Maik Duwensee: The Future of Hardware Integrity & Reliability

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.

Maik’s experience at Cruise marries these two approaches: As a startup, Cruise carries the urgency of Tesla but benefits from General Motors’ long institutional history of reliability testing – as well as access to the whole lifetime of data. While such an approach can cause friction, his team balances doing extensive testing early and tight schedules. Maik says, “Reliability testing can take a couple of weeks, but you can define ‘hey if I’m 60% confident that I have this level of reliability,’ then you can make a risk decision. If I ship it to the field, I know there’s a remaining risk, but I don’t hold the program.” A full picture of the data, including knowing the remaining risk, lets him develop fixes that can be applied on the fly without delaying the schedule.

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. “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 and organized with a thought toward future needs.

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.”