Speed and precision are non-negotiables in manufacturing. From identifying equipment defects and ensuring worker safety to adjusting assembly lines, these jobs require split-second decisions made in real time. This is where Edge AI helps.
In this article, we will explore what Edge AI means for manufacturers, how it helps, some real-world use cases and some of the major challenges involved.
What Is Edge AI?
Edge AI performs computations locally at or near the data source, rather than relying on distant servers or the cloud. This localized processing enables real-time responses, like detecting defects, identifying safety hazards or adjusting machine performance, typically within milliseconds.
In manufacturing, if a defective product isn’t caught instantly and continues down the line, even a split-second delay can lead to rework, downtime, or equipment damage. Being split-second faster can prevent these issues by ensuring immediate action.
For example, NVIDIA uses Jetson modules, the company’s small, powerful computers designed to run AI at the edge, to enable real-time defect detection in factories. Moreover, cameras and sensors analyze products locally, instantly finding flaws and alerting operators to prevent downtime and reduce waste, without relying on slower cloud connections..
How Edge AI Helps Manufacturers
Despite the advancements in manufacturing technologies, businesses still face several challenges, including rising costs, capacity constraints, etc., some of which are directly and indirectly addressed by Edge computing and AI.
Reduced latency: In large, distributed manufacturing plants, detecting issues like machine overheating or malfunction can be the difference between a quick fix and hours of costly downtime. Edge AI allows operations and plant managers to process sensor data locally, right at the source, enabling real-time decisions such as stopping production or initiating corrective action. By eliminating the need to send data to the cloud, Edge AI significantly reduces latency, ensuring faster response times in critical, time-sensitive situations.
Lower bandwidth and cloud costs: Sending all data to the cloud drives up bandwidth and storage costs, while Edge cuts expenses by sending only critical anomalies, reducing data traffic and overhead.
For example, Hitachi deployed Edge AI sensors on factory equipment to analyze vibration and temperature data locally, sending only alerts to the cloud when anomalies occur. This reduced data transmission by over 90%, cutting bandwidth and storage costs significantly. Additionally, Edge AI-driven predictive maintenance lowered unplanned downtime by up to 30%.
Increased uptime and reliability: With cloud systems, if the internet connection stalls, production systems can become slow or unresponsive. Edge AI avoids that by making decisions and running models on-site, ensuring operations continue reliably even with poor or interrupted connectivity.
This independence from internet connectivity helps keep production running smoothly, which is beneficial in remote or infrastructure-hit locations.
Real-World Applications of Edge AI
Visual inspections: Modern factory floors use Edge AI-enabled cameras to automate visual monitoring and detect issues like cracks, misalignment, missing parts and other anomalies in the production lines.
For example, Ford collaborated with IBM to leverage computer vision and Edge AI for real-time, on-site vehicle inspections. Using IBM’s Maximo Visual Inspection platform helped the automaker deploy the solution at several plants at multiple inspection points. The goal was to detect and correct vehicle body defects in the production line. The solution increased vehicle quality, according to Ford’s advanced manufacturing IT principal technologist, and did not require data scientists to implement.
Smarter, self-regulating machines: Many production lines still depend on reactive maintenance measures, leading to operational inefficiencies and downtime.
Edge AI technologies eliminate the need for manual adjustments and monitor machine performance accordingly. They adjust speeds, calibrations and workflows based on real-world conditions and do not strictly adhere to paper manuals. The purpose is to enable machines to self-optimize, learning through patterns and predictions.
However, this isn’t exclusive to Edge AI. Cloud-based systems can also enable predictive maintenance and optimization. The key difference is that Edge AI processes data locally and in real-time, without needing to send data to the cloud.
Safety and compliance monitoring: Regulatory compliance related to occupational and worker safety is challenging in non-static and potentially hazardous factory environments; even more so when relying on manual monitoring.
Edge AI systems allow production leaders to analyze video feeds, floor data and sensors to catch unsafe behaviors in real time, such as workers without protective gear, equipment overheating or blocked or malfunctioning emergency exits. Edge AI does this by running trained computer vision and sensor-analysis models directly on local devices at the factory floor.While it may not directly resolve these issues, Edge AI can improve risk mitigation by facilitating faster responses, preventive actions and improved compliance monitoring.
Challenges in Full Deployment of Edge AI
With more than 80% of AI initiatives failing, Edge AI,too, has its fair share of challenges when it comes to full deployment.
Lack of usable data and skilled talent: AI systems today rely less on complex coding and more on quality data. However, many businesses find their operational data difficult to access as it's spread across various systems. In such cases, you can start by auditing and consolidating your data sources before implementing Edge; i.e., identifying the location of critical operational data, breaking down silos between systems and cleaning data to ensure its accurate and usable.
Second, people with extensive operational knowledge aren’t trained in AI or analytics; there’s a typical shortage of professionals who can successfully connect business needs and AI capabilities.
Companies must consider investing in cross-functional workshops and partnerships with training providers to equip operational teams with the basics of AI, data literacy and practical use cases relevant to their roles. Encouraging collaboration between your teams and domain experts will help bridge the gap between business needs and technical opportunities, keeping your AI projects up and running in real operational challenges.
Legacy systems and high costs: Many businesses still operate on obsolete systems and platforms that weren't designed considering modern AI and other emerging tech stacks. Updating or replacing these legacy systems can be costly and disruptive, let alone steering your workforce out of their comfort zone to adopt modern processes and workflows.
If you're a medium-sized manufacturing business, you can start by analyzing which legacy systems are causing the most problems and prioritizing them for upgrade. Second, consider modernizing in stages, such as integrating application programming interfaces (APIs) to connect old systems with new platforms, rather than replacing the entire platform at once.
Conducting a clear cost-benefit analysis and involving frontline teams early can also reduce disruption and improve adoption of new processes and workflows.
Furthermore, increasing computational power in your existing systems to support Edge AI adds another layer of challenge. This is because many existing machines or devices on the factory floor might not be capable of handling the processing demands of AI models locally.
Upgrading them may require new hardware, specialized chips, or retrofitting edge devices, all of which add cost, require technical expertise and can disrupt production during the installation process.
Security gaps and manageability
System management and maintenance issues, coupled with complex integrations with existing systems, make it challenging to deploy Edge AI fully.
Business owners worry about ensuring reliable performance and maintaining oversight when Edge AI runs across multiple locations. Deploying Edge AI outside of centralized systems raises serious data privacy risks, especially when we talk about vulnerable devices that are often physically accessible.
Getting Started
Despite the challenges I’ve outlined, you don’t need to overhaul your entire factory to make room for Edge AI. You can start with a focused pilot project; i.e., identify a high-impact area like quality inspection or equipment monitoring where there is a real need for real-time insights.
Remember, employing Edge AI is not just a tech upgrade but a business transformation. You can start with bottom-line activities, including ensuring your existing machines can support data collection.
Then, collaborate with your IT and ops teams to build a secure, scalable foundation and scale the implementations to other functional areas on the floor.
