2026-04-28T11:00:47Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/76652024-05-13T08:07:20Zcom_10259_6336com_10259_6335com_10259.4_106com_10259_2604com_10259_3830com_10259_5086col_10259_6337col_10259_3832

Santos Martín, José Ignacio 526 600 0000-0002-6653-043X Pereda, María c64c3885-d555-4b37-9378-32af8bc6c908 600 Ahedo García, Virginia 10 600 0000-0002-9812-388X Galán Ordax, José Manuel 188 600 0000-0003-3360-7602 2023-05-03T08:29:25Z 2023-05-03T08:29:25Z 2023-06 0360-8352 http://hdl.handle.net/10259/7665 10.1016/j.cie.2023.109261 Project control is a crucial phase within project management aimed at ensuring —in an integrated manner— that the project objectives are met according to plan. Earned Value Management —along with its various refinements— is the most popular and widespread method for top-down project control. For project control under uncertainty, Monte Carlo simulation and statistical/machine learning models extend the earned value framework by allowing the analysis of deviations, expected times and costs during project progress. Recent advances in explainable machine learning, in particular attribution methods based on Shapley values, can be used to link project control to activity properties, facilitating the interpretation of interrelations between activity characteristics and control objectives. This work proposes a new methodology that adds an explainability layer based on SHAP —Shapley Additive exPlanations— to different machine learning models fitted to Monte Carlo simulations of the project network during tracking control points. Specifically, our method allows for both prospective and retrospective analyses, which have different utilities: forward analysis helps to identify key relationships between the different tasks and the desired outcomes, thus being useful to make execution/replanning decisions; and backward analysis serves to identify the causes of project status during project progress. Furthermore, this method is general, model-agnostic and provides quantifiable and easily interpretable information, hence constituting a valuable tool for project control in uncertain environments. application/pdf image/jpeg eng Elsevier Computers & Industrial Engineering. 2023, V. 180, 109261 https://doi.org/10.1016/j.cie.2023.109261 Atribución 4.0 Internacional http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess Project management Stochastic project control Earned value management Shapley values Explainable machine learning SHAP Gestión de empresas Ingeniería Informática Industrial management Engineering Computer science Explainable machine learning for project management control info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Computers & Industrial Engineering 180 109261 THUMBNAIL Santos-cie_2023.pdf.jpg Santos-cie_2023.pdf.jpg IM Thumbnail image/jpeg 4935 https://riubu.ubu.es/bitstream/10259/7665/5/Santos-cie_2023.pdf.jpg e70c6b0e147f2b45bf247d16e635e1c1 MD5 5 Santos-cie_2023-Graphical_abstract.jpg.jpg Santos-cie_2023-Graphical_abstract.jpg.jpg IM Thumbnail image/jpeg 1601 https://riubu.ubu.es/bitstream/10259/7665/6/Santos-cie_2023-Graphical_abstract.jpg.jpg 4aa25c5a1fd6f3f1e7c8825782009dcb MD5 6 LICENSE license.txt license.txt text/plain; charset=utf-8 999 https://riubu.ubu.es/bitstream/10259/7665/3/license.txt b295bcbce42e2caabeb0c623d3860c06 MD5 3 CC-LICENSE license_rdf license_rdf application/rdf+xml; charset=utf-8 908 https://riubu.ubu.es/bitstream/10259/7665/2/license_rdf 0175ea4a2d4caec4bbcc37e300941108 MD5 2 ORIGINAL Santos-cie_2023.pdf Santos-cie_2023.pdf application/pdf 2259402 https://riubu.ubu.es/bitstream/10259/7665/1/Santos-cie_2023.pdf 31d82fb3cf19451541f1d85250fb745c MD5 1 Santos-cie_2023-Graphical_abstract.jpg Santos-cie_2023-Graphical_abstract.jpg Resumen gráfico image/jpeg 190255 https://riubu.ubu.es/bitstream/10259/7665/4/Santos-cie_2023-Graphical_abstract.jpg 71d035cf07b791b4ad4b7e9ab8f7501e MD5 4 10259/7665 oai:riubu.ubu.es:10259/7665 2024-05-13 10:07:20.245 Repositorio Institucional de la Universidad de Burgos bubrep@ubu.es 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