Connected visibility: how automotive manufacturers are uncovering the "hidden factory"
From micro-stops to stakeholder buy-in, panellists from Magna, Daimler Truck, BorgWarner and L2L explain how better visibility in shopfloor data is helping manufacturers maximise existing assets and scale pilot improvements into lasting operational change
With capital budgets under closer scrutiny than ever, automotive manufacturers are being pushed to extract more value from existing assets, systems and people rather than defaulting to new investment. Yet micro-stops, disconnected data, siloed teams and poorly integrated technology can quietly erode productivity – often invisibly, until the losses compound.
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That was the starting point for AMS's livestream with L2L, "Connected Visibility: Automotive Production's Resilience Advantage," which brought together operations, engineering and digital transformation leaders from across the sector: Murugan Boominathan, Senior Analyst, Manufacturing Solutions at Magna IT – Industrial Solutions; Adam Polcha, Manufacturing Engineering Manager at Daimler Truck North America; Trent Randles, Engineering Manager at BorgWarner; and Curtis Bird, Director of Implementations at L2L. The panel explored how connected visibility – from shopfloor to network level – can expose hidden losses, support faster decision-making, and help manufacturers convert one-off pilot wins into sustained operational change.
A small microstop can add up pretty quickly to a major breakdown...If the delay is more than half a second, you will have a visual impact where there's a lag or delay in the process
Where hidden and how losses can be found
Boominathan opened by identifying three common blind spots that cut across both greenfield and brownfield sites: barcode scanning delays, system-to-system interfacing lags, and informal workarounds that become normalised without anyone tracking them. "A small microstop can add up pretty quickly to a major breakdown," he explained, pointing to PLC-to-system handoffs as a particular culprit: "If the delay is more than half a second, you will have a visual impact where there's a lag or delay in the process."
He also drew a clear distinction between greenfield and brownfield environments. "The greenfield sites have a lot of design capability... you have time and scope to improve your process. But in terms of brownfield, it's not that easy," he said, citing the difficulty of retrofitting a quality routing point into an existing conveyor system without significant controls and workflow modification.
Polcha offered a contrasting perspective from heavy/commercial trucking, where cycle times are measured in minutes rather than seconds. "We don't deal with milliseconds or microseconds or even seconds. We deal with minutes of downtime," he said. For Daimler Truck, hidden losses often sit behind the KPIs themselves – in the ability, or inability, to correlate metrics like first-time quality with vehicle attributes, supplier involvement or production timing. "The biggest shift in improving our throughput and our quality has been around understanding the relationships between vehicle attributes or other aspects of the plant."
Bird brought a maintenance-floor perspective, describing micro-stops as part of "the hidden factory "They are the disruptions nobody asks about in tier meetings" he said, recounting an operator's observation that "it's easier to recover from a 30-minute downtime" than from three separate 10-minute events, even though the total time lost is identical. Randles confirmed he sees a similar pattern at BorgWarner: "If you stop two or three seconds... 50 times a shift that adds up," he said, noting that with cycle times under a minute, those seconds translate directly into lost units. Addressing micro-stops, he added, "has given us insight into our manufacturing processes we didn't have before."
Building the business case for visibility
Asked how to justify investment in visibility tools when the return isn't as obvious as traditional CapEx, Polcha described the need for "a 360 view of the business" that goes beyond downtime, design issues or warranty data in isolation, tying performance factors back to margin. Without that comprehensive analysis, he said, "you're relying on people's gut and their experience."
Randles described a practical workaround for the difficulty of quantifying visibility's value: looking back at historical incidents and identifying what data would have accelerated root-cause analysis. "I could have prevented this much downtime or this much production loss," he explained of the approach, which converts a subjective argument into "quantifiable information" that can underpin a funding request.
The operators can provide insight that myself, engineers, maintenance technicians cannot provide, because they are running the equipment every day
The value of operator insight
All four panellists emphasised that operator experience remains essential, not a relic to be replaced by dashboards. "The operators can provide insight that myself, engineers, maintenance technicians cannot provide, because they are running the equipment every day," said Randles, describing multiple "sources of truth" that need to be combined for a clear picture.
Polcha went further, calling operator perspective "profound": "They can literally see all of the cracks or gaps or maybe poor decisions, some of which might have happened five years ago upstream in the organisation." The real challenge, he argued, is conveying that insight back up the organisation to influence design and strategic decisions, not just plant-floor execution.
Bird noted that operator comments and system data tend to align well in practice, provided operators are given a straightforward way to contribute. "Looking at the data but then going back to the floor... saying here's what the data is telling us. Do you agree or disagree," he said, describing how L2L's phased rollout approach deliberately mirrors existing workflows –replacing whiteboards with simple iPad entry, for instance – rather than disrupting them. One unexpected benefit: "On the whiteboard, they put down the problems that they have in the comments section, but for some reason when it's digitally, they put their ‘attitude’ in there as well," which Bird said helps gauge operator frustration levels.
You can throw as much money as you want at an individual facility, but if you don't have a good connection to the people that are there, you need to work out how do you bring them along the journey, from inception to the changeover through to execution
Scaling pilots into lasting change
On why pilot projects succeed in one plant but fail to scale, Polcha pointed to culture as the decisive factor. "You can throw as much money as you want at an individual facility, but if you don't have a good connection to the people that are there, you need to work out how do you bring them along the journey, from inception to the changeover through to execution." He also flagged a common failure mode: deploying new technology that initially replicates the old system's output without visible early benefit, creating a high risk that frustrated operators simply "unplug this thing."
Bird reinforced that leadership engagement is the clearest predictor of success: "I can tell it's going to be successful when the plant manager is actually in a lot of the meetings and sitting in and participating." He described telling management teams directly that follow-through, not initial deployment, is the hard part: "If this fails, it's because of you."
Boominathan distilled the requirements into three "magic words": buy-in, alignment and success criteria, the last of which he stressed is often underweighted. "Unless you set this expectation at the very beginning, it's really difficult to move beyond the rollout," he said, describing how Magna standardises templates in advance across product lines – plastics, leather trims, steel components – to make rollouts repeatable rather than one-off exercises. Bird agreed, framing it simply as "building standards for everyone to follow."
Data complexity at scale
An important moment came when Polcha and Boominathan discussed assumptions about data complexity. While Boominathan noted that 2,000 product variants are common in tier manufacturing, Polcha clarified Daimler Truck's reality is several orders of magnitude greater: "I'm not talking about 2000 variants, for us it's literally on the scale of millions of variants a year." He described the challenge of conveying this complexity to vendors accustomed to standard automotive data structures as a persistent barrier to deploying off-the-shelf solutions in commercial trucking.
Next steps to improving visibility
Looking ahead, Randles predicted AI's biggest near-term impact would be in automating data collection, structuring and analysis — while human judgement remains essential. "It still takes a person looking at the information, making a decision, taking action on that," he said. Bird shared a concrete example of AI already delivering value: suggesting adjusted preventive maintenance frequencies that freed up "five more man hours per week" for one customer.
Polcha argued a real opportunity lies in giving subject matter experts – not just coders or data analysts – the power to query combined datasets directly, removing the translation barrier between business and IT. Boominathan closed with a quantified ambition: citing a recent study suggesting most manufacturers currently achieve only 20-30% shopfloor visibility, he set a goal of reaching 80-90% within five years. "It's about helping the right personnel at the right time, making the right decision, with the right data," he said.
The session underscored a consistent theme across all four panellists: technology alone rarely determines whether a visibility initiative succeeds. Alignment, operator trust, phased rollout and sustained leadership follow-through are what separate a one-off pilot from genuine, scalable operational resilience.
