Transforming Industrial Manufacturing with Edge AI

Leveraging Edge Data with AI

In manufacturing, “the edge” is the production environment, where cameras, sensors, machines and assembly lines generate data. Using edge computing technology, enterprises collect and translate data from these sources . The data is analysed using technologies such as streaming data analytics and AI to enable immediate insights for rapid decision making and instantaneous action.  

Before exploring how Edge AI is impacting industrial manufacturing, it’s necessary to define what Edge AI is. To achieve this, it’s also necessary to introduce Edge computing, as it plays an essential role in the application of Edge AI.

Edge Computing stores data and performs computing tasks in the same place that the data is initially generated. It brings the functions of gathering, analyzing, and processing information to the network’s edge.

Thus, Edge AI can be understood as artificial intelligence that’s performed locally. It uses technology like sensor fusion, video analytics, machine vision, and advanced analytics.

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Industrial manufacturers operating large facilities face challenges managing numerous devices. A 2023 survey by Arm identified Edge Computing and Machine Learning as two of the top five technologies poised to significantly influence manufacturing in the near future.‍

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What is all the fuss about Edge AI?

IDC predicts that by 2026, 75% of large enterprises will rely on AI-infused processes to enhance asset efficiency, streamline supply chains, and improve product quality across diverse and distributed environments.

By 2026 at least 50% of edge computing deployments will involve machine learning (ML), compared with 5% in 2022.

The Advent of AI/ML in the Edge

From Observability to Actionable Insights: Initially, digital edge technologies primarily focused on observability and reporting. The aim was to gather data from various sources, monitor system health, and provide insights into operational performance. While these functions are essential, the digital edge's true potential lies in its ability to not just observe but to act intelligently and autonomously.

The Advent of AI/ML in the Edge: The incorporation of AI and ML into edge computing nodes is a game-changer. It enables these devices to process and analyze data locally, make decisions in real-time, and execute actions without the need for constant cloud connectivity. This shift towards a more intelligent edge is driven by the need for speed, efficiency, and reduced latency in data processing.

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Key use cases powered by Edge AI in Manufacturing

Industrial manufacturing leveraging AI are seeing benefits across the value chain, from engineering to production, through predictive maintenance. This includes improved efficiency, lower machine failure costs, and cost-effective preemptive repairs, preventing breakdowns.

1. Predictive Maintenance: Edge AI enables predictive maintenance by analysing real-time data from sensors and machinery at the edge of the network. By detecting anomalies and patterns in equipment behaviour, manufacturers can anticipate potential failures and schedule maintenance tasks proactively, thereby reducing downtime and preventing costly disruptions to production.

2. Quality Control and Inspection: Edge AI facilitates real-time quality control and inspection processes on the manufacturing floor. By deploying AI-powered vision systems at the edge, manufacturers can inspect products for defects, deviations from specifications, and other quality issues with greater accuracy and efficiency. This ensures that only high-quality products reach the market, enhancing customer satisfaction and reducing rework.

3. Optimised Energy Management: Edge AI helps manufacturers optimise energy consumption and reduce operational costs by analysing energy usage data in real-time. By deploying AI algorithms at the edge, manufacturers can identify opportunities to improve energy efficiency, minimise waste, and streamline resource allocation across production processes, leading to significant cost savings and sustainability benefits.

4. Real-time Production Monitoring: Edge AI enables real-time monitoring of production processes by analysing sensor data and machine performance metrics at the edge of the network. Manufacturers can use AI algorithms to identify bottlenecks, optimise production workflows, and make timely adjustments to meet production targets and maintain product quality standards.

5. Supply Chain Management: Edge AI enhances supply chain management by providing real-time visibility and insights into inventory levels, logistics operations, and demand forecasting. By analysing data from sensors, RFID tags, and other IoT devices at the edge, manufacturers can optimise inventory levels, improve demand forecasting accuracy, and streamline logistics operations, resulting in reduced costs and improved efficiency throughout the supply chain.

6. Worker Safety and Security:  AI-powered video analytics systems deployed at the edge can detect safety violations, unauthorised access, or hazardous conditions, enabling prompt intervention and mitigating risks to personnel and assets.

