Despite healthy demand, it’s a challenging time for the tire industry. While the market for tires is expected to grow at an annual rate of more than 6% through 2027, the industry remains plagued by pandemic-related supply chain issues.
If you’re like many tire producers I’ve met with recently, you’re also facing a labor shortage with no end in sight. Workers you’ve depended on for decades are leaving the workforce in record numbers. And despite higher wages and other incentives, replacements are difficult to find.
The solution? Now you can build a new paradigm that’s less dependent on labor – and continually optimizes the process, too.
The Vision: From Automation to Autonomy
Across the tire industry, the technology momentum is beginning to swing from automation in the traditional sense to autonomous operations, enabled by advancements in artificial intelligence (AI) and machine learning technology.
What’s the fundamental difference? An automation system is programmed to perform tasks in the absence of human intervention, while an autonomous system adds the ability to learn how to perform tasks more effectively by leveraging reinforcement learning techniques.
In other words, an autonomous system is built to replicate the cognitive ability we humans take for granted as we learn and make decisions in daily activities – like driving a car.
Think about it for a moment. How do you approach a stop sign? You intuitively predict the optimal brake pressure to apply when you see the sign. And over the course of your approach, you may adjust brake pressure based on your vision and comprehension of vehicle speed to ensure a complete stop at the sign. With each stop, you instinctively evaluate your performance and reinforce your understanding to improve your stopping ability in the future.
With autonomous control technology, we can apply these same reinforcement learning capabilities to machines across your tire plant.
The Power of Closed-Loop Optimization
Autonomous systems use real-time sensing, data, and modeling capabilities to make predictions – and then use decision optimization to prescribe machine adjustments in real time that result in desired outcomes, such as consistently producing a high-quality product. A closed feedback loop reinforces optimal outcomes.
Closed-loop optimization can be a game-changer in tire plants, where many applications are notoriously difficult to improve due to the behavior of viscoelastic materials.
Today, operators are expected to react to raw material variations and environmental disturbances, which leads to dramatic throughput fluctuations based on skill level. In fact, I’ve visited plants where the output varies by up to 50%, depending on which operator is running the equipment.
You know the reason why. The experienced operator has gained an almost intuitive understanding of the machine and knows how to tweak parameters to stay within specification. The new operator doesn’t understand those advanced tactics.
Autonomous control helps drive consistent performance and quality despite differences in operator proficiency. And it goes a step further – autonomous control enables a level of continuous improvement not possible in systems that rely on manual intervention.
Driving Real Performance Gains – Today
Autonomous control applies to almost every application in the tire plant – and significant performance gains are already being realized by innovative companies.
