Boeing Wichita, with its distinguished history spanning nearly a century of aerospace manufacturing excellence, faced a critical operational challenge that threatened to constrain future production capabilities. The facility, which has produced over 1,600 B-29s, 744 B-52s, and more than 10,000 737 fuselages since the 1920s, operates a specialized paint facility responsible for engine cowlings, thrust reversers, and inlets—the visible exterior components of commercial aircraft engines.
The capacity planning manufacturing challenge emerged from two converging factors that fundamentally altered the operational landscape. Legacy aircraft models were being phased out while new product lines introduced significantly larger components that strained existing equipment capabilities. The new parts were substantially larger than their predecessors, requiring different paint materials and curing processes that demanded two complete cycles through the paint and cure operations instead of the traditional single pass.
Boeing’s existing paint facility, originally sized for product demand from the 1980s and 1990s, operated with sand, wash, paint, cure, and general part buffer systems that could no longer accommodate the evolving requirements. The facility processed approximately 20 different parts across three aircraft model families, each with unique production rates measured in aircraft per month (APM). The system’s complexity was amplified by unique part routings where some components required multiple passes through paint operations, variable process durations for each step, and individual rework probabilities for different part types.
Critical Operational Constraints:
Parts could only be batched if they were identical types with matching finishes arriving within one hour of each other
The traditional Excel-based capacity studies that Boeing had relied upon proved inadequate for handling the intricate routing patterns, batching behaviors, and dynamic scheduling requirements. These spreadsheet-based approaches provided no visibility into buffer sizing requirements and could not predict on-time delivery performance by individual parts. What is a capacity plan became a fundamental question as Boeing needed sophisticated analytical capabilities that could model the complex interdependencies within their paint facility operations.
The convergence of increasing demand with the introduction of larger, more complex parts created unprecedented capacity pressure on the existing system. The new model change required parts to loop through painting and curing processes twice, effectively doubling the processing time for these components while consuming the same physical equipment space. This constraint, combined with the inability to batch larger parts due to equipment size limitations, threatened to create bottlenecks that could impact Boeing’s ability to meet customer delivery commitments.
The facility’s paint booth and oven systems, designed for smaller legacy products, faced utilization rates that approached maximum capacity even before the full implementation of new product lines. Boeing’s leadership recognized that without accurate capacity planning manufacturing analysis, they risked either over-investing in unnecessary equipment or under-preparing for future demand scenarios that could compromise operational performance.
Boeing partnered with Simio to develop a comprehensive aerospace simulation approach that could accurately model the complex paint facility operations and provide data-driven insights for capacity planning decisions. The collaboration leveraged Simio’s discrete event simulation capabilities to create two distinct analytical models designed to answer critical strategic questions about facility capacity and equipment requirements.
The aerospace engineering software implementation utilized Simio’s object-oriented architecture to create detailed digital representations of Boeing’s paint facility operations. The modeling approach recognized that discrete event simulation could handle the operational complexity that traditional Excel-based methods could not address, including complicated routing patterns, batching behaviors, special processing rules, and comprehensive performance scoring for individual parts and overall system effectiveness.
The simulation models operated as input-process-output systems where inputs included demand forecasts, part routings, worker schedules, process durations, and rework probabilities for each component type. The process section described the complete system behavior, including demand conversion to part arrivals, custom processing rules governing paint operation initiation, resource availability constraints, and part movement delays between operations. Output metrics focused on on-time delivery percentages, equipment and worker utilization rates, space consumption logs, and real-time tracking of parts within the system.
The current state model focused on the existing system configuration, incorporating sand, wash, paint, cure, and general part buffer operations sized for legacy product demand. The aerospace modelling approach captured the reality that batching could theoretically occur for smaller parts but typically did not happen due to low production rates that prevented simultaneous arrivals at the paint system.
The model incorporated critical operational constraints including the fact that new model parts barely fit existing equipment, making batching impossible for these larger components. The requirement for new parts to complete two cycles through painting and curing processes was accurately represented, along with the capacity pressure created by increasing demand combined with these processing changes.
To examine maximum throughput potential, the current state model included both the primary paint booth and oven systems as well as an additional paint booth currently used for training purposes. This configuration allowed Boeing to understand the absolute capacity limits of their existing infrastructure and identify when demand would exceed available capacity.
The future state model addressed Boeing’s strategic question about greenfield building expansion requirements by modeling optimal facility layout and equipment configurations. The aerospace simulation software enabled evaluation of various scenarios assuming the same part routings, durations, and rework rates from current operations while incorporating enhanced operational parameters.
The future state configuration assumed two-shift, five-day-per-week operations with new equipment sized to accommodate batching of two parts of any size throughout the entire process, effectively eliminating equipment size limitations. The model design followed theory of constraints principles, identifying the paint booth as the system constraint due to its high equipment cost and skilled labor requirements.
The facility layout optimization utilized a pull-based system where parts moved from arrival holding buffers through sanding, wash bay, and masking operations, then were pushed through cure, demask, stencil, quality assurance, and staging for shipment. When parts completed painting operations, the next most urgent component would advance to take its place, creating a streamlined flow that minimized bottlenecks and maximized equipment utilization.
