In Electronics Manufacturing, AI is only as good as the data beneath it
Manufacturing has spent the last decade being told that hardware wins factories and software wins markets. On the shop floor, that distinction is dissolving fast. Precision manufacturers — especially in electronics, where tolerances are unforgiving and OEM customers increasingly expect near-zero defects — are discovering that competitiveness no longer hinges on which machines they own, but on how well those machines talk to each other, to enterprise systems, and to the people making decisions in real time.
Few sectors feel that shift more acutely than electronics manufacturing services (EMS), where a single contract manufacturer might run dozens of product lines, each with its own customer specifications, tolerances, and compliance requirements. For Naman Shah, MD and CEO of LeSol Group, that complexity is exactly why digital infrastructure has stopped being a back-office upgrade and become a front-line competitive lever.
“Manufacturing competitiveness today is increasingly determined by how effectively information flows across the factory, not just how efficiently machines operate,” says Naman Shah.
Software as the Connective Tissue
At LeSol, Shah describes digital manufacturing as the connective layer between design, engineering, and production — the thing that turns three separate functions into one coordinated system. Digitizing workflows and improving visibility across the manufacturing process, he says, cuts down on engineering changes, sharpens production planning, and keeps execution consistent even as customer requirements multiply.
That consistency matters more in EMS than almost anywhere else. Managing multiple product categories and a wide range of customer requirements means process standardization and real-time production visibility aren’t nice-to-haves — they’re what keeps quality from drifting between one production run and the next. Digital tools, Shah says, let LeSol monitor production performance, improve traceability, catch quality deviations earlier, and move faster on operational decisions, while also tightening collaboration between design and manufacturing teams to shorten development cycles.
Where the ROI Actually Shows Up
Ask Shah which Industry 4.0 technology has delivered the strongest return, and he resists naming just one. Instead, he points to a category: anything that improves visibility and traceability across the shop floor. Digitized production tracking and real-time monitoring have produced measurable gains in first-pass yield, faster identification of quality deviations, and better line utilization. Automated inspection at critical stages, he notes, tends to pay for itself quickly simply by catching problems while they’re still cheap to fix.
What might be the least glamorous investment on his list has arguably delivered the most consistent payoff: digitizing engineering and production documentation. Reducing manual paperwork and engineering iterations doesn’t generate headlines the way robotics or AI does, but Shah says it directly shortens development cycles and reduces errors — a reminder that in manufacturing, ROI often comes from unglamorous plumbing rather than the flashiest technology on the floor.
AI and ML, from Planning to Predictive Maintenance
Artificial intelligence and machine learning are increasingly embedded across the EMS production lifecycle, but Shah frames their impact as inseparable from the connected, data-driven manufacturing environment they run on. As digital workflows tie design, engineering, and production together, he says, production planning becomes more streamlined, engineering iterations drop, and product development moves faster — with AI and ML playing a growing role in optimizing planning, reinforcing quality assurance, and enabling more predictive operations as the underlying data infrastructure matures.
Precision Through Automation, Vision, and Real-Time Data
In electronics manufacturing, precision is less about any single piece of automation than about disciplined process control applied consistently. Shah points to critical assembly and testing stages as where automation delivers the clearest benefit, reducing variability and human error, while vision-based inspection confirms quality at multiple points along the line rather than relying solely on an end-of-line check — catching drift closer to its source and cutting rework.
“The objective isn’t automation for its own sake, more like a well-instrumented line where each stage is measurable, traceable, and always being refined,” says he.
Real-time production data, he says, is what ties it all together — giving teams the ability to respond to early warning signals in process parameters and quality trends, rather than discovering defects after the fact. Over time, that same data supports process fine-tuning, faster qualification of new products, and more consistent output across production batches.
Data as the Core Manufacturing Asset
For an EMS company juggling multiple products, customers, and processes, Shah argues that planning, quality, and capacity decisions are only as good as the data behind them. LeSol has been steadily digitizing shop-floor processes so production status, quality parameters, and material movement are captured in real time instead of reported after the fact — and integrating that data with enterprise systems is, in his view, what actually turns information into decisions.
When shop-floor data flows into planning, procurement, and quality systems, teams can respond faster to shifts in demand, material availability, or line performance, while customers gain more confidence in the consistency of what they receive. Shah describes this integration as an ongoing journey rather than a finished project, with each step delivering measurable gains in responsiveness and operational discipline.
Digital Twins and the New Product Introduction Problem
Simulation technologies are becoming central to how LeSol approaches both production planning and new product introduction. Virtual models of production lines let manufacturers simulate workflows, spot where queues start to build, and validate process changes before they touch the physical shop floor — supporting production planning, reducing downtime, and improving resource use along the way.
For new product introduction specifically, Shah says simulation allows manufacturers to test manufacturability, run through multiple production scenarios, and tune the assembly approach before full-scale production begins — typically resulting in less engineering rework, fewer defects, and a shorter time to market. As products grow more complex and customer expectations keep shifting, he expects digital twins to play an even larger role in data-driven decision-making.
Toward Near-Zero-Defect Quality
As OEM customers push toward near-zero-defect expectations, Shah sees quality assurance shifting from a reactive inspection function into something proactive and data-driven. Digital quality systems bring process visibility, traceability, and real-time data capture to every stage of production, allowing teams to catch deviations earlier, sharpen root-cause analysis, and act faster on corrective measures — lowering rework, lifting first-pass yield, and keeping results consistent across production batches.
In electronics manufacturing, where precision and reliability aren’t negotiable, Shah says these systems also strengthen compliance, documentation, and customer confidence — and as manufacturing complexity increases, embedding digital quality throughout the production lifecycle becomes less a competitive nicety and more table stakes.
Where GenAI Actually Earns Its Keep
Generative AI draws plenty of attention, but Shah is deliberately narrow about where he sees practical value beyond experimentation: places where manufacturing generates large volumes of documents and institutional knowledge. Engineering documentation, work instructions, quality reports, and customer communication consume significant time today, and GenAI can accelerate that work while improving consistency — including letting teams query production and quality data in plain language, making insights accessible beyond a handful of specialists.
Knowledge management is the other area he flags. EMS companies accumulate deep process knowledge across products and customers over time, and GenAI, in his view, can help capture and retrieve that expertise so lessons from one program carry over to the next. LeSol’s approach, he says, is deliberately pragmatic — starting with use cases where accuracy can be verified and the benefit is clear, rather than deploying GenAI broadly before it’s ready.
The One Technology Bet — With a Caveat
Asked to name the single technology that will create the biggest competitive advantage for electronics manufacturers over the next five years — AI, automation, robotics, industrial IoT, or digital twins — Shah picks artificial intelligence. But he’s quick to qualify the choice.
“AI is only as good as the data foundation it’s built on. Automation, robotics, industrial IoT, and digital twins are all key building blocks — in many ways, they’re the pieces that generate the data that makes AI valuable in the first place,” states Shah.
The manufacturers who pull ahead over the next five years, he argues, will be the ones who connect these layers so that data from machines, processes, and quality systems feeds intelligent decision-making across the factory — not just in isolated dashboards. AI can strengthen production planning, quality assurance, and predictive operations, translating directly into advantages in cost, speed, and reliability.
But the real edge, Shah maintains, comes down to execution: building the digital infrastructure, developing the talent, and creating the operational discipline to actually use these technologies well. Technology on its own doesn’t create advantage — the ability to absorb it into daily operations does.