How widespread are AI applications in manufacturing? This post via the AI Accelerator Institute states that back in 2020 around 60% of manufacturers had already adopted AI in a variety of use cases. I would love to know what that number is today, but it’s got to be pushing +80%. Those AI use cases from that post include, but are definitely not limited to:
1. AI & computer vision
AI and computer vision are facilitating production, logistics optimization, fleet management and quality inspection. Machine vision can help to increase capacity and reduce transactions in logistics and warehouse areas. This can translate to the elimination of scanning needed by human employees or by making sure that customer orders are packed as accurately as possible. Dash cameras are another example of machine vision. In fleet management, these cameras can often include options such as in-cab monitoring.
2. Predictive maintenance
Pinpointing potential errors and downtime by analyzing sensor data helps manufacturers to predict when machines will stop working and schedule maintenance before it happens. This leads to improved efficiency, as operations don’t need to stop, and to reduced costs from machines failing and needing to be replaced. Businesses can also automate visual inspections with computer vision in order to save potential losses of up to 20% annual sales.
3. Cybersecurity
With manufacturing and operations technology environments generating big amounts of data in security logs, filtering normal day-to-day actions with suspicious ones can be a massive undertaking. AI can autonomously detect fraud, intruders, malware, and much more, helping to tackle modern cybersecurity threats and challenges at a faster and more accurate rate than a human employee.
4. Edge analytics
Offering decentralized and fast insights from datasets, edge analytics collects data from sensors or machines. This data collected on the edge can then be analyzed and transformed into insights that can be used to optimize operations. Edge analytics can then:
- Track employee health and safety
- Improve both production yield and quality
- Detect the beginning stages of performance deterioration and risk failure
5. Robotics infused with AI
Manufacturing robots are excellent to automate repetitive tasks and help eliminate human employee mistakes. From welding to product inspection and assembly, these robots let employees shift their attention to other areas. Adding AI into the mix, we’re left with robots capable of not only monitoring themselves but also training themselves to get even more efficient. And with computer vision, robots can accomplish precise operations.
6. Inventory management
Due to their capacity for forecasting and planning supply, machine learning solutions can be used to predict results more accurately. This will enable businesses to manage their inventory by planning when products need to be replenished, offering more accurate arrival times for goods. AI can also create models to predict future demands based on existing inventory data.
Given the inherit nature of manufacturing, that it is made up of a series of repetitive tasks in a relatively fixed environment with a minimum of variables, it is a perfect platform for the widespread use of artificial intelligence. It could be argued that in many ways manufacturing was among the first categories to begin adopting AI applications as they were just emerging during the 1980s. On top of these use cases there are the obvious corporate applications in finance and accounting, sales and marketing. Manufacturing might already be the category with the widest adoption of AI in the most diversity of applications.
John Schneider
I have always been an early adopter. Ideas, concepts, technologies and methodologies, learning and testing these is a personal passion. This puts me in the unique position of usually having perspective, POV and experience with this newness before everyone else. I use this to both my and my consulting clients advantage. Let me show you.