The Challenge
The organization operated within a highly complex industrial environment involving:
- Alarm-intensive process systems
- Large-scale pulp and recovery operations
- High operator interaction volumes
- Variable process workflows
- Manual operational response procedures
- Limited cross-system visibility
- Reactive process optimization
Operations teams struggled with:
- Alarm floods and standing alarms
- High operator burden
- Workflow inconsistency
- Delayed operational response
- Process variability across mill areas
- Limited visibility into operational patterns
- Difficulty identifying automation opportunities
Operational data existed across multiple disconnected systems, including:
- Historian databases
- Alarm management systems
- Process control systems
- Operator workflow logs
- Production reporting systems
Because these systems operated independently, identifying operational inefficiencies and optimization opportunities required significant manual engineering analysis.
B3 Solution
B3 implemented an AI-powered Operational Intelligence and Agentic AI framework designed to modernize industrial decision-making and operational visibility across mill operations.
The deployment unified:
- Alarm histories
- Process control data
- Operator interactions
- Workflow behavior
- Historian trends
- Operational events
- Production metrics
The architecture included:
- AI-powered process analytics
- Workflow reconstruction
- Alarm intelligence
- Operational pattern detection
- Opportunity analysis
- Intelligent recommendations
- Automation readiness scoring
- AI-generated operational summaries
This enabled operations and engineering teams to correlate process variability, alarm activity, operator response behavior, and production trends within a single operational intelligence environment.
Alarm Intelligence & Alarm Management
B3 identified significant operational burden associated with recurring alarm activity and nuisance alarm conditions across mill operations.
Key Findings
- 19,721 total alarm activations analyzed
- 657 average activations per day
- 214 standing/chattering alarm tags identified
- High operator burden from recurring alarm events
The platform identified the top operational alarm actors, including:
- Digester feed pump trips
- Washer low-level alarms
- Evaporator steam pressure events
- Conveyor jam alarms
- Boiler fan vibration alarms
Measured Operational Impact
- 15,721 estimated annual alarm events reduced
- 1,237 operator hours saved
- 12% reduction in ack-only response behavior
Recommended Actions Included
- Alarm rationalization initiatives
- Standing alarm remediation
- Response workflow standardization
- Alarm prioritization optimization
- Operator burden reduction strategies
Operator Process Intelligence
B3 deployed AI-powered workflow intelligence capable of reconstructing and analyzing operator process behavior across mill operations.
The system identified:
- 128,456 operator actions analyzed
- 1,842 unique workflows identified
- 342 automation opportunities surfaced
- 1,926 operator hours evaluated
The platform reconstructed operational workflows including:
- Boiler startup sequences
- Grade change operations
- Paper break recovery procedures
- Wet-end chemical adjustments
- Steam system reset workflows
Process Variability Analysis
B3 identified significant workflow variability across several operational areas, including:
- Recovery boiler operations
- Paper machine workflows
- Power boiler systems
- Evaporation systems
- Utilities and water treatment
This enabled operations teams to identify:
- Workflow inconsistency
- Operational bottlenecks
- Standardization opportunities
- Automation candidates
- High-variability operational zones
Agentic AI & Rate Surge Optimization
B3 also implemented AI-assisted operational optimization capabilities focused on rate surge control and throughput recovery analysis.
The platform analyzed:
- Process deviations
- Recovery timelines
- Operator response behavior
- Throughput instability
- Surge scenarios
- Corrective operational actions
Key Operational Insights
- 47 recommended operational actions identified
- 28 automated adjustment opportunities surfaced
- 17-minute average response time analyzed
- 3,410t throughput recovery opportunity evaluated
Estimated Annual Operational Opportunity $2.35M+
Recommended Optimization Actions Included
- Process pacing optimization
- Surge response standardization
- Automated blend correction recommendations
- Throughput stabilization workflows
- AI-assisted operational guidance
The deployment established the foundation for future AI-assisted operational control and industrial copilot systems.
Results & Business Impact
Using B3’s Operational Intelligence platform, the manufacturer established a scalable industrial AI framework capable of improving operational visibility, reducing operator burden, and identifying high-value process optimization opportunities.
Key Outcomes
- Reduced alarm fatigue
- Improved operational visibility
- Faster operational response
- Standardized operator workflows
- Increased process intelligence
- Improved process consistency
- Enhanced operational decision-making
- Identified automation opportunities
- Reduced operational variability
- AI-assisted optimization readiness
Quantified Impact
|
Metric
|
Value
|
|
Alarm Events Reduced
|
15,721
|
|
Operator Hours Saved
|
1,237
|
|
Automation Opportunities
|
342
|
|
Operator Actions Analyzed
|
128,456
|
|
Throughput Recovery Opportunity
|
3,410t
|
|
Estimated Annual Opportunity
|
$2.35M+
|
Why It Matters
Modern forestry, pulp & paper operations generate massive volumes of process and operational data, but many organizations still struggle to transform that information into actionable operational intelligence.
By connecting alarms, workflows, historian data, process behavior, and operational events into a unified AI-powered intelligence environment, B3 enabled this manufacturer to move from reactive operational management toward intelligent, data-driven industrial optimization.
The result was improved operational visibility, reduced operator burden, increased process intelligence, and a scalable foundation for future industrial AI deployment across mill operations.
