Month: July 2021

Artificial Intelligence (AI) is most often used in manufacturing 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. 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 don’t know how to get started, and often attribute their caution in implementing AI to cost, IT requirements and/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

Some of the biggest downtime in manufacturing can be caused by a major piece of equipment not working due to a mechanical or electrical failure. Breakdowns can usually be easily prevented by following the recommended preventive maintenance schedule for equipment. Often, preventive maintenance is overlooked or not optimized in terms of optimal 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. Maintenance schedules can be optimized prior to predicted breakdowns to keep machines in perfect condition and keep the production floor running smoothly.

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. AI is becoming an essential tool for the rapid delivery of products from production to the consumer. With machine learning algorithms, manufacturers can determine the optimal supply chain solution for all their products.

Managing internal inventory can be a major challenge in itself. A production line relies heavily on inventory to keep the lines running and produce products. Each step in the manufacturing process requires a certain amount of components to operate; once they are used up, they need to be replenished in time to continue the process. Making sure the factory floor has all the supplies it needs is a task that artificial intelligence can help with. The AI can learn the quantities of components, expiration dates, and optimize their distribution 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 more and more large companies are developing devices for this market, it’s only a matter of time before the manufacturing industry fully embraces it. Virtual reality can help better train product assemblers to perform assembly or preventive maintenance tasks. 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. Manufacturing AR/VR applications are endless and can play an important role in solving today’s problems.