TIRE MANUFACTURING (DISCRETE + PROCESS OPERATIONS) CASE STUDY
Increased plant efficiency 10 to 15% for a premium tire manufacturer
Delivered Vision AI line monitoring with IIoT flow tracking and MES analytics for real-time, multi-plant production control.
30% better tracking
15% less material loss
10 to
Tire manufacturing (discrete + process operations) Case Study
THE CHALLENGE
What was holding them back
Operational pain
Manual monitoring created bottlenecks, missed stoppages, and slow response.
Business risk
Material losses increased due to weak WIP and inventory visibility.
Why tools failed
No real-time, cross-plant visibility to act on issues as they happened.
CLIENT SNAPSHOT
About the client
Industry
Tire manufacturing (discrete + process operations)
Geography
India
Service
AI & Automation
Existing Tools
Manual logs + CCTV + basic shopfloor reporting
THE SOLUTION
Our Tire manufacturing (discrete + process operations) Solution
Vision AI Line Monitoring (Object + Action Intelligence)
- Tracked material movement and line states using object tracking.
- Detected key actions/events (stoppage, pile-up, misfeed) in real time.
IIoT Layer for Machine + Material Flow Signals
- Captured machine status and production signals for continuous context.
- Mapped material flow checkpoints to reduce blind spots in WIP.
MES Visibility Across Plants (Unified Operations View)
- Centralized multi-plant dashboards for production, WIP, and losses.
- Enabled shift-wise analytics to pinpoint recurring constraints.
Alerts + Escalation (Faster Response Cycles)
- Triggered alerts on abnormal conditions and bottleneck patterns.
- Enabled role-based notifications for operators, supervisors, and heads.
Governance + Adoption (Control Without Chaos)
- Implemented role-based access for secure operational control.
- Standardized definitions for events, losses, and reporting across plants.
THE IMPACT
Measurable Results
30%
higher WIP and inventory tracking accuracy
15%
reduction in material loss and scrap
20%
faster bottleneck detection and resolution
6
weeks to measurable impact post rollout
TECH STACK
Technologies Used
AI Vision
MES
IIoT
