Quality is perhaps best understood and enforced in high precision component industries such as aerospace and defense, as well as medical device manufacturing. With that said, all manufacturers take into consideration defects, rework, scrap, and their associated costs.
On the forefront of quality metrics, First Pass Yield isa helpful KPI to monitor as it answers a key question for managers:How effective are we at manufacturing quality parts?
第一通过的收益率（FPY），也称为吞吐量，测量产生的质量单位是开始该过程的总单位的百分比。
There’s a very straightforward reason for manufacturers to track and improve FPY: Reducing waste. When scrap parts are created, or units that require re-work, there is waste generated in several forms:
Having an above standard first-pass yield is a competitive advantage. Not only are you using your resources more effectively, but this could lead to several bottom-line benefits:
FPY is by no means a magic bullet. Focusing on it exclusively without factoring in other metrics could prove harmful. This is why it is important to focus on several metrics (or a composite metric such as OEE) when monitoring production performance.
Calculating FPY is rather simple, as it simply divides the number of good parts by the total number of parts that began the process, and accounts for the parts that require rework.
这是最简单形式的公式的样子：
FPY = Quality Units / Total Units Produced
To run through an example. Here is the situation:
所以：
第一次通过屈服= 93 /100 = 93％
Improving throughput yield requires a variety of factors including understanding操作员的性能, equipment, and processes, ensuring the quality supply of material, and having accurate data to track KPIs.
Let’s discuss some of the practical ways you can approach FPY, and boost your quality program.
Process optimization is a key component to ensure quality standards are met on the shop floor. By analyzing existing processes, identifying bottlenecks, and visualizing production data, manufacturing engineers and shop floor managers can develop more efficient processes that can also improve quality.
By observing how quality components are manufactured, supervisors can ensure that processes are well-detailed to include not only instructions (see training below) but also key metrics in regards to production time, such as setup and changeovers, cycle times, job times, etc.
Layering quality data on production metrics will help to identify the most accurate job standards required for producing quality units. This data can be used to ensure expectations are appropriate for the first pass yield an organization is aiming for.
Optimizing job standards是一个MachineMetrics的核心用例。与公关oduction data and quality data (which we touch upon below), job standards can be automatically updated so that existing work standards remain accurate.
Having a solution in place to automatically collect quality data in real-time is incredibly important to establishing quality benchmarks. Manual collection solutions will not only be time-consuming but error-prone, leading to inaccurate quality standards.
With an automated data collection solution, expectations are kept in line and appropriate goals can be both established and measured. MachineMetrics is able to collect both production data from the machine control as well as key contextual data from operators.
This data is standardized and contextualized in real-time, and can even be used to automatically trigger workflows, such as alerting the quality team.
Using tablets at each machine, operators can categorize the reason in which a part does not reach spec. This provides data on the top reasons that parts are rejected and allows for deeper analysis to determine why these defects are occurring and how they can be addressed.
With appropriate changes in place, such as adjusting a process, first pass yield can be improved.
An example of the MachineMetrics Quality Report. With a Pareto chart, it is easy to identify the most common causes of quality issues so that you can begin rolling out process changes to address them.
Collecting machine performance and health data in real-time offers a variety of benefits. Below are just a few that fall under the umbrella of quality.
Machine events and thresholds can be set that automatically notify team members. For example, a downtime event may trigger a text message to the shop floor supervisor, or a part quality issue logged by an operator could immediately notify the maintenance team. This ensures issues are resolved as soon as they happen, and can have an immediate impact on first pass quality yield.
When machine tools fail, they generally produce scrap parts that could have been avoided. Sometimes, the machine will continue to run, producing hundreds of parts that will be scrapped.Tool monitoringis a solution that uses tool data to understand the performance of the tool to prevent quality issues from occurring, or at the very least, minimizing their impacts.
作为一名经理，一眼就能深入了解商店地板，这有助于您在发生问题后立即解决问题。无论是否设置警报，商店地板仪表板都可以轻松指示机器落后于何处，机器何时降低或达到质量标准，从而为您提供商店地板控制every manager hopes for.
Justin Garland, Director of Sales, walks through how you can create a workflow in MachineMetrics to automate quality inspections.
您的运营商是生产的命脉，也是最接近每天从事工作的机器。通过可见性，指导和问责制使他们能够确保个人可以将表现掌握在自己手中。
有了准确的生产数据，可以对流程进行调整和充分的详细信息，以便操作员可以更多地专注于从事生产力工作，而不是做出许多低价值决策。此外，有了准确的工作标准，将达到适当的期望，因此操作员认为目标是可以实现的。
This allows managers to roll out incentive programs as well as enable operators with fully detailed instructions as well as visibility right from the machine, via operator tablets and shop floor dashboards. In real-time, operators will have a view into where they stand against expected production performance.
One of the most basic components of improving first pass yield is ensuring that the material purchased from suppliers meets quality standards upon arrival.
有一个variety of ways to vet and select quality suppliers, including asking for their relevant certifications, ensuring they have experience in your industry, knowing their capacity potential, talking with other customers, asking for a quote, engaging in an inspection of their supply, etc.
If poor quality material comes through the door, first pass quality yield will only become more of a challenge for your team.
MachineMetrics工业数据平台捕获，标准化和上下文化实时生产数据，并在预先构建和自定义报告以及仪表板中传播。
经理，运营商和各种利益相关者跨维护，生产和质量可以立即采取对数据采取行动，或者使用历史报告来深入研究更深入的分析和过程优化策略。
将您的质量计划提升到一个新的水平。学习更多关于how MachineMetrics reduces scrap, orbook a demo with our team today.
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