FleetSight: A Simulation Tool for Cost, Availability and Remaining Service Life of Aging Military Systems
Dr. Louis E. Alfred, ScD.
Victor J. Thombs

Extending the life of aging aircraft (or any supported system) depends upon many tradeoffs among performance and economic variables. FleetSight analysis software shows how alternative maintenance actions and technology upgrades, designed to combat aging and obsolescence, can extend the life and lower the support requirements of products, components and personnel under various logistical assumptions and mission scenarios.


FleetSight unravels complicated relationships and improves understanding of complex dynamic processes inherent in the aircraft aging and obsolescence cycle. FleetSight was developed to simplify the creation of a good plan and, more importantly, revising the plan, with a robust understanding of the impact of those revisions. Using system dynamics modeling and simulation techniques, FleetSight simulates the interaction of each aircraft attribute with the other attributes, following each element of the system through years of use and abuse, repair and deterioration. The result is a model that can quantify alternative life cycle extension options and allows for ranking those options according to differing mission requirements.

In addition to simulating changes in availability and average condition, FleetSight allows program managers to project operations and maintenance (O&M) costs, manpower requirements, spares and other essential logistical and operational characteristics of an aircraft fleet.

FleetSight incorporates important feedback structures to ensure the model behavior will mirror reality. The model behavior duplicates real world behavior because it uses the same reasoning to formulate responses. The transparency of the modeling methodology and its flexibility in allowing users to quickly alter model assumptions or test alternative life extension options contributes to model credibility and encourages its application. The choice of a system dynamics modeling method gives DoD program managers more realistic projections than those obtained with conventional, static forecasting methods. A dynamic model also enables management to explore the reasons why long-term life cycle support rises or falls in response to alternative technology decisions and support policies.


Figure 1 diagrams the primary feedback linkages that describe the maintenance elements of FleetSight. The center of the diagram identifies Active Aircraft (1) and Inactive Aircraft (2). Aircraft move to inactive status through Down for Maintenance (3) while Return to Active (4) replenishes the pool of active aircraft. Over time, all aircraft, sub-systems, and components cycle through the maintenance loop many times.

Two drivers determine the rate at which aircraft are sent to maintenance, Scheduled Maintenance (5) and Unscheduled Maintenance (6). Scheduled maintenance consists of regular hourly, calendar and primary depot maintenance (PDM), as well as scheduled upgrades. Scheduled maintenance also depends upon the number of active aircraft. Unscheduled maintenance includes all unexpected maintenance and accidents. Some scheduled maintenance may also be accomplished while an aircraft is down for unscheduled maintenance work.

The scheduled and unscheduled maintenance work associated with inactive aircraft constitutes the Repair Backlog (7). The Rate of Repair (8) controls the return of aircraft from maintenance to active status. As a maintenance facility completes work in the repair backlog, aircraft return to active status. The rate of repair equals the minimum of either the repair backlog or Maintenance Capacity (9). The rate of repair, in turn, reduces the repair backlog.

Maintenance capacity includes the facilities, equipment parts and personnel available to work against the repair backlog. Capacity may vary according to pressures generated within the system. The first pressure comes from Maintenance Performance (10). Dividing the repair backlog (measured in labor hours) by the rate of repair (measured in labor hours per day) gives the average number of days of work in the repair backlog, called the average repair time. As the average repair time rises, for example, relative to a desired goal for minimum repair time, pressure will rise within the system to add more personnel in order to increase maintenance capacity. Because increases in average repair time are quickly apparent, a rise in the repair time often results in corrective action within a week or two.

Figure 1: Primary Feedback Relationships

The second pressure that may induce changes in capacity comes from a measure of the Fraction Active (11). For example, as the fraction active falls relative to a desired level, pressures in the system may tend to shift budget resources from other activities to maintenance in order to expand capacity and increase the fraction active. This feedback loop can be traced from (11) Fraction Active, (9) Maintenance Capacity, (8) Rate of Repair, (4) Return to Active, (1) Active Aircraft, and back to (11) Fraction Active. Generally, problems that reduce fraction active will tend to be more systemic and take longer to correct. Time is required to add significant resources to capacity, and a drop in the active fraction may take longer to bring about corrective action than the shorter-term actions triggered by a rise in the average repair time.

As aircraft age, Average Condition (13) will decline. Lower average condition will, in time, tend to generate an increasing amount of unscheduled maintenance and will also tend to extend the time required to complete maintenance tasks in the repair backlog. Older aircraft and components lead to higher Disposals (14) as repairs become harder to accomplish and are no longer economically justified considering the shorter remaining useful service life. Calculating an average air fleet condition offers a simple method for generating the slow rise in the frequency and duration of unscheduled maintenance tasks over time. In the model, average condition is primarily a function of aircraft aging, both chronological and technological. As time passes, unscheduled maintenance items may appear with increasing frequency as average condition declines. Mishaps (14) also contribute to unscheduled maintenance.

Three factors modify the impact of the aging rate on average condition. The first is the Average Quality of Work (15). Changes to maintenance capacity that place unusual pressure on labor and equipment that can affect the quality of the maintenance work. Pressures to accomplish more work in less time can cause individuals to skip optional preventive tasks, to rush some work and to take shortcuts. Over time, work quality will affect the rate of decline in average condition and the rate at which unscheduled maintenance increases.

Mission Stress (16), the second factor, can also modify the effect of aging on average condition. Mission stress is a function of the Mission Profile (17) relative to the fraction of active aircraft. Increasing the operational tempo during special training exercises or raising the number of sorties demanded during a conflict adds to the average stress placed on a given number of aircraft. Added stress will tend to degrade aircraft condition and can increase the number of accidents. Mission stress will also affect scheduled maintenance. During periods of high stress, for example, personnel may skip some scheduled maintenance while during periods of low stress personnel may give extra attention to equipment maintenance.

Departures from the planned maintenance schedule are the third factor to affect how aging impacts average condition. Over time, deferred maintenance, can cause an increase in the amount of unscheduled maintenance while, conversely, extra maintenance may prevent unexpected problems.


Within the model, the rate of aging controls average condition for any system, sub-system, or component. Figure 2 shows average condition as a distribution of three performance states (P1, P2 and P3) with the rates of flow between the states representing the aging process.

Figure 2: Aging Function

New aircraft enter the system in P1 condition, age over time, and eventually retire as P3. (Figure 1 does not show aircraft losses due to accident and/or attrition.) At any moment in time, the average condition of the fleet is computed by the weighted average of the number of aircraft in each performance state. Over time the majority of aircraft will tend to age from P1 to P3, with some aircraft in each category. The rate of aging (the average time required for an aircraft to move from P1 to P2 or from P2 to P3) is given as a "normal" aging rate. Normal aging assumes normal scheduled maintenance of normal average work quality and normal mission stress. Normal rates are then modified by dynamic multipliers representing factors such as work quality, mission stress and deferred maintenance.

Although the forces of time will eventually succeed in pushing the average condition towards the P3 category, maintenance actions can slow and even temporarily reverse the aging process. Upgrades and replacements can move systems and components back up to the P1 category and thus raise the average condition of the fleet. The model treats upgrades as a special form of scheduled maintenance that can "reset the clock" on the aging process.


One of the reasons it is so difficult to foresee the full impact of alternative choices is that, although all of the feedback loops in Figure 1 all affect system behavior, some loops act much faster than others, and some are more powerful than others. Although each loop can affect the system over time, different loops may provide maximum leverage at different points in the production cycle.

A powerful loop with short delays, for example, forces change very quickly. Identification of such loops is critical for controlling system behavior. Weak loops with long delays, on the other hand, offer little leverage, and seldom respond in the short-term to even heroic management efforts. System dynamics models allow decision-makers to differentiate between weak and strong feedback relationships and identify the critical leverage points within a complex system.

By itself, the model structure in Figure 1 is too aggregated to address complex management issues that arise in such complex processes as maintaining aging aircraft, shipbuilding, or space vehicle development. However, the modular flexibility of system dynamics allows model-builders to create a single structural module, test it for parameter accuracy and behavioral realism, and replicate it. The model structure in Figure 1 can be replicated dozens or hundreds of times to depict each component or subsystem (e.g., engines, compressors) within a larger system (e.g., aircraft). Further, each of the submodels is interrelated -- as in the real world, the rate of work progress on any one element can be dynamically linked to the rate of progress on related elements. An extremely sophisticated model, then, may be easily developed by aggregating a large number of simpler modules. The result is a complex, but realistic model that remains easy to understand.

System dynamics models explicitly incorporate causality, including feedback and delays, to build a system structure in which assumed causal relationships can be directly tested. Such causal models help to identify effective management options and avoid costly mistakes.

Another important structural element in system dynamics is nonlinear relationships, critical to realistic simulations and projections. A non-linear relationship exists when the rate of change of one variable speeds up or slows down relative to a constant rate of change of another, related variable.

For example, suppose that as a fleet ages, the frequency of required maintenance increases. A maintenance action that may have occurred once every four or five months when the aircraft was new may need to be done every two or three months as the aircraft nears the end of its expected service life.


From among many alternative simulation outcomes, it is then possible to choose those strategies that meet the established program objectives at the lowest cost. Models help to quantify cost and performance tradeoffs so that subsequent decision-making is based on more complete and accurate information. Models also help management explore the reasons why long-term costs rise or fall in response to alternative assumptions and policies.

Further, all system dynamics models utilize the same graphic language and hierarchical organization, creating a universal, highly intelligible language for exploring system behavior. Simple diagrams, and their underlying mathematical expressions, greatly facilitate communication. This common language removes many of the ambiguities that plague conventional decision analysis techniques and overcomes the "black box" phenomenon of traditional econometric models.

System dynamics can fundamentally improve the effectiveness of management decision-making and integrate all elements of management processes. What were once believed to be isolated management problems suddenly become interrelated pieces of a much larger puzzle. While system dynamics models will never substitute for practical management experience, models help to capture and structure that experience, and extend decision support throughout an organization.

Using system dynamics, the FleetSight life cycle management tool provides DoD program managers with new insights on the impact their decisions have on budget, performance and the life cycle of any aging aircraft or weapon system. FleetSight performs the following functions in order to achieve this objective:

  • accommodates differing technology development cycles and program lifetimes,
  • assesses new technology opportunities,
  • tracks component operation and component failure points,
  • identifies the links among interdependent subsystems,
  • traces the "what-if?" consequences of alternative life extension strategies,
  • adapts to rapidly changing requirements,
  • applies to past and current weapons systems as well as to future systems,
  • supports logistics planning, and
  • forecasts annual maintenance and support efforts

FleetSight allows program managers to define and test alternative "what-if?" scenarios that trace the consequences among competing policy options, quantify the impact of specific decisions, and test the sensitivity of system response to different parameter assumptions. FleetSight offers an innovative simulation tool for designing long-term resource allocation strategies to support aging aircraft fleets.


Decision Dynamics, Inc.
11718 Yates Ford Road
Fairfax Station, VA 22039
Phone: (703) 988-0623
Fax: (703) 250-2987