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Project monitoring is treated as a critical mechanism to ensure successful delivery within time and budget constraints. The first study develops and evaluates control-chart-based duration indicators and performance metrics, including the Duration Performance Index (DPI) and robust measures for signaling accuracy and overreaction. It also studies how alternative probability distributions, such as Gamma and Conway-Maxwell-Poisson (CMP), affect modeling and control-chart signaling.","ANDRÉ HENRIQUE ALVES CARNEIRO  \nStatistical Project Monitoring: contributions in the literature through the use of control charts  \nand machine learning  \nSão Paulo, Brazil  \nANDRÉ HENRIQUE ALVES CARNEIRO  \nStatistical Project Monitoring: contributions in the literature through the use  \nof control charts and machine learning  \nOriginal version  \nDoctorate thesis presented to Polytechnic School of University of São Paulo to obtain the Doctorate in Science.  \nAdviser: Prof. Dr. Linda Lee Ho  \nSão Paulo, Brazil  \nI authorize the total or partial reproduction and dissemination of this thesis, by any conventional or digital media, for study and research purposes, as long as the source is cited.  \nANDRÉ HENRIQUE ALVES CARNEIRO  \nStatistical Project Monitoring: contributions in the literature through the use of control charts and machine learning/ ANDRÉ HENRIQUE ALVES CARNEIRO. -São Paulo, Brazil, 2025-  \n96 p.  \nAdviser: Prof. Dr. Linda Lee Ho  \nThesis (Doctor)– University of São Paulo – USP  \nPolytechnic School  \nGraduate Program in Production Engineering, 2025 .  \n1. Statistical Project Control 2 . Monte Carlo Simulation 3 . Control Charts 4 . Machine Learning  \n5. Artificial Intelligence I. Linda Lee Ho. II. University of São Paulo. III. Polytechnic School.  \nIV. Doctorate  \nCDU XX:XXX:XXX.X  \nANDRÉ HENRIQUE ALVES CARNEIRO  \nStatistical Project Monitoring: contributions in the literature through the use of control charts and machine  \nlearning  \nOriginal version  \nDoctorate thesis presented to Polytechnic School of University of São Paulo to obtain the Doctorate in Science.  \nThe approved work. São Paulo, Brazil, July, 03, 2025:  \n\n| Prof. Dr. Linda Lee Ho\u003Cbr>Adviser |\n| --- |\n| Prof. Dr. Roberto da Costa Quinino\u003Cbr>UFMG |\n| Prof. Dr. Frederico R. Borges Cruz\u003Cbr>UFMG |\n| Dr. Rodrigo Goulart Votto\u003Cbr>Voith Paper Máquinas e Equipamentos |\n\nProf. Dr. Leandro Alves da Silva  \nEP-USP  \nSão Paulo, Brazil  \nAcknowledgements  \nFirst of all, I would like to thank my family for showing me the importance of a high-quality education and for unconditionally supporting my academic decisions.  \nSecondly, I would like to thank my advisor, Linda Lee Ho, who has supported and guided me from my bachelor’s degree through my doctorate. With your help, I was able to successfully transition into the data science field, where I feel much more fulfilled.  \nI would also like to thank all my amazing friends, who are a significant part of my life, bringing joy and providing invaluable emotional support for my decisions. A special thanks to Marcela Okuyama, my best friend, for always being by my side and for being the person I admire the most.  \nAbstract  \nThe objective of this thesis is to unify two distinct studies related to project monitoring. Project monitoring is a critical process for ensuring project success by delivering activities on time and within budget. The first study explores the use of statistical control chart methodologies to evaluate novel duration indicators derived from traditional techniques, as well as new performance metrics designed to assess control chart behavior. It also investigates the impact of alternative probability distributions, such as Gamma and Conway-Maxwell-Poisson (CMP), on modeling project activities. The main contributions of this study are threefold: (i) the Duration Performance Index (DPI), from the Earned Duration Management (EDM) approach, is confirmed to be a reliable indicator for tracking overall project duration; (ii) the probability of overreaction (􀁐􀁏) and accuracy (􀁁􀁣) are identified as the most robust performance metrics for evaluating control chart behavior; and (iii) while the choice of probability distribution, particularly Gamma and CMP, does not significantly affect total project duration, it can interfere with the correct signaling of control charts. The second study addresses both an academic and practical question: When should a project be monitored? It proposes an integrated methodology that co","cbCaigFPtFVg7vVh","https://ap.wps.com/l/cbCaigFPtFVg7vVh","pdf",4006545,1,97,"English","en",105,"# Abstract\n## Objectives and overview\n## Study 1: control charts and duration indicators\n## Study 1: probability distributions and performance metrics\n## Study 2: optimal monitoring review periods\n## Machine learning and feature selection\n## Evaluation design and simulation\n## Main contributions\n## Keywords","[{\"question\":\"What problem does the thesis address in project monitoring?\",\"answer\":\"It targets how to monitor projects effectively to ensure delivery on time and within budget by combining statistical control chart methods with data-driven techniques.\"},{\"question\":\"What are the main contributions of the first study?\",\"answer\":\"It confirms the Duration Performance Index (DPI) as a reliable duration tracking indicator and identifies robust metrics—probability of overreaction and accuracy—for evaluating control chart behavior, while analyzing distribution choices such as Gamma and CMP.\"},{\"question\":\"How does the second study decide when to monitor a project?\",\"answer\":\"It proposes integrated methodology to determine optimal review periods using machine learning with Boruta feature selection and experimental design, identifying periods that predict overall delay with minimal performance loss while testing preprocessing and model choices.\"}]","Statistical Project Monitoring - 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