Managing technology development for safety-critical systems

This paper presents a model that determines the optimal budget allocation strategy for the development of new technologies for safety-critical systems over multiple decision periods. The case of the development of a hypersonic passenger airplane is used as an illustration. The model takes into account both the probability of technology development success as a function of the allocated budget, and the probability of operational performance of the final system. It assumes that the strategy is to consider (and possibly fund) several approaches to the development of each technology to maximize the probability of development success. The model thus decomposes the system's development process into multiple technology development modules (one for each technology needed), each involving a number of alternative projects. There is a tradeoff between development speed and operational reliability when the budget must be allocated among alternative technology projects with different probabilities of development success and operational reliability (e.g., an easily and quickly developed technology may have little robustness). The probabilities of development and operational failures are balanced by a risk analysis approach which allows the decision maker to optimize the budget allocation among different projects in the development program at the beginning of each budget period. The model indicates that by considering reliability in the R&D management process, the decision maker can make better decisions, optimizing the balance between development time, cost, and robustness of safety-critical systems.Technology development; system reliability; risk analysis; project management

Risk and Uncertainty Analysis in Government Safety Decisions

Probabilistic risk analysis (PRA) can be an effective tool to assess risks and uncertainties and to set priorities among safety policy options. Based on systems analysis and on Bayesian probability, PRA has been applied to a wide range of cases, three of which are briefly presented here: the maintenance of the tiles of the space shuttle, the management of patient risk in anesthesia, and the choice of seismic provisions of building codes for the San Francisco Bay Area. In the quantification of a risk, a number of problems arise in the public sector where multiple stakeholders are involved. In this paper, I describe different approaches to the treatments of uncertainties in risk analysis, their implications for risk ranking, and the role of risk analysis results in the context of a safety decision process. I also discuss the implications of adopting conservative hypotheses before proceeding to what is, in essence, a conditional uncertainty analysis, and I explore some implications of different levels of "conservatism " for the ranking of risk mitigation measures

Risk and Uncertainty Analysis in Government Safety Decisions

Probabilistic risk analysis (PRA) can be an effective tool to assess risks and uncertainties and to set priorities among safety policy options. Based on systems analysis and Bayesian probability, PRA has been applied to a wide range of cases, three of which are briefly presented here: the maintenance of the tiles of the space shuttle, the management of patient risk in anesthesia, and the choice of seismic provisions of building codes for the San Francisco Bay Area. In the quantification of a risk, a number of problems arise in the public sector where multiple stakeholders are involved. In this article, I describe different approaches to the treatments of uncertainties in risk analysis, their implications for risk ranking, and the role of risk analysis results in the context of a safety decision process. I also discuss the implications of adopting conservative hypotheses before proceeding to what is, in essence, a conditional uncertainty analysis, and I explore some implications of different levels of ''conservatism'' for the ranking of risk mitigation measures