Developing a Theoretical Real System Age

Enos, James R.

doi:10.1109/jsyst.2020.2983852

Cited by 3 publications

(11 citation statements)

References 34 publications

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“…The real system age also captures the potential negative aspects of subsequent changes as presented in both the literature for biological and software systems. 2 Figure 1 presents the logistics curve, S-curve, which is adjusted to begin at negative one and approaches positive one as the number of changes increase. The literature on both aging in biological and software systems provides a basis for the logistics curve as both fields have observed increased aging as errors are introduced with more changes or reproductions of systems.…”

Section: Changeability and Attribute Factorsmentioning

confidence: 99%

“…Generally, DoD systems experience seven major changes during their lifecycle, so the equation is calibrated to cross the y-axis at seven changes. 2 Past seven changes, changes to the system no longer reduce the real system age but begin to increase the real system age. These types of changes do not include periodic software or maintenance upgrades but focus on major changes to system capabilities.…”

Section: Changeability and Attribute Factorsmentioning

confidence: 99%

“…The attribute factors, interoperability, robustness, and versatility, of real system age may represent both external changes to the environment or internal changes to the system. 2…”

Section: Mathematical Representationmentioning

confidence: 99%

“…As the real system age equation does not attempt to quantify the attribute factors, the percentage change provides a means to evaluate various changes to the system's interoperability, robustness, or versatility and sum these impacts. 2 An additional benefit of using percentage changes to the factors is that it enables the factor changes to remain dimensionless, so how a system measures interoperability, robustness, and versatility does not matter for the real age. As shown in Equation ( 4), the interoperability at time t and the previous time determine how much the interoperability changes between time periods.…”

Section: Factor Magnitudementioning

confidence: 99%

“…1 These attributes serve as the basis for calculating an engineered system's real system age as a function of changes to these attributes and the overall number of engineering changes to a system. 2 With this mathematical representation of real system age, this paper applies the real system age to several DoD systems to include the B-52 bomber, A-10 aircraft, F-117 stealth fighter, and the OH-58D helicopter. This paper is organized into four additional sections to include a literature review, methodology, analysis and results and a conclusion.…”

Section: Introductionmentioning

confidence: 99%

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Applying real system age to DoD systems

Enos

2022

Systems Engineering

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Why do some systems exceed their planed lifecycle while others retire at or before their planned retirement date? A possible answer to this question is that systems that exceed their planned lifecycle have several common non‐functional attributes that enable them to continue to provide value to their stakeholders well beyond their planned lifecycle. As this is the case in several Department of Defense systems, it should be possible to develop a mathematical representation of the real system age for these engineered systems to compare that age to their chronological age and determine when to retire a system. This paper expands on previous work to develop a mathematical relationship for real system age by applying it to several DoD systems to include the B‐52 bomber, A‐10 aircraft, F‐117 stealth fighter, and the OH‐58D helicopter. It identifies that the systems the DoD retired have similar traits as their real system age closely follows the chronological age and approaches a 25‐year threshold for retirement. With this equation, it is possible to identify when a system is due for retirement or if an upgrade to the system may be able to extend the useful life of the system.

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Section: Changeability and Attribute Factorsmentioning

confidence: 99%

Section: Changeability and Attribute Factorsmentioning

confidence: 99%

“…The attribute factors, interoperability, robustness, and versatility, of real system age may represent both external changes to the environment or internal changes to the system. 2…”

Section: Mathematical Representationmentioning

confidence: 99%

Section: Factor Magnitudementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Applying real system age to DoD systems

Enos

2022

Systems Engineering

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Mechanisms to support changeability during sustainment: Modularity, excess, and operations

Singh,

Szajnfarber

2024

Systems Engineering

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Complex engineered systems face protracted design cycles and indefinite lifetimes, leading to discrepancies between expected and actual operating conditions. The field of design for changeability has focused on developing strategies to cope with these discrepancies. Literature has implicitly assumed that future changes can be predicted by system designers, but this assumption does not match observed reality. Often, systems must be modified in unexpected ways to maintain value, limiting the usefulness of standard change mechanisms. To understand how unexpected changes are implemented in practice, we studied the C‐130, a setting where one platform performed many missions, and close air support in Desert Storm, a setting where many platforms performed one mission. Through this examination, we find that excess and operator change are key mechanisms for changeability that have been largely ignored by literature. Excess can enable additions to systems without having to remove or swap subsystems out. It is also a critical enabler of modular changes during the operational phase. Operator changes are non‐form changes driven by system users who change how their systems are used to gain new capabilities rather than relying on changes to the form of the system. This paper also studies the relationship between these mechanisms to shed light on how changeability is achieved in practice, building the foundation for future work to study tradeoffs of implementing valuable excess during design.

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