5 research outputs found

    Reconciling Engineer-To-Order Uncertainty by Supporting Front-End Decision-Making

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    This paper presents the dynamics of engineer-to-order (ETO) practice through Integration Definition for Function Modelling (IDEF) practice. The paper describes and defines how an ETO manufacturer utilised IDEF-QA in order to manage project uncertainties within the tendering process. The research is conceptualised through an empirical action research approach, involving an active role in the assessment of the ETO process. The paper revisits the use of IDEF, showcasing an assessment of output quality. It also suggests a road map for resource uncertainty within ETO, specifically when scoping the supply chain for ETO projects. The paper then presents an IDEF Quality Assessment model for improving the tendering process of ETO, and it examines the importance of evaluating project behaviour for supporting new future projects. The principal contribution is in how a structured approach provides IDEF with a quality assessment of resources, thereby consolidating and establishing a relationship for highlighting the uncertainties experienced by ETO manufacturers within the decision-making process

    A comparative study for outlier detection techniques in data mining

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    Existing studies in data mining mostly focus on finding patterns in large datasets and further using it for organizational decision making. However, finding such exceptions and outliers has not yet received as much attention in the data mining field as some other topics have, such as association rules, classification and clustering. Thus, this paper describes the performance of control chart, linear regression, and Manhattan distance techniques for outlier detection in data mining. Experimental studies show that outlier detection technique using control chart is better than the technique modeled from linear regression because the number of outlier data detected by control chart is smaller than linear regression. Further, experimental studies shows that Manhattan distance technique outperformed compared with the other techniques when the threshold values increased
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