First pass yield: What it is and how to improve it?

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First Pass Yield: What It Is And How To Improve It?

December 19, 2024

Maintaining the quality of products is a major concern for every manufacturing industry. As the cost associated with poor quality is constantly increasing, decreasing rework and defective products is the primary focus nowadays. To effectively assess quality performance, it is quite important to choose the right Key Performance Indicator (KPI).

First Pass Yield (FPY) stands out as a vital metric for evaluating manufacturing quality. It provides valuable insights into the effectiveness of your production process in delivering quality products consistently.

What is First Pass Yield

First Pass Yield (FPY), also known as Throughput Yield (TY), is a quality metric that measures the percentage of products that meet quality standards without the need for rework or adjustments during the manufacturing process. It indicates the effectiveness of quality control measures in the manufacturing line.

First Pass Yield Formula

The formula for calculating First Pass Yield (FPY) is quite simple.

FPY = (Number of good products produced / Total number of products entering the process) × 100%

Note that good units mean products that meet quality standards and can be shipped directly to customers without adjustments. The total number of products is input into the process regardless of the outcome. The result is multiplied by 100 to express it in percentage.

Identifying Root Causes of Low FPY

Once you calculate your FPY, you will have an idea of your production process efficiency and initial product quality. Generally, first pass yield with value of 90% and over is considered good in manufacturing. If your first pass yield value does not fall in this range, you should look out for strategies to improve it. However, before directly jumping into improving it, look for the causes for low FPY value to ensure you implement the most suitable methodology to improve your FPY. Here are some approaches by which you can identify the root causes of low FPY.

1. Data-driven analysis: Data-driven analysis can be used in modern complex manufacturing process that generate vast amount of data through various sources. Some of the analysis methods are:

Process Capability Studies: Process capability studies evaluate whether a manufacturing process can produce products as per defined limits by comparing the natural variability of a process to its specification limits.
Utilize Cp and Cpk indices to quantify process capability: Cp measures the range of process relative to the specified limits while Cpk measures the distance of the center of the process to the nearest specified limit. Cp or Cpk values of less than 1.33 indicate that the process cannot constantly meet specifications.
Action steps: While using process capability studies to identify the root cause of low FPY, identify the process with the lowest Cp and Cpk. Focus on centering the process for low Cpk but acceptable Cp and address process variations for low Cp.

Control Charts: Control charts plot process data over time to monitor process stability and detect unusual variations.
Implement appropriate control charts based on data type: There are various types of control charts. Use X-bar are R charts for variable data (continuous measurement process data, such as length or pressure) while p-charts or u-charts for attribute data (number of products or characteristics of a product that do not conform to criteria).

Analysis Techniques: While identifying low cause for low FPY, observe numbers above or below control points. Look for runs, trends, and cyclical patterns. Detect special cause variations that may impact FPY.
Pareto Chart: It is a bar graph based on the pareto principle (80/20 rule). It is used to highlight the most significant cause for the effect.
Analysis: Focus on “vital few” causes that account for 80% of the problem. You can use the Pareto chart to directly find out the major causes contributing to low FPY.

2. Root cause analysis techniques: Data-driven analysis techniques are generally used for quantitative data. To analyze qualitative data, you can use these following root cause analysis techniques:

5 Whys: The 5 whys technique involves asking “why” multiple times, with each answer forming the basis for the next question. The question is repeated until the cause is found out for the problem.

Example:
Defect: Misaligned Components
Why? Incorrect assembly
Why? Unclear assembly instructions
Why? Outdated work standards
Why? Lack of regular process review
Why? No standardized update procedure in place

Fishbone Diagram: The Fishbone diagram, also known as the cause-and-effect diagram is a technique used to brainstorm and categorize potential causes for a problem.

Example:

Failure Mode and Effects Analysis (FMEA): FMEA analyzes potential failures of the process along with their severity to affect the process. It can also be automated using Kaizen Copilot which detect the failures of the process after you enter the details of it.

Example: FMEA table for a Printed Circuit Board (PCB) assembly process:

How to Improve FPY

Now, we will explore the methodologies to improve FPY, extracted from case studies and industry-specific research. By examining these evidence-based techniques, you can gain deeper insights into improving you FPY.

1. Implementing Six Sigma for FPY Enhancement

Six Sigma is the most commonly used approach to improve FPY. It follows the DMAIC methodology, which stands for Define, Measure, Analyze, Improve, and Control. A study was conducted to improve the first pass yield of an engine using DMAIC approach. Following approach was used:

Define:

The article clearly identifies the problem as the low FPY in the production of ALH4CT engines, with the initial FPY being around 95%.
The goal is stated as increasing the FPY to over 99%.
A cross-functional team was formed to identify problems and objectives.

Measure:

Data on engine rejection causes was collected over a 6-month period.
Pareto analysis was used that identify the “loss of power” as a major cause contributing to low FPY.

Analyze:

Cause-and-effect (fishbone) diagrams were developed to investigate the root causes of the “loss of power” issue.
The primary root cause was determined to be the wrong flywheel marking due to fixture-related problems.

Improve:

A technical solution was implemented which was modifying the flywheel fixture by adding a sleeve in the dwell pin locator.
This reduced the play between the flywheel and the rotary table, correcting the flywheel marking issue.

Control:

Standard Operating Procedures (SOPs) were developed to maintain the improvements.
Critical To Quality (CTQ) checks were integrated into the process audit checklists.
Control charts were used to monitor the process and detect any deviations.

Statistical Tools and Analysis:

Gage R&R studies were conducted to validate the measurement system’s reliability.
Process capability analysis, using Cp and Cpk indices, was performed to quantify the improvements in the manufacturing process.
Control charts were utilized for ongoing process stability monitoring.

Results and Impact:

After following the approach, the FPY was reduced from 95% to 99%. Engine rejection rate was significantly reduced and the production targets per shift were achieved.

2. Statistical Process Control (SPC) for FPY Improvement

SPC is a quality control method that uses statistical techniques to control and monitor the process. It involves data collection from the process, plotting it on the graph, and analyzing it to identify process variations that affect product quality. Let’s take a look at an example from the automotive industry that uses SPC to improve FPY:

Data Collection:

The researchers collected data on the quantity of products produced, accepted, and rejected for 250/18 and 300/18 inner tube components over a 3-month period.
This allowed them to calculate the initial FPY for the processes.

Statistical Analysis:

Pareto charts were used to identify the most significant defect types, which were identified as splice crack, splice open, and bulging.
This helped to prioritize the areas to focus improvement efforts on.
Fishbone (cause-and-effect) diagrams were constructed to identify the potential causes for the critical defect types.

Action Plan for Improvement:

Based on the root cause analysis, the researchers proposed the following actions:

Implement a poka-yoke (mistake-proofing) system in the splicing machine to eliminate operator errors.
Incorporate a cleaning cycle in the curing machines to address issues like dirty molds and blocked vents.
Improve material handling practices to reduce exposure to foreign contaminants.
Maintain proper steam temperature and pressure during the curing process.

Results:

After implementing the action plan, the researchers observed an increase in the average FPY from 96.35% to 97.25%
This corresponded to a reduction of around 6,000 defective parts per month.

3. Using Lean Tools to Improve FPY:

Lean tools are used to recognize and eliminate waste in the process. When used in combination, they provide a systematic approach to identify waste, analyze root causes, and foster a continuous improvement culture to improve first-pass yield. Following is a case study of a Small Manufacturing Enterprise (SME) that used lean tools to improve FPY:

Achieving Visibility Across the Value Stream Mapping:

Mapped the complete value stream for a key product line (needle valves) to identify the critical points.
Value Stream Mapping helps to identify the area where orders can potentially get held up or defective.

Tackling Root Causes with Data-Driven Analysis:

Used Pareto charts to identify the most significant quality issues impacting FPY.
Conducted fishbone analysis to uncover root causes of poor FPY performance

Fostering a Kaizen Culture:

Formed cross-functional teams to analyze FPY data and develop counter measures.
Tied the FPY measurement system into the ISO 9001 quality management framework.
Empowered employees to participate in problem-solving and process improvement.

Measurable FPY Improvements:

Increased average FPY for needle valve components from 96.35% to 97.25%.
Eliminated approximately 6,000 defective parts per month.
Improved responsiveness by reducing lead times in the sales department.

4. General Tips to Improve FPY:

While methodologies like Six Sigma, statistical process control, or lean tools provide a structured approach to improve FPY, there are some general principles and tips that can be applied to enhance your FPY.

Process Optimization: Optimize your process by simplifying your production process where necessary. Start the optimization by observing and analyzing the process and eliminating unnecessary activities that can cause errors.

Employee Training: Train your employees regarding quality standards and procedures. Encourage a culture of quality awareness among all staff so that they would have a sense of ownership to improve quality.

Improve Material Quality: Work with suppliers to ensure your raw material meets specifications. Implement strict quality checks for all incoming material to guarantee high-quality material.

Visual Management Techniques: Use visual cues and displays to communicate quality standards and current performance properly. This makes it easy for operators to identify and respond to quality issues.

Standardize Procedures: Ensure your production process is consistent as the shift or operator changes. Develop and implement Standard Operating Procedures (SOPs) to ensure process consistency.

Utilize Root Cause Analysis: When defects occur, conduct root cause analysis to determine the cause of these defects. Once detected, implement corrective actions to prevent the recurrence of identified issues.

By implementing these general tips along with structured methodologies, you can create a comprehensive approach to improve FPY.

Improving FPY through Retrocausal

Retrocausal’s Assembly Copilot and Kaizen Copilot significantly enhance first pass yield through their cutting-edge technology and computer vision. The real-time feedback and guidance system of the Assembly Copilot helps workers do the task right the first time, thus preventing errors and improving FPY. Assembly Copilot also collects data for every cycle and classifies it into value-added and non-value-added activities. This data-driven approach helps to identify inefficiencies in the process, enabling targeted improvements that lead to higher FPY.

Moreover, optimizing manufacturing process required extensive data-collection and time studies. Kaizen Copilot makes it easier by automating the time study process, allowing you to focus on improvement projects rather than collecting data. This results in faster adjustments and enhancement in FPY. Additionally, Kaizen Copilot conducts FMEA based on AI technology that analyze your process and suggest possible failures in it along with causes. This aid to conduct root cause analysis to find out cause of low FPY.

By integrating Kaizen and Assembly Copilot, you can improve FPY significantly through real-time guidance, data-driven insights, and continuous improvement methodologies. These AI powered technologies reduce errors and waste, enhancing quality of the products and ultimately increasing FPY.

Conclusion

First pass yield is a significant metric for manufacturing efficiency and product quality. This article explores several methods to improve it, from structured methodologies to general tips. Also, AI-powered solutions like Kaizen Copilot and Assembly Copilot offer new avenues for improvement through real-time guidance and AI-powered insights that can significantly improve FPY. Improving FPY is an ongoing process that requires commitment, continuous monitoring, and adaptation to changing conditions.