Obsolescence of integrated systems which contain hardware and software is a problem that affects multiple industries and can occur for many reasons,including technological,economic,organizational,and social factors.It...Obsolescence of integrated systems which contain hardware and software is a problem that affects multiple industries and can occur for many reasons,including technological,economic,organizational,and social factors.It is especially acute in products and systems that have long life cycles,where a high rate of technological innovation of the subcomponents result in a mismatch in life cycles between the components and the systems.While several approaches for obsolescence forecasting exist,they often require data that may not be available.This paper describes an approach using non-probabilistic scenarios coupled with decision analysis to investigate how particular scenarios influence priority setting for products and systems.Scenarios are generated from a list of emergent and future conditions related to obsolescence.The key result is an identification of the most and least disruptive scenarios to the decision maker’s priorities.An example is presented related to the selection of technologies for energy islanding,which demonstrates the methodology using six obsolescence scenarios.The paper should be of broad interest to scholars and practitioners engaged with enterprise risk management and similar challenges of large-scale systems.展开更多
基金This work was supported in part by the National Science Foundation Grant 1747767"Planning IUCRC University of Virginia:Center for Hardware and Embedded System Security and Trust(CHEST)",and in part by the Commonwealth Center for Advanced Logistics Systems(CCALS).
文摘Obsolescence of integrated systems which contain hardware and software is a problem that affects multiple industries and can occur for many reasons,including technological,economic,organizational,and social factors.It is especially acute in products and systems that have long life cycles,where a high rate of technological innovation of the subcomponents result in a mismatch in life cycles between the components and the systems.While several approaches for obsolescence forecasting exist,they often require data that may not be available.This paper describes an approach using non-probabilistic scenarios coupled with decision analysis to investigate how particular scenarios influence priority setting for products and systems.Scenarios are generated from a list of emergent and future conditions related to obsolescence.The key result is an identification of the most and least disruptive scenarios to the decision maker’s priorities.An example is presented related to the selection of technologies for energy islanding,which demonstrates the methodology using six obsolescence scenarios.The paper should be of broad interest to scholars and practitioners engaged with enterprise risk management and similar challenges of large-scale systems.
