…by Saket Gaurav, Chairman & Managing Director, Elista
Walk into any modern electronics plant today and you’ll notice a very different kind of decision-making at work. What earlier depended on instinct, experience, and after-the-fact troubleshooting is now guided by systems that can see ahead of time. Artificial Intelligence (AI) hasn’t simply added sophistication to the shop floor; it has changed the fundamentals of how manufacturing thinks — from reacting to anticipating. And this shift is beginning to redefine both quality and cost in the consumer durables sector in a way we have never experienced before.
A Shift from Firefighting to Foresight
In the old model, breakdowns were treated almost like weather events — inconvenient but expected. Predictive systems are breaking that mindset. Today, machines flag abnormalities long before they disrupt production. This ability to detect risk early is not just a technical upgrade; it brings discipline and stability to an environment where every minute counts.
Quality Moving From Samples to Certainty
For decades, quality checks depended on representative sampling. It worked, but it always carried an element of uncertainty. With AI-backed computer vision, the line of inspection runs on every unit, not just a selected few. The system doesn’t tire and doesn’t miss. It recognises deviations that even trained eyes can overlook. This is how the industry’s long-standing ambition of near-zero defects becomes genuinely achievable.
Digital Twins and the Power of Safe Experimentation
The rise of digital twins has silently accelerated innovation cycles. Earlier, validating a design change could take weeks of trials. Now, engineers test failure scenarios virtually and refine decisions without risking material or time. It pushes manufacturing teams to be more confident and decisive, because they are working with clearer evidence, not assumptions.
Production Lines That Learn and Course-Correct
A traditional line requires constant supervision. Operators adjust parameters through experience. With AI in place, the line starts adjusting itself. When drift appears — a small shift in temperature, pressure, torque — the system steps in and stabilises it. The workforce moves from manual correction to higher-order problem-solving, which is exactly where skilled labour creates the most value.
Real Savings Through Quiet Optimisation
Cost reduction in manufacturing rarely comes from dramatic interventions. It comes from consistent, everyday improvements: better planning, fewer urgent shipments, reduced wastage, optimised energy use. AI nudges these improvements into the system continuously. The cumulative savings are what give factories greater competitive strength without compromising on quality.
Data From the Field Bringing Products Back to the Line
With customer consent, connected devices offer anonymised insights into real-world product behaviour. For manufacturers of consumer durables, this closes a long-standing gap. You no longer rely only on lab tests. You learn how devices cope with dust, voltage fluctuations, or prolonged usage. This intelligence feeds back into design, procurement, and testing — tightening quality from end to end.
Customisation at Scale Without Penalising the Line
Consumers want more choice today — and historically, variety raised manufacturing costs. AI helps manage that complexity. It predicts demand patterns more accurately, shortens testing cycles, and reduces the chaos of small-batch production. Factories can offer diversity without being burdened by inefficiency.
What Predictive Manufacturing Means for the Future
The consumer durables industry is entering a phase where quality expectations will keep rising and pricing pressures will remain unrelenting. Companies that adopt predictive technologies early will find themselves with an outsized advantage: fewer surprises, tighter cost structures, and more consistent output.
From what I have seen firsthand, AI is not replacing human judgement. It is amplifying it. Teams become more strategic. Engineers get clearer visibility into problems. Operators handle more impactful tasks rather than repetitive correction work. Factories begin functioning like intelligent systems — learning continuously, adapting quickly, and improving without waiting for crises.
Predictive manufacturing is no longer a distant ambition. It is already reshaping the sector. And the organisations that embrace it with discipline and intent will define the next era of efficiency and quality in India’s consumer durables landscape.
