Industries
Collaborations of Industrial Expertise
and AI Solutions Across All Industries
Tire Industry
Semiconductor Industry
Shipbuilding Industry
Logistics Industry
Social Infrastructure
Logistics Industry
AI Ecosystem
in Action
Our partners are revolutionizing their industries with transformative AI solutions
> Autonomous Quality Control
> Predictive Maintenance of Industrial Equipment
> Real-Time Process Anomaly Detection
> AI-Powered Energy Management
> Logistics & Supply Chain Optimization
> AI-Assisted Human-Machine Interface
Selected
Use cases
Discover how the AI Ecosystem works for each decision - making objective
Autonomous Quality Control
Predictive Maintenance
Real-Time Process Anomaly Detection
Autonomous Quality Control
Objective
Autonomous Quality Control
Industry Use Case
For Company “A” in the manufacturing industry, each production batch requires manual machine adjustments by the operator to maintain product quality
Painpoints
1. Extended Cycle Time
Manual adjustments cause longer and inconsistent cycle times
2. Inconsistent Yield Rate
yield rates vary with operator experience
3. Dangerous Work Conditions
machine adjustments increase accident risks
Solution
SmartICS
AI Ecosystem
Outcome
Implementing the AI Ecosystem improves all KPI
1. Reduced Cycle Time
AI inferences run parallel to operations, eliminating any production interruptions
2. Consistently Improved Yield Rate
continuous AI maintenance through MLOps ensures consistently high yield rates
3. Reduced Worker Accident Risk
eliminating manual adjustments completely removes the risk of operator accidents
Predictive Maintenance
Objective
Predictive Maintenance
Industry Use Case
At Company “B”, a team of maintenance engineers, experts in vibration analysis, manually checks each piece of heavy machinery
Painpoints
1. Limited Engineer Availability
with few experts in vibration analysis, their limited availabilit creates a bottleneck
2. Manual Analysis & Data Overload
analyzing vast data from numerous machines and sensors manually makes it hard to spot critical patterns
3. Scaling Challenges
as the factory and number of machinery increases, the small team struggles to meet increasing maintenance demands
Solution
SmartCMS (Condition Monitoring System)
AI Ecosystem
Outcome
Implementing the AI Ecosystem allows the team to:
1. Scale Operations
efficiently expand predictive maintenance across more machinery as the factory grows
2. Analyze Data Efficiently
quickly process large datasets, ensuring no critical patterns are overlooked
3. Enhance Engineer Capabilities
augment maintenance engineers’ decision-making capabilities with AI-driven insights
Real-Time Process Anomaly Detection
Objective
Real-Time Process Anomaly Detection
Industry Use Case
At Company “C”, managers use limited data from traditional enterprise software to analyze faults in manufacturing processes.
Painpoints
1. Difficulty Identifying Root Causes
when machine faults or quality issue arise, limited data makes pinpointing the root cause challenging
2. Prolonged Downtime
analyzing and identifying the source of issues can take up to days, resulting in extended downtime
3. Reduced Yield and Longer Cycle Times
extended troubleshooting impacts production efficiency and output
Solution
SmartPDM (PLC Diagnostics & Monitoring)
AI Ecosystem
Outcome