By Ram Bhutani
AI is revolutionizing manufacturing and supply chains. Organizations must fully leverage AI coupled with emerging technologies such as IoT and Automation to remain competitive and resilient.
While AI is now a buzzword, we have seen large-scale deployments of multiple AI-based use cases across the end-to-end supply chain. These use cases help organizations drive enhanced quality, efficiency, visibility, and sustainability across their manufacturing and supply chains.
Enhanced Quality through Machine Vision: Machine vision has played an integral role in transforming supply chain processes by enhancing quality control. Deploying machine vision can help enterprises visually inspect and identify manufacturing defects. Utilizing high-speed cameras and processors, along with AI algorithms, manufacturers can accurately identify defects at high speeds while improving traceability. Integrating Machine Vision with existing Manufacturing Execution Systems reduces human error and boosts process efficiencies and quality.
Predictive Maintenance with AI and IoT: AI-driven predictive maintenance helps manufacturers anticipate equipment failures, reduce downtime, and enhance overall equipment effectiveness (OEE). IoT-enabled devices gather and transmit real-time data on equipment performance, environmental conditions, and power usage. This seamless connectivity, combined with Machine Learning Algorithms, enables predictive and condition-based maintenance.
Automation with AI-powered Cobots and Robots: AI-powered robotic systems facilitate precise assembly and quality inspection tasks, ensuring consistent product quality while reducing labour costs and cycle times. They can also catalyze high-volume personalized manufacturing.
Enhanced Planning with Machine Learning and Predictive Analysis: Machine learning is deployed across supply chain planning, encompassing demand forecasting, capacity and production planning. Leveraging these advanced technologies, organizations can effectively analyze big data sets, uncovering patterns and trends to enhance visibility and mitigate quality and production bottlenecks. These applications have delivered significant results by accurately predicting customer needs, streamlining operations, and optimizing capital allocation.
The ultimate use case – Digital Twins: We anticipate the emergence of AI in more advanced supply chain use cases. Digital twins, integrating AI with supply chain software like MES, hardware, and IT/OT technologies, will be central to this evolution. In essence, a supply chain digital twin replicates the characteristics, behaviour, and performance of its real-world counterpart.
In conclusion, AI is transforming manufacturing and supply chains, boosting efficiency, quality, and competitiveness. However, successful AI deployments require considerations across talent, processes, data security, and infrastructure. Establishing a robust platform and ensuring high-quality data generation is vital. This involves deploying systems like Manufacturing Execution Systems, Enterprise Resource Planning software, and Warehouse Management Systems, alongside leveraging sensors and IoT devices across the supply chain to generate valuable data for AI-driven insights and decision-making.
The author is Senior Vice President of Business Operations, BCI
Disclaimer: Views expressed are personal and do not reflect the official position or policy of Financial Express Online. Reproducing this content without permission is prohibited.
