淡江大學機構典藏:Item 987654321/91984
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    題名: Mining business knowledge for developing integrated key performance indicators on an optical mould firm
    作者: Liao, Shu-Hsien;Hsiao, Pei-Yuan
    貢獻者: 淡江大學管理科學學系
    關鍵詞: data mining;association rules;cluster analysis;optical mould firm;key performance index \(KPI\);business intelligence
    日期: 2013-12-01
    上傳時間: 2013-08-12 13:43:19 (UTC+8)
    出版者: Abingdon: Taylor & Francis
    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.
    關聯: International Journal of Computer Integrated Manufacturing 26(8), pp.703-719
    DOI: 10.1080/0951192X.2013.766933
    顯示於類別:[管理科學學系暨研究所] 期刊論文

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