Real-World Impact of Seewise AI on Manufacturing Operations

A leading foundry operating multiple batch-type furnaces needed better visibility into furnace utilization, idle time, and energy consumption across production cycles. Manual monitoring made it difficult to track preheat states, operator delays, and actual furnace readiness, resulting in avoidable downtime and excessive energy usage.

The Challenge

Furnace operations relied heavily on manual observation and delayed reporting, making it difficult to track performance and energy efficiency across shifts.

Key challenges included:

• Low OEE and inconsistent furnace utilization
• High material and energy wastage during cycles
• No clear tracking of idle, preheat, and ready states
• Unidentified delays affecting throughput
• Lack of real-time visibility into furnace activity

The Seewise AI Solution

Seewise deployed an Vision AI-driven monitoring system using camera feeds and production intelligence models to track furnace operations in real time and analyze cycle behavior.

The solution enabled:

• Real-time monitoring of furnace active, idle, and preheat states
• Cycle-level energy consumption tracking
• OEE and hourly productivity analytics
• Detection of delays, downtime, and inefficiencies
• Central dashboards for production and energy insights

All furnace activity and performance data were captured and analyzed continuously through the Seewise platform.

How It Works

• Camera feeds covering furnace areas are processed through the Seewise AI platform
• AI models track furnace cycle states including active, idle, and preheat phases
• Operational data is analyzed to calculate OEE, cycle time, and energy usage
• Dashboards provide real-time visibility into productivity and delays
• Supervisors receive alerts and insights for immediate action

Impact

• Improved furnace utilization and production output
• Reduced downtime and unplanned delays
• Lowered excessive energy and material usage
• Enabled continuous monitoring of furnace operations
• Established data-driven decision-making on the shopfloor

Business Value

• Higher production efficiency across furnace cycles
• Better control over energy consumption and wastage
• Improved adherence to production targets
• Eliminated manual reporting inaccuracies
• Saved time, energy, and operational cost in every cycle

Power transmission tower beams and angles move through a multi-stage galvanization cycle involving molten zinc, acid baths, and chemical treatments. Continuous monitoring of every stage is critical to maintain productivity, safety, and process consistency. Manual tracking limited visibility into operations and made it difficult to control performance across shifts.

The Challenge

The galvanization plant operated continuously but relied heavily on manual tracking and reporting. This created gaps in visibility, accuracy, and control across the dipping line.

Key challenges included:

• Inaccurate manual production records
• Process and cycle-time violations
• No reliable OEE tracking for tanks and beams
• Limited traceability of assets and movement
• Lack of real-time shopfloor visibility

The Seewise AI Solution

Seewise deployed Production AI and Tracker AI across the galvanization line using existing camera infrastructure. The system continuously monitored tanks, cranes, beams, and process cycles to deliver real-time operational intelligence.

The solution enabled:

• Monitoring of zinc and chemical tanks across stages
• Tracking of cranes, beams, and job movement
• Cycle-time and SOP compliance monitoring
• Temperature and process condition visibility
• Real-time dashboards for performance tracking

How It Works

• Cameras capture the galvanization line, tanks, and loading stations
• Seewise AI processes video feeds to track beams, cranes, and process stages
• The system analyzes cycle time, OEE, and process compliance in real time
• Operational data is visualized through dashboards and alerts
• Supervisors receive live updates on delays, violations, and performance

Impact

• 12% increase in OEE and production output
• 11% reduction in downtime and unplanned stops
• 8% reduction in excess material and energy usage
• Complete real-time visibility across operations

Business Value

• Improved adherence to process SOPs
• Accurate production and performance tracking
• Stronger traceability of assets and jobs
• Reduced manual reporting and errors
• Time, material, and cost savings per cycle

A leading beverage manufacturing facility needed a reliable way to prevent human entry into active machine zones during automated operations. High-risk areas such as palletization and stretch-wrapper zones required immediate intervention to eliminate accidents caused by unintended machine motion.

The Challenge

Automated packaging machines continue operating unless explicitly stopped, creating serious safety risks when operators enter machine zones. Existing safety processes relied heavily on manual lockout–tagout methods, which were prone to delays and human error.

Key challenges included:

• No real-time detection of human presence
• Risk of injury from unintended machine motion
• Manual safety protocols frequently bypassed
• Poor coordination between operator movement and machine logic

The Seewise AI Solution

Seewise deployed an AI-driven safety system using existing CCTV cameras and direct PLC integration to enforce real-time human–machine interlocks.

The solution enabled:

• AI-based detection of human presence in restricted zones
• Continuous monitoring of machine key signals
• Instant PLC signal writes to halt machine operation
• Automatic machine resume once the area is verified safe
• All events were centrally logged and monitored through the Seewise platform.

How It Works

• Seewise AI processes camera feeds covering machine entry points and operator zones
• The AI system detects human presence in active machine areas in real time
• A stop signal is sent directly to the PLC when a safety breach is detected
• Machine motion halts immediately to prevent hazardous operation
• Operations resume only after the zone is verified as clear

Impact

• Prevented machine operation during human presence
• Enforced strict human–machine interlock compliance
• Created a complete audit trail of entry and stop events
• Achieved 100% real-time safety automation on the shopfloor

Business Value

• Reduced near-miss incidents and injury risk
• Improved SOP adherence and safety compliance
• Strengthened safety culture across shifts
• Minimized downtime caused by safety violations

In heavy manufacturing and tool management environments, identifying tools and components often depends on engraved or embossed text on metal surfaces. Glare, scratches, and lighting variations make manual reading and conventional OCR unreliable. A robust, automated OCR solution was required to ensure accurate identification and traceability of metallic parts.

The Challenge

Conventional OCR systems are not designed for reflective or irregular metal surfaces commonly found on shopfloors, leading to errors and operational delays.

Key challenges included:

• Low OCR accuracy due to glare and surface texture irregularities
• Manual identification causing delays and human errors
• Limited integration with MES / ERP systems
• High cost of alternate hardware solutions such as RFID or laser scanners
• Quality and maintenance issues caused by mis-tracking of tools

The Seewise AI Solution

Seewise deployed a custom AI-driven OCR system purpose-built for industrial metal surfaces. The solution combined surface-aware vision intelligence with a robust OCR pipeline to reliably read engraved and embossed characters under real shopfloor conditions.

The solution enabled:

• AI models optimized for reflective and textured metal surfaces
• Adaptive illumination correction and contrast enhancement
• Accurate recognition of faint, distorted, or partially damaged engravings
• Structured data output ready for production systems

How It Works

• Seewise AI processes camera feeds capturing metal tools and components
• AI preprocessing removes glare, noise, and enhances contrast on reflective surfaces
• The OCR engine interprets engraved or embossed alphanumeric characters
• Validation logic improves recognition accuracy
• Structured data is pushed directly to MES / ERP systems or databases

Impact

• Automated part identification directly from camera feeds
• Improved traceability and tracking accuracy
• Eliminated manual logging and data entry errors
• Reduced inspection time and operational downtime

Business Value

• Lower operating cost compared to RFID or laser-based systems
• Stronger quality control and digital traceability
• Reduced operator dependency
• Improved productivity through automated ID capture

In logistics and loading yard environments, ensuring that all cargo is securely fastened before truck dispatch is critical. Improperly fastened safety belts can cause load displacement during transit, leading to product damage, driver risk, and safety incidents. A real-time automated system was required to verify belt fastening and eliminate inconsistencies caused by manual inspections.

The Challenge

Manual safety belt verification during outbound loading was slow, inconsistent, and heavily dependent on human vigilance, creating gaps in compliance and accountability.

Key challenges included:

• Manual inspections prone to delays and oversight
• No real-time alerting for missing or loose belts
• Inconsistent fastening practices across operators and shifts
• No centralized system to track violations or audit evidence

The Seewise AI Solution

Seewise deployed an AI-based vision system to automatically verify safety belt fastening before truck dispatch. Using camera-based detection and real-time analytics, the system ensured compliance checks were enforced without slowing down operations.

The solution enabled:

• AI models trained to detect belt presence and fastening status
• Strategic camera placement at loading lane exit points
• Real-time alerts when non-compliance was detected
• Centralized logging of compliance events with visual evidence

How It Works

• Seewise AI analyzes camera feeds at loading exits to monitor cargo and belt fastening
• The AI system detects the presence and fastening status of safety belts
• Edge-based processing enables real-time inference
• Instant alerts are triggered when belts are missing or improperly fastened
• Compliance logs are stored with timestamps and visual evidence

Impact

• Automated safety belt compliance checks before dispatch
• Eliminated manual inspection errors
• Reduced risk of cargo shifting during transit
• Improved dispatch quality and safety standards

Business Value

• Faster, more consistent outbound operations
• Stronger enforcement of safety SOPs
• Improved accountability across shifts
• Complete audit trail for compliance and investigations

Collaborative robots operate in close proximity to humans in modern manufacturing environments. While designed for shared workspaces, high-speed operations in areas such as palletization still pose safety risks when humans enter active robot zones. An intelligent, real-time system was required to detect human presence and immediately trigger robot control actions without relying solely on manual intervention.

The Challenge

Despite being collaborative by design, cobots operating at production speeds require additional safeguards to prevent contact injuries during manual intervention.

Key challenges included:

• Accidental entry into active robot movement paths
• Lack of direct signal-level interlock between AI detection and control systems
• Delays in stopping robot motion during manual intervention
• Dependence on emergency stops and physical sensors alone

The Seewise AI Solution

Seewise deployed an edge-based AI safety system to monitor cobot workspaces and enforce real-time control through safety relays. The solution enabled immediate robot response when human presence was detected, without introducing operational latency.

The solution enabled:

• AI-based human detection within cobot operating zones
• Local edge processing for low-latency decision making
• Real-time RS-485 output to trigger safety relays
• Automatic robot halt and safe resume once the area is clear

How It Works

• Seewise AI processes camera feeds covering entry paths and cobot workspaces
• The AI system detects human presence within the active robot operating zone
• Inference is processed locally on the edge device for low-latency response
• An RS-485 signal is sent to the safety relay
• Robot motion halts instantly and resumes only when the area is safe

Impact

• Enabled safe human–robot collaboration
• Immediate response to human presence in robot zones
• Eliminated risk of contact injuries during palletization
• Fully automated safety control via edge device and safety relay

Business Value

• Reduced injury risk in collaborative operations
• Improved compliance with industrial robot safety standards
• Faster reaction times through real-time signal intelligence
• Avoided downtime and liability from safety incidents
• Cost-efficient integration with existing safety relay systems