The solution methodology incorporated multiple demand scenarios testing various combinations of equipment configurations and operational methods. Each scenario consisted of three part families with individual aircraft per month program rates, with demand held constant for 12-month periods to evaluate system performance under sustained operational conditions.
The aerospace engineering software enabled testing of different methods including batching versus non-batching approaches and oven curing versus spin booth curing technologies. Equipment quantities were systematically adjusted and model iterations executed until optimal configurations were identified for each demand scenario and method combination.
Performance quantification utilized planned flow days from Boeing’s ERP system compared against modeled flow days to calculate on-time delivery percentages and total past-due days. This scoring methodology enabled objective comparison of different scenarios and equipment configurations to identify optimal solutions for various operational requirements.
The Simio-based paint facility capacity planning analysis delivered comprehensive insights that fundamentally transformed Boeing’s approach to facility expansion and equipment investment decisions. The discrete event simulation models provided precise answers to critical strategic questions while revealing optimization opportunities that traditional analytical methods could not identify.
The current state model definitively established that existing facility equipment could not meet future demand requirements. Using the “number of parts in system” graph analysis, the simulation identified the precise point where system capacity would be exceeded, with the sharp positive slope indicating when the facility could no longer maintain pace with increased arrival rates.
The validation process compared simulation predictions with Excel-based load chart forecasts, confirming that demand would exceed capacity at approximately the same timeframes when accounting for assumption modifications including no overtime, no rework, and no additional primer flash-off time due to environmental conditions. The model’s use of subject matter expert times with variability provided more accurate representation of actual process durations compared to Excel load charts that used part standards without variability and consequently overestimated capacity.
Multiple iterations of the current state model explored various capacity extension strategies, including reactivating a retired paint booth previously relegated to training new painters. However, even with these additional resources and their associated limitations, the analysis confirmed that existing infrastructure could not satisfy projected demand increases within the required timeframe.
The future state model analysis revealed significant insights about optimal equipment configurations and operational methods. Testing four different demand scenarios across four distinct methods—batching with oven, batching with spin, no batching with oven, and no batching with spin—provided comprehensive data for strategic decision-making.
Equipment Configuration Results:
Batching with oven combination required four paint booths and three ovens for a total of seven equipment pieces
The analysis demonstrated that maximizing paint booth flow through separate oven curing, while seemingly optimal for minimizing expensive paint booth equipment, actually proved less efficient when the entire system was considered. The spin technology approach, which combined painting and curing in the same equipment space, reduced overall equipment count and facility layout requirements.
One of the significant advantages of the discrete event simulation approach was the quantification of buffer size requirements and space consumption throughout the facility. Each part type consumed different amounts of square footage at various process steps, with additional space requirements for proper work execution—two extra feet around parts in ovens for airflow, three feet around manual operation areas for portable stairs access, and six feet around wash bay operations for pressure washer operation.
The aerospace simulation software tracked space consumption at each process step, particularly in staging areas, enabling accurate allocation of square footage requirements for optimal facility layout. This capability provided Boeing with precise data for facility expansion planning and space utilization optimization.
The model’s ability to evaluate various rework scenarios provided critical insights for quality planning and equipment sizing. Testing rework rates from 0% to 25% in 5% increments revealed the dramatic impact of quality performance on equipment requirements:
At 10% rework rate: Additional oven and paint worker required
The analysis clearly demonstrated that rework rates for new model parts would significantly influence equipment needs, providing Boeing with data-driven quality targets that directly impacted facility investment requirements.
The comprehensive analysis led Boeing to adopt the spin approach for curing operations, recognizing that space optimization and reduced equipment count provided greater value than maximizing individual paint booth utilization. All equipment was sized to accommodate batching of large parts, and the facility layout was optimized based on simulation-driven insights rather than traditional capacity planning methods.
The aerospace modelling approach proved superior to Excel-based capacity analysis for complex systems because it could handle intricate part routings, incorporate rework rates, quantify buffer sizes, add duration variability to analysis, and evaluate complete system performance rather than individual equipment pieces.
The Boeing paint facility capacity planning project demonstrates how advanced simulation technology transforms traditional manufacturing planning processes while delivering measurable strategic value. The partnership between Boeing and Simio illustrates the potential for aerospace simulation software to address complex operational challenges through data-driven analysis and optimization.
The discrete event simulation approach provided Boeing with confidence in their facility expansion decisions, accurate equipment sizing recommendations, and optimized operational methods that minimized both space requirements and capital investment. The ability to test multiple scenarios without operational disruption enabled Boeing to make informed decisions about technology adoption and facility layout that would have been impossible with traditional analytical methods.
This implementation validates Simio’s position as a leader in intelligent digital twin simulation technology, demonstrating how modern simulation platforms can address complex aerospace manufacturing challenges while delivering measurable business value. The successful analysis provided Boeing with a concrete blueprint for facility expansion that balanced operational requirements with capital efficiency, ensuring optimal performance for future production demands.
The project’s success reinforces the importance of sophisticated capacity planning manufacturing analysis in aerospace operations, where complex constraints and high-value products require precise optimization to maintain competitive advantage. Boeing’s experience demonstrates that organizations willing to invest in advanced simulation capabilities can achieve superior operational outcomes while minimizing risks associated with facility expansion and equipment investment decisions.
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