| Summary: | 碩士 === 國立中央大學 === 工業管理研究所在職專班 === 101 === Cluster analysis is one of the data mining methods that are commonly applied in the fields including business analysis, medicine, pathology, and natural science. In recent years, it has also been largely applied to the genetic data analysis or image analysis fields.
Nowadays, the majority of people who apply the cluster analysis technique are on qualitative data, quantitative data or mixed data types and it’s noted that fewer people discuss whether the cluster analysis theory can also be applied on process data or flow data.
The aim of this thesis is to establish a similarity model that contains the objects of process data when applying cluster analysis. It’s found that a better grouping effect can be achieved when the model is based on the agglomerative hierarchical method of the cluster analysis and that the average linkage is used when calculating distance between objects.
Nevertheless, the assessment of similarity of process order requires different scores depending on individual study subject; therefore users need to set appropriate criteria that cater for their specific research environments.
The case study used in this thesis is based on the data collected from an engine maintenance plant during the overhaul process on the GE CF6-80C2 Engine. Several in-house repair parts are being identified in this process and by studying the of similarity degree of these in-house repair parts, it’s hoped that the findings can be useful to the management in planning shifts or making other managerial decisions including competency planning for maintenance technicians, determining new maintenance capacity or acquisition of maintenance machinery.
In conclusion, the study shows that after applying this similarity model, there are 205 in-house repairing parts identified in the engine overhaul process and those can be grouped into 8 major clusters. Having discussed further with production staff, each group can be given a similar meaning that corresponds to its overhaul process. As such, it shows that cluster analysis theory can also be applied in the process data type when carrying out grouping analysis based on their similarities.
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