Challenges We Solve
Common pain points that structured decision models eliminate.
Capacity Guesswork
Demand spikes catch you off guard. Balance production capacity against forecast demand with labor, equipment, and overtime factored in.
Inventory Imbalance
Too much stock ties up capital; too little loses customers. Compute optimal reorder points and economic order quantities.
Logistics Blind Spots
Route inefficiencies and supplier concentration risk go undetected. Map dependencies and optimize delivery paths.
Reactive Maintenance
Equipment fails before it's serviced. Predict failure probability from age, usage, and maintenance history to schedule proactively.
Agent Orchestration in Action
Autonomous AI agents collaborate to detect, diagnose, and resolve operational disruptions with human-in-the-loop guardrails at every step.
Detects anomalous vibration signature from sensor feed
Monitors IoT telemetry after every data sync. Vibration frequency on Line 3 compressor exceeds 2-sigma threshold, triggering maintenance prediction pipeline.
Estimates 72% failure probability within 14 days
Runs maintenance_prediction model using equipment age (4.2 years), usage hours (18,400), vibration trend, and maintenance history. Weibull distribution projects mean time to failure of 11 days.
Models production impact of Line 3 downtime
Runs demand_capacity_planner with Line 3 offline scenario. Identifies 340-unit/day shortfall, models overtime on Lines 1-2 to recover 80% of capacity. Estimates $180K revenue impact if unaddressed.
2 of 3 agents agree: schedule preventive maintenance this week
Multi-agent consensus confirms failure risk is high, parts are in stock, and production can be redistributed. Preventive window: Thursday 6 PM to Friday 6 AM.
Generates maintenance plan with production rebalance schedule
Synthesizes failure prediction, capacity rebalance plan, and parts availability into a prioritized action brief. Estimates $180K saved vs reactive failure. Routes to Plant Manager.
Built-in Guardrails
Monday Morning Intelligence Briefing
This is what your VP Operations sees every Monday at 6 AM, automatically generated from 9+ model outputs, zero analyst hours required.
WORKFORCE INTELLIGENCE BRIEFING
Generated: Mon May 19, 2026 6:00 AM ET · 3 plants, 12 production lines, 800+ tracked assets
Vibration anomaly detected 48 hours ago. Failure probability: 72% within 14 days. Last maintenance: 97 days ago.
- Vibration frequency 2.4x baseline (3.2 mm/s vs 1.3 mm/s normal)
- Equipment age: 4.2 years (fleet avg 2.8 years)
- Similar failure pattern matched to Line 1 compressor failure in Oct 2025
Preventive maintenance window recommended: Thursday 6 PM. Production rebalance plan attached. Cost avoidance: $180K.
Single-source dependency on TitaniumCo (92% of supply). Lead time increased from 4 weeks to 7 weeks.
- TitaniumCo plant in region affected by trade policy changes
- No qualified secondary supplier currently approved
- Current inventory covers only 3.2 weeks at current consumption rate
supply_chain_risk model recommends emergency dual-sourcing qualification. 6-week qualification timeline.
First-pass yield dropped from 96.2% to 93.8% over 4 weeks. Defect rate up 63% in welding station.
- New operator batch started 3 weeks ago (learning curve effect)
- Material lot #4472 showing higher variability than spec
quality_yield_tracker model run #R-5089 attached with root cause analysis.
Plant 1 at 94% utilization (overtime risk), Plant 3 at 67% (underutilized). Load imbalance score: HIGH.
- Q3 demand surge concentrated in Product Line A (Plant 1 primary)
- Plant 3 retooling for new product delayed by 2 weeks
demand_capacity_planner recommends shifting 120 units/day of Line A production to Plant 3.
| Metric | This Week | Prior Week | Trend |
|---|---|---|---|
| OEE (Overall) | 82.4% | 84.1% | ↓ ⚠ |
| Unplanned Downtime | 3.2% | 2.1% | ↑ ⚠ |
| First-Pass Yield | 94.8% | 95.6% | ↓ ⚠ |
| Inventory Turns | 8.4x | 8.2x | ↑ |
| Supplier On-Time | 91% | 94% | ↓ ⚠ |
| Capacity Utilization | 81% | 83% | ↓ |
| Safety Incidents | 0 | 1 | ↓ |
Use Cases
How teams use DecisionLedger to make better decisions.
Runs the demand-capacity planner weekly to balance production loads across 3 plants, factoring in labor availability, equipment constraints, and overtime costs.
Eliminated overtime overspend by matching capacity to demand 2 weeks ahead
Uses the supply chain risk model to score all Tier 1 suppliers by concentration risk, geographic exposure, and lead-time volatility - flagging single-source dependencies.
Identified and dual-sourced 4 critical single-supplier dependencies
Deploys the maintenance prediction model across 200+ assets, scheduling preventive maintenance based on failure probability instead of fixed calendar intervals.
Reduced unplanned downtime by 40% with condition-based maintenance
From 48-Hour Reactive to 4-Hour Predictive
See how agent orchestration compresses an 8-week manual process into same-day resolution.
Reactive Process
48 hours
12 people, $50K-$250K in lost production
With DecisionLedger
4 hours planned downtime
2 technicians, $0 lost production
Measurable Impact
Based on platform benchmarks across early adopters.
