Read how the power of AI and edge computing is critical to driving operational efficiencies and productivity gains.
Automation and monitoring of industrial assets, systems, processes, and environments are increasingly important across manufacturing industries, including transportation, electronics, mining, and textiles. In order to implement safer and more productive practices, companies are automating their manufacturing processes with IoT sensors. IoT sensors generate vast amounts of data that, when combined with the power of AI, produce valuable insights that manufacturers can use to improve operational efficiency.
Edge computing allows sensor-enabled devices to collect and process data locally to deliver insights on the factory floor without having to communicate with the cloud. Edge AI enables any device or computer to process data and make AI-led decisions in real time, with minimal latency. This convenience gives rise to new use cases where fast, real-time insights are required, like when scanning for product defects on assembly lines, identifying workplace hazards, flagging machines that require maintenance, and more.
By bringing AI processing tasks closer to the source, edge computing provides many advantages to manufacturers, including:
- Ultra-Low Latency Processing: In manufacturing scenarios, throughput is critical. Inspection processes can be a key bottleneck in the overall process. Processing data at the edge saves valuable microseconds as the data does not need to be sent to and from the cloud.
- Enhanced Security: A manufacturer’s data is key IP. Keeping data within the device compared to sending it through the cloud means that it stays secure and is less vulnerable to attacks or data breaches.
- Bandwidth Savings: Sending only AI processed smart data to the cloud and processing the remaining high velocity (for example, vibration) and high volume (for example, image and video) data locally on the device lowers data transmission rates and frees up bandwidth, cutting costs.
- Harnessing OT Domain Knowledge: Empowering OT domain experts to control the data processing AI parameters by leveraging their tacit knowledge enables them to create a highly adaptive and outcome focused agile solution.
- Robust Infrastructure: Processing data on site through edge devices allows companies to keep their manufacturing processes moving without disruption, even if network outages occur.
Use Cases of Edge Computing in Manufacturing
Manufacturers globally have started to use AI at the edge to transform their manufacturing processes. The following use cases explore how edge computing is promoting enhanced efficiency and productivity in manufacturing.
- Predictive Maintenance: Sensor data can be used to detect anomalies early and predict when a machine will fail. Sensors on equipment scan for flaws and alert management if a machine needs a repair so the issue can be addressed early, avoiding downtime. The combination of sensor data, AI, and edge computing accurately assesses equipment condition and allows the manufacturer to avoid costly unplanned downtime. For example, sensor-equipped video cameras in chemical plants are used to detect corrosion in pipes and alert staff before they can cause any damage.
- Quality Control: Defect detection is an essential part of the manufacturing process. When running an assembly line where millions of products are made, defects need to be caught in real time. Devices that use edge computing can make decisions in microseconds, catch defects instantly, and alert staff. This capability provides a significant advantage to factories as it can reduce waste and improve manufacturing efficiency.
- Equipment Effectiveness: Manufacturers are continuously looking to improve processes. When combined with sensor data, edge computing can be used to assess overall equipment effectiveness. For example, in the automotive welding process, manufacturers need to meet many requirements to ensure that their welding is of the highest quality. Using sensor data and edge computing, companies can monitor the in real time, and catch defects or safety risks before products leave the factory.
- Yield Optimization: In food production plants, it is critical to know the exact quantity and quality of the ingredients being used in the manufacturing process. By using sensor data, AI, and edge computing, machines can recalibrate instantly if any parameters need to be changed in order to produce better quality products. There is no need for manual supervision, or to send data to a central location for review. The sensors on site are capable of making decisions in real time to improve yields.
- Factory Floor Optimization: Manufacturers must understand how factory spaces are being used in order to improve processes. For example, in a car manufacturing plant it is inefficient if workers must walk to different locations to complete tasks. Supervisors may be unaware of this bottleneck if the data is not available. Sensors help analyze factory spaces—how are they being used, who is using them and why. Data and critical Edge AI processed information is sent to a central location for a supervisor to review. The supervisor can then make informed optimizations to factory processes.
- Supply Chain Analytics: There is a growing need for companies to have constant visibility on procurement, production, and inventory management. By automating these processes with AI and edge computing, companies can better predict and manage their supply chain. For example, an electronic manufacturing company with automated processes can immediately alert other production facilities across the country to generate more of a needed raw material so production is not affected.
- Worker Safety: Industrial workers often operate heavy machinery and handle hazardous materials at manufacturing sites. Using a network of cameras and sensors equipped with AI-enabled video analytics, manufacturers can identify workers in unsafe conditions and quickly intervene to prevent accidents. Edge computing is critical to worker safety since life-saving decisions need to be made in real time.
Edge computing will continue to transform the manufacturing industry by bringing about AI-driven operational efficiencies and productivity gains. Download this free e-book to learn how edge computing is helping build smarter and safer spaces around the world.