This thesis describes the application of a novel decision support process for machined part estimating in small and medium-sized engineering companies. Many SMEs tend to adopt manual estimating techniques, however this dependence on human expertise represents a risk to such organizations. Better information management in estimating can improve process performance and contribute to increased competitiveness. The research which is the subject of this thesis investigated whether a systems approach to machined part estimating would extend the capacity of an SME to manage knowledge more effectively. The research explored the workplace learning context, the provision of learning opportunities and the management of organizational knowledge; before determining that an intelligent information system offered the most beneficial solution to the situation-of-interest. The case study company produce low-volume, make-to-order, medium and large sized machined steel forgings; utilising conventional machine tool equipment. The application of the decision support system enabled novice estimators to produce viable cost estimates; reducing the risk from reliance on human expertise inherent in manual estimating. The hybrid feature-based costing / case-based reasoning estimating technique, which is the core of the novel METALmpe cost model, proved exceptionally well suited to the SME environment. Estimates produced using METALmpe were consistently more accurate than those of the human expert; with a level of accuracy that exceeds the initial research aim, i.e. a tolerance of -5% / +10%. Significantly, implementation of METALmpe (hardware, software and support for 5 users), can be provided at a cost which is within the typical information technology budget of many SMEs. With demands on organizations to process and disseminate ever increasing volumes of information, METALmpe can improve an SME’s information management capabilities and contribute to competitive advantage through strengthening strategic assets and core competencies
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