How to Improve First Pass Yield with Instrumental

One of the core KPIs for manufacturing leaders and teams is First Pass Yield (FPY). Improving it can save organizations significant costs – and protecting it is paramount for many manufacturing leaders. 

Given this focus, there’s a strong cultural aversion to adding tests or inspections to production lines – even if warranted – because they will inevitably reduce FPY (all testing does… initially). The irony is that testing and inspection are often the only way to actually improve FPY.

This article describes how manufacturing teams implement Instrumental technology to drive FPY improvements without taking an initial hit to FPY. These implementation strategies can be adopted by any organization looking to improve FPY.

Why First Pass Yield is your most important production metric

First Pass Yield is the number of units that pass through all testing on your line on the first try, with no retests, over the total number input at the beginning of the line. 

FPY is one of the most important production metrics. Mark Watson, Manufacturing and Supply Chain Executive, says, “First Pass Yield is the best on-the-line predictor of field quality because it considers workmanship, design for manufacturing (DFM), and process capability.” On a related point, it’s a key predictor of escapes – if you’re relying on human inspectors who are, at best, catching 80% of real issues, you can expect a lot higher escape rate if you have a low FPY. Low FPY causes significant costs due to the churn of units through repair and retesting on the factory floor. 

What is considered a good FPY depends on your volume – but generally, anything less than 80% should be improved. That additional 20% of circulating units will be a massive drain on resources and capital equipment efficiency – and minimizing it will save significant costs.

Instrumental provides real-time FPY for both visual and functional tests via a manufacturing data analytics dashboard, broken down by station and test. Those who don’t use Instrumental should set up regular reporting from the factory on this metric and its trends over time. The cadence of this reporting will be volume and product value dependent. For development build, reporting should be daily at a minimum. For production, it would be reasonable to request reporting for every $50,000-$100,000 of input.

Implementing yield protection without reducing First Pass Yield

Many production teams are measured on FPY, in addition to throughput, so it’s a number they only want to see go up. The only way to improve FPY is to systematically understand where failures are coming from and eliminate those root causes in design or in process. This is possible to do over time – but is generally a laborious and slow process. In the meantime, lots of failures are circulating on the line and costing the organization money.

What if it was possible to get more data directly from the line about these failures that would support systematic failure analysis – including stopping them in real-time – without reducing FPY? There is – and a leading electrification brand leveraged it to improve FPY by 3% points.

Case Study: Electrification leader improved FPY by 3% points

In our work with an electrification leader, their top priorities were quality and efficiency during a large global scale-up. Improving FPY is at the core of both: better FPY will improve field quality as the process will be more stable and we should expect fewer escapes, and better FPY will increase factory efficiency by reducing units circulating through repair and retesting.

The manufacturing leadership decided to implement Instrumental’s manufacturing AI and data platform at several critical assembly steps across every one of their production lines (in five global sites) to collect multiple images of every unit. Aware of the sensitivity around FPY, the leadership instructed that failures intercepted by Instrumental Discover AI and Instrumental’s Monitors should not count towards FPY. Instead, the results were to be used as a tool for the manufacturing team to identify potential areas for upstream improvement. As the manufacturing engineers made improvements, they would then be able to track the delta in downstream FPY and report back to leadership on the gains.

For example, some units had failures caused by poor workmanship – unmated connectors or misrouted wires – which would cause functional failures downstream. The manufacturing team was able to intercept these failures leveraging Instrumental’s technology, quickly identify the operators who needed training, and get them re-trained within the same shift. When amplified across multiple defect types on multiple lines in multiple global sites, this faster feedback loop allowed the team to meaningfully improve downstream failures. On average, across all sites, they improved FPY by 3% points – a huge operational improvement for the business worth millions of dollars of savings.

Improve First Pass Yield to improve field performance

While improving FPY will often have a large financial impact on streamlining factory operations, headcount, and costs – it is likely that it will have an even larger financial impact in the field. Field operations and costs are often separated from “manufacturing costs”, even though their root causes often lie with manufacturing or even engineering. In building value studies for our customers, some of the largest ROI contributors are often from reducing field failures and associated customer satisfaction costs.

To really understand the impact that FPY has on your field performance, start by tying together one set of shared metrics between manufacturing and the field. You can use cohort analysis to understand how upstream improvements in the manufacturing process are impacting downstream field performance and associated costs. Ideally this will provide critical information back to manufacturing leadership to support prioritization and further investments.