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7.3.2 Monte‐Carlo Simulation Approach for System Readiness Level Estimation
Author(s) -
Tan Weiping,
Sauser Brian,
RamírezMárquez Jose
Publication year - 2009
Publication title -
incose international symposium
Language(s) - English
Resource type - Journals
ISSN - 2334-5837
DOI - 10.1002/j.2334-5837.2009.tb01008.x
Subject(s) - probabilistic logic , computer science , monte carlo method , estimation , metric (unit) , point (geometry) , scale (ratio) , industrial engineering , maturity (psychological) , machine learning , reliability engineering , artificial intelligence , systems engineering , mathematics , engineering , statistics , psychology , developmental psychology , operations management , geometry , physics , quantum mechanics
A system‐focused prescriptive metric, called System Readiness Level (SRL), which incorporates both the current Technology Readiness Level (TRL) scale and the concept of an Integration Readiness Level (IRL) has recently been established to measure current and future readiness status of a system under development. In previous research, deterministic values for components' TRLs and IRLs estimation were assumed, which involves much human subjectivity. Moreover, only point estimation from calculation was proposed in previous research. In order to reduce the subjective influence, we propose a probabilistic method here to combine all evaluators' estimation towards a system's components and integration points. Based on the probabilistic form of TRLs and IRLs, a Monte‐Carlo simulation methodology is followed herein to assess the maturity status (SRL) of a system. An illustrative example is examined to show how to employ the proposed methodology. The paper concludes with the discussion of the gained value of the new methodology as well as its limitation.