
For the 3 weeks prior, defect rates fluctuated widely around a mean of 8 per day. Once humidity control began, the average immediately dropped to around 3 per day, meeting our goal. The control chart visually verifies that the process change drove a sustained reduction in defects.
Control charts are invaluable for confirming stability and process capability over time. In this case it proved that addressing the root cause dropped PCB failure rates by over 60%.
Conclusion
While sophisticated quality programs often involve advanced statistical and analytical methods, the foundational seven QC tools continue to deliver immense value. As demonstrated in our toaster defect reduction project, systematically applying the 7 QC tools enabled us to:
- Map the production process with a flow chart
- Quantify problems with check sheets
- Focus on the vital factors with a Pareto chart
- Perform root cause analysis using a cause and effect diagram
- Validate causes with a scatter diagram
- Characterize variations with a histogram
- Monitor improvements with a control chart
By leveraging these simple but statistically powerful instruments, our cross-functional team was able to achieve a 25% reduction in product defects in less than a month. The seven QC tools are indispensable for surfacing quality issues, identifying solutions, and controlling processes, and should be part of every quality program.