Industrial Engineering at the Boeing Composite Wing Center
Executive Summary
As an Industrial Engineer on the Boeing 777X program, I was responsible for analyzing and scaling a highly complex manufacturing system composed of dozens of interdependent work cells, each constrained by physical resources such as machines, labor, tooling, and material flow.
My core focus was “rate readiness” - determining whether the system could meet future production targets, and if not, defining the optimal set of actions to close the gap. This required balancing tradeoffs between throughput improvements, capital investment, and operational risk under uncertainty.
I built data-driven models and decision frameworks to identify system bottlenecks, forecast future performance, and guide high-stakes decisions, including whether to invest millions in new equipment or instead rely on engineering and process improvements.
System Overview
Before diving into the analysis, it’s important to understand the physical system being optimized. So first, a quick summary of the engineering of an airplane wing:
The main load-bearing structure of an airplane wing (also known as the wing-box) is made of up several different components:
The Upper and Lower “Skins.” Also known as Wing Panels. The upper panel is labeled 5 in the diagram below.
The Front and Rear Spars. Labeled as 2 and 3. These are like the “spines” of the wing skeleton.
The Stringers. Labeled as 7. These structures add rigidity to the wing panels (and allow for the movement of fuel between compartments in the wing).
The Ribs. Labeled as 4. These plates line the interior of the wing-box, just like the ribs of a skeleton.
*Second image is the full scale 777X wing panel.
For the Boeing 777X, both pairs of panels and spars are over 100 ft long, and the ~90 stringers can range from 2 ft to 100 ft long. All of these components are fabricated at the Composite Wing Center in Everett, Washington. This was the building that I worked at as an Industrial Engineer from 2019 - 2022, focusing on system-level production performance.
Because some of the specific details are sensitive, proprietary information, I will have to be somewhat vague about the engineering process. But everything I’m going to share in this case study has been released by Boeing publicly.
Problem Framing: Scaling a Physical System
The core problem was straightforward in concept: Given a fixed set of physical resources and an increasing production target, how do we ensure the system can scale to meet demand?
This required continuously answering:
Where are the system’s true constraints?
What is the most effective way to remove or mitigate them over time?
These constraints were governed by physical realities: machine cycle times, labor availability, floor space, material constraints, and process variability. Even small inefficiencies or disruptions could propagate across the system and impact overall throughput.
Theory of Constraints
I will very briefly touch on a fundamental Industrial Engineering concept called the Theory of Constraints. It states that in order to improve the performance of a system, you must follow these steps:
Identify the system's constraint(s)
Decide how to exploit the system's constraint(s)
Subordinate everything else to exploit the constraint(s)
Elevate the system's constraint(s)
If in the previous steps a constraint has been broken, go back to step 1, but do not allow inertia to cause a system's constraint.
In practice, this became a continuous decision system. Constraints shifted over time due to changing demand, engineering improvements, and operational variability, requiring constant reassessment and reprioritization.
Manufacturing Process
Broadly speaking, the manufacturing process for all composite products follows the same general steps (including the components I described above):
Raw carbon fiber material is formed into the shape of the part
The part is cured in an autoclave (high pressure + temperature)
Post-processing brings the part to final specification
This process is executed across many interconnected work cells, each responsible for a portion of the workflow. Below is a precedence diagram that visualizes the whole workflow of the manufacturing process.
Even though the above diagram seems complex, this is only a tiny fraction of the complexity of the system.
Each work cell depends on a combination of:
Machinery and automation
Tooling and transport equipment
Skilled operators and inspectors
Raw and non-production materials
Time (ranging from hours to multiple shifts)
While this appears linear on paper, real production behavior was highly variable. Machine downtime, staffing gaps, quality issues, and upstream delays introduced non-deterministic behavior that had to be accounted for in all planning and forecasting.
Rate Readiness
My primary responsibility was driving rate readiness: ensuring that each part of the system could meet future production targets defined by the program’s rate ramp.
A rate ramp defines how many airplanes must be produced per month over time. Meeting this demand requires every work cell in the system to operate within strict time constraints.
These rates directly informed decisions around:
Capital investment
Engineering prioritization
Staffing and operational planning
If any work cell failed to meet required throughput, it risked becoming a bottleneck that constrained the entire system.
Decision Framework: Increasing Throughput
When a work cell could not meet future rate requirements, there were two primary levers:
1. Improve Flow (Reduce Cycle Time)
Add staffing (when effective)
Optimize processes through engineering improvements
Introduce new methods or technologies
Tradeoffs:
Engineering effort and time
Implementation risk
Cost of experimentation
2. Increase Capacity
Add machines or equipment
Increase staffing to fully utilize existing equipment
Tradeoffs:
Multi-million dollar capital costs
Floor space limitations
Long lead times
Additional labor requirements
Choosing between these options required evaluating cost, risk, timing, and system-wide impact. These were rarely obvious decisions and often had long-term consequences.
Example: Capacity vs Throughput Tradeoff
In one of the most critical bottleneck work cells, there was strong pressure to purchase an additional machine: a multi-million dollar investment with significant space and staffing implications.
I led the analysis to determine whether this investment was necessary.
By combining:
Historical flow data
Time studies
Projected improvements from engineering initiatives
I built a forward-looking performance model under multiple scenarios.
The conclusion: With targeted process improvements, the system could meet future demand without additional capital investment.
This required aligning engineering and operations stakeholders and accepting some level of risk, but ultimately avoided unnecessary cost while maintaining rate readiness.
System-Level Bottleneck Analysis
Because no work cell operates in isolation, we extended this analysis across the entire system.
For each work cell, we compared:
Actual performance
Projected performance
Required performance based on rate
This allowed us to identify gaps between current capability and future requirements.
We then aggregated this into a system-level view, where:
Each column represented a work cell
Each value represented the performance gap
This made it possible to:
Identify the most critical bottlenecks
Prioritize interventions
Sequence improvements over time
Importantly, this was about identifying both the severity of the gap and timing. Some bottlenecks only became critical at higher production rates, so planning had to account for when constraints would emerge.
Data & Decision Infrastructure
To support this work, I developed analytical tools using SQL, Excel, and BI dashboards that transformed raw production data into actionable insights.
These tools enabled:
Real-time visibility into work cell performance
Projection of future throughput under different scenarios
Alignment across engineering and operations teams
This effectively created a decision-making layer that connected physical production behavior with planning and execution.
The below chart is an example of an analysis of an individual work cell where we can visualize actual and average current performance with projected rates into the future (if grey line is above red line, then there is a performance gap).
A consolidated version of this system analysis might look like this (columns are workcells, rows are production rates, and values are the severity of the performance gap):
Conclusion
The challenge was not optimizing individual processes, but orchestrating a complex, interdependent system under evolving constraints.
Every decision - staffing, tooling, process improvements, or capital investment - had cascading effects across the system. And the complexity magnifies when we take into account the resources of
headcount
tooling
raw material
As well as factors like
part quality
scrapped parts
inventory
logistical constraints and floorspace
raw material “expiration”
raw material supply chain considerations
and the interaction effects between all of these things.
This experience shaped how I approach complex systems today: Start from first principles, understand the real constraints, and design solutions that balance throughput, cost, and reliability at scale.