Lockheed Martin Transforms Military Training with Simio’s Digital Twin Technology

Executive Summary

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’s 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.

Business Challenge

The Turnkey Training Challenge

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:

• Complex maintenance requirements
• Weather-dependent availability
• Competing demands between training and maintenance

Forecasting Uncertainty: Training operations involve:

• Multiple pipelines with varying resource requirements
• Unpredictable student progression and attrition
• Complex dependencies between courses and resources

Strategic Decision Support Needs: Management required:

• Accurate forecasting of program performance
• Quantification and mitigation of operational risks
• Optimization of resource procurement and utilization
• Support throughout the program lifecycle

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.

Solution Approach: The Training Enterprise Digital Twin

Creating a Digital Replica of Training Operations

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:

1. Program Validation Model

• This component simulates how students progress through training pipelines:
• Models complete training syllabi from basic training through specialized courses
• Accounts for different student paths (fixed wing, rotary wing, electronic warfare, etc.)
• Incorporates variable attrition points where students may fail or require additional training
• Tracks instructor availability and qualifications
• Simulates academic and flight training events with appropriate resource requirements

2. Sustainment and Logistics Model

This component simulates asset availability and maintenance requirements:

• Models both planned and unplanned maintenance events
• Accounts for detailed maintenance schedules and resource constraints
• Predicts asset availability based on usage patterns
• Integrates weather impacts on training operations
• Optimizes maintenance scheduling to maximize asset availability

“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:

• Simulation produces forecasts and recommended resource plans
• Plan Selection identifies optimal resource allocation strategies
• Execution implements selected plans in actual operations
• Data Collection captures real-world performance data
• Continuous Improvement refines models based on operational data

This approach ensures the digital twin maintains fidelity with actual operations and enables continuous improvement through data-driven decision-making.

Technical Implementation

Bringing the Digital Twin to Life

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:

Simulation Architecture

The simulation architecture integrates several key components:

• Database Integration: Connects with exterior systems for data input and output
• Monte Carlo Simulation: Runs multiple replications to account for variability
• Dashboard Support: Provides visualization of key performance indicators
• Scheduling Interface: Enables integration with operational scheduling systems

“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.”

Implementation Process

Lockheed Martin developed a structured approach to digital twin implementation:

Team Structure: Separated specialized modeling and analysis roles:

• Modelers focused on understanding and improving the simulation product
• Analysts specialized in understanding customer requirements and generating reports

Implementation Workflow:

1. Update baseline data with current operational information
2. Run simulation scenarios to forecast outcomes
3. Verify results against historical data or expectations
4. Refine model parameters based on verification results
5. Identify and resolve scheduling conflicts
6. Present outcomes to stakeholders for decision-making
7. Implement selected approach and continue monitoring

This structured approach ensures the digital twin provides accurate, actionable insights while maintaining alignment with operational realities.

Results and Business Impact

Transforming Decision-Making and Operations

The Training Enterprise Digital Twin delivered significant measurable improvements across multiple dimensions of Lockheed Martin’s training operations.

Syllabus Optimization Case Study

A comparative analysis of syllabus changes demonstrates the model’s capability to evaluate operational impacts:

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.

Strategic Business Impact

Beyond specific optimization examples, the digital twin delivered significant strategic benefits:

1. Improved Stakeholder Communication

• Clear forecasts of expected performance
• Data-driven understanding of resource requirements
• Transparent view of operational constraints
• No surprises for customers or management

2. Cost Avoidance Through Asset Optimization

• Accurate modeling of actual asset requirements
• Reduced procurement of high-cost training assets
• Optimized maintenance planning
• Better utilization of existing resources

“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.”

3. Enhanced Operational Planning

• Accurate forecasting of class dates throughout the training pipeline
• Better allocation of instructors and resources
• More efficient scheduling of maintenance activities
• Reduced bottlenecks and waiting time

4. Improved Risk Management

• Ability to test “what-if” scenarios without operational disruption
• Quantification of operational risks
• Data-driven mitigation strategies
• Enhanced adaptability to changing requirements

The digital twin has become an essential strategic asset, providing both immediate operational benefits and a foundation for future innovation and optimization.

Future Applications

Expanding the Digital Twin Ecosystem

Based on the success of the initial implementation, Lockheed Martin is exploring several expansion opportunities for their Training Enterprise Digital Twin:

1. Enhanced Integration with Scheduling Systems

• Real-time operational scheduling assistance
• Dynamic resource allocation
• Automated conflict resolution

2. Expanded What-If Analysis Capabilities

• Equipment modernization impacts
• Training method changes
• Resource constraint evaluations

3. Application to Additional Training Domains

• Expanded beyond flight training to other military specialties
• Cross-domain training coordination
• Joint training exercises

4. Advanced Analytics Applications

• Predictive maintenance integration
• Student performance prediction
• Early identification of training challenges

“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.

Conclusion

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.