Abstract:
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.
