Blog post

Revolutionizing Smart Manufacturing with Edge AI

edge ai
By Edge Impulse Team
Revolutionizing Smart Manufacturing with Edge AI

Smart manufacturing integrates advanced technologies such as the Internet of Things (IoT), big data analytics, and artificial intelligence (AI) to enhance efficiencies, improve product quality, and gain a competitive advantage. Leveraging edge AI enables real-time decision-making and process optimization directly at the edge of the manufacturing network.

Edge AI refers to the deployment of artificial intelligence (AI) algorithms on edge devices such as sensors, cameras, and industrial controllers, rather than relying solely on cloud-based systems. This approach reduces latency and enhances data privacy, but also helps manufacturers harness the power of AI at the source of data generation. 

Fortune Business Insights predicts that the global smart manufacturing market will grow to $658.41 billion by 2028. The market size is projected to increase from $310.92 billion in 2023 to $754.1 billion by 2030. Emerging technologies such as artificial intelligence, cloud computing, big data, and machine learning are expected to drive this growth.

Key Technologies Driving Smart Manufacturing Adoption

According to Rockwell Automation’s 9th Annual State of Smart Manufacturing report, manufacturing technology investment was up 30%. AI use cases are delivering outcomes that drive ROI, with AI/ML delivering more significant business outcomes than any other smart manufacturing technology.

Industrial Internet of Things (IoT)
This is the backbone of smart manufacturing, facilitating seamless connectivity and data exchange between machines, sensors, and systems across the entire production life cycle.

Edge computing and edge AI
Computational power is brought closer to the source of data generation, enabling real-time processing and analysis of data. This allows smart factories to make intelligent decisions and take immediate actions.

Advanced robotics and automation
Smart factories are increasingly dependent on advanced robotics and automation technologies to streamline processes, improve accuracy, and enhance productivity.

3D printing
Also known as Additive Manufacturing, 3D printing has revolutionized how products are designed and manufactured. Smart factories are leveraging 3D printing technologies to produce complex and customized components on-demand, reducing lead times, minimizing waste, and enabling greater design flexibility.

Cloud computing and big data analytics
They play a crucial role in smart manufacturing by enabling the storage, processing and analysis of vast amounts of data generated by IoT devices and other sources. By utilizing cloud-based platforms and advanced analytics tools, manufacturers can gain insights into production processes, identify bottlenecks, and optimize operations.

The Impact: Applications and Benefits of Edge AI in Smart Manufacturing

Predictive maintenance
Manufacturers continuously monitor equipment performance by analyzing data from sensors and IoT devices in real time. Edge AI algorithms enable the detection of anomalies, potential failures, and recommend preemptive maintenance actions, helping to minimize downtime and maximize asset utilization. 

Quality control
With edge AI for real-time inspection and defect detection, manufacturers can automate the inspection process by deploying computer vision models in edge devices, enabling real-time detection of defects, anomalies, or non-conformities in products. Edge Impulse’s FOMO is a groundbreaking algorithm that brings real-time object detection, tracking and counting to microcontrollers for the first time. 

BMW utilizes edge AI by placing inspection cameras on the factory floor, giving them a 360-degree view of their assembly line.

Process optimization
By deploying AI models on the edge, manufacturers can continuously monitor and analyze various aspects of their manufacturing processes such as resource utilization, energy consumption, and production bottlenecks, empowering them to make data-driven decisions and optimize resource allocation in real-time.

Energy optimization
Because of its ability to analyze real-time data from various sources, edge AI algorithms can identify inefficiencies and recommend actions to optimize energy usage, leading to significant cost savings and reduced environmental footprint.

Flexibility and agility
Edge AI enables the rapid reconfiguration and adaptation of production lines to accommodate changing customer demands, new product introductions, or shifts in market conditions. By processing data locally and making real-time decisions, edge AI empowers manufacturers to respond swiftly to dynamic market forces, providing a competitive edge.

According to Deloitte’s 2024 Manufacturing Industry Outlook report, manufacturers should continue to embrace digital transformation to contribute to a competitive, resilient future, and to help tackle ongoing manufacturing challenges. Some of the key trends identified in the report include:

Navigating the talent shortage
Labor market tightness is expected to continue into 2024. In a survey conducted by the National Association of Manufacturers (NAM), more than 75% of surveyed manufacturing executives feel that attracting and retaining a quality workforce is their primary business challenge.

Source: Data from BLS and Oxford Economics Model, Deloitte and Manufacturing Institute skills research initiative.

