Prediction of the project schedule performance outcome with last planner system and social network analysis indicators

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dc.catalogadorgrr
dc.contributor.authorLagos Crua, Camilo Ignacio
dc.contributor.authorHerrera Valencia, Rodrigo Fernando
dc.contributor.authorMac Cawley Vergara, Alejandro Francisco
dc.contributor.authorAlarcón Cárdenas, Luis Fernando
dc.date.accessioned2025-10-27T18:15:26Z
dc.date.available2025-10-27T18:15:26Z
dc.date.issued2025
dc.description.abstractPurpose:Building earlier last planner system (LPS) only schedule predictors, this study integrates social network analysis (SNA) collaboration metrics and earned value method (EVM) indicators with LPS data to forecast the final schedule performance index (SPI) at planned completion. The approach offers earlier, more actionable diagnostics by having dependence on lagging EVM measures and highlighting team-interaction drivers of schedule risk. Design/methodology/approach:The model was developed using machine learning (ML) regression trained on data from 160 projects across ten progress intervals. It incorporated five scope metrics, nine EVM metrics, eight LPS performance metrics and eight LPS collaboration metrics to predict the schedule performance index (SPI) at the end of the initial plan. Data Science and SNA techniques were applied to extract these metrics from LPS software. Findings: The LPS-based model using support vector regression achieved an R2 = 0.861 and a mean absolute error of 0.067. The model exhibited greater robustness across 90% of the sample and significantly reduced reliance on result-oriented metrics from 88% to 49%. Research limitations/implications: Future research should explore the applicability of the model in different project contexts and further refine the integration of LPS metrics with predictive analytics. Limitations include the reliance on historical data and potential variations in LPS implementation across projects.Practical implications: The proposed model provides a proactive approach to schedule compliance prediction, reducing dependence on traditional result-oriented metrics. This enables project managers to anticipate schedule risks earlier and take corrective actions based on LPS performance and collaboration indicatorsOriginality/value: This study presents a novel approach to outcome prediction by leveraging LPS and SNA data for ML regression, demonstrating its potential to enhance schedule performance assessments while shifting the focus from reactive to proactive planning.
dc.format.extent26 páginas
dc.fuente.origenORCID
dc.identifier.doi10.1108/ECAM-03-2025-0424
dc.identifier.eissn1365-232X
dc.identifier.issn0969-9988
dc.identifier.urihttps://doi.org/10.1108/ECAM-03-2025-0424
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/106369
dc.identifier.wosidWOS:001595487800001
dc.information.autorucEscuela de Ingeniería; Herrera Valencia, Rodrigo Fernando; 0000-0001-5186-3154; 1030570
dc.information.autorucEscuela de Ingeniería; Mac Cawley Vergara, Alejandro Francisco; 0000-0002-4848-4732; 81775
dc.information.autorucEscuela de Ingeniería; Alarcón Cárdenas, Luis Fernando; 0000-0002-9277-2272; 100096
dc.information.autorucEscuela de Ingeniería; Lagos Crua, Camilo Ignacio; S/I; 194017
dc.language.isoen
dc.nota.accesocontenido parcial
dc.revistaEngineering, Construction and Architectural Management
dc.rightsacceso restringido
dc.subjectLast planner system
dc.subjectSocial network analysis
dc.subjectSchedule performance
dc.subjectMachine learning
dc.subjectPrediction
dc.subject.ddc620
dc.subject.deweyIngenieríaes_ES
dc.titlePrediction of the project schedule performance outcome with last planner system and social network analysis indicators
dc.typeartículo
sipa.codpersvinculados1030570
sipa.codpersvinculados81775
sipa.codpersvinculados100096
sipa.codpersvinculados194017
sipa.indexWOS
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