Manufacturing & Supply Chain
Optimize production, predict failures, and de-risk supply chains
Predict equipment failures, score supply chain concentration risk, optimize production schedules, and balance demand against capacity — with LP optimization and Monte Carlo stress testing.
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Models
LP
Optimization
Predictive
Maintenance
Challenges We Solve
Industry-specific pain points that structured decision models eliminate.
Unplanned Downtime
Equipment fails before it's serviced. Predict failure probability from age, usage patterns, and maintenance history to schedule proactively and avoid production stops.
Supply Chain Concentration
Critical suppliers become single points of failure. Score supplier dependency, geographic concentration, and lead-time risk across your entire supply chain.
Quality Yield Variance
Yield drops erode margins before root causes are identified. Track yield by line, shift, and material lot to detect degradation early and drive corrective action.
Demand-Capacity Mismatch
Over-capacity ties up capital; under-capacity loses orders. Balance production capacity against forecast demand with labor, equipment, and overtime factored in.
Use Cases
Industry-specific scenarios powered by DecisionLedger.
Uses the production scheduling optimizer to balance demand across 3 production lines, factoring in equipment availability, labor shifts, and material constraints — maximizing throughput under real-world limits.
Increased production throughput 15% without adding capacity
Deploys the maintenance prediction model across 500+ assets, replacing fixed-interval maintenance with condition-based scheduling driven by failure probability curves.
Reduced unplanned downtime 40% and maintenance costs 22%
Runs the supply chain risk model quarterly to score all suppliers by geographic concentration, lead-time volatility, and financial health — dual-sourcing decisions backed by quantified risk.
Eliminated 6 single-source supplier dependencies before disruptions hit
Measurable Impact
Based on platform benchmarks across early adopters.
Production Scheduling
Manual spreadsheet plans
LP-optimized schedules
Unplanned Downtime
Calendar-based maintenance
Predictive condition-based
Supply Chain Risk
Annual vendor reviews
Continuous risk scoring
Quality Yield
Detected after production
In-line anomaly detection
Engineered for Manufacturing
Linear programming, Monte Carlo stress testing, and vendor scorecards for production and supply chain decision-making.
Linear Programming
Optimize production schedules, inventory levels, and resource allocation under constraints using mathematical programming solvers.
Monte Carlo Stress Testing
Simulate thousands of demand, supply, and quality scenarios to quantify uncertainty and build resilient production plans.
Vendor Scorecards
Score and rank suppliers on delivery performance, quality, cost stability, and risk factors with automated monitoring and alerts.
Connects With
Featured 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.
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.
Workplace Safety Risk
Predict workplace incident risk and target prevention investments.
How It Works
Three steps to structured, auditable decisions.
Connect Operational Data
Upload production logs, maintenance records, and supply chain data. Map equipment, inventory, and quality fields once.
Optimize & Simulate
Run LP optimization for scheduling, Monte Carlo for demand uncertainty, and predictive models for maintenance and quality yield.
Monitor & Act
Track vendor scorecards, equipment health, and yield trends. Trigger alerts and intervention playbooks when thresholds breach.
A single hour of unplanned downtime costs $50K-$250K. How many of your maintenance decisions are still based on calendar intervals instead of failure probability?
ERP scheduling modules
Production planning that can't optimize across constraints with LP solvers
Calendar-based PM schedules
Servicing equipment every 90 days whether it needs it or not — or failing before 90 days
Spreadsheet vendor scorecards
Annual supplier reviews that miss supply chain concentration risk between cycles
Standalone CMMS systems
Maintenance tracking without predictive analytics or integration with demand planning