Can a recently growing defense budget or maintenance and supply chain initiatives such as Condition Based Maintenance Plus (CBM+) (more here) reverse this trend? It’s unclear— and that’s the point. Today’s product support managers (PSMs) must have decision-quality information to know the net result of strategies chosen. Until then, a fundamental, overarching question remains invariably unanswered:
The Complex Problem
Without this answer, a PSM is left to make decisions on where to spend limited sustainment dollars to maximize aircraft availability without data-driven knowledge. Unfortunately, it is extremely difficult to quantify a product support course of action’s impact. Why? Because a product support strategy is typically an amalgamation of organizational maintenance, depot maintenance, and supply chain components which lacks sufficient integration to rigorously understand the components’ interrelationships and marginal effect on aircraft availability.
Without this critical understanding, the program cannot address key analytical questions, such as:
- What is currently happening across fleet product support? Specifically, what is the effect of current resourcing (funding, personnel, equipment) and processes across maintenance and supply chain elements on aircraft availability?
- How well is the interaction of product support elements in this complex system understood? What “dials” can be turned across these elements to optimize aircraft availability and at what incremental cost?
- What aircraft availability predictions can be made based on changes to contributing variables?
Though these questions may seem impossible to answer, it can be done—but only with the right capability and resources to model product support element performance and cost in relation to aircraft availability at a fidelity necessary to support sound judgement.
Enter Data-driven Decision Making and Data Analytics
Today’s world is data driven. Decisions at all levels demand it. Data informs planning, assesses alternatives, and predicts outcomes. Management requires user-friendly analytic tools to make these data-driven decisions. Fortunately, advanced modeling tools capable of accessing large volumes of unstructured, dissimilar data exist and make this analysis possible.
Predicting aircraft availability is a perfect application of these tools and data sets. The real task is fitting a multivariate prediction model to disparate product support data sets to support decision-quality results. Accomplishing this task requires experts with:
- A deep understanding of all relevant product support data such as personnel resourcing, infrastructure capacity, repair times, failure rates, parts availability, and funding levels
- Expertise to model and assess multiple outcomes which consider trades among all variables
Dayton Aerospace Support
Dayton Aerospace is well positioned to help aircraft programs address its aircraft availability challenges. Our analytics and product support subject matter experts (SMEs) have successfully performed multiple analyses assessing the costs and benefits of product support strategy alternatives, as well as integrating depot maintenance and aircraft modifications to optimize aircraft availability. We have substantial experience tackling the five phases of the analytics process—problem definition; data definition; data collection and integration; analysis and results interpretation; results evaluation against objectives; and communication and deployment of recommendations. In doing so, Dayton Aerospace provides the customer with data-driven decisions and the analytics necessary to perform additional inquiries.