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NPI: A How To Guide for Engineers & Their Leaders
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Leading from the Front
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Marcel Tremblay: The Olympic Mindset & Engineering Leadershippopular
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Anurag Gupta: Framework to Accelerate NPI
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Kyle Wiens on Why Design Repairability is Good for Business
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Nathan Ackerman on NPI: Do The Hard Thing First
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JDM Operational Excellence in NPI
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Building the Team
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Quality is Set in Development & Maintained in Production
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3 Lessons from Tesla’s Former NPI Leader
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Maik Duwensee: The Future of Hardware Integrity & Reliability
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Reject Fake NPI Schedules to Ship on Time
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Leadership Guidance for Failure to Meet Exit Criteria
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Screws & Glue: Getting Stuff Done
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Choosing the best CAD software for product design
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Screws vs Glues in Design, Assembly, & Repair
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Best Practices for Glue in Electronics
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A Practical Guide to Magnets
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Inspection 101: Measurements
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OK2Fly Checklists
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Developing Your Reliability Test Suite
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Guide to DOEs (Design of Experiments)
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Ten Chinese phrases for your next build
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NPI Processes & Workflows
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Production: A Primer for Operations, Quality, & Their Leaders
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Leading for Scale
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Proven Strategies for Collaborating with Contract Manufacturers
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Greg Reichow’s Manufacturing Process Performance Quadrants
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8D Problem Solving: Sam Bowen Describes the Power of Stopping
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Cut Costs by Getting Your Engineers in the Field
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Garrett Bastable on Building Your Own Factory
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Oracle Supply Chain Leader Mitigates Risk with Better Relationships
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Brendan Green on Working with Manufacturers
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Surviving Disaster: A Lesson in Quality from Marcy Alstott
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Ship It!
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Production Processes & Workflows
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Failure Analysis Methods for Product Design Engineers: Tools and Techniques
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Thinking Ahead: How to Evaluate New Technologies
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How to Buy Software (for Hardware Leaders who Usually Don’t)
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Adopting AI in the Aerospace and Defense Electronics Space
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Build vs Buy: A Guide to Implementing Smart Manufacturing Technology
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Leonel Leal on How Engineers Should Frame a Business Case for Innovation
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Saw through the Buzzwords
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Managed Cloud vs Self-Hosted Cloud vs On-Premises for Manufacturing Data
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AOI, Smart AOI, & Beyond: Keyence vs Cognex vs Instrumentalpopular
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Visual Inspection AI: AWS Lookout, Landing AI, & Instrumental
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Manual Inspection vs. AI Inspection with Instrumentalpopular
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Electronics Assembly Automation Tipping Points
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CTO of ASUS: Systems Integrators for Manufacturing Automation Don't Scale
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ROI-Driven Business Cases & Realized Value
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Webinars and Live Event Recordings
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Build Better 2024 Sessions On Demand
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Superpowers for Engineers: Leveraging AI to Accelerate NPI | Build Better 2024
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The Motorola Way, the Apple Way, and the Next Way | Build Better 2024
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The Future of Functional Test: Fast, Scalable, Simple | Build Better 2024
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Build Better 2024 Keynote | The Next Way
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Principles for a Modern Manufacturing Technology Stack for Defense | Build Better 2024
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What's Next for America's Critical Supply Chains | Build Better 2024
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Innovating in Refurbishment, Repair, and Remanufacturing | Build Better 2024
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Leading from the Front: The Missing Chapter for Hardware Executives | Build Better 2024
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The Next Way for Reducing NPI Cycles | Build Better 2024
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The State of Hardware 2025: 1,000 Engineers on Trends, Challenges, and Toolsets | Build Better 2024
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Scaling Manufacturing: How Zero-to-One Lessons Unlock New Opportunities in Existing Operations | Build Better 2024
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Design for Instrumental - Simple Design Ideas for Engineers to Get the Most from AI in NPI
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Webinar | Shining Light on the Shadow Factory
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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 best on-the-line predictor of field quality because it considers workmanship, design for manufacturing (DFM), and process capability.
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.
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.
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.