Erin T. Ryan

An ontological framework for clarifying flexibility-related terminology via literature survey

Despite its ubiquity in the systems engineering literature, flexibility remains an ambiguous concept. There exist a multitude of definitions, which vary not only by domain, but within domains as well. Furthermore, these definitions often conflict with one another, making it difficult to discern the intended meaning in a given study or to form generalizations across studies. Complicating matters, there is a plethora of related terminology that is often used carelessly and/or inter-changeably with flexibility. In this paper, we employ a novel ontological framework for clarifying salient aspects of extant flexibility-related terminology. While it was not possible to distill consensus definitions from the literature, we did identify certain dominant characteristics that enabled us to formulate a set of democratic definitions for flexibility, adaptability, and robustness, as well as recommended definitions for agility and versatility. We believe that the proposed definitions of these key system design principles may provide a baseline for improving analysis and communication among systems engineering practitioners and academics.

A Proposed Methodology to Characterize the Accuracy of Life Cycle Cost Estimates for DoD Programs

Abstract For decades, the DoD has employed numerous reporting and monitoring tools for characterizing the acquisition cost of its major programs. These tools have resulted in dozens of studies thoroughly documenting the magnitude and extent of DoD acquisition cost growth. Curiously, though, there have been extremely few studies regarding the behavior of the other cost component of a systems life cycle: Operating and Support (O&S) costs. This is particularly strange considering that O&S costs tend to dominate the total life cycle cost (LCC) of a program, and that LCCs are widely regarded as the preferred metric for assessing actual program value. The upshot for not examining such costs is that the DoD has little knowledge of how LCC estimates behave over time, and virtually no insights regarding their accuracy. In recent years, however, enough quality LCC data has amassed to conduct a study to address these deficiencies. This paper describes a method for conducting such a study, and represents (to the authors’ knowledge) the first broad-based attempt to do so. The results not only promise insights into the nature of current LCC estimates, but also suggest the possibility of improving the accuracy of DoD LCC estimates via a stochastically-based model.

Characterizing the accuracy of dod operating and support cost estimates

For decades, the Department of Defense (DoD) has employed numerous reporting and monitoring tools for characterizing the acquisition cost estimates of its programs. These tools have led to dozens of studies thoroughly documenting the magnitude and extent of DoD acquisition cost growth. However, little attention has been paid to the behavior of the other main cost component of a systems life cycle cost: Operating and Support (O&S) costs. Consequently, the DoD has little knowledge regarding the accuracy of O&S cost estimates or how that accuracy changes over time. In a previous paper, the authors described an analytical methodology for remedying this deficiency via a study to characterize the historical accuracy of O&S cost estimates. The results are presented here, and indicate there tend to be large errors in DoD O&S cost estimates, and that the accuracy of the estimates improves little over time

A Macro-Stochastic Model for Improving the Accuracy of Department of Defense Life Cycle Cost Estimates

The authors present a prognostic cost model that is shown to provide significantly more accurate estimates of life cycle costs for Department of Defense programs. Unlike current cost estimation approaches, this model does not rely on the assumption of a fixed program baseline. Instead, the model presented here adopts a stochastic approach to program uncertainty, seeking to identify and incorporate top-level (i.e., “macro”) drivers of estimating error to produce a cost estimate that is likely to be more accurate in the real world of shifting program baselines. The predicted improvement in estimating accuracy provided by this macro-stochastic cost model translates to hundreds of billions of dollars across the Department of Defense portfolio. Furthermore, improved cost estimate accuracy could reduce actual life cycle costs and/or allow defense acquisition officials the ability to make better decisions on the basis of more accurate assessments of value and affordability.

Health monitoring impact on non-repairable component supply methods

Purpose From on-board automotive diagnostics to real-time aircraft state of health, the implementation of health monitoring and management systems are an increasing trend. Further, reductions in operating budgets are forcing many companies and militaries to consider new operating and support environments. Combined with longer service lives for aircraft and other systems, maintenance and operations processes must be reconsidered. The majority of research efforts focus on health monitoring techniques and technologies, leaving others to determine the maintenance and logistics impact on the systems. The paper aims to discuss these issues. Design/methodology/approach This research analyzes the impact of a health monitoring system on a squadron of aircraft. Flight, maintenance and logistics operations are stochastically modeled to determine the impact of program decisions on supply metrics. An arena discrete event simulation is utilized to conduct this research on 20 components on each of the 12 aircraft modeled. Costs and availability are recorded for comparison across three sparing scenarios to include economic order quantity (EOQ) for baseline and health monitoring cases and a just-in-time (JIT) health monitoring set of simulations. Findings Data are presented for EOQ and JIT supply methods. A comparison of health monitoring enabled supply to current methods shows cost savings and availability gains. The different methodologies are compared and discussed as a trade-space for programmatic decisions. Originality/value This work demonstrates the ability of health monitoring systems and condition based maintenance to affect supply ordering decisions. The development of trade-spaces within operating environments is demonstrated along with the ability to conduct cost benefit analyses.

A Cost-Based Decision Tool for Valuing DoD System Design Options

Abstract The DoD has frequently demonstrated its ability to procure phenomenal systems; however, these accomplishments are often tarnished by substantial cost and schedule overruns. While defense acquisition policies are continually being revised to address these perennial problems, many believe that a more fundamental source of these overruns is the lack of flexibility in the systems being developed, which tend to preclude effective responses to unexpected events. However, providing justification to invest in flexibility is a tough sell when the measure of value is a military capability or political outcome, as there is no extant method to demonstrate the potential return on investment. This paper introduces a decision tool for valuing the inherent ability of different systems or designs to respond to uncertainty. The proposed tool is essentially a modification of the current life cycle cost model and is premised on the notion that the need for capability changes in a system arises in a stochastic manner that can be incorpo- rated into a continually updated, expected value model presented in terms of total life cycle cost. The cost-based decision tool presented here quantifies the ability of competing designs to respond to these capability changes via a cumulative distribution function (CDF). The design with the most favorable CDF (i.e., the one that is most likely to meet the most likely set of require- ments at the lowest expected value curve of life cycle cost) is deemed to be the “best” design.