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PROCEEDINGS OF THE FIFTH INTERNATIONAL CONFERENCE ON ENGINEERING COMPUTATIONAL TECHNOLOGY
Edited by: B.H.V. Topping, G. Montero and R. Montenegro
A Model of Performance Improvement Strategy Planning for a Construction Knowledge Management System
W.D. Yu1, P.L. Chang1 and S.J. Liu2
1Institute of Construction Management, Chung Hua University, Hsinchu, Taiwan
W.D. Yu, P.L. Chang, S.J. Liu, "A Model of Performance Improvement Strategy Planning for a Construction Knowledge Management System", in B.H.V. Topping, G. Montero, R. Montenegro, (Editors), "Proceedings of the Fifth International Conference on Engineering Computational Technology", Civil-Comp Press, Stirlingshire, UK, Paper 70, 2006. doi:10.4203/ccp.84.70
Keywords: knowledge management, construction, consulting firms, quantitative benefits, strategy planning, data mining.
Construction has been conceived as an experience-based discipline. Knowledge learned from previous projects plays important role in the successful performance of future projects. This has made construction an ideal industry for knowledge-based economy. In the past few years, the Taiwan Government has spent tremendous efforts in promoting the eTaiwan project, which emphasizes the wide application of information and communication technology (ICT) to transform the local construction industry from a traditional to a knowledge-based one. In responding to this policy, the leading construction firms (including A/E firms and general contractors) in Taiwan have implemented knowledge management systems (KMS) in various formats in their organizations. Since the performance of knowledge activities such as acquiring, creating, storing, sharing, and the reuse of knowledge have a significant impact on the competitiveness of the firm, effectiveness measurement and improvement of the KMS became a key issue for top managers of the firm.
Very limited work was found from a literature survey of the benefit measurement of KMS. This was due to the difficulty in identification of the knowledge artifacts and the quantification of their benefits. This paper describes the results of research on developing a quantitative benefit measuring method for KMS and the mining of improvement strategies for the KMS using data mining techniques. A leading local A/E consulting firm was selected as the industrial partner for implementation and verification of the proposed methodology. More than fifty real cases of KMS activities, collected from the case firm, were used for the study with respect to the benefits, behaviour, and models of the knowledge creation activities. Questionnaires were designed for engineers and staff of the case firm to recover the time and costs spent in solving the engineering and management problems by the traditional and KMS approaches, respectively. Totally, more than eight hundred questionnaires were submitted and more than three hundred valid responses were collected. Quantitative methods were developed to calculate the performance of the two approaches from the valid questionnaire responses. Then, several data mining techniques were adopted for the two ends: (1) evaluation of the performance of the KMS; (2) planning of the KMS improvement strategies. For the former, quantitative analyses on problem solving time, cost, and man hours for the collected cases were performed in terms of departments, problem types, locations; and timing of respondents. For the latter, Nonaka's four-dimensional organizational knowledge creation theory was adopted to investigate the micro behaviour of the participants of the KMS; the patterns of high-performance combinations of knowledge creation activities were identified using clustering, classification, association, and time-series techniques. Improvement strategies were then proposed based on the data mining results.
The proposed methodology described in this paper provides a first-of-its-kind quantitative measurement of the performance of a KMS. While integrated with data mining and KDD techniques, a systematic strategy planning model for improving KMS of construction firms is proposed and developed. With such a model, not only the performance of KMS can be improved but also the competitiveness of the firm can be strengthened. It is concluded that the result of the research has developed a strong decision-making model based on the business intelligence of the firm, which support the top management in all types of decisions including risk assessment, costing, scheduling, and scope management.
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