Integrated Cost and Schedule Control Automationby Osama Y. Abudayyeh, North Carolina State Univ, Raleigh, United States,
William J. Rasdorf, North Carolina State Univ, Raleigh, United States,
Abstract: Effective management of construction projects depends on good access to and control of data, especially data pertaining to cost and schedule control functions. Long recognizing the need to integrate these interrelated functions and to improve the quality, integrity, and timeliness of data, researchers have proposed conceptual models to achieve this purpose. However, the proper design of an automated solution has been lacking that would support the needs of integrated cost and schedule control and would acquire and store quality data in a timely manner. The purpose of this paper is to discuss the design of an automated data acquisition and storage model that overcomes the above-mentioned deficiencies. The data storage component is developed using a four-step process used to model engineering problems. The first step, problem definition, is accomplished by using the work packaging model to solve the integration problem and by using data collection forms developed by R.S. Means Company to identify what data items are collected in cost and schedule control. The second step, conceptual modeling, utilizes Nijssen's Information Analysis Methodology (NIAM). For the third step, computational modeling, the relational data model is selected. The Optimal Normal Forms (ONF) algorithm is used for mapping from the conceptual to the computational model. The fourth and final step, computer modeling, is accomplished by mapping the computational data model onto a computer database management system. The paper then discusses how bar coding will be used for automating the data acquisition component in support of integrated cost and schedule control. We believe that bar coding can improve the quality, integrity, and timeliness of data when coupled with a good database design.
Subject Headings: Data processing | Data collection | Computer models | Scheduling | Information management | Automation | Construction management
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