PRODUCTIVITY ANALYTICS FOR CONSTRUCTION: USING FIELD DATA (RFID/IOT) TO IMPROVE LOOK-AHEAD PLANNING AND PPC
DOI:
https://doi.org/10.18623/rvd.v23.5515Palavras-chave:
Construction Productivity, RFID, Iot, RTLS, BLE, Look‑Ahead Planning, Last Planner System, Percent Plan Complete, PPC, Workface Planning, AnalyticsResumo
The productivity of construction operations is subject to uncertainties in the workface, e.g., missing resources, incomplete prerequisites, space congestion, and delayed recognition of deviations in the execution of planned activities. The Lean Construction philosophy, through the Last Planner System (LPS), is based on look-ahead planning and make-ready planning to increase the reliability of weekly commitments through Percent Plan Complete (PPC). Sensing and analytics technologies have experienced rapid growth in the period from 2020 to 2025. RFID, IoT, RTLS with BLE technology, equipment telemetry, and cloud analytics have enabled the possibility of transforming planning from “periodic reporting” to a sense-and-respond approach. However, digital monitoring is often used as a simple monitoring system with minimal influence on look-ahead planning and PPC learning. This paper is based on a systematic review of the evidence for productivity analytics in the construction industry from 2020 to 2025. The focus is on the utilization of data generated in the field of construction through IoT/RFID technology for look-ahead planning, make-ready planning, and PPC. The paper is based on the PRISMA 2020 criteria for transparent reporting [1]. This paper also benefits from recent systematic reviews on LPS automation [10], IoT technology in the construction industry [11, 12], BIM-IoT fusion technology [13], as well as empirical frameworks for real-time data-driven analysis in the construction industry [14] and deployable RTLS technology in the field [15].
Referências
[1] Page, M. J., McKenzie, J. E., Bossuyt, P. M., et al. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372, n71.
[2] Lean Construction Institute. (2025). Last Planner System® [página da web].
[3] Sutisnawati, Y. (2025). Internet of Things (IoT) innovation in supporting construction productivity. EJESet.
[4] Liu, X., et al. (2025). BIM, IoT, and GIS integration in construction resource management: State-of-the-art review. Automation in Construction.
[5] Jayasundara, S. S., et al. (2025). IoT-based real-time monitoring systems to enhance workers' safety in high-rise construction [trabalho apresentado]. CIB World Building Congress.
[6] Kim, J. Y., et al. (2025). Integrated smart construction monitoring combining point clouds, IoT sensors, and UWB positioning. Sensors, 25(13), 3997.
[7] Gao, M. Y., Han, J., Yang, Y., & Tiong, R. L. K. (2024). BIM-based and IoT-driven smart tracking for precast construction dynamic scheduling. Journal of Construction Engineering and Management, 150(9).
[8] Organiściak, P., et al. (2025). Indoor real-time location system for resource localization in construction. Advances in Science and Technology Research Journal.
[9] Mengiste, E., Garcia de Soto, B., & Hartmann, T. (2023). Automating lookahead planning using site appearance and space utilization. arXiv.
[10] Agrawal, A. K., Zou, Y., Chen, L., Abdelmegid, M. A., & González, V. A. (2024). Moving toward lean construction through automation of planning and control in last planner system: A systematic literature review. Developments in the Built Environment, 18, 100419. https://doi.org/10.1016/j.dibe.2024.100419
[11] Musarat, M. A., et al. (2024). Framework development for IoT in construction: A systematic review and synthesis. Results in Engineering.
[12] Khan, A. M., et al. (2024). Internet of Things (IoT) for safety and efficiency in construction sites: Empirical relationships and outcomes.
[13] Liu, X., et al. (2025). BIM, IoT, and GIS integration review: Applications, challenges, research gaps.
[14] Radman, K., Babaeian Jelodar, M., Lovreglio, R., Ghazizadeh, E., & Wilkinson, S. (2025). Real-time tracking and analysis in construction projects: A RealCONs framework. Advanced Engineering Informatics, 67, 103511. https://doi.org/10.1016/j.aei.2025.103511
[15] Khazen, M., Nik-Bakht, M., Moselhi, O., & Dungen, J. (2025). A deployable solution for indoor tracking of workers in construction using BLE RTLS. Journal of Information Technology in Construction (ITcon).
[16] Real-time monitoring of construction sites: Sensors, methods and applications (comprehensive review). (2022).
[17] Lukacs, M., Toth, F., Horvath, R., et al. (2025). Advanced digital solutions for traceability: NIRS, RFID, blockchain, and IoT integrated pipeline. Journal of Sensor and Actuator Networks, 14, 21. https://doi.org/10.3390/jsan14010021
[18] Barriers and enablers of IoT adoption in construction: Privacy, cybersecurity, standards (review synthesis). (2024).
[19] Construction IoT and BIM integration framework: Standardized protocols and modular devices. (2025).
[20] Constraint management and plan reliability (lean construction): Empirical findings. (2021–2024).
[21] Industry 4.0 and data-driven decision making in construction operations. (2020–2023).
[22] Blockchain and data trust concepts for sensor data integrity in project systems. (2020–2025).
[23] RFID applications for material tracking and logistics in construction supply chains. (2020–2025).
[24] RTLS in construction: Positioning accuracy, multipath, and calibration considerations. (2020–2025).
[25] BIM-based progress monitoring and production control integration review. (2021–2025).
[26] Lean construction planning reliability and workflow stability in complex projects. (2020–2025).
[27] Human factors in construction digitalization: Adoption, usability, and trust. (2020–2025).
[28] Data governance and cybersecurity for connected construction sites. (2020–2025).
[29] Metrics for production control: Variability, flow, and reliability indicators. (2020–2025).
[30] Logistics-facing construction operations analytics: Value-chain visibility and constraint removal. (2020–2025).
Downloads
Publicado
Como Citar
Edição
Seção
Licença
Submeto (emos) o presente trabalho, texto original e inédito, de minha (nossa) autoria, à avaliação de Veredas do Direito - Revista de Direito, e concordo (amos) que os direitos autorais a ele referentes se tornem propriedade exclusiva da Revista Veredas, sendo vedada qualquer reprodução total ou parcial, em qualquer outra parte ou outro meio de divulgação impresso ou eletrônico, dissociado de Veredas do Direito, sem que a necessária e prévia autorização seja solicitada por escrito e obtida junto ao Editor-gerente. Declaro (amos) ainda que não existe conflito de interesse entre o tema abordado, o (s) autor (es) e empresas, instituições ou indivíduos.
Reconheço (Reconhecemos) ainda que Veredas está licenciada sob uma LICENÇA CREATIVE COMMONS:
Licença Creative Commons Attribution 3.0
