IndiaManufacturing2026 – ongoingApollo Tyres
AI-driven advanced process control for tyre extrusion
Apollo Tyres cut setup rework by about 26% with AI that sets and adjusts extrusion line speed
Problem
Each recipe change on a tyre extrusion line needed a manual startup speed and a few minutes of stabilization. Startup rework equalled about 5-6% of production volume, with 30-35 setup changes a day.
Approach
Apollo's team built a closed-loop control system on Amazon SageMaker, Amazon Bedrock and AWS IoT Greengrass. A predictive model on edge hardware picks the startup line speed. An AI agent then reads tread and sidewall profiles and scale data and adjusts speed in real time, with guardrails and human oversight. It ran on the passenger car radial extrusion line at the Andhra Pradesh plant, and rollout to other extrusion lines began.
Results
Takeaway
The gain came from fixing one recurring step, the startup after a recipe change, rather than the whole line. The result is reported by the supplier of the cloud platform.
Categories
- Size
- Large enterprise
- Industry
- Manufacturing
- Business function
- Production and quality
- Innovation type
- Process innovation
- AI technology
- Predictive analytics and MLAI agents
- Adoption
- Built in-house
- Evidence
- Vendor case study
Sources
Source summaries
AWS describes Apollo Tyres' in-house closed-loop process control for extrusion lines. It gives a 26% cut in setup rework, a 40% cut in stabilization time and about 20 extra production hours a month. The post does not state when the project began.
Full details are in the original sources.