Data Center / Cloud

On-Demand Session: Deploying Edge AI in Manufacturing

When it comes to production, companies spend endless cycles improving their processes to drive the most revenue. Manufacturing lines are rigorously tested, and any changes require downtime that can eat up a company’s profits. That’s where AI comes in.

Manufacturing as an industry is ripe to experience the benefits of AI because it performs highly repeatable tasks that can each be tuned and optimized for overall performance. AI takes readily-available historical data from sensors, cameras, and even outcomes and processes it faster than any human could, without getting tired. Once the data is fed into the AI, the AI makes sense of it, then it has to make a prediction based on past data, it makes a choice based on the best option available, and finally it takes action.

At GTC ’21, Data Monsters, who builds AI solutions for production and packaging, discussed the growth of AI in manufacturing and how AI is being used to optimize every part of the supply chain, from forecasting and production planning to quality control. The session “Getting Started with AI in Manufacturing” shared how AI could be used to improve the Overall Equipment Effectiveness (OEE) of any organization using data that is already available today. 

OEE consists of three factors: availability, performance, and quality. Each of these factors can be optimized to improve the effectiveness and therefore profits of manufacturers. Let’s take a look at the various AI techniques that can be used for each.

Figure 1. Overall Equipment Effectiveness KPIs for manufacturing

Availability is measured by the amount of uptime compared to downtime. As downtime at any part of the system can result in dramatic productivity loss, predictive maintenance is something many manufacturers are looking to in order to improve the uptime of machinery. Predictive maintenance models learn from the system and identify indicators that predict a failure. This model can alert the team prior to a failure and make recommendations about what needs to be fixed, both of which can reduce downtime. 

Performance looks at how fast products are being produced compared to how fast they could be produced. With highly repetitive tasks in the manufacturing space, AI can be used to help identify the most efficient schedule based on objective function parameters, and make suggestions on where bottlenecks can be removed. Depending on the parameters, process optimization can determine the most efficient outcome based on technology variables and historical outcomes, thus maximizing throughput, minimizing cost, and reducing leftover stock.

Quality of production means looking at what proportion of products are being produced without defects. Here, computer vision provides a lot of data for analysis. Manufacturers can improve the overall quality by identifying where in the process the defects are happening so they can be prevented in the future. Reducing defects and improving the overall quality of products can have a dramatic impact on not only productivity, but also revenue. 

Figure 2. Data Monsters trained AI that identifies visual anomalies on the product and alerts the packaging line operator in real-time.

AI becomes a huge differentiator in the manufacturing space, as it reduces manual operation, and improves efficiency and the competitive position in the market with optimized costs and scheduling. 

Due to the intense calculations of AI required to perform these tasks, manufacturers are bringing the compute close to sensors generating the data. Moving compute to the edge has the benefit of lowering latency and bandwidth requirements to run AI applications, ensuring the fastest and most accurate responses. With numerous compute systems on production lines, AI models are downloaded from the cloud, data is collected and processed locally. Models are fine-tuned and uploaded back to the cloud for further distribution between several edge systems.

To learn more about implementing inspections, diagnostics, and predictive maintenance in the manufacturing pipeline, check out the Data Monster’s session “Getting Started with AI in Manufacturing“. 

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