Leaderboard

Domain: Aerospace, MRO (Maintenance, Repair & Overhaul) - Engine shop for narrow body turbofan engines (€220M turnover facility, covering CFM56, V2500, and LEAP Shop Visits of various airlines and lessors) Specific Improvement Initiative: Reducing engine module Turn Around Time (TAT) from 45 – 60 days to <35 days during performance restoration visits in a span of 6 Months (This effectively represents another high-value chokepoint because releasing underlying store engines into service reduces costly leases and makes airline dispatch more dependable. We recently initiated an AI-driven project in late 2025 that seeks to incorporate predictions of module status using borescope, oil analysis and test cell data, then auto generates work scopes and part forecasts.) How we adapted DMAIC with AI — stage by stage Define This will still remain 100% human owned with no shortcuts. Simply, Artificial Intelligence cannot describe what "good" means for airworthiness parts. For few weeks we worked with airline customers, lessors and regulatory representatives to map CTQ’s: TAT < 35 days, zero escape on critical elements (HPT, HPC), > 12% cost reduction, and full traceability for EASA/FAA. AI also helped in the visualization of the current vs. target Pareto of delays. Boundaries (no compromise in Safety and/or Compliance) remained the responsibility of the MBB and the project sponsor. Measure "AI becomes super strong here — it accelerates data collection and baseline accuracy massively." Rather than manually sampling 200 historic Shop Visits, AI ingested 1800+ data points, including borescope images, oil debris, vibration trend, test cell parameters, etc., and created a clean baseline in a few days: · Avg breakdown TAT: TAT breakdown – disassembly 8d, Inspection 12d, Repair 28d, Assembly/test 12d. · Variation drivers (HPT blade rework 42% of variance) The judgment of humans is still predominant, i.e., validating data quality, excluding outliers resulting from non-standard visits, and checking if there is no survivorship bias present in the data set itself. Analyze This stage roles flip: AI takes the lead on root-cause discovery, humans will challenge and refine it. AI ran pattern recognition across thousands of features · "Predicted that 68% of HPT delays are caused by unexpected coating wear (not visible with a standard borescope). " · Simulated ‘what if’ scenarios (add ultrasonic inspection on blades → TAT -4 days, cost + €8k) etc. MBB owns: · Forcing AI to explain (SHAP values & counterfactuals). · Rejecting Correlations which Violate Physics/Engineering Judgment. · Prioritizing causes with the team (fishbone + AI insights). Improve AI excels in solution generation and testing, whereas piloting decisions fall under human expertise. AI produced 12 different workscope variants, ranked according to their TAT/cost/risk. We tested top 3 on 8 engines: · Added Predictive coating inspection → Reduced Surprise Rework · Auto parts pre-kitting based on AI prediction → Assemble wait time Human judgment prevails: deciding which approved variant will go ‘live’, managing change with technicians and getting sign-off from regulators. Control Human + AI Hybrid With Human ownership of Sustainability. AI Monitors and tracks real-time adherence (daily TAT tracker for deviances). MBB owns: · Control plan update (with new SOPs on the use of AI). · Monitoring adoptions: Technicians' override of AI predictions (<15%). · Monthly review: review of escapes/misses to retrain the model. · Celebrating success with the shop floor (sharing savings made visible) Which of the phases will be made stronger with the use of AI · Measure: 10× faster, more granular baseline · Analyze: reveals patterns not yet perceived by humans · Improve: generates/test thousands of scenarios in hours Where human judgment still needs to dominate? 1. Define: framing value and non-negotiables such as safety, 2. Analyze: rejecting physically impossible correlations. 3. Improve: Piloting real engines, i.e., one can’t simulate customers trust. 4. Control: Sustaining Culture & Accountability Practical result after 7 months · TAT average 33.8 days · Cost per visit down by 14% · No Escapes · Technician satisfaction up (less firefighting, more predictable work) Bottom line from the engine shop DMAIC may not be replaced, but it’s turbocharged! The AI takes care of the hard work on data and simulation, but MBB makes sure the method remains disciplined . right problem definition, right boundaries and right decision. Without humans "owning" Define and "challenging" Analyze, AI is a matter of "optimizing the wrong things faster, With humans owning, you get sustainable, compliant, high value improvement that regulators and customers actually trust.