Problem
Enable reliable, data-driven maintenance prioritization maximizing workshop production capacity through quantified scenario analysis at 24-72 hour horizons.
- +Recency bias: Teams prioritize machines with recent issues rather than systematic capacity impact analysis.
- +Local optimization: Individual machine health metrics miss workshop-level throughput dependencies.
- +Resource constraints: Limited maintenance windows require optimal allocation; wrong choices mean unrecoverable lost capacity.
Approach
- +Processing time characterization: Analysis at standard, historical, and best levels.
- +Scenario-based simulation: Aggregates machine-level performance into workshop-level maintenance decisions.
- +Load balancing engine: Distributes forecasted workload across machines to calculate total time required per scenario.
Delivered
- +Python package with CLI for daily ranking generation.
- +Jupyter notebooks for EDA and impact studies.
- +Maintenance ranking dashboard with time-savings estimates (24h/72h horizons).
- +Integration specifications for MES/ERP data pipelines.
Result
- +Quantified gains: Top machine showed 5.3 hours recoverable over 72h.
- +ROI: 480 hours/year × $5K/hour = $2.4M annual value.
- +Payback period: 30-60 days.
- +Ranking stability: Top 6 machines showed 80%+ overlap across 24h/72h horizons.
Where it ran
- +Daily operations: Morning planning meetings prioritize interventions.
- +Strategic planning: Budget allocation informed by capacity-at-risk.
- +Root-cause workflows: Ranking changes trigger diagnostics.
- +Benchmarking: Track alignment with top-ranked machines.
Stack
Scenario Simulation · Capacity Optimization · Load Balancing · Python/Pandas
Related solutions
Predictive Maintenance
Catch degradation early, rank maintenance by what it costs you in throughput, and cut unplanned downtime before it hits OEE.
Production Optimization
Find the bottlenecks, kill the micro-stops, and turn real operating constraints into schedules that recover throughput.
OEE Monitoring
Give leaders and production teams one shared view of Availability, Performance, Quality, and the losses sitting behind them.
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