RT info:eu-repo/semantics/article
T1 Explainable machine learning for project management control
A1 Santos Martín, José Ignacio
A1 Pereda, María
A1 Ahedo García, Virginia
A1 Galán Ordax, José Manuel
K1 Project management
K1 Stochastic project control
K1 Earned value management
K1 Shapley values
K1 Explainable machine learning
K1 SHAP
K1 Empresas-Gestión
K1 Industrial management
K1 Ingeniería
K1 Engineering
K1 Informática
K1 Computer science
AB 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.
PB Elsevier
SN 0360-8352
YR 2023
FD 2023-06
LK http://hdl.handle.net/10259/7665
UL http://hdl.handle.net/10259/7665
LA eng
DS Repositorio Institucional de la Universidad de Burgos
RD 26-abr-2024