Industry: Aviation / Manufacturing Operations
Role: Aerospace Performance Analyst
Context
This project focuses on optimizing the assembly flow and operational efficiency of the Final Assembly Lines (FAL) based in Toulouse, France. This analysis bridges the gap among raw plant-floor execution, quality control, and supply chain logistics using a complex operational dataset that includes manufacturing metrics across multiple commercial aircraft programs (including the A320neo Family, A350 XWB, A330neo, and A220 Series).
Business Objective
The primary objective was to transition fragmented data into a centralized, high-performance Star Schema data warehouse to eliminate assembly line bottlenecks, evaluate shift performance, and quantify the exact cycle-time penalties caused by external supply chain disruptions and component shortages.
Tools Used
- Database Management: SQL Server (SSMS) for backend data staging and relational modeling.
- ETL & Data Engineering: Power BI Power Query for localization handling, column type conversions, and automated dimension table generation.
- Data Modeling: Power BI Desktop (Star Schema relationship mapping).
- Analytics & Business Intelligence: DAX (Data Analysis Expressions) for custom performance tier modeling.
Key KPIs
- Total Throughput Units: Cumulative volume of completed aircraft sub-assemblies.
- Average Cycle Time (Hours): The mean duration required for an aircraft component to clear a specific assembly stage.
- First Time Yield (FTY): The ultimate quality standard tracking the percentage of units passing inspections with zero defects or reworks.
- Downtime Impact Rate: The percentage of total operational time lost to unscheduled mechanical or machine stoppages.
- Supply Chain Time Penalty: The localized hourly delay added to production cycles specifically due to material shortages or vendor delays.
Key Deliverables
Key Insights
- Quality Bottlenecks: Defects and reworks are heavily clustered within the Engine Mounting and Structural Assembly stages, acting as the primary drag on the plant’s overall First Time Yield (FTY).
- Supply Chain Drag: Active material shortages or vendor delays cause immediate spikes in production
Supply Chain Time Penalty, proving that external logistics friction rather than internal mechanics is the main driver of line stalls. - Overtime Strain: The high-volume A320neo Family consistently meets its aggressive delivery targets but relies on a disproportionate accumulation of overtime hours to compensate for localized process friction.
- Downtime Hotspots: The Jean-Luc Lagardère Site and automated sections of FAL Line 3 experience the highest rates of unscheduled mechanical losses, with disruptions peaking during the Night Shift.
Strategic Recommendations
- Buffer Strategic Stock: Establish targeted inventory safety buffers for high-risk components on critical-path programs to isolate assembly lines from external vendor shocks.
- Targeted Quality Audits: Deploy dedicated quality engineering sprints specifically to the Engine Mounting and Structural Assembly phases to permanently design out the root causes of recurring rework.
- Realign Maintenance Windows: Shift preventative machine maintenance schedules at the Jean-Luc Lagardère Site to clear-out or transition periods, maximizing active floor uptime.
- Dynamic Rescheduling: Utilize active supplier risk flags in the data model to proactively pivot weekly production schedules toward assembly tasks that are independent of delayed parts.
Important: This case study draws on fictitious data.
Marie Camara 