This thesis describes research that has developed a decision model for the analytical selection of manufacturing best practices. The competitiveness and growth in the manufacturing sector is critical for Singapore economy. Design and improvement of manufacturing systems is imperative to sustain the competitiveness of manufacturing organisations in the country. It is common for companies to adopt manufacturing best practices in this design process to emulate the success and performance of their counterparts. However, practices should be adapted to the competitive environment and strategy of the company to yield the desired results. Therefore, linkages between best practices and their associated competitive priorities will present useful guidelines for action to help manufacturing organisations achieve superior performance. The research programme has set out to define a decision model for best practice adoption. A broad taxonomy of manufacturing strategies and concepts has been used to identify and cluster a list of popular best practices commonly adopted. The decision framework for best practice adoption process is then formulated and a preliminary decision model constructed. This model is verified through semistructured interviews with industry and academic experts. Validation of model is conducted via case study research on eight manufacturing organisations. Linkages between practices and competitive strategies are then constructed to establish the final decision model. Finally, this decision model is illustrated in the form of a guidebook to help practitioner in the best practice selection process. This research has bridged the fields of manufacturing strategy and best practice research by establishing a comprehensive taxonomy of manufacturing strategies and concepts to classify the popular and commonly adopted best practices. A decision model that links best practices to competitive strategies has been developed to select the most appropriate practices for an environment. Thus, the work presented in this thesis has made a significant and original contribution to knowledge on the provision of analytical decision support for practitioners engaging in the manufacturing best practice adoption process
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