By drawing on the power of AI, smart manufacturers are realising the very tangible and measurable business benefits
Real-time data is enabling new levels of innovation and the rise of smarter factories when combined with potent tools like edge computing, AI/ML, and streaming analytics. Data is revolutionizing manufacturing. Forward-thinking businesses are combining edge and artificial intelligence (AI) with operational technology (OT) to offer use cases that yield impressive advantages.
The smart manufacturing
The production environment, also known as "the edge" in manufacturing, is where data is generated by cameras, sensors, machinery, and assembly lines. Businesses gather and translate data from these sources, or automation control systems linked to these sources, using edge computing technologies. Technologies like artificial intelligence (AI) and streaming data analytics are used to examine the data to provide quick insights for quick decision-making and quick action. AI at the manufacturing edge
AI can advance your organization’s ability to protect workers, enhance production quality, avoid maintenance issues and fill in skills gaps with machine intelligence. The benefits of AI in action at the edge include:
Lower number of defects: AI can track parts coming into and moving through the factory. Computer vision helps speed and automate the work in process throughout the entire production cycle. Defects can be identified, flagged, and tracked back to individual processes or components in real-time.
Minimal breakdowns: AI-driven predictive maintenance systems use data from sensors and IoT data to pinpoint the exact location of maintenance requirements — saving technicians significant amounts of time in diagnoses and allowing the organization to proactively predict and prevent future equipment failures.
Addresses knowledge gaps: Augmented reality (AR)–-based AI systems allow off-site specialists to visit the factory virtually, using the AR interface to directly evaluate a situation and guide or train on-site workers. The AI can also understand situational context and load standard processes for recommended action, with each step demonstrated in AR, allowing untrained workers to perform complex tasks in cases where specialists are unavailable.
Use edge AI for more value
Moving AI to the manufacturing edge promises a lot of tantalizing benefits, but it also poses some unique challenges that need to be overcome for manufacturing-edge AI deployments to be successful. To further simplify deployment, integration, security and management, configured systems built by manufacturing AI experts can accelerate time to value with solutions designed especially for smart manufacturing use cases. Outcome-driven use cases
Edge computing with AI and streaming data analytics is increasingly deployed for use cases such as predictive maintenance, computer vision, production quality and digital twins, all of which require analyzing vast volumes of multi-dimensional data such as images, audio and sensor readings from connected devices and equipment and other assets. Certain use cases, such as those that enable the connected worker to be more productive and safer, rely on high-speed and ultra-low latency connectivity. Together, these technologies and use cases can help manufacturers give their customers what they want when they want it: innovative, high-quality products at competitive prices while meeting increasingly stringent profitability, sustainability and safety goals.
By drawing on the power of AI at the edge, smart manufacturers are realizing the very tangible and measurable business benefits that come with better, faster insights at the point of need. This intelligent approach to manufacturing gives them the ability to differentiate and compete in a competitive global marketplace.
(The writer is, a lead-solutions architect, at Dell Technologies India, views are personal)
