Evaluation of Asset Performance Using Integral Data Mining Scheme

Abstract

The asset evaluation is an indispensable process in asset management and asset disposal. Seldom studies were found in exploring asset-usage performance of laboratory equipment in universities. The influential parameters, such as source, usage, and benefit that influence the performance of asset management in universities should be considered more structurally and deeply. In this study, a structural and complete index system and an integral data mining method are proposed to investigate the effect of related index parameters on the asset-usage performance of laboratory equipment in universities. The proposed integral method includes two phases. The first phase is to find out the relative weight of each influential parameter using the integral Analytic Hierarchy Process (AHP) scheme which extensively contains the index-weight and time-weight models based on Orness measure in conjunction with the method of relative closeness degree. The second phase is to investigate the global correlation between second-level indexes and the final performance using Grey System Theory (GST). The results of case study indicate that the proposed integral AHP-and-GST method can not only be used to globally and locally understand the effect of influential parameters on fixed asset of laboratory equipment, but also provide a decision-making basis for asset allocation.

Keywords

• Analytic hierarchy process
• asset management
• grey system theory
• performance evaluation
• relative closeness degree