Proposição de uma estrutura de planejamento e controle de médio prazo com apoio de BIM

Abstract

The inefficiency of traditional construction project management systems has resulted in the dissociation between the stages of Engineering, Procurement and Construction (EPCM), contributing to increase fragmentation between different processes in project management. EPCM projects are characterized by large contracts, high costs, long planning and execution periods with fixed completion dates, specialized technical staff, and the involvement of several stakeholders. In this context, Building Information Modeling (BIM) has been used to facilitate information sharing and support decision making that, associated with the planning approach called Location Based Management (LBMS), allows the stages of a project to be structured as a flow pulled by the construction planning. The objective of this research is to propose an information modeling structure to allow a more systematic planning and control in the medium term through the use of BIM. The 4D model is used to represent and simulate the construction progress, while the look-ahead planning information is related to the creation of the construction-oriented engineering and procurement packages. The model adopted features of the Advanced Work Package (AWP) approach to support the delivery of all detailed work packages. The balance line was used to represent the workflows through the hierarchical structure of locations. As a methodological approach, constructive research (design science research) was chosen, and the model was developed through an exploratory study and an empirical study applied in collaboration with an engineering company specialized in the pharmaceutical and food industry. It was concluded that the integration of preconstruction processes promoted by location-based planning, with the support of BIM, provided a more effective control of the look-ahead planning, allowing the visualization of the variability of production processes in order to eliminate or reduce the uncertainties for the execution of tasks.