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Paper w78_2007_16:
Industrial case study of innovative managerial control system applied to site control process (IMCS-CON)

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Anfas Thowfeek, Nashwan Dawood, Ramesh Marasini, John Dean

Industrial case study of innovative managerial control system applied to site control process (IMCS-CON)

Abstract: Construction projects are complex, fragmented and highly risk business, due to the nature of construction operations. Therefore project managers require more efficient techniques and tools to plan and monitor the construc-tion project. In recent years many research studies have been carried out in order to make construction industry more efficient, profitable and attractive business. The IMCS-CON developed as decision support system for project mangers to assist project-controlling processes using a holistic approach. The IMCS-CON provide a framework to measure, analyse, review, and report performance data and enabling project management team to make corrective decision and keep project on track. The IMCS-CON system was evaluated using a case study of 2.3 million, three-story residential apartment building project in UK. The IMCS-CON system utilises multivariate statistical process control techniques to monitor the construction site variables. The MSPC combines a large number of variables into few independent vari-ables, which then can be monitored and any process deviations from the normal operating conditions can be identified with corrective actions suggested. The IMCS-CON models on-site information as quantitative variables and uses his-torical data and establishes patterns of correlated variables and assists project management in making future decisions. The outputs can also be visualised in multi-dimensional graphs. Statistics of external variables and internal variables influencing construction site operations were identified using a real life case study. The results of modelling the vari-ables and conducting experiments with IMCS-CON are analysed and discussed in this paper.

Keywords: performance measurement, construction process variables, statistical process control, construction proc-ess benchmarking, construction process improvement, construction productivity


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