A novel meta-synthesis approach to technology maturity management for project risk control

Student thesis: Doctoral Thesis

Abstract

With the rapid development of science and technology in modern society, “high-tech” is becoming the main characteristic of complex systems. However, due to the large scale of projects, and the intricate architecture and functionalities involved in these systems,traditional project development processes often encounter schedule delays, cost increases or performance degradation. Most of these are related to technical risk. Moreover, rapid change in both domestic and international markets and the acceleration of technology upgrading are bringing more challenges to risk management for complex system development.To improve the risk management of key technology developments aiming to ensure the success of technical-critical complex systems, a Meta-Synthesis Approach (MSA) with technology assessment is proposed in this thesis. It combines quantitative and qualitative methods for assessing the maturity of technology (Technology Readiness Level, TRL) and also the difficulty of enhancing the technology maturity (Advancement Degree of Difficulty,AD2). The research focuses on the qualitative analysis of TRL assessment and quantitative assessment of AD2. Concepts and characteristics of technology maturity are discussed. An attributes-based qualitative TRL assessment method was developed and a unified maturity evidence chain is defined to capture and manage the maturity assessment information. A genetic algorithm based Numerical Integration method was developed for the quantitative assessment of AD2, embodying key calculation of parameters for maturity weight factors.After that, a visualisation method with technology maturity diagrams is proposed. All the above methods were integrated into MSA to assist decision-making of technology risk management.The advantages of the MSA are then discussed, detailing the spiral development process of Machine-factor (data modelling, simulation and analysis) and Man-Factor (Expert Decision-making System) to produce an iterative refinement of the technical risk management. The optimised methods and process are supported by an information and procedure management software tool. To evaluate the approach, the author employed nine case studies and questionnaires to illustrate the usefulness and efficacy of new methods and integrated processes. The thesis concludes with a discussion of the research including the limitations of the results and future research.The key technological innovations of the research conducted in this thesis are listed below:(1) A multi maturity attributes based qualitative TRL assessment method was proposed,demonstrating four views of the technology risk/maturity aspect, including (a) representation of the formation of the critical technology element state, (b) integration of the critical technology elements towards the system, (c) the fidelity of the testing or demonstration environments, together with (d) key performance indicators..(2) A computational method for calculating AD2 is presented. Based on the unified assessment evidence chain, the method establishes a numerical integration calculation of AD2. During the numerical integration, a genetic algorithm is used to optimise the assessment features weight factors.(3) A visualisation modelling based decision-making and information management software was developed in the research. It supports technology risk identification and the management process in aiming to facilitate technical decision-making, which is essential for a complex technology system management.(4) A systematic approach based on MSA is proposed, which combines qualitative and quantitative assessment methods. Assessment information and a procedural management software platform acting as a “Machine” are concatenated to an experts group acting as a“Man”, the whole composing a “Man-Machine System”. To enhance the veracity of the TRLand AD2 assessment and apply the assessmen
Date of Award1 Nov 2010
LanguageEnglish
Awarding Institution
  • University Of Strathclyde
SupervisorXiu Yan (Supervisor) & William Ion (Supervisor)

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