Apollo Tyres, one of India’s largest tyre manufacturers, sought to transform its manufacturing processes with an ambitious digital strategy. The company worked with AWS Partner Deloitte to implement an Internet of Things (IoT) solution on Amazon Web Services (AWS), connecting its production equipment to a data lake. Real-time data collection, integration, and advanced analytics through a centralised dashboard have enabled a 9% increase in productivity on primary equipment and a 3% reduction in energy usage.
Apollo Tyres has grown rapidly in the past few decades. Faced with limited insight into the performance of its costly equipment, the company sought an IoT solution to digitalise and standardise its manufacturing processes. Machine data held the key to process efficiency.
Tyre manufacturing involves numerous steps and a variety of heavy equipment. Apollo’s machines were equipped with supervisory control and data acquisition (SCADA) systems, which collect data on production capacity and other metrics. But this data was siloed, offering a window into the performance of individual machines only, with no basis for comparison between machines or plants.
This limited visibility was particularly concerning in the case of Apollo’s tyre rubber mixers. These machines are crucial to the manufacturing process. They are also extremely capital intensive—representing an investment of about US$24 million each, including related infrastructure—labour intensive, and energy intensive. Any improvement to their performance promised significant returns.
AWS Partner Deloitte was working with AWS on other projects when the opportunity to test new technologies in the tire industry arose. Seeing an ideal fit between Deloitte and Apollo, AWS prompted a collaboration.
Deloitte leveraged its expertise in building secure, scalable IoT solutions to develop the solution architecture with AWS using various AWS services. These included AWS IoT SiteWise, a managed service for collecting and analysing data from industrial equipment, running on AWS IoT Greengrass, a cloud service for building, deploying, and managing device software at the edge. This enabled ingestion of a very large volume of raw data packet streams from Apollo’s equipment into an AWS data lake.
The complete solution consists of a smart manufacturing platform with a centralised dashboard. To test the proof of concept, Apollo started with its mixers, feeding data from the mixers into the AWS data lake. Amazon Redshift enabled data visualisation on the dashboard, presenting a detailed, near real-time view of equipment metrics for faster, more informed decision making by plant personnel.
The newfound connectivity finally enabled Apollo to compare data. “That was the beauty of it,” said Shibu George, global head advanced manufacturing at Apollo Tyres. “When we started streaming data to AWS, we could compare the performance within the plant, and across plants. That was a unique opportunity.”
With seamless access to mixer data, Apollo could identify performance discrepancies and take corrective actions. The company proceeded with deeper analytics and improved productivity by 9%—equivalent to the capacity of more than one mixer. This makes it possible to increase production without investing US$24 million in another mixer and related infrastructure.
Apollo’s teams initially expressed some unease about the visibility of their data; however, management was able to alleviate this by showing them the benefits. “With the help of Deloitte, we could shine a light and show our teams how the data could help them improve. It was a great experience,” said George. This, along with training and support from AWS, helped Apollo make the cultural shift into a data-driven organisation.
The boost in productivity from Apollo’s mixers also reduced its energy usage by 3%. Viewed in isolation, this reduction might seem negligible. However, a single mixer has a massive energy load of about 10 megawatts—enough power to illuminate a town of about 200,000 people. Reducing CO2 emissions in this energy load by a mere 3% is equivalent to cutting emissions from 4,000 vehicles traveling for an entire year.
Apollo then explored doing more with its data using artificial intelligence and machine learning, such as predicting compound properties and testing process improvements. The company also started streaming data from other equipment, including its curing presses and tire building machines, ultimately realising a 50% reduction in idle time on curing presses.
Numerous other projects are in the works, and the company sees virtually limitless potential. Armed with advanced analytics, and the ongoing support of Deloitte and AWS, Apollo adds it is confident in its ability to continue unlocking value from its machine data.