Factories embracing a data-driven and AI-optimised future

By Arash Ghazanfari, CTO (UK), Dell Technologies
By combining data with powerful tools such as edge computing, AI/ML and streaming analytics, real-time data is enabling the rise of smarter factories

Data is revolutionising the manufacturing industry. By combining data with powerful tools such as edge computing, artificial intelligence/machine learning and streaming analytics, real-time data enables entirely new levels of innovation and, consequently, the rise of smarter factories. 

In 2022 the total value of manufacturing product sales was a staggering £203.7 billion (US$259.1bn). Worldwide, companies are desperate to keep up with the rate of innovation happening within the industry, with the UK committing £50 million (US$63.6bn) to a data innovation hub to support UK manufacturers to accelerate the development of digital technologies. Ultimately, forward-looking enterprises should prioritise pairing operational technology (OT) with edge and AI to enable use cases that deliver remarkable benefits.

Unleashing the evolution of smart manufacturing 

In manufacturing, "the edge" is the production environment, where cameras, sensors, machines, and assembly lines generate data. Using edge computing technology, enterprises collect and interpret data from these sources or automation control systems connected to these sources. The data is then analysed using streaming data analytics and AI to enable real-time insights for rapid decision-making and immediate action. 

However, that same influx of data at the edge can paradoxically become a barrier to transformation. Expanding data sets, including new data types across new edge locations, can overwhelm edge technology with its sheer volume, creating data silos. Having a well-structured edge infrastructure will be critical to its success.

Despite these issues, manufacturers and other industrial firms continue to innovate at the edge, differentiating themselves based on their ability to derive value from edge data. Today that means using AI and ML to process massive data sets and return insights in near real-time at the point of data creation and consumption.

Revolutionising manufacturing: AI at the edge

AI can improve an organisation's security, efficiency, skills, and product quality.  – all will help organisations stay relevant and competitive in an ever-evolving landscape. The impactful and unique benefits of AI are:

  • A reduction in defects: AI can track the journey of a product through the factory right from arrival. Computer vision helps speed up 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 instant remediation rather than doing so after a defective product is complete.
  • Minimal breakdowns: AI-driven predictive maintenance systems use data from sensors and IoT devices to pinpoint the exact location of maintenance requirements. This saves technicians significant time usually used to diagnose the problem and allows the organisation to predict and prevent similar future equipment failures proactively. Proactively keeping equipment and processes up and running at an optimal level of performance helps organisations protect workers, avoid disruptions, and reduce maintenance costs. 
  • 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 to remedy it. 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 usually required but unavailable.

Generating more value at the edge

AI at the manufacturing edge promises several attractive benefits but poses some unique challenges that must be addressed. 

Organisations 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 – one of which might be a lack of on-site AI expertise. Validated designs are tested and proven configurations, designed from the beginning to dynamically fit needs based on specific use cases. These integrated solutions have been rigorously tested and documented to help speed and simplify deployment.

Empowering results

The use cases behind today's success stories are as varied as manufacturing subsectors, but recurring themes are emerging. These include connected workers, overall equipment effectiveness, predictive maintenance, production quality, yield optimisation, enhanced logistics, production optimisation and digital twins – all of which are among the most common manufacturing edge use cases.

Frequent use cases for AI-enabled edge computing and data analytics are predictive maintenance, computer vision, production quality and digital twins. These all require analysing immense volumes of multi-dimensional data such as images, audio and sensor readings from connected devices, equipment, and other assets. Use cases 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 phone data, to deliver just-in-time productivity and safety information. Other emerging use cases, such as AR and mixed reality for maintenance and training applications, will require the flexibility and cost-effectiveness of 5G networks to solve age-old connectivity and Wi-Fi data throughput issues. 

In an increasingly competitive and demanding world, 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 stringent profitability, sustainability, and safety goals.

By drawing on the power of AI at the edge, smart manufacturers are realising the tangible and measurable business benefits that come with better, faster insights right when they need it. This intelligent manufacturing approach allows them to differentiate and compete in a highly competitive global marketplace.

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