In the US, the gap is predicted to reach 2.1 million roles by 2030, says Industry Today. In fact, manufacturers cite a deficit of talent and skills as their toughest challenge in scaling AI use cases. The closer use cases get to production, the harder the deficit bites, according to MIT Technology Review’s Taking AI to the Next Level in Manufacturing report.

Supply chain resilience and agility
The COVID pandemic underscored the vulnerabilities of traditional supply chain models and the importance of resilience and agility. Now and in the future, manufacturers will need to focus on diversifying their supply chains, implement real-time visibility, and explore nearshoring and reshoring strategies to mitigate risk and maintain business continuity. 

Smart factories (powered by edge AI)
Smart factories are rapidly becoming the new standard in the manufacturing sector’s digital transformation. This latest innovation leverages advanced technologies such as the Internet of Things (IoT), artificial intelligence (AI), robotics, and big data analytics to create interconnected systems that stream-line production, minimize downtime, and improve product quality. 

Sustainable and circular manufacturing
With growing concerns over climate change, sustainability has become a top priority for manufacturers. We can expect to see greater emphasis on circular manufacturing practices, which involve designing products for longevity, reuse, and recycling. Additionally, the adoption of renewable energy sources and energy-efficient technologies will become more prevalent.

Edge AI Platforms and Tools for Smart Manufacturing (Hardware and Software)

To effectively implement edge AI solutions, smart manufacturing requires a variety of hardware, software and edge devices specifically designed for edge computing and AI workloads. 

Edge AI hardware and chipsets

AI-accelerated edge devices
Devices like the NVIDIA Jetson series, Google Coral, and Intel NCS are built for edge AI workloads, featuring powerful AI processors and accelerators.

Edge AI chipsets
Companies like Qualcomm, AMD and Arm are developing specialized chipsets optimized for low-power, high-performance edge computing. 

Edge AI software platforms

Edge Impulse
Edge Impulse helps developers and machine learning engineers build, test, and deploy edge AI algorithms on edge devices, covering the entire workflow from data collection to model optimization and production monitoring.

Google Cloud IoT Edge
Google’s edge computing platform for deploying and managing AI models on edge devices.

Microsoft Azure IoT Edge
Microsoft’s solution for cloud-to-edge AI, enabling deployment of AI models on IoT devices.

OpenSource frameworks
Platforms like TensorFlow Lite and PyTorch Mobile enable efficient deployment of AI models on edge devices.

From Sensors to Gateways: The Infrastructure Powering Edge AI in Manufacturing

Smart cameras
AI-enabled cameras are designed for vision inspection and defect detection.

Industrial IoT sensors
Connected sensors equipped with edge computing capabilities.

Industrial edge gateways
Such devices are typically deployed on the plant floor, close to manufacturing devices and sensors at the edge. The primary goal is to ingest the data produced on the plant floor and forward it upstream for further processing. 

These tools and platforms provide manufacturers with the necessary infrastructure to deploy edge AI solutions tailored to their specific needs, whether it’s for predictive maintenance, quality control, process optimization, or collaborative robotics.

Factors to Consider When Choosing an Edge AI Platform

When selecting an edge AI platform, manufacturers should consider factors such as performance requirements, integration with existing systems, scalability and security, and vendor support. Additionally, many manufacturers opt for hybrid solutions, combining on-premises edge computing with cloud-based AI services for more complex workloads or centralized model training and management.

By leveraging the right combination of edge AI hardware, software, edge devices, manufacturers can effectively harness the power of AI at the edge, enabling real-time decision making, optimizing processes, and driving the digital transformation of their operations.

AI in Manufacturing Use Cases in Action

When it comes to edge AI use cases, the possibilities are endless. From autonomous vehicles to smart cities, retail, healthcare, and beyond, the number of use cases will continue to expand. 

Image source: Birlasoft: 17 Remarkable Use Cases of AI in the Manufacturing Industry

Pallet tracking and inventory management

Many industrial companies rely on tracking and managing the inventory of their products accurately. But such processes can be manual, prone to human error, and don’t offer real-time visibility into inventory. In this case study, learn how pallet counting, a key part of global shipping and logistics, can be automated to reduce inefficiencies and lost value.

Looking Ahead

The manufacturing sector will continue to transform with innovative technologies such as edge AI leading the way to the future they envision. A future defined by profitability, sustainability, agility, and efficiency. While there is much to be excited about, headwinds and challenges such as talent shortages, cybersecurity, and supply chain resilience must be addressed so that manufacturers can continue to thrive in this brave new digital landscape.



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