Miles and Snow proposed their theory about organisation strategy, structure and process in 1978. The key point of their theory is the adaptation cycle of an enterprise among entrepreneurship domain, engineering domain and administration domain. They propose three successful strategy types: Defender, Prospector and Analyser, which can successfully adapt to changes in the environment in their own way. Since the theory first appeared in the 1970s, it has been applied and examined in strategy, organisational theory, human resource management, operations management, marketing and accounting, but has not been rigorously applied to nmodern manufacturing. This research aims to test and develop Miles and snow's engineering solutions of Defender, Prospector and Analyser in the adaptive cycle model. A series of propositions and hypotheses have been generated based on the EE (Entreneurship - Engineering) and the EA (Engineering - Administration) models that created in this research, which are based on the literature review of manufacturing technologies, strategic theories and Miles and Snow typology. AMTs (Advanced Manufacturing Technologies) are chosen to test the EE and EA models, as well as original Miles and Snow's engineering solution. Both quantitative and qualitative methods have been applied in this research in order to validate these propositions and hypotheses. The research has generated a number of successful outcomes relating to its aim and objectives: Firstly, the Miles and Snow model is extended into an area that was not only under represented in the original exposition, but also has changed dramatically since their framework was derived. Secondly, the research places manufacturing strategy issues into a well-validated organisational model, thereby reducing the implicit passivity of many manufacturing strategy prescriptions. These two contributions will enable further contributions of value to both theorists and practicioners. Implementation difficulties associated with organisational obstacles can be assessed in terms of organisational solutions and these might be contrasted with technical solutions prevalent in the literature. Further research can be explored in the following areas based on the validation of the EE and the EA models: in other technologies, other industries, and other countries.

The software industry is facing a great challenge due to the demand of quality and complexity on an ever increasing number of software products. The software industry has been dealing with this challenge by software process assessment and improvement, and by improving and developiong more sophisticated software development methodologies. Quality concepts and practices have been well formulated in traditional manufacturing industries, especially in Japan. However many successful and influential manufacturing quality techniques have not been examined systematically in context of software quality. To tackle the existing problems in improving the software process maturity and software product quality, I investigate the quality principles and techniques developed in manufacturing industries and study the feasibility and ways in applying them to software quality. The similarities and differences between software and manufacturing industries are analysed. Motivated by the TQM (Total Quality Management) concept, the Japanese quality control tools and quality management tools are examined together with the Deming cycle in the context of software process improvement. The manufacturing process capability indices Cp and Cpk are examined and it is shown how they can be used in quantitatively monitoring, controlling and improving the software process. The very successful Japanese manufacturing quality techniques, Taguchi methods, are investigated. Two of these methods are shown to be appropriate to apply to software quality. The Taguchi's optimal parameter design method is used to produce the Orthogonal Software Testing Methodology to optimise software testing. Taguchi's fractional factorial experimentation and Analysis of Variance (ANOVA) method is used to produce an Orthoganol Software Performance Evaluation Methodology which provides an experimental method for effective software performance evaluation.