Artificial Intelligence (AI) is most commonly applied in Manufacturing to improve overall equipment efficiency (OEE) and first-pass yield in production. Over time, manufacturers can use AI to increase uptime and improve quality and consistency, allowing for better forecasting.
As with many components of digitization, AI implementation can seem overwhelming. Concerns about how to effectively use and manage billions of data points generated by intuitive computing power and their connected machines are common amongst manufacturers. Many are uncertain how to get started and often attribute their caution in AI adoption to cost, IT requirements, and/or fear of not being “Industry 4.0” ready.
To stay competitive, it’s important manufacturers adapt to a more data-driven business model. This often includes reorganization of staff, hardware and software upgrades.
AI, a concept often associated with the future, is now a reality and can be applied to your factory today. Here are 5 Ways Industrial AI is Revolutionizing Manufacturing and tips on implementation:
Some of the biggest downtimes for a production operation can be caused by a core piece of machinery being offline due to mechanical or electrical failure. Usually, the failure can be easily prevented by following up on the machine’s recommended preventative maintenance schedule. Often PMs are overlooked or not optimized for the best timeline to complete. With the power of IoT devices, sensors, MES data, and machine learning algorithms, manufacturers can utilize many machine data points to predict breakdowns. PM schedules can be optimized before the predicted breakdown to keep machines in top-notch condition and the production floor running smoothly.
Today’s supply chains are super complex networks to manage, with thousands of parts and hundreds of locations. AI is becoming a necessary tool to get products from production to customer promptly. With machine learning algorithms, manufacturers can define the optimized supply chain solution for all their products. Questions like ‘How many resistors should be ordered for the next quarter?’ or ‘What’s the best shipping route for product A’ can finally be answered without relying on a best guess approximation.
In-house inventory management can be a major challenge in itself. The production line heavily relies on inventory to keep the lines fed and producing products. Each process step requires a certain amount of components to operate; once consumed, it needs to be replenished on time to continue processing. Keeping the factory floor stocked with all necessary inventory is a challenge that AI can help manage.
