The Factory Of The Future Will Be Data-Driven And AI Optimized

The Factory Of The Future Will Be Data-Driven And AI Optimized

Authored by Srinivaschary T, Lead-Solution Architect, Dell Technologies India

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 evolution of 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 from 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 in order to provide quick insights for quick decision-making and quick action. However, even as user expectations for real-time insights rise, growing data sets, including new data types across new edge locations, may become too much for edge technology to handle.

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 for immediate remediation, as opposed to after a defective product is discovered.

  • 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 clearly demonstrated in AR, allowing untrained workers to perform complex tasks in cases where specialists are unavailable.

Use edge AI to generate 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. 

Organizations need to set up a strong foundation of back-end infrastructure and consulting services to fully understand the entire journey from ingesting edge data to getting the desired business outcome from beginning to end. 

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. Choosing an engineering-validated solution for AI can help businesses overcome barriers to adoption, including a lack of on-site AI expertise. Validated designs are tested and proven configurations, designed from the start to dynamically fit needs based on specific 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, such as Wi-Fi and cellular, to deliver just-in-time productivity and safety information. 

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.

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