Beyond the Balance Sheet: How the NASH Group is engineering digital transformation on the factory floor
Walk onto most factory floors and you’ll hear the same story: legacy systems bolted onto newer ones, pockets of automation surrounded by paper logs, and a leadership team perpetually firefighting instead of forecasting. NASH Group, a major precision sheet-metal and engineering components manufacturer serving the automotive and industrial sectors, has spent the last several years deliberately rewriting that script — not by chasing the latest AI headline, but by doing the unglamorous work first.
“At NASH, I do not view digital transformation merely as a cost-efficiency lever,” says Sanjay Wadhwa, Chairman & Managing Director, NASH Group of Companies. “I see it as a strategic enabler that shapes how we design, engineer, manufacture, and compete globally. Efficiency is certainly important, but our larger focus is on building products, processes, and capabilities that are future ready.”
That distinction — efficiency lever versus strategic enabler — is more than semantics. It shapes every investment decision NASH makes, from ERP rollouts to sensor deployments to where AI gets applied first.
Standardisation Before Sophistication
Ask any CIO what derails an AI or IIoT initiative, and the answer is rarely the algorithm — it’s the data underneath it. Wadhwa learned this early, and it has become almost a doctrine at NASH.
“A principle I strongly believe in is that we need to bring in standardisation across plants in SAP, shop-floor data, etc.,” he explains. “This lays the foundation for the future, because as we scale the IT and digital strategy, it would be easy to implement due to the standardisation activity.”
The numbers behind that discipline are significant: SAP has been implemented across more than 15 plants, giving NASH what Wadhwa calls “a common backbone for our processes” and materially improved visibility across the supply chain. But he’s candid that ERP alone isn’t enough. “SAP gives us transaction-level visibility. The next critical layer is machine-level connectivity — installing sensors and enabling data capture from presses, laser cutting machines, turret punching machines, compressors, and other critical equipment leveraging IoT,” he says. “This allows us to understand what is happening on the shop floor in real time, rather than relying only on information entered after the event.”
Even seemingly basic definitions had to be standardised first. In a network of 15-plus plants, Wadhwa notes, terms like downtime, scrap, and OEE (Overall Equipment Effectiveness) “can be interpreted differently unless there is a common framework.” Getting that framework right — along with digitising maintenance records that many plants still kept as paper logs — wasn’t glamorous work, but as Wadhwa puts it, “it is the real foundation on which predictive maintenance is built.”
From Firefighting to Foresight
If there’s a single thread that defines NASH’s transformation story over the last two to three years, it’s a shift in operating posture. “A few years ago, much of maintenance and production planning was reactive; issues were addressed when they occurred,” Wadhwa recalls. “Today, we are increasingly building the ability to anticipate, plan, and act before they disrupt operations.”
That shift extends beyond maintenance into how departments talk to each other. Design, manufacturing, quality, and maintenance functions, historically siloed, are now converging onto a common data platform. “Earlier, these functions often worked in silos, and issues surfaced later in the process,” he says. “Today, feedback loops are becoming shorter. A manufacturing issue can reach design teams faster, and corrective action can move more quickly through the system.”
None of this happened by decree. Wadhwa is candid about the organisational grind involved: “Each plant has its own legacy practices and operating habits. Bringing them onto common processes requires patience, discipline, and change management.” The payoff, he argues, is optionality — standardisation “is what will enable us to scale digital initiatives, including AI, across the organisation.”
There’s also a market-facing dimension to this rigor that many manufacturers underestimate. Tier-1 automotive customers, Wadhwa points out, increasingly evaluate suppliers on digital maturity itself.
“Many of our Tier-1 automotive customers now assess digital maturity, including traceability and real-time quality data, as part of vendor qualification,” he says. “In that sense, digital maturity has become a marker of trust and reliability, not just an internal efficiency metric.”
AI With Its Feet on the Shop Floor
Where does artificial intelligence actually fit into a precision manufacturing business? At NASH, Wadhwa describes a deliberately dual mandate — one facing customers, one facing internal operations.
“AI will play a dual role at NASH,” he says. “Some of our customer-facing solutions, particularly in the Smart Vision and IoT domains, are AI-enabled, allowing customers to benefit from enhanced intelligence, automation, and decision-making. At the same time, AI is becoming an increasingly important enabler within our own operations.”
The internal use cases read like a checklist of the manufacturing sector’s most persistent pain points, now being tackled with data rather than intuition alone:
Real-time OEE and downtime analytics that don’t just report metrics but diagnose root causes — breakdowns, changeovers, or material shortages — cutting down manual analysis time.
Predictive maintenance, using vibration and thermal sensors on compressors and high-speed presses to catch early warning signs “before failures occur, thereby reducing unplanned downtime.”
AI-based vision inspection to catch sheet-metal defects — burrs, dents, dimensional variations — with a consistency manual inspection struggles to match, directly improving first-pass yield.
Capacity and demand planning for new programme ramp-ups, including AI-assisted recommendations on buffer stock levels when volumes fluctuate sharply.
Yet for all the technology, Wadhwa is emphatic that AI is meant to augment, not replace, the people running the plants. “Throughout this journey, our approach remains people-first,” he says. “Technology is meant to strengthen the judgement of our operators and engineers, not replace it. The shop floor continues to run on human expertise; AI and digital tools provide better information, faster insights, and stronger decision support.”
He’s equally measured about where these initiatives stand today: “These are evolving initiatives, and we continue to learn as we scale them. What is important is that each use case is anchored in a real operational problem and is designed to deliver practical value on the shop floor.” It’s a notably unglamorous framing for AI investment — and possibly the reason NASH’s use cases seem to stick rather than stall in pilot purgatory.
What’s Next: A Roadmap of Connected Priorities, Not a Single Big Bet
Ask most manufacturing leaders about their “next big IT investment” and you’ll typically get a single named system. Wadhwa’s answer is deliberately plural. “Our next major IT investment is not a single standalone system,” he says. “It is a set of connected priorities that together will strengthen our digital backbone and operational intelligence.”
Top of that list is a comprehensive CMMS (Computerised Machine Management System) rollout across all plants — the backbone, in Wadhwa’s words, of NASH’s “transition from reactive maintenance to predictive maintenance.” Alongside it, NASH is building a single, standardised leadership dashboard linking maintenance maturity, OEE, and capacity-versus-demand across every facility — whether in Bangalore, Pune, Chennai, or elsewhere — to give leadership “a clearer and more consistent view of performance across the network.”
Other priorities round out an increasingly comprehensive digital agenda: digitising tool-health and changeover tracking that remains largely manual today; building skill-matrix systems that link operator certifications to specific machines for smarter shift planning as automation increases; and — notably — extending traceability beyond NASH’s own plant walls into its network of subcontractors and suppliers.
And running underneath all of it, non-negotiably, is cybersecurity. “In a connected manufacturing environment, protecting operational technology, enterprise systems, and customer data is fundamental,” Wadhwa says.
Taken together, it’s a roadmap that resists the temptation to lead with the most exciting technology. Instead, it leads with the plumbing — standardised data, connected systems, digitised records — betting that the AI and automation layered on top will be more durable, more trusted, and ultimately more valuable for it.
As Wadhwa sums it up: “Our roadmap is about building a digitally intelligent company, one where manufacturing and engineering excellence remain at the centre, supported by technology and data systems that are steadily being implemented to empower our people and strengthen our competitiveness.”