Demand Planning
Monthly spreadsheet updates
Weekly LP-optimized plans
Supplier Risk
Annual vendor reviews
Continuous risk scoring
Unplanned Downtime
Calendar-based maintenance
Predictive scheduling
Inventory Turns
Safety stock guesswork
EOQ-optimized reorder points
Compliance on Autopilot
Every HR decision model includes built-in regulatory compliance checks. Always audit-ready, never scrambling before a review.
ISO 9001
Automated quality management system evidence collection with corrective action tracking and management review data
OSHA
Workplace safety risk scoring with incident prediction, training compliance monitoring, and near-miss analysis
EPA
Environmental compliance monitoring with emissions tracking, waste management documentation, and permit status
FDA cGMP
Current Good Manufacturing Practice compliance with batch record review, deviation tracking, and CAPA management
Supply Chain Due Diligence
Supplier qualification, conflict mineral tracing, and forced labor risk screening per EU CSDDD and US UFLPA
Immutable Audit Trail
Every model run archived to S3 Object Lock WORM storage for ISO certification audits and regulatory review
The HR Automation Stack
Six end-to-end automated workflows that chain multiple decision models together. Each pipeline runs autonomously with governance gates.
Predictive Maintenance
IoT sensor anomaly
Preventive maintenance scheduled with production rebalance and parts ordered automatically
Demand Planning
Weekly cycle
Optimized production schedule with inventory replenishment and delivery routing
Quality Monitoring
Continuous (per-lot)
Real-time yield tracking with root cause isolation and supplier quality correlation
Supplier Risk
Continuous + quarterly deep dive
Supplier risk scores with dual-source recommendations and safety stock adjustments
Built for Every People Leader
Whether you lead the function or the analytics, DecisionLedger delivers the outputs your role demands.
For the VP Operations
- Weekly intelligence briefings synthesizing OEE, yield, capacity, and supplier risk into one page
- Cross-plant capacity optimization with demand-driven production rebalancing
- Real-time supplier risk monitoring with dual-source qualification tracking
- Autonomous anomaly detection that triggers maintenance before failures occur
For the Plant Manager
- Predictive maintenance scheduling based on failure probability, not calendar intervals
- Production line capacity modeling with overtime, shift, and equipment constraint optimization
- Quality yield tracking by line, shift, and material lot with early degradation alerts
- Integrated view of maintenance, capacity, and quality across all production assets
For the Supply Chain Director
- Supplier concentration risk scoring with geographic, financial, and lead-time factors
- Inventory optimization with EOQ, safety stock, and demand uncertainty factored in
- Logistics routing optimization across multi-modal transportation networks
- Continuous vendor performance scoring replacing annual review cycles
For the Quality Director
- First-pass yield monitoring with statistical process control and trend detection
- Root cause analysis correlating quality issues to operators, materials, and equipment
- ISO 9001 evidence packages auto-generated from model runs and corrective actions
- Process bottleneck identification with throughput optimization recommendations
Connects With
Part of 150+ native integrations across CRM, marketing, finance, HR, ecommerce, and analytics
Jira
ServiceNow
Asana
GitHub
GitLab
ClickUp
Linear
Notion
Airtable
Freshservice
Slack
StatuspageJiraServiceNowAsanaGitHubGitLabClickUpLinearNotionAirtableFreshserviceSlackStatuspageFeatured Models
Pre-built decision models ready to run with your data.
Demand Capacity Planner
Demand and capacity planning model. Balances production capacity against forecast demand, factoring in labor, equipment, and overtime to identify capacity gaps and recommend staffing or CapEx actions.
Inventory Optimization
Inventory optimization model. Determines optimal stock levels by balancing carrying costs against stockout risk, computing reorder points and economic order quantities.
Logistics Routing
Logistics and routing optimizer. Evaluates delivery routes and transportation modes to minimize cost, transit time, and carbon emissions while meeting service level requirements.
Maintenance Prediction
Predictive maintenance model. Estimates equipment failure probability based on age, usage hours, and maintenance history to optimize maintenance scheduling and reduce unplanned downtime.
Process Bottleneck
Process bottleneck identifier. Analyzes workflow stages to find constraints limiting throughput, quantifies bottleneck impact, and prioritizes lean improvement and automation opportunities.
Production Scheduling
Production scheduling optimizer. Determines optimal job sequencing to minimize changeover time, maximize throughput, and meet delivery commitments across multiple product lines.
Quality Yield Tracker
Quality and yield tracking model. Monitors defect rates, rework costs, scrap losses, and first-pass yield to identify process improvement opportunities and vendor quality issues.
Supply Chain Risk
Supply chain risk analyzer. Maps supplier dependencies, evaluates single-point-of-failure exposure, and scores overall supply chain resilience to inform diversification and contract negotiation decisions.
How It Works
Three steps to structured, auditable decisions.
Connect Operational Data
Upload operational data from CSV, connect via data warehouse, or use API integrations. Map equipment, inventory, and production data fields.
Optimize & Simulate
Run linear programming, risk matrices, and anomaly detection across capacity, inventory, and supply chain models.
Execute & Track
Push recommendations to operational systems, monitor KPIs, and compare predicted vs actual outcomes.
Your best plant manager retires next year. How much of your operations playbook lives only in their head?
Spreadsheet capacity plans
Static models that can't optimize across constraints in real time
Calendar-based maintenance
Servicing equipment on schedule, not on condition - wasting budget or missing failures
Annual vendor scorecards
Point-in-time reviews that miss supplier risk between assessment cycles
ERP reporting modules
Historical dashboards that tell you what happened, not what to do next