From the program manager to the program executive officer (PEO) to the Air Force depot complex commander, all stakeholders share a common imperative: Provide maximum warfighting capacity, capability, and readiness. In meeting that imperative, a primary analytic question is whether sufficient depot capacity exists throughout the fleet’s lifecycle to accommodate expected repair and modification workloads. Since the Air Force Life Cycle Management Center Logistics Directorate (AFLCMC/LG) Product Support Business Case Analysis (PS-BCA) Organic Strategy Assessment process now assumes organic support as the baseline condition, the fleet manager’s consideration of depot complex enterprise capacity only increases in importance. (Read the latest AFLCMC PS-BCA Training here.)
While the program office’s assessment of depot capacity is necessary, it may be insufficient and limited in its effectiveness. Why? It’s typically narrow in its analytical scope, only factoring in a particular fleet’s requirements and what the program controls. However, risk exists in what the individual program office does not control—the depot complex enterprise-level current and projected production requirements and resourcing decisions. This enterprise is the summation of all current and planned fleet repair and modification programs—each competing for possibly constrained depot manpower and facility resources.
Hence, the objective is to work the fleet management challenge in a data-driven, analytically based manner from both ends—the individual aircraft fleet and the depot complex enterprise—and to do so within a coordinated, collaborative construct that accommodates changing program dynamics and timely resourcing decisions. This results in proactive, higher-confidence, individual aircraft fleet strategies and depot complex industrial decisions.
To achieve this objective, the primary question is:
The Modeling Construct: Scaling at the Depot Complex Enterprise Level
As discussed in part one of this series, the Input-Process Model-Output method of defining boundaries and scope is a good starting point to evaluate alternative depot complex capacity scenarios and their effect on aircraft fleet measures of merit (MoMs). To “build out” the construct, one approach is to focus first on desired outputs. These outputs drive the model structure and the data inputs and analysis necessary to produce those outputs. Fundamentally, the candidate MoMs shown in the diagram below provide the stakeholders within the depot complex, aircraft PEOs, and program offices with decision-quality results.
Measures of primary interest to the depot complex are the:
- Location, characterization (bay quantity and size, overhead lift requirements, immoveable stand requirements, specialized facilities (e.g., paint), etc.), duration, and timing of any physical capacity constraints
- Lead time required for alleviation of physical capacity constraints and the degree and timing of that alleviation
- Location, characterization (quantity, skill set requirements, etc.), duration, and timing of any personnel constraints
- Lead time required for alleviation of personnel constraints and the degree and timing of that alleviation
Modeling Construct Outputs: Turning the Dials of Scope, Schedule and Resourcing
The depot complex enterprise measures of merit are extremely important since they form the basis for exploring the trade space of alternative depot support postures for varying fleet management alternatives. Based on the number of modifications to be performed in conjunction with programmed depot maintenance (PDM), modification timing and predecessor-successor relationship with other modifications, user priority and completion requirements, etc., the number of possible scenarios for each fleet at the depot complex enterprise-level can be quite large. An analysis model is absolutely essential to adequately explore the trade space and produce a solution that optimizes production while meeting warfighter requirements at lowest cost. Beyond that, such a model could effectively provide depot complex leadership with insights relative to 50/50 and industrial base core predictions over time.
Model: Process Flow of the Depot Complex Enterprise Production Machine
The depot complex enterprise decision support model itself is a structural and procedural representation of the entire depot complex production machine. It is a scaled version of each aircraft program’s fleet management decision support model. Structurally, the model depicts the production steps, capacity (number of docks/bays, resources applied, shifts per day, etc.), and the dynamics of shared resources across the complex. Procedurally, aircraft fleets flow through the machine based on Little’s Law, where Work in Progress (WIP) equals Throughput multiplied by Flowtime. Important, primary sources of this information are facilitated, cross-functional, production estimating workshops that estimate flow days for all combinations of each fleet’s heavy maintenance and modifications.
Modeling Construct Inputs: Fleet Requirements with Priority as a Variable
As stated in part one, modeling efforts live and die by the quantity, accuracy, and fidelity of the data required to feed the model. No utility exists in a discretely detailed model lacking the commensurate data inputs. The challenge is to define the minimum level of fidelity required to meet decision requirements. This is as true at the enterprise level as it is at the individual aircraft fleet management level.
Inputs to the depot complex enterprise decision support model are the aggregation of individual fleet inputs: PDM schedules, modification installation schedules, production machine construct and flow, and resourcing. Again, well-structured, facilitated production estimating workshops involving production planners, engineers, and maintenance stakeholders are a necessary starting point for producing the needed data inputs, and facilitating data validation and verification.
Data produced from these workshops include:
- Production flow days for each modification,
- Composite flow days for modifications performed in parallel or in conjunction with PDM or both,
- Personnel required per modification by specialty, and
- Required support equipment.
In addition, aircraft or modification priority defined by the warfighter can be factored into the trade space in order to make resourcing decisions. The derivation of all of this data, nor its integration at the enterprise level, is trivial. Without it, however, truly effective enterprise fleet management lacks the necessary analytic rigor required to address its complexity.
Optimum Enterprise Fleet Management: Depots and Programs Working Hand-in-Glove
In summary, effective fleet management is a product of data-driven analysis at both the individual aircraft fleet and depot complex enterprise levels. The latter addresses the critical variable—capacity—and provides answers based upon a balanced view across the enterprise relative to priority and workload. Implementing an analysis rhythm based on funding cycles can serve to enhance and even stabilize program and depot complex planning well in advance of execution.
Dayton Aerospace Support
Dayton Aerospace is well positioned to help aircraft programs address their fleet management challenges. Our analytics and product support subject matter experts (SMEs) have successfully performed multiple analyses assessing the trade-offs between competing priorities by integrating depot maintenance and aircraft modifications to optimize aircraft availability. We have substantial experience tackling the phases of the analytics process—problem definition; business and analytic objective definition; ground rules and assumption identification; 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 customers with data-driven decisions and the analytics necessary to perform additional inquiries. Our approach to effective fleet management utilizes the high value, authoritative sources of system data and models described in the Air Force Research Laboratory’s (AFRL) Digital Hangar as a continuum across disciplines to support effective fleet management strategies.