Lockheed Martin, a global leader in aerospace and defense technology, faced significant challenges in managing their complex military training operations under performance-based contracts. By implementing Simio’4 Process Digital Twin technology, they created a comprehensive Training Enterprise Digital Twin that revolutionized their approach to resource planning, scheduling, and operational decision-making.
The solution enabled Lockheed Martin to accurately model student progression through training pipelines alongside asset availability and maintenance requirements. This digital replica of their training enterprise delivered remarkable results, including 25% faster training completion times, 20% reduction in peak student loads, and potential savings of tens of millions of dollars through optimized asset procurement.
This case study examines how Lockheed Martin leveraged Simio’s simulation technology to transform their training operations while providing unprecedented visibility into operational dynamics and resource requirements.
Lockheed Martin’s innovative “turnkey training” approach represents a fundamental shift in military training delivery. Rather than simply providing training equipment, Lockheed Martin delivers a comprehensive performance-based service focused on training outcomes. This approach creates unique operational challenges:
Performance-Based Contract Risk: Payment is contingent on producing qualified graduates who meet strict specifications, creating significant financial exposure if training goals aren’t met.
“It’s performance based. That means we only get paid when we produce a finished product that meets or exceeds specifications. We have student candidates coming in one side as the raw material, and graduates are produced at the other end as the finished product.”
Resource Optimization Complexity: Training facilities include high-value assets like aircraft and simulators with:
Forecasting Uncertainty: Training operations involve:
Strategic Decision Support Needs: Management required:
Traditional planning approaches couldn’t adequately capture these complexities or provide the decision support needed to optimize operations. Lockheed Martin needed a solution that could model the intricate dependencies within their training enterprise while supporting data-driven decision-making.
Lockheed Martin implemented a comprehensive digital twin approach using Simio’s simulation technology to create a virtual representation of their entire training enterprise. This digital twin incorporated both student progression through training pipelines and the availability of assets supporting that training.
The solution included two interconnected modeling components:
This component simulates asset availability and maintenance requirements:
“We’ve run through the simulation and let’s take a look at our outputs. The simulation produces gigabytes of Gantt charts. You know everything there is to know about a student. You see what they’re doing at each point in time of their day.”
Crucially, the digital twin implementation follows a closed-loop feedback system:
This approach ensures the digital twin maintains fidelity with actual operations and enables continuous improvement through data-driven decision-making.
The technical implementation of the Training Enterprise Digital Twin leveraged Simio’s advanced simulation capabilities to create a comprehensive virtual replica of Lockheed Martin’s training operations.
Data Inputs and Model Parameters
The model incorporates a wide range of data inputs to ensure accurate simulation:
| Data Category | Examples | Impact on Simulation |
| Training Syllabi | Course structures, lessons, training events | Defines student progression paths |
| Attrition Percentages | Failure rates at different checkpoints | Models realistic student flows |
| Resource Availability | Instructor and device availability factors | Captures resource constraints |
| Continuation Training | Instructor qualification maintenance | Accounts for additional resource demands |
| Rest Requirements | Crew rest periods and duty day limitations | Ensures realistic scheduling |
| Weather Impacts | Seasonal variations and delays | Models key environmental factors |
| Maintenance Schedules | Planned and unplanned maintenance | Predicts asset availability |
| Working Hours | Operational time constraints | Defines available training windows |
The simulation architecture integrates several key components:
“We want to run these analyses on a regular basis. And I just we just scratched the tip of the iceberg because there’s hundreds of parameters that you can change in this model. And each of them can represent a different insight depending on what situation you are up against.”
Lockheed Martin developed a structured approach to digital twin implementation:
Team Structure: Separated specialized modeling and analysis roles:
This structured approach ensures the digital twin provides accurate, actionable insights while maintaining alignment with operational realities.
The Training Enterprise Digital Twin delivered significant measurable improvements across multiple dimensions of Lockheed Martin’s training operations.
A comparative analysis of syllabus changes demonstrates the model’s capability to evaluate operational impacts:
| Performance Metric | Basic Syllabus | Hybrid Syllabus | Improvement |
| Avg. Training Time (with weather) | 180 days | 155 days | 25 days faster (14%) |
| Training Time Variability | High (±30 days) | Moderate (±15 days) | 50% reduction |
| Meeting 180-day Requirement | ~60% of students | ~90% of students | 30% improvement |
| Peak Student Load | 75 students | 60 students | 20% reduction |
| Annual Graduation Rate | Maintained | Maintained | No negative impact |
| Facility Requirements | Higher | Lower | Reduced footprint |
This example illustrates how moving selected training events from aircraft to simulators not only reduced average training time but also significantly decreased variability, enabling more predictable operations while maintaining graduation rates.
Beyond specific optimization examples, the digital twin delivered significant strategic benefits:
“Think about the cost of an airplane. Just the purchase cost is pretty high. But now think about the sustainment cost of that airplane. It might be three times the purchase price of the aircraft. So if you can prove that you can train with fewer aircraft, you may have saved tens of millions of dollars.”
The digital twin has become an essential strategic asset, providing both immediate operational benefits and a foundation for future innovation and optimization.
Based on the success of the initial implementation, Lockheed Martin is exploring several expansion opportunities for their Training Enterprise Digital Twin:
“We support decisions throughout the program life cycle. We have solution development, stand up and service delivery.”
As Lockheed Martin continues to refine and expand their digital twin implementation, they anticipate even greater operational improvements and cost savings across their training enterprise.
Lockheed Martin’s implementation of Simio’s Digital Twin technology represents a transformative approach to managing complex training operations. By creating a comprehensive virtual replica of their training enterprise, they’ve gained unprecedented visibility into operational dynamics, enabling data-driven decisions that optimize performance across multiple dimensions.
The results speak for themselves: faster training completion, more efficient resource utilization, enhanced predictability, and significant cost avoidance. Most importantly, the digital twin has become an integral part of Lockheed Martin’s operational and strategic decision-making process, creating a continuous improvement cycle that drives ongoing optimization.
This implementation demonstrates the power of Simio’s advanced simulation technology to address complex operational challenges in the aerospace and defense industry. The Training Enterprise Digital Twin has become an essential strategic asset for Lockheed Martin, providing both immediate operational benefits and a foundation for future innovation.