Biswajit Biswas, Chief Data Scientist, Tata Elxsi, is a seasoned expert with over 25 years of experience in AI, Generative AI, Quantum Computing, and wireless communication. He has led strategic initiatives at Tata Elxsi, focusing on autonomous systems, predictive models, and AI solutions. Skilled in deep learning, machine learning, DSP, and product development, he holds four patents and contributes to national standards in AI and Quantum applications.
The article explores the evolving landscape of smart manufacturing, emphasizing AI, edge computing, and cognitive enterprises, while stressing sustainability and India’s potential leadership in this transformation.
Manufacturing is shifting fast; connected factories are giving way to adaptive, data-driven enterprises. Industrial IoT first wired machines and factory lines; now AI systems guide decisions, predict outcomes, and refine production, quality, energy, and supply flows. In the next decade, smart manufacturing will be defined by applied intelligence, edge autonomy, and measurable, sustainable outcomes.
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The first wave of digital manufacturing focused on connecting machines, sensors, and production lines to central systems, enabling visibility into operations. This setup brought transparency, yet it rarely delivered real-time smarts. Cognitive enterprises make the next jump: they blend AI, digital twins, and closed-loop learning setups that sense issues, think them through, and fix them on their own.
Digital twins drive this change. They create continuously synchronized virtual representations of assets, production lines, and even entire plants. Manufacturers can now simulate scenarios, predict failures, optimize throughput, and orchestrate resources dynamically. When engineers train these models on operational, quality and environmental data, they become active decision tools that adjust with use.
Platforms such as IRIS, Tata Elxsi’s industrial AI system, run inference on the shop floor using sensors, video feeds, and anomaly models to improve safety and output Generative AI adds another working layer: it helps operators trace root causes, refine processes, and coordinate tasks.
Sustainability is no longer a parallel initiative—it is becoming a design principle for manufacturing systems.
The factory of the future is not fully autonomous—it is collaborative. Cobots working safely alongside humans are redefining flexibility on the shop floor, enabling rapid reconfiguration, mass customization, and higher productivity without sacrificing safety. However, true flexibility emerges when cobots are augmented with Edge AI and Generative AI.
Edge AI allows real‑time perception, decision‑making, and control to happen close to machines—where latency, reliability, and data sovereignty matter most. Vision‑based inspection, adaptive robotic control, predictive maintenance, and energy optimization are increasingly executed at the edge, ensuring deterministic performance even in disconnected or bandwidth‑constrained environments. IRIS demonstrates this by deploying AI inference directly at production lines, improving quality accuracy and reducing defects through localized intelligence.
Generative AI adds a new cognitive layer. By learning from vast operational histories, engineering documentation, and process knowledge, GenAI can assist operators and engineers with root‑cause analysis, process recommendations, and even autonomous workflow orchestration. Paired with cobots and edge intelligence, it hits zero flaws: teams catch slips, name them, and mend in near-instant time.
Smart plants in the coming decade mix edge and cloud compute smoothly. Clouds train big models, tune across sites, and cross-plant benchmarking. The edge is where manufacturing intelligence must execute.
This combo wins big:
Tata Elxsi’s Tether Platform follows this mix. Built for cloud-to-edge deployment, it manages devices, ingests data, and runs analytics across distributed sites. In plants, it connects assets, twins, and AI workflows into one operating layer.
Sustainability is no longer a parallel initiative—it is becoming a design principle for manufacturing systems. Net‑zero goals, energy efficiency mandates, and circular economy models require intelligence spanning across production, utilities, logistics, and suppliers.
AI‑driven manufacturing platforms enable granular energy monitoring, emissions tracking, and process optimization at machine and line levels. AI systems track energy use, emissions, and process performance at the machine level. Edge models adjust consumption based on load and renewable supply. Digital twins let engineers test trade-offs before altering equipment.
Resilient supply chains are equally critical. Cognitive enterprises use AI to sense disruptions, predict demand fluctuations, and reconfigure sourcing and production strategies dynamically. Platforms such as Fabrix, Tata Elxsi’s manufacturing and operations transformation platform, are positioned to integrate production intelligence with enterprise workflows—enabling synchronized planning, execution, and sustainability reporting across the value chain.
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India is uniquely positioned to lead the next decade of smart manufacturing. With a strong engineering talent base, rapidly digitizing industries, and national initiatives around manufacturing competitiveness and sustainability, India can leapfrog legacy models and adopt AI‑native factories.
The roadmap involves:
And with the above, a true AI-native platform for manufacturing automation should have a three-layer architecture to embrace AI-led transformation for leapfrogging the growth envisaged.
Indian enterprises that combine domain depth with platforms like IRIS, Tata Elxsi's Tether - Connected Digital Platform and Fabrix can accelerate this transition—moving from connected factories to truly cognitive enterprises that are intelligent, resilient, and sustainable by default.
Smart manufacturing over the next decade will be defined by cognition, collaboration, and conscience. The convergence of digital twins, Edge AI, GenAI, and hybrid compute architectures is re‑engineering factories into adaptive systems that learn continuously and operate sustainably. As India steps into a leadership role, Platform-driven ecosystems driven by edge-cloud synergy will be the foundation of manufacturing excellence in the era of cognitive enterprises as India assumes a leadership position.
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