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Journal articles on the topic 'Predictive project management'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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1

Clayton, Mike. "Classic, predictive project management." Business & Management Collection 2024, no. 11 (2024): e1006386. http://dx.doi.org/10.69645/sysp8079.

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Cruz, Mia Torres-Dela, Subashini A/P Ganapathy, and Noor Zuhaili Binti Mohd Yasin. "Knowledge Management and Predictive Analytics in IT Project Risks." International Journal of Trend in Scientific Research and Development Special Issue, Special Issue-ICAEIT2017 (2018): 209–16. http://dx.doi.org/10.31142/ijtsrd19142.

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Mohammad, Abdullah, Minhajul Amin Md, Yasmin Tisha Sakina, Samin Kafil Sabera, and Kownine Antor Tahsin. "Utilizing Artificial Intelligence for Predictive Project Management." International Journal of Novel Research in Engineering and Science 11, no. 2 (2024): 36–43. https://doi.org/10.5281/zenodo.14293301.

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<strong>Abstract:</strong> The integration of Artificial Intelligence (AI) into predictive project management has revolutionized the way projects are planned, monitored, and executed. This study explores the application of AI-driven models, including Artificial Neural Networks (ANN), Support Vector Machines (SVM), and Random Forest (RF), to predict project timelines and costs accurately. The models were trained on real-world data, with results showing ANN as the most effective in reducing errors and improving reliability. Key insights reveal that AI enhances decision-making, minimizes deviatio

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Khandhar, Aangi B. "A Review on Parking Occupancy Prediction and Pattern Analysis." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 03 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem29597.

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Parking occupancy prediction and pattern analysis is a crucial component of modern urban management systems. Utilizing advanced data analysis techniques, this project aims to develop a predictive model for forecasting parking occupancy levels and analyzing patterns within parking data. By leveraging machine learning algorithms and statistical methods, the project seeks to provide insights into parking behavior and optimize resource allocation in urban areas. The implementation of parking occupancy prediction and pattern analysis contributes to efficient urban planning, improved traffic managem

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Theingi Aung, Sui Reng Liana, Arkar Htet, and Amiya Bhaumik. "Using Machine Learning to Predict Cost Overruns in Construction Projects." Journal of Technology Innovations and Energy 2, no. 2 (2023): 1–7. http://dx.doi.org/10.56556/jtie.v2i2.511.

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Addressing the persistent issue of cost overruns in construction projects, our study explores the potential of machine learning algorithms for accurately predicting these overruns, utilizing an expansive set of project parameters. We draw a comparison between these innovative techniques and traditional cost estimation methods, unveiling the superior predictive accuracy of machine learning approaches. This research contributes to existing literature by presenting a data-driven, reliable strategy for anticipating and managing construction costs. Our findings have significant implications for pro

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Setiyono, Setiyono, Apif Miptahul Hajji, Aisyah Larasati, and Imam Alfianto. "Simulasi Progres Proyek