3 ways in which industrial AI is revolutionizing manufacturing

July 30, 2021

Manufacturers most often use Artificial Intelligence (AI) to improve overall equipment effectiveness (OEE) and first-cycle output. Over time, manufacturers can use AI to increase uptime and improve quality and consistency, enabling better forecasting.

Like many other components of digitalization, implementing AI can seem like an overwhelming task. Additionally, concerns about how to effectively use and manage the billions of data points generated by AI computing power and connected machines are common among manufacturers. Many people don’t know how to start; moreover, they often attribute their caution in implementing AI to costs, IT requirements, or fear of not being ready for Industry 4.0.

To stay competitive, it is important for manufacturers to adapt to a more data-driven business model. This often involves reorganizing personnel, upgrading equipment and software.

AI, a concept often associated with the future, is now a reality and can be applied to your facility today. Here are 3 ways in which industrial AI is revolutionizing manufacturing and tips on how to implement it:

1. Predictive and preventive maintenance

Major equipment failures, whether mechanical or electrical, cause significant manufacturing downtime. Following recommended preventive maintenance schedules can prevent these breakdowns. However, preventive maintenance is often overlooked or not optimized for quick turnaround times. Thanks to the capabilities of IoT devices, sensors, MES data and machine learning algorithms, manufacturers can use multiple points of machine data to predict breakdowns. Optimize maintenance schedules before predicting breakdowns to maintain machines and ensure smooth production floor operations.

2. Supply chain automation

Today’s supply chains are ultra-complex networks that need to be managed, with thousands of parts and hundreds of locations. Moreover, AI is becoming an essential tool for the rapid delivery of products from production to the consumer. By leveraging machine learning algorithms, manufacturers can determine the optimal supply chain solution for all their products.

Furthermore, managing internal inventory can be a major challenge in itself. A production line heavily relies on inventory to maintain operations and meet production targets. Throughout the manufacturing process, operators must replenish a specific amount of components at each step to sustain efficiency. Ensuring the factory floor has all necessary supplies is critical, and artificial intelligence can play a pivotal role in optimizing this process. AI can learn component quantities, track expiration dates, and efficiently distribute them across the shop floor.

3. Production optimization

Optimizing a manufacturing process can be a data-intensive task that involves countless historical data sets. Determining which process parameters provide the highest quality products is no easy task. Production and quality engineers are constantly conducting dozens of experiments to optimize process parameters, but they can often be costly and time-consuming. By using artificial intelligence to process data quickly, engineers can find the best process recipe for different products. The AI will constantly learn from all points of production data to continuously improve process parameters.

Bonus: Augmented and virtual reality

As augmented and virtual reality technology improves every day, and with more and more large companies developing devices for this market, it’s only a matter of time before the manufacturing industry fully embraces it. Furthermore, virtual reality can help better train product assemblers to perform assembly or preventive maintenance tasks. Similarly, augmented reality provides real-time, machine-learning-based reporting in the factory or in the field, helping to quickly identify defective products and areas of improvement. The applications of AR/VR in manufacturing are endless and can play an important role in solving today’s problems.