[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117866-en":3,"doc-seo-117866-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},117866,34359740700684,"Finn","https://ap-avatar.wpscdn.com/avatar/1f400023980c374ae676?_k=1777273430885731487",8,"Research & Report","Explainable machine learning for project management control - SHAP methods - methodology for prospective and retrospective analysis","Project control is central to project management, ensuring objectives are achieved according to plan despite deviations. Earned Value Management is widely used, and under uncertainty, Monte Carlo simulation and statistical or machine learning models extend it to analyze expected times and costs. The work introduces a general, model-agnostic explainability layer using SHAP (Shapley Additive exPlanations) on models fitted to Monte Carlo simulations, enabling prospective and retrospective interpretations. Forward analysis supports replanning and identifying key task–outcome relations, while backward analysis explains causes of status during progress.","Computers & Industrial Engineering 180 (2023) 109261  \nContents lists available at ScienceDirect  \nComputers & Industrial Engineering  \njournal [homepage: www.elsevier.com/locate/caie](homepage: www.elsevier.com/locate/caie)  \n| Explainable machine learning for project management control Jos´e Ignacio Santos a, María Peredab, c, Virginia Ahedod, Jos´e Manuel Gal´an e, *\u003Cbr>a Departamento de Ingeniería de Organizaci´on, Escuela Polit´ecnica Superior, Universidad de Burgos, Avenida Cantabria S/N, 09006 Burgos, Spain\u003Cbr>b Grupo de Investigaci´on Ingeniería de Organizaci´on y Logística (IOL), Departamento Ingeniería de Organizaci´on, Administraci´on de empresas y Estadística, Escuela T´ecnica Superior de Ingenieros Industriales, Universidad Polit´ecnica de Madrid, C/Jos´e Guti´errez Abascal, 2, 28006 Madrid, Spain\u003Cbr>c Grupo Interdisciplinar de Sistemas Complejos (GISC), Madrid, Spain\u003Cbr>d Departamento de Ingeniería de Organizaci´on, Escuela Polit´ecnica Superior, Universidad de Burgos, Avenida Cantabria S/N, 09006 Burgos, Spain e Departamento de Ingeniería de Organizaci´on, Escuela Polit´ecnica Superior, Universidad de Burgos, Avenida Cantabria S/N, 09006 Burgos, Spain |  |  |  |\n| --- | --- | --- | --- |\n| A R T I C L E I N F O |  | A B S T R A C T |  |\n| Keywords:\u003Cbr>Project management Stochastic project control Earned value management Shapley values\u003Cbr>Explainable machine learning SHAP |  | 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. |  |\n\n1. Introduction  \nProject control consists of monitoring project progress and performance, controlling the expected output(s), and taking the necessary corrective actions when deviations from the original plan occur. This role is the cornerstone of any project manager and is key to project success (Pellerin & Perrier, 2019).  \nProject control management methods typically aim to quantify project progress and predict the final outcome if no corrective actions are taken. This prediction should be made as soon as possible so that the range of corrective measures available is as wide as possible. Integrated project management and control systems generally consist of three elements: a baseline schedule, periodic progress data, and a set of analysis techniques capable of identifying potential problems —and perhaps opportunities—in the project (Vanhoucke, 2019). Based on this information, if the control system indicates any possible difficulty to meet the  \nproject obj","cbCaimwAd3tFejtD","https://ap.wps.com/l/cbCaimwAd3tFejtD","pdf",2259402,1,20,"English","en",105,"# Introduction\n## Project control and uncertainty\n## Earned Value Management and extensions\n## Explainable machine learning with SHAP\n# Methodology and analysis approach\n## Prospective (forward) analysis\n## Retrospective (backward) analysis","[{\"question\":\"What problem does the paper address in project management control?\",\"answer\":\"It targets how to perform project control when uncertainty exists, so that deviations from the plan can be analyzed and explained effectively while meeting project objectives.\"},{\"question\":\"How does the proposed method use SHAP in project tracking control?\",\"answer\":\"It adds an explainability layer based on SHAP to machine learning models fitted to Monte Carlo simulations of the project network, enabling interpretable attribution to activity characteristics.\"},{\"question\":\"What is the difference between prospective and retrospective analyses in the method?\",\"answer\":\"Prospective (forward) analysis identifies key relationships between tasks and desired outcomes to support execution and replanning decisions, while retrospective (backward) analysis identifies causes of project status during progress.\"}]","Explainable machine learning for project management control - 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