A recent Harvard Business Review article, “Why You Need Systems Thinking Now”, argues that the most effective leaders understand systems—not just problems. While the article focused on business and societal challenges, its insights apply even more directly to the aerospace and defense industry, where complex systems interact in unpredictable ways.
Have you ever heard the story of the blind men and the elephant? Each blind man felt the elephant and described it differently, as they were each feeling a different part. The man whose hand had reached an ear said, "it is large, rough and wide, like a rug." The one who felt the trunk said, "it is straight and hollow pipe, and very strong." The one who felt the feet and legs said, "it is mighty and firm, like a pillar." Each one had felt one part out of many, but had not understood what the elephant was. This story illustrates a lesson we all know, but is still a challenge: the behavior of a system cannot be known just by knowing the elements of which the system is made.
In this industry, data often lives in silos—maintenance databases, production systems, engineering models, and logistics records. Each tells part of the story, but not the whole. Without seeing how these systems influence each other, decisions are delayed, perpetuating the problem, or worse, well-intended changes can create unintended consequences—reducing readiness, increasing costs, or shifting bottlenecks elsewhere.
Systems thinking provides the framework to understand interconnections and feedback loops within large, complex environments. It moves organizations beyond reactive problem-solving toward holistic insight and proactive decision-making.
At SkyPath Analytics, we use systems thinking as the foundation of our work. Through model-based reliability analysis and digital MRO and manufacturing process simulation, we help aerospace and defense leaders integrate their data into context—transforming fragmented information into a unified, reliable picture of system behavior.
Aerospace platforms degrade over time not because of a single defect, but due to interacting factors—environmental conditions, maintenance quality, supply chain variability, and design trade-offs.
By applying system functional models grounded in model-based systems engineering (MBSE), engineers can simulate how components, subsystems, and support processes influence each other, and map out how component failures cascade through the system. It is interesting that after a system is fielded, the failure modes and effects analysis (FMEA) is rarely referenced or updated. These functional models are living and dynamic FMEAs that can be leveraged for other reliability, maintainability and logistics product data analysis.
When maintenance and diagnostic data are integrated into the model, it becomes possible to identify true failure modes and root causes of reliability issues—those that emerge only from the system’s combined behavior.
This approach accelerates reliability analysis, improves fleet availability, and prevents the costly cycle of addressing symptoms instead of causes. When the corrective action benefits compound over time, quicker identification and correction of the root cause can lead to tremendous cost saving and availability gains.
In MRO and production environments, complexity takes a different form. Changing inspection sequences, automation, machine down time, or staffing levels can unintentionally move constraints elsewhere in the process.
By building discrete event simulation (DES) models of the production processes, SkyPath helps organizations replicate their end-to-end production system in a virtual environment. Leaders can test “what-if” scenarios—adding shifts, changing routing logic, introducing automation—and predict how these changes affect throughput, utilization, and turnaround time. They can be leveraged for both strategic investment decisions, as well as day-to-day tactical decisions.
The result: decisions that are data-driven, predictive, and free of the blind spots and unintended consequences that come from viewing the process in isolation.
Systems models bring data into the system context, revealing the dependencies, feedback loops, and trade-offs that shape outcomes. Instead of asking, “What’s happening?” decision-makers can ask, “Why is it happening—and what will happen next?”
This systems-level understanding doesn’t simplify reality; it makes it comprehensible and actionable. It replaces isolated metrics with true insight—clarity that supports better engineering, maintenance, and operational decisions.
At SkyPath Analytics, our purpose is to connect data silos and create clarity through systems thinking. We combine model-based engineering, data analytics, and AI-powered insight to help aerospace and defense organizations understand how every part of their ecosystem affects the whole.
Whether diagnosing aircraft reliability degradation, optimizing production throughput, or predicting future system performance, SkyPath brings together people, data, and models into one coherent framework—enabling smarter, faster, and more confident decisions.
As the Harvard Business Review notes, “Systems thinking helps leaders see patterns that linear thinking misses.” In aerospace and defense, this isn’t just good management—it’s a strategic necessity.
Those who understand their systems holistically will not only solve today’s problems more effectively but also anticipate tomorrow’s challenges before they emerge.
In the near future, the integration of AI and systems thinking transforms analytics from reactive pattern recognition to proactive, model-informed reasoning—bringing the industry closer to autonomous, adaptive decision-making. Keep your eyes on this space for more more on that in the near future.
That’s the power of systems thinking—and it’s the foundation of SkyPath Analytics.