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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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How to Prepare for Tariffs in 2025: Leaders Share Lessons and Strategies
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Tactics in Failure Analysis : A fireside chat with Dr. Steven Murray
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Maik Duwensee has worked on autonomous sensor reliability at Apple, led the team for Model 3 Manufacturing Noise/Vibration/Harshness (NVH) at Tesla, served as the Head of Sustaining Engineering and Executive Product Bike Lead for Uber, and is now Head of Manufacturing at next-gen battery materials company Sila Nanotechnologies. While navigating from consumer electronics to automotive and onward, he’s become a leading expert in hardware integrity and reliability.
Maik positions his career development as a natural evolution of his background: Growing up in East Germany, “We basically had to take everything apart to keep it going.”
He is renowned for his strategic mindset – guiding design decisions, impacting specification requirements, collaborating on material/process development, and defining and sustaining QA processes & controls.
Maik and I spoke about hardware integrity in autonomous automotive fleets while he was the head of that team at Cruise. Read on for his wisdom on how to think about reliability – and what he thinks will change in the next 5-10 years.
Cruise’s reliability experiment
Attitudes towards reliability at Apple, Tesla, and Cruise
What’s next in reliability
Improving Reliability with a 360° view of the Product
Shifting from consumer electronics to automotive opened new ways of approaching reliability for Maik. He says at Cruise, his team has had to "reconsider the entire concept.” Because Cruise owns its fleet and all its data, his team’s work became an ongoing reliability experiment.
What emerged from this experiment? Maik takes a holistic, 360° view of reliability that includes both the system level and the component level – and tight feedback loops across both. Maik says, “That was the attractive part for me going back into reliability – we have General Motors as a partner to build the base vehicle, and my group has to okay every component that is part of the autonomous vehicle system.” Because of this more streamlined process – and lacking the privacy concerns in consumer electronics manufacturing – Maik and the team could more easily close the loop on what happens on the line and improve on future development, developing extremely good models to predict failures and optimize the fleet.
These models are critical in automotive, where you deal with much smaller sample sizes than consumer electronics. In consumer electronics, you can create large configurations of units to run through reliability testing – but that’s cost-prohibitive for automotive. Instead, Maik and the team do a lot of module testing, but because the numbers are so low they still rely on simulation and institutional knowledge. “There's always a little bit of tea leaf reading left over in the end,” Maik says.
The hardware integrity team at Cruise starts with a global checklist tailored to the product need or point in the development process. The checklist (which covers test engineering, quality, supply chain, and more) lets them confidently determine the remaining risk. The goal isn’t to fully eliminate the risk but to use the program to decide whether or not to move forward.
Reliability and Attitudes Toward Risk at Apple, Tesla, & Cruise
At Apple, Tesla, and then Cruise, Maik experienced three very different approaches to reliability.
“Everyone likes to use the word reliability,” Maik says. At Apple, “if I found an issue, it was taken seriously.” Products are tested at the component and module level under an exhaustive range of conditions. Then, as the product is built up, the same rigor is applied to the system level.
Tesla took a different approach. Maik says, “They had to become the Apple of automotive first,” so the process was more “get it out, and we’ll address it later.” Tesla’s attitude shifted once the Model 3 shipped and began to be reviewed by agencies like J.D. Power and Associates. Reliability and quality were then more important – and that’s easier because there are fewer moving parts.
If I ship it to the field, I know there’s a remaining risk, but I don’t hold the program.
Maik also framed these differences as between the dynamism of electronics and the established processes of the automotive industry. “Automotive is a hundred years older as an industry than consumer electronics, so there are generally specifications that have been established over decades of time.” But because in consumer electronics, the products are new, processes like Design Failure Mode and Effect Analysis mean more.
For Cruise, Maik says, “They have to meet in the middle.”
Data Access and the Future of Reliability
Maik believes the blueprint for the future of reliability testing is providing teams access to the lifetime of data they need to make confident risk decisions. The combination of digital twin technology with digital thread manufacturing helps order and analyze this data. “If I have a field issue on a large scale,” he says, “I want to push a button and get the whole history of everything in the impacted products.”
But that access requires organizations to change their approach to data maintenance and storage – a shift that has already begun for many. Data must be easy to access, organized with a thought toward future needs and well-integrated in a manufacturing analytics system.
It’s a chicken-and-egg problem: Storing data costs money, so it’s hard to justify if you can’t say for certain why you need it – just that you might. While decisions about data management are often left to IT or operations, reliability and quality are important stakeholders in those conversations. The latter teams can often generate the largest returns on investment in data through process and quality improvements that impact the bottom line.
“That is gonna be part of a company’s secret sauce: how it handles data. That will set it apart in crisis mode because [with data] you can quantify the crisis and you can solve the crisis.”