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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