Mining business knowledge for developing integrated key performance indicators on an optical mould firm
學年 102
學期 1
出版（發表）日期 2013-12-01
作品名稱 Mining business knowledge for developing integrated key performance indicators on an optical mould firm
作品名稱（其他語言）
著者 Liao, Shu-Hsien; Hsiao, Pei-Yuan
單位 淡江大學管理科學學系
出版者 Abingdon: Taylor & Francis
著錄名稱、卷期、頁數 International Journal of Computer Integrated Manufacturing 26(8), pp.703-719
摘要 The supply chain for Taiwanese optical components accounts for 39.7% of the total supply chain of the optical mould industry. However, some critical elements of the optical mould industry are difficult to predict; these include personnel, mechanical equipment, material, environmental and complex management factors. Therefore, these enterprises need flexibility to fine-tune their organisational structure, so that the main functions of various departments operate with the best processes. Beside case firm database, this study collects subjective data by designing a questionnaire with nominal scale question to investigate employees’ potential attitude and behaviour in relation to the case firm's key perfomance indicators KPIs. A total of 250 questionnaires were sent and 220 questionnaires were returned, including 207 effective questionnaires. All data source are designed on a entity relationships ER model and constructed on a relational database. In addition, this study applies a data mining approach using association rules, an Apriori algorithm, and cluster analysis to develop the integrated KPIs for a Taiwanese optical mould company. This study investigates the data mining process and considers how the development of the integrated KPIs for this company might serve as a business intelligence example for other firms and industries.
關鍵字 data mining; association rules; cluster analysis; optical mould firm; key performance index \(KPI\); business intelligence
語言 en_US
ISSN 0951-192X
期刊性質 國外
收錄於 SCI
產學合作
通訊作者
審稿制度 否
國別 GBR
公開徵稿
出版型式 ,紙本
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