Before he ever walked a production line in Beijing, Martin Hess Pedersen spent nearly twenty years inside Nokia writing and leading software. Then a simple obsession changed his path: what are our consumers actually telling us, and how fast can we learn from them? That question carried him from software quality into the full stack of hardware, testing, and manufacturing, and eventually into a 12-year stint in Asia.
This story is for leaders who want to transform messy signals from factory data, field feedback, and human judgment into repeatable excellence grounded in trust.
Martin Hess Pedersen on the Great Wall of China.
Nokia’s Foundation for Data and Trust
When Martin Hess Pedersen talks about his years at Nokia, there’s no hesitation in his admiration.
“If Nokia were still alive and thriving today,” he says with a smile, “I’d probably still be there.”
Those were the years that shaped him, two decades spent at the heart of one of the most admired engineering organizations in the world. He began in software, leading teams and managing development programs, but his curiosity quickly grew beyond code. He wanted to understand how products lived once they left the lab. What did consumers experience? What could their feedback teach engineers about design, quality, and manufacturing?
That question led him into the factory. “We built five million phones a month out of a single plant,” Martin recalls. “The supply chain was extraordinary, world-class in efficiency and precision.”
At Nokia, the concept of data-driven quality was ingrained in the company's culture. Even in the early 2010s, “we’d combine component and finished-goods data, track every KPI, and move quickly to the right part of the supply chain the moment something started to drift.”
Nokia’s structure — owning most of its factories and keeping suppliers close — created a culture of transparency.
“When you own both the process and the data,” Martin says, “trust is the default.”
Across Cultures: Microsoft and Foxconn
After nearly twenty years at Nokia, the next chapters of his career would expose Martin to entirely different cultures of hardware engineering and manufacturing execution.
When Microsoft acquired Nokia in 2014, Martin continued to lead manufacturing quality. If “Nokia’s Way” was data-driven quality and world-class efficiency, he describes “Microsoft’s Way” as deep specialization and process discipline.
“It was a huge learning curve,” he remembers. “Nokia was flat and fast; Microsoft was structured and siloed. Neither was wrong — they just moved differently.”
In 2016, still in Beijing, Martin joined Foxconn when the company purchased Nokia’s mobile assets and sought to relaunch a licensed Nokia brand under its umbrella. Martin initially hesitated. “Foxconn’s culture was unlike anything I’d experienced — very hierarchical, very top-down,” he says. “But I also saw the potential to blend what I’d learned at Nokia about quality and trust into a new system built on a massive scale.”
While at Foxconn, he and a small team built an end-to-end mobile phone division — design, development, manufacturing, and supply chain — inside Foxconn.
At Foxconn, there’s only one voice — Terry Guo’s. You don’t bring bad news. But when you’ve seen what openness can accomplish, you realize how much that transparency enables both speed and quality.”
The three experiences — Nokia’s collaborative precision, Microsoft’s structure, and Foxconn’s command hierarchy — have provided Martin with a strong perspective on the importance of transparency and data in building great products.
Why Data Transparency Isn’t the Default in Contract Manufacturing Relationships
Even today, with AI systems on every roadmap, leaders at electronics brands both large and small still struggle to get raw manufacturing data from their contract partners. Martin knows both sides of that tension — and the psychology behind it.
“Eighty to ninety percent of the time,” he says, “when something goes wrong, the supplier’s first thought is liability. They ask, ‘What am I going to be held accountable for?’”
Fear, not technology, is the barrier. Suppliers hesitate because data can expose risk — or margin. And when every percentage point matters, some suppliers might be tempted at various times to fudge the numbers – rework not reported, yield numbers “smoothed,” only sharing incomplete test data.
Martin doesn’t demonize it. He understands it. “Margins are narrow, and brands push for cost-down every year. Suppliers look for little buffers to survive. If full transparency means losing those buffers, hesitation is human.”
But he’s clear about the fix: trust through shared accountability. “It takes two to tango. If the brand dictates the process — from SOPs to test limits — then the brand owns part of the result. Contracts should reflect that.” Unfortunately, by the time many leaders – on engineering, or quality, or even automation teams – get involved with their suppliers, procurement has already stamped a contract that will drive behavior. Shared accountability needs to be built in up front, so that liability and fear are not a barrier to sharing the information that enables high-quality execution at rapid speeds.
The “Logitech Way”: Partners, Not Suppliers
When Martin joined Logitech, that idea became doctrine. “We don’t have suppliers; we have partners. We grow together and learn together.”
The principle sounds simple, but it plays out in deliberate ways:
- Shared Risk. Logitech’s manufacturing contracts explicitly balance responsibility. Brands accept part of the operational risk when they drive design and process decisions.
- Presence and the Personal Touch. During new site ramps, Martin’s team sends people to “walk the line” — observing, testing, and debugging shoulder-to-shoulder with factory teams. “We don’t push the big green button until we’re confident,” he says.
- Transparency as Learning. When a partner shares early issues, they’re rewarded with support, not penalties.
Owned factories and contract sites both feed into Logitech’s data lake, but their data looks different. Owned sites are automated and structured; manufacturing partners require translation. Instead of forcing uniformity, Martin’s team invests in infrastructure to normalize diverse data sets and achieve consistent insights regardless of source.
“We treat our manufacturing partners as extensions of our own operations,” he explains. That includes sharing learnings. Manufacturing partners are often eager to learn from their customers – but many customers are reluctant to share information from their sales channels or market data.
“They want to know the happiness of the consumer, star ratings, return rates, reviews,” he says. “That’s their achievement, too.”
They also want to see what’s ahead: roadmap visibility, early access to innovation plans, and how they rank against their peers. Logitech shares anonymized performance comparisons, not as punishment, but as motivation.
“When a supplier knows they’re outperforming the average, it builds pride. When they’re not, it builds focus. Either way, it drives improvement.”
The Future: AI, Blended Data, and Closed-Loop Quality
Martin’s excitement about AI isn’t theoretical — it’s grounded in years of working with messy, real-world data. “AI is already helping us mine call transcripts, repair logs, and manufacturing records for root causes,” he says. “You can get to an 80 or 90 percent correct hypothesis in minutes. That’s game-changing.”
The next step is even more ambitious: blending consumer data with factory data to build predictive quality systems. “Imagine connecting every test station, every yield report, every customer review,” he says. “You could see a pattern forming before the first return hits the field.”
To get there, data governance matters. “Without structured lineage and disciplined data ownership, the models can’t learn correctly. But when they do, they’ll give us insights we can’t even imagine today.” Building these systems will be what’s next for top-tier electronics brands and manufacturers.