How AI Will Transform Manufacturing and Supply Chains: Insights from Bidyut Sarkar

As semiconductor demand fluctuates amid economic uncertainty and geopolitical tensions, manufacturing leaders urgently seek new ways to enhance efficiency, continuity, and quality. According to digital industrial manufacturing expert Bidyut Sarkar, thoughtfully adopting Internet of Things (IoT) infrastructure integrated with artificial intelligence (AI) promises to transform and optimize operations for chipmakers worldwide.

Sarkar draws from over two decades of leading advanced manufacturing and supply chain analytics solutions for major tech firms across Asia, Europe, and the United States. He explains how interconnected IoT systems, including sensors, controllers, and automated equipment, generate absolutely massive amounts of real-time production data.

In addition to speaking globally on smart manufacturing, Sarkar is authoring a forthcoming book on IoT and AI convergence in the industrial realm, sharing insights from integrating these exponentially powerful technologies.

"When applied judiciously to this wealth of manufacturing data, AI techniques like machine learning and deep learning can uncover patterns, correlations, and insights that drive significant improvements," says Sarkar. He has recently won two prestigious Gold Globee 2023 Business Awards in individual categories, beating 2,200 applicants for delivering AI-powered supply chain solutions using cloud, IoT, and machine learning. To address rampant disruptions plaguing global supply chains, Sarkar has successfully deployed AI-powered solutions for mitigating logistics risks through reinforcement learning algorithms and predictive analytics.

Sarkar details several high-impact examples of how chipmakers can leverage AI and IoT on the factory floor. For instance, predictive maintenance applications use AI algorithms to analyze IoT sensor streams from equipment to forecast signs of impending failures accurately. This allows proactively addressing wear and tear issues before they cause disruptive downtime. One semiconductor manufacturer reduced downtime events by 35% using such AI predictive maintenance, saving millions in costs.

Deep learning image analysis models can also detect minute defects and process deviations early. This visibility enables optimizing manufacturing parameters in real-time to boost yield, prevent wastage, and ensure consistent product quality. According to Sarkar, "Such AI techniques exploit hyperscale data far beyond human capacity to process manually. This empowers a perpetually self-improving environment."

To realize the potential of AI, Sarkar emphasizes that manufacturers must follow a solution strategy in data curation, model validation, and system integration. "The axiom of 'garbage in, garbage out' absolutely holds true with AI," he cautions. "Meticulous data preprocessing, feature engineering, model robustness testing, and explainability are all mandatory."

Furthermore, as AI biases can emerge unexpectedly, Sarkar advocates collaborative human-machine decision-making. "Keep knowledgeable engineers and technicians in the loop - AI should augment human capabilities, not replace them outright," he says. People must interpret insights, continuously improve models, handle edge cases, and maintain oversight. Transparent AI is key to building operator trust and adoption on the factory floor.

Sarkar also highlights the imperative of digital ethics and cybersecurity in industrial AI adoption. "Rigorously audit data and algorithms for biases that could propagate unfairness," he says. "And access controls, encryption, and resilience testing safeguard physical equipment as well as intellectual property." Such prudence ensures fairness and builds public and investor trust.

As semiconductor supply chains contend with market turbulence, AI and IoT offer indispensable visibility and control. According to Sarkar, manufacturers worldwide can rely on these technologies to navigate uncertainty - but only with thoughtful implementation. By curating data judiciously, validating predictively, and collaborating inclusively, chipmakers can leverage AI and IoT to enhance productivity, continuity, sustainability, and human capabilities. The future for manufacturers who embrace AI responsibly remains one of immense possibilities.