From Retrofit Sensors to AI Vision: The Pragmatic Playbook for Factory Floor Growth

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“Smart manufacturing isn’t a tech initiative; it’s a business growth strategy. It isn’t about replacing people, but empowering legacy machines to perform with modern precision.” - Dr. C.N. Jayapragasan, Lakshmi Electrical Control Systems Ltd.

August 2026 : Walk onto the floor of a traditional textile or component manufacturing plant, and you likely won't find pristine, automated robotics fresh out of a sci-fi film. Instead, you'll see decades-old conventional machinery - rugged, reliable, but running far below modern efficiency standards.

During the “Driving Profitable Growth Through Smart Manufacturing” roundtable - presented by Dassault Systèmes and Tata Technologies, powered by Pro MFG Media, and supported by ACMA India - Dr. C.N. Jayapragasan from Lakshmi Electrical Control Systems Ltd. offered a grounded, refreshing perspective on industrial evolution. Rather than scrapping legacy assets, Dr. Jayapragasan explained how combining grassroots workforce education with targeted IoT retrofits, machine learning vision systems, and predictive maintenance can turn aging equipment into high-yield smart systems.

When smart manufacturing tools are introduced, floor-level operators often fear one thing: job replacement. Dr. Jayapragasan emphasized that the first step toward successful transformation isn't buying sensors - it's educating staff from bottom to top.

“Smart manufacturing isn't just a technology initiative; it’s a business growth strategy,” he noted. By clearly communicating that digital platforms exist to boost capacity rather than reduce headcount, management creates a receptive workforce. To build true operational resilience, his team conducts cross-training every 10 days, ensuring three operators are fully skilled on every station to neutralize unexpected absences without halting lines.

Many manufacturers hesitate to adopt digital models because their fleet consists of older, conventional machines. However, after calculating an initial Overall Equipment Effectiveness (OEE) of around 35%, Dr. Jayapragasan’s team took a pragmatic route: retrofitting existing equipment with IoT sensors and real-time connectivity instead of buying new assets.

By streaming live operational data directly onto unified digital dashboards, cross-departmental teams - from quality planning to production - gain complete visibility. The immediate visibility into downtime causes and real-time usage allows quick, precise decision-making that steadily pushes factory capacity upward.

Quality assurance is often bottlenecked by manual inspection, where fatigue leads to costly human error. To solve this, Machine Learning (ML) vision systems are deployed to handle real-time automated inspection.

Dr. Jayapragasan shared a striking example from industrial knitting machines. When a single needle breaks during a 24-hour continuous run, four to five tons of fabric can end up with micro-holes - causing entire batches to be rejected. By placing digital cameras equipped with AI/ML systems directly on the machines, the system catches needle defects the moment they occur, stopping the feed instantly.

The result? A 32% drop in material rejection and a 45% reduction in inspection time.

When asked which AI application delivers the highest business value, Dr. Jayapragasan did not hesitate: Predictive Maintenance.

By analyzing historical database logs along with real-time sensor streams, plants move away from reactive firefighting and scheduled downtime. Instead, predictive analytics flag micro-variations before failures happen, protecting production continuity.

Combined with vision-based quality control and automated dashboard intelligence for component procurement, factories can scale profits while keeping their existing machinery - and workforce - intact and thriving.

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