Comprehensive Metric Info
First Pass Yield (FPY) KPI in Manufacturing
First Pass Yield (FPY) is a critical Key Performance Indicator (KPI) in manufacturing that measures the percentage of units that are produced correctly the first time through a production process, without requiring any rework, repair, or scrap. It's a direct reflection of process efficiency and quality.
Data Requirements for FPY Calculation
To accurately calculate FPY, you need specific data points collected throughout the manufacturing process. Here's a breakdown:
Specific Fields and Metrics:
- Total Units Started (Input):
The total number of units that enter the production process at the beginning of a defined period (e.g., a shift, a day, a week). This is a crucial starting point.
- Total Units Passed (Output):
The number of units that successfully complete the production process without any defects or need for rework. These are the "good" units.
- Total Units Reworked:
The number of units that required some form of rework to correct defects. This is a key indicator of process issues.
- Total Units Scrapped:
The number of units that were deemed unusable and had to be discarded. This represents a complete loss of resources.
- Process Step Data (Optional but Recommended):
If you want to analyze FPY at specific stages of the process, you'll need data on units started, passed, reworked, and scrapped at each step. This allows for more granular analysis and identification of bottlenecks.
- Time Period:
The specific timeframe for which you are calculating FPY (e.g., shift, day, week, month). This is essential for tracking trends over time.
- Product Type/SKU:
If you manufacture multiple products, you'll need to track FPY for each product separately to identify variations in performance.
Data Sources:
- Manufacturing Execution System (MES):
This is often the primary source for real-time data on production activities, including units started, passed, reworked, and scrapped.
- Enterprise Resource Planning (ERP) System:
ERP systems can provide data on planned production, material usage, and overall production schedules, which can be used to validate MES data.
- Quality Management System (QMS):
QMS data can provide details on defects found, rework performed, and scrap reasons, which can be used to understand the root causes of low FPY.
- Manual Data Entry:
In some cases, data may be manually entered by operators, especially in smaller operations. It's important to ensure the accuracy of this data.
- Sensor Data:
In automated processes, sensor data can provide information on machine performance and potential issues that might affect FPY.
Calculation Methodology
The basic formula for calculating FPY is:
FPY = (Total Units Passed / Total Units Started) * 100%
Here's a step-by-step explanation:
- Gather the Data:
Collect the required data for the chosen time period from your data sources. This includes the total units started and the total units passed.
- Calculate the Ratio:
Divide the total units passed by the total units started.
- Convert to Percentage:
Multiply the result by 100 to express FPY as a percentage.
Example:
Let's say in a shift, you started with 1000 units. After the process, 850 units passed without any issues. 100 units were reworked, and 50 units were scrapped.
FPY = (850 / 1000) * 100% = 85%
This means that 85% of the units were produced correctly the first time through the process.
Application of Analytics Model
An AI-powered analytics platform like 'Analytics Model' can significantly enhance the calculation and analysis of FPY. Here's how:
Real-Time Querying:
Users can use free-text queries to extract FPY data from various sources in real-time. For example, a user could ask, "What is the FPY for product X in the last 24 hours?" The platform would automatically retrieve the necessary data and calculate the FPY.
Automated Insights:
The platform can automatically identify trends and patterns in FPY data. For example, it could detect a sudden drop in FPY and alert users to potential issues. It can also identify which process steps are contributing most to low FPY.
Visualization Capabilities:
Analytics Model can present FPY data in various visual formats, such as charts and graphs. This makes it easier to understand trends and identify areas for improvement. Users can visualize FPY over time, by product, or by process step.
Root Cause Analysis:
By integrating data from different sources (MES, QMS, etc.), the platform can help users perform root cause analysis. For example, if FPY is low, the platform can analyze defect data to identify the most common causes and suggest corrective actions.
Predictive Analytics:
Using machine learning algorithms, the platform can predict future FPY based on historical data and current trends. This allows manufacturers to proactively address potential issues before they impact production.
Business Value of FPY
FPY is a powerful KPI that directly impacts several key business outcomes:
Reduced Costs:
Higher FPY means less rework, less scrap, and lower material costs. This translates to significant cost savings for the manufacturer.
Improved Quality:
A high FPY indicates that the manufacturing process is producing high-quality products consistently. This leads to increased customer satisfaction and reduced warranty claims.
Increased Efficiency:
By identifying and addressing the root causes of low FPY, manufacturers can improve the efficiency of their production processes. This leads to higher throughput and reduced lead times.
Better Decision-Making:
FPY data provides valuable insights that can be used to make informed decisions about process improvements, resource allocation, and quality control measures.
Enhanced Competitiveness:
Manufacturers with high FPY are more competitive in the market due to their ability to produce high-quality products at lower costs.
In conclusion, FPY is a crucial KPI for manufacturing that provides valuable insights into process efficiency and quality. By leveraging an AI-powered analytics platform like 'Analytics Model,' manufacturers can effectively calculate, analyze, and improve their FPY, leading to significant business benefits.