Shifting the needle on quality and efficiency in your tire and rubber production facility through AI-driven Guidance and Optimisation.

tire-manufacturing-process

Tire manufacturers are looking for innovations that will drive business growth and efficiency amidst the demand for increasingly reliable, sustainable, and innovative products. 

To customers, sustainable tires have a smaller environmental footprint, last longer, and improve fuel economy. To manufacturers, sustainable tires improve market share, reduce rejects, and strengthen margins. Scrapped tires have a significant environmental impact as they can pile up in landfills or contaminate water sources

Across tire manufacturers, sustainability remains a major commitment among top executives. Michelin and Continental have invested considerably in research into the use of sustainable materials, looking to reduce the impact tires have on agriculture and landfills. Goodyear has looked to smart monitoring to extend the lifespan of existing tires and Bridgestone has invested considerably in renewable energy. 

Many of these initiatives look to alternative resources, but what about resource efficiency? Tire manufacturers scrap thousands of tires each year due to the strict standards on product reliability that dominate the industry. If tire manufacturers can produce tires more efficiently, they can reduce costs while offering customers a more reliable, longer-lasting product.

The three substantial expenses for tire manufacturers are equipment, energy, and raw materials. To ensure these resources are being used effectively, manufacturers regularly look to improve machine availability and reduce defective products by optimizing their production processes. From compounding through to vulcanization, there are thousands of small parameter changes that affect how a tire is produced. 

Improving Efficiency with Artificial Intelligence

Manufacturing plants are complex, producing an abundance of data required for product quality control. The major challenge for tire manufacturers is how to continue optimizing these processes as tires become more advanced and the volume of data produced throughout the manufacturing process continues to grow. Without the help of AI solutions to understand these processes, improving quality and availability across automated production may result in business disaster. 

Artificial Intelligence technologies excel in analyzing complex datasets. AI systems have the ability to learn the sophisticated, non-linear relationships that exist between parameters while significantly reducing the global manufacturing risks in tire manufacturing. This is due to the unique advantage of AI systems in understanding the intricate web of interdependencies that exist between past and present steps in manufacturing processes. 

The manufacturing plants of the future

Prescriptive AI is a particular kind of AI technology that is able to learn the complex relationships between process parameters and plant metrics and generate prescriptive actions for operators to optimize product quality. In manufacturing, these prescriptive AI solutions are called Expert Execution Systems (EES).

At DataProphet, we provide manufacturers with the next generation of manufacturing solutions, which are able to continuously learn from the data and provide actionable insights to the plant floor. Our solutions use plant data on scrap, defects, or non-quality to provide continuous, pre-emptive feedback to machines and equipment to optimize the plant. 

Introducing DataProphet PRESCRIBE 

Our AI-as-a-service product, DataProphet PRESCRIBE, proactively prescribes changes to plant control plans from compounding to curing. DataProphet PRESCRIBE is the homepage to your factory, offering a web-interface to aid engineers and operators to help prioritize important setpoint changes. Using DataProphet PRESCRIBE, operators are shown the latest measurements across each step of their process along with target bounds which provide instructions on the process control changes that should be made first.

DataProphet PRESCRIBE is easy to integrate into your existing systems, identifying the optimal process parameters to reduce non-quality. Using DataProphet ET and DataProphet EDGE, DataProphet PRESCRIBE can connect to and integrate data across a range of existing production Historians, Quality Management Systems (QMS), ERP, MES, ODBC Databases, and machine PLCs.  

DataProphet PRESCRIBE comes with a web interface that provides operators with clear control bounds to improve system quality. Using this end-to-end approach, our solution has seen reductions of 50 percent and more in the cost of non-quality, through a customized single model approach, which takes into account higher-order effects or interactions across processes which might constrain viable process control changes for operators. 

When you combine sustainability with an AI-driven agenda, your plant becomes one that can thrive in the future business landscape.