Mining business knowledge for developing integrated key performance indicators on an optical mould firm

Abstract

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.