This thesis describes the application of a novel decision support process formachined part estimating in small and medium-sized engineering companies. ManySMEs tend to adopt manual estimating techniques, however this dependence on humanexpertise represents a risk to such organizations. Better information management inestimating can improve process performance and contribute to increasedcompetitiveness.The research which is the subject of this thesis investigated whether a systemsapproach to machined part estimating would extend the capacity of an SME to manageknowledge more effectively. The research explored the workplace learning context, theprovision of learning opportunities and the management of organizational knowledge;before determining that an intelligent information system offered the most beneficialsolution to the situation-of-interest.The case study company produce low-volume, make-to-order, medium and largesized machined steel forgings; utilising conventional machine tool equipment. Theapplication of the decision support system enabled novice estimators to produce viablecost estimates; reducing the risk from reliance on human expertise inherent in manualestimating. The hybrid feature-based costing / case-based reasoning estimatingtechnique, which is the core of the novel METALmpe cost model, proved exceptionallywell suited to the SME environment. Estimates produced using METALmpe wereconsistently more accurate than those of the human expert; with a level of accuracy thatexceeds the initial research aim, i.e. a tolerance of -5% / +10%.Significantly, implementation of METALmpe (hardware, software and supportfor 5 users), can be provided at a cost which is within the typical informationtechnology budget of many SMEs. With demands on organizations to process anddisseminate ever increasing volumes of information, METALmpe can improve anSME’s information management capabilities and contribute to competitive advantagethrough strengthening strategic assets and core competencies.
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