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Application of virtual manufacturing to predict scenarios and reduce physical and non-physical losses in construction

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Abstract

The production management models currently adopted in the construction industry do not adequately describe the relationships between activities and processes. Such models place emphasis on conversions to the detriment of internal logistics of input distribution for various work fronts. In the planning phase, tasks are commonly estimated using the critical path method, program evaluation and review technique, and Gantt charts. However, techniques capable of optimizing physical flows are not widely adopted. In view of this limitation, this study aimed to assess the potential of virtual manufacturing tools in estimating virtual production parameters based on product lifecycle management and real-life data. Data on mortar production and distribution to work locations were collected on site. Then, two simulations were performed. The first simulation was used to construct a virtual model of the mortar distribution process. The second simulation examined the impact of a mortar transportation device on the activity. Subsequently, the efficiency of both simulations was compared. The efficiency of the mortar transportation device was confirmed by the improvements provided by its implementation. Given their limited example of use in civil construction, it will need to be expanded in this study to validate their potential in all activities of the sector. Despite the limitation presented in this work, the virtual manufacturing can be used as one tool for the integrated product development and product lifecycle management to improve the quality, and to reduce the losses during the production lifecycle.

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Acknowledgements

The authors thank Dassault Systèmes – 3DS for providing the computational tools used by the GETin research group, at the State University of Londrina, Brazil.

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Correspondence to Altibano Ortenzi.

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Junior, P.C.D., Francisco, S.A.M. & Ortenzi, A. Application of virtual manufacturing to predict scenarios and reduce physical and non-physical losses in construction. Int J Adv Manuf Technol 132, 5473–5485 (2024). https://doi.org/10.1007/s00170-024-13702-9

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