Turnaround Planning Digital Worker
Illustrative workflow design. Validate capabilities, performance, controls and integration requirements before implementation.
Problem Statement
The challenge addressed
Solution Architecture
AI orchestration approach
Illustrative preview (sample data, not verified outcomes): AI Agent Orchestration - Multi-step planning workflow with live activity stream
Illustrative preview (sample data, not verified outcomes): Scope Review Checkpoint - Human-in-the-loop approval for AI-generated work packages
Illustrative preview (sample data, not verified outcomes): AI Planning Analysis - Critical issues detection with plan details and early warnings
AI Agents
Specialized autonomous agents working in coordination
Orchestrator Agent
Turnaround planning involves multiple interdependent phases that must be coordinated - scope development, scheduling, resource allocation, risk analysis, and cost estimation all influence each other.
Core Logic
Illustrative workflow design. Validate capabilities, performance, controls and integration requirements before implementation. The Orchestrator Agent manages the complete planning workflow, delegating tasks to specialist agents, tracking progress, resolving conflicts between agent recommendations, and synthesizing the final turnaround plan. It handles inter-agent handoffs with context preservation and ensures all planning deliverables are consistent and complete.
Scope Intelligence Agent
Determining the right work scope for a turnaround is critical but difficult. Under-scoping leads to equipment failures; over-scoping wastes resources. Analyzing equipment condition data and historical patterns to identify necessary work requires significant engineering expertise.
Core Logic
Illustrative workflow design. Validate capabilities, performance, controls and integration requirements before implementation. This agent analyzes equipment condition data, criticality ratings, and failure probability scores using ML models trained on historical maintenance patterns. It generates work package recommendations with AI confidence scores, categorizes items by priority (critical, high, medium, low), and provides reasoning for each recommendation. The agent cross-references historical overrun data to improve duration estimates.
Schedule Optimizer Agent
Building optimal turnaround schedules requires balancing work package dependencies, resource constraints, and duration targets. Manual CPM scheduling is time-intensive and often misses optimization opportunities.
Core Logic
Illustrative workflow design. Validate capabilities, performance, controls and integration requirements before implementation. The Schedule Optimizer Agent builds Critical Path Method (CPM) schedules from approved work packages, identifying the critical path and total float. It performs resource leveling to smooth demand peaks, generates multiple schedule scenarios with different duration/cost trade-offs, and calculates schedule confidence using probabilistic analysis. Outputs include Gantt charts, milestone tracking, and P6-compatible exports.
Resource Planner Agent
Resource planning for turnarounds involves allocating hundreds of workers across multiple crafts while avoiding conflicts, minimizing peak staffing costs, and ensuring qualified personnel are available when needed.
Core Logic
Illustrative workflow design. Validate capabilities, performance, controls and integration requirements before implementation. This agent allocates resources to scheduled activities based on craft requirements and skill levels, detects resource conflicts and proposes resolutions, optimizes crew compositions to minimize idle time, and generates mobilization/demobilization plans. It produces resource histograms showing daily headcount by craft and identifies peak demand periods requiring contractor support.
Risk Analysis Agent
Turnarounds face numerous risks including schedule delays, cost overruns, safety incidents, and resource shortages. Without quantitative risk analysis, contingency planning is often based on arbitrary percentages rather than data-driven assessments.
Core Logic
Illustrative workflow design. Validate capabilities, performance, controls and integration requirements before implementation. The Risk Analysis Agent performs Monte Carlo simulations (typically 10,000+ iterations) to quantify schedule and cost uncertainty, generating P10/P50/P80/P90 confidence levels. It identifies specific risks with probability and impact scores, performs sensitivity analysis to highlight key risk drivers, and recommends mitigation strategies with expected risk reduction values.
Cost Estimation Agent
Accurate cost estimation for turnarounds is challenging due to the complexity of labor, materials, equipment, and contractor costs, plus the need to calculate appropriate contingencies based on risk levels.
Core Logic
Illustrative workflow design. Validate capabilities, performance, controls and integration requirements before implementation. This agent estimates turnaround costs by category (labor, materials, equipment, contractors, overhead), calculates contingency based on Monte Carlo risk results, generates cash flow projections aligned to the schedule, and performs cost-benefit analysis for scope trade-offs. It produces detailed cost breakdowns and compares estimates against industry benchmarks.
Worker Overview
Technical specifications, architecture, and interface preview
System Overview
Technical documentation
Tech Stack
5 technologies
Architecture Diagram
System flow visualization