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BEYOND MACHINES: INTEGRATING HUMAN-MACHINE SYNERGY IN A CONSCIOUS FACTORY

The Machinist

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August 2025

In the era of Industry 4.0, manufacturing is evolving towards intelligent collaboration, combining human insight with machine intelligence. Central to this shift is the conscious factory, which emphasises harmony over replacement. A recent panel discussion featured industry leaders exploring the connections between cognition, emotion, and computation in this new landscape.

BEYOND MACHINES: INTEGRATING HUMAN-MACHINE SYNERGY IN A CONSCIOUS FACTORY

In a world increasingly captivated by the potential of Industry 4.0, the discussion is evolving from simple automation for efficiency to intelligent collaboration between human insights and machine intelligence. Central to this shift is the concept of the conscious factory production ecosystem that doesn't just mechanise but harmonises, where human decisions are enhanced, not replaced, by machines.

Recently, a high-level industry panel moderated by Vipul Bhatia, Director, KPMG India at the ET Now Machinist Tech in Manufacturing Summit, brought together several industrial leaders to explore this intersection of cognition, emotion, and computation.

THE ROI OF AUTOMATION: MYTH VS. REALITY

For Runal Dahiwade, CEO of Peppermint Robotics, the question of ROI is not just routine, it's fundamental. "We often assume that India has a surplus of unskilled labour, and thus automation has little value here," he observed. "But the truth is exactly the opposite, especially in urban India."

Dahiwade debunked the myth that automation is a luxury in low-wage economies. "Gig economy jobs have changed aspirations. Today, even those who might earlier take up mundane cleaning jobs would rather deliver food or work in logistics," he noted. "And with attrition, inconsistency, and the cost of relievers across multiple shifts, the ROI on robots often lands within the first year, sometimes within eight months."

Automation, for Dahiwade, is no longer about replacing labour, but filling a widening gap in supply and quality.

SURVEILLANCE, PRIVACY & ETHICS IN MACHINE LEARNING

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