Finding the signal in maintenance anomalies.
How Lean Six Sigma, enterprise data engineering and Tableau turned fragmented parts-usage records into actionable insight across a Maintenance, Repair & Overhaul network.
- Business problem
- Parts usage varied across comparable engines, making maintenance exceptions difficult to investigate.
- Adam’s contribution
- Adam’s work connected maintenance data and analysis with a structured investigation of anomalous parts usage.
- What this demonstrates
- Lifecycle parts-usage insight supported maintenance investigation and cost forecasting. Outcomes are qualitative.
A better answer to a costly question.
Cost managers were seeing different lifecycle costs for comparable engines maintained at different MRO facilities. By building granular parts-usage data supply chains, consolidating the data in an enterprise warehouse, and delivering Tableau self-service analytics, the team enabled investigations into anomalous consumption—and translated those findings into maintenance-plan and forecasting improvements.
Comparable engines. Different costs.
Traditional cost reporting revealed the variance, but not the operational behaviour behind it.
Across multiple Maintenance, Repair & Overhaul facilities, engines of the same type and with similar utilisation profiles were experiencing different lifecycle maintenance costs. The key investigative question was whether differences in parts consumption could explain the exceptions.
pattern
pattern
pattern
DMAIC gave the analytics a purpose.
Five stages connected a cost-management problem with operational remediation and sustained control.
- 01
Define
Frame lifecycle cost variation across comparable engine cohorts as the business problem.
- 02
Measure
Increase the coverage and granularity of parts-usage records across MRO facilities.
- 03
Analyse
Surface anomalous consumption by engine type, location and lifecycle stage.
- 04
Improve
Investigate root causes and refine maintenance plans where evidence supports change.
- 05
Control
Keep exceptions visible with repeatable self-service monitoring and review.
Connect the data. Then connect the decisions.
The solution linked source-system improvements to the people able to act on the findings.
Consistent, more detailed parts-usage data
Consolidated new and historical records
Engine, facility and lifecycle analysis
Self-service anomaly detection and exploration
Root-cause enquiries and plan remediation
The decisive design choice: capture sufficiently granular parts usage at source, then centralise it alongside historic enterprise data. Tableau was the consumption layer—not a substitute for a reliable data supply chain.
Make abnormal consumption visible.
From aggregate spend to a contextual view of what an engine consumed at a particular point in its life.
Parts-usage deviation
Compare like with like
Focus on similar engines and usage histories rather than confusing natural lifecycle variation with an exception.
Surface the unusual
Identify atypical parts usage for a specific engine type at a specific stage of its operational lifecycle.
Investigate, don't assume
Give cost managers a clear starting point for enquiries with local MRO teams and validation of root causes.
The dashboard was designed to trigger better decisions, not merely display more data.
The closed-loop improvement cycle
Finding an outlier is the beginning, not the end- DetectSpot unusual consumption
- EnquireEstablish operational cause
- RemediateRefine maintenance plans
- MonitorReview subsequent patterns
Operational insight became a working capability.
Value came from a repeatable way to find, investigate and act upon exceptions.
One enterprise view
Cross-facility visibility into granular parts consumption and maintenance history.
Self-service enquiries
Cost managers could explore anomalies and focus attention on meaningful exceptions.
Better maintenance plans
Evidence from root-cause investigations could inform targeted plan improvements.
Stronger cost forecasts
Observed lifecycle parts usage became an input to cost forecasting activities.
The combination of standardised data, accessible insight and disciplined follow-through became central to maintenance-plan optimisation and lifecycle cost forecasting.
Why this is a strong enterprise exemplar.
Expert delivery meant combining method, architecture, analytical design and user adoption.
DMAIC provided the improvement discipline; enterprise data engineering provided dependable information; Tableau put the analysis in the hands of cost managers.
Execution hallmarks
- Start with the decision. Connect the cost-variance question to the operational signals needed to explain it.
- Engineer the supply chain first. Good visualisation depends on comparable, granular and accessible data.
- Apply lean thinking end to end. Treat root-cause analysis and corrective action as part of the delivery, not an afterthought.
- Design for enterprise adoption. Make exceptions accessible to the cost managers who own the decisions.