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Many businesses are already advancing on their digital transformation path and are positioned to benefit from early adoption of Edge AI. Yet, obstacles like cost, complexity, security issues, and a lack of experience can hinder the deployment of edge initiatives.‍

Challenges of getting started with Edge AI

1. Data acquisition and protocol fragmentation is a general challenge

‍Valuable industrial edge data is typically locked within a vast array of Operational Technology (OT) systems. However these systems are highly heterogeneous, they come from different vendors, feature different characteristics and capabilities, and the components within these systems can’t readily communicate with one another. As a result, organizations struggle to access, analyze, and make use of that data.

2. IT/ OT converge and aligning facilities goals with enterprise strategy

OT runs the technology that creates the data. IT wants rapid innovation, while OT wants reliable business operations.

As edge computing empowers smart manufacturing, the industrial sector is often left to struggle with the gap between enterprise strategy and individual facilities deploying pointed solutions. This issue is due in part to the existence of diversity at the industrial edge, which is associated with operational technologies (OT), IT infrastructures, applications, and people skills.

The OT role will continue to evolve because IT departments are already stretched too thin, and the edge is simply not a top priority for them. OT will assume more responsibility for those technologies that are critical to them, such as Edge Computing.‍

3. Lack of internal skills to build/ operate the edge

Organizations must invest in the correct skillsets – including AI, cybersecurity and automation – to ensure their edge infrastructure is utilized correctly and to its fullest.

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Want to know more about "real applications of Edge AI" in Industry? Donwload customer stories here

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Harnessing the Potential of Edge AI "Today"

Edge environments are highly complex and heterogeneous. If you are thinking to scale at speed, it’s worthwhile considering an edge computing platform that can support your growth. The task of managing edge operations across diverse locations, devices and applications with the highest security standards can be daunting and expensive.

When it comes to integrating Edge AI infrastructure within the industrial sector, organisations face a pivotal decision: should they build their own custom solution or buy a third-party offering? Both approaches have their advantages and disadvantages, and the right choice depends on various factors such as budget.

Building vs. Buying: Evaluating Edge AI Platforms for Your Business

The decision to build or buy an edge computing platform requires careful consideration of various factors, each with its own set of advantages and disadvantages.

Buying from a Third Party

Advantages:

1. Speed to Market: Purchasing a third-party solution can significantly accelerate deployment time, allowing businesses to benefit from Edge AI capabilities more rapidly compared to developing a system in-house.

2. Reduced Initial Investment: Building an Edge AI infrastructure requires a substantial upfront investment in research, development, and testing. Buying a solution can lower these initial costs.

3. Expert Support: Vendors often provide ongoing support and maintenance, ensuring the system remains up-to-date and operates efficiently without requiring in-house expertise.

4. Proven Solutions: Third-party products have typically been tested and validated across multiple deployments, offering a level of reliability and performance assurance.

Disadvantages:

1. Less Customisation: Off-the-shelf solutions may not fit every unique operational requirement, potentially leading to compromises in functionality or performance.

2. Ongoing Costs: While the initial investment might be lower, recurring licensing fees, subscriptions, or service charges can add up, impacting long-term budgets.

Building Your Own Edge AI Infrastructure

Advantages:

1. Customisation: Building in-house allows for bespoke solutions tailored precisely to an organisation's specific needs, offering optimal integration with existing systems and processes.

2. Control and Independence: Owning the infrastructure reduces dependency on external vendors, providing more control over the technology stack, data security, and future developments.

Disadvantages:

1. Higher Initial Costs: The costs associated with research, development, and deployment of a custom solution can be significantly higher, requiring substantial initial investment.

2. Longer Deployment Time: Designing and building a bespoke system is time-consuming, potentially delaying the realisation of benefits from Edge AI.

3. Maintenance and Support: Organisations must allocate resources for ongoing maintenance, updates, and troubleshooting, requiring in-house expertise or external consultants.

In conclusion, the choice between buying and building Edge AI infrastructure in the industrial world hinges on balancing the need for customisation and control against the desire for speed, reduced initial outlay, and vendor support. Organisations must carefully assess their specific requirements, capabilities, and strategic objectives to make the most informed decision.

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Continue reading Part 2 -> Scaling Edge AI in Manufacturing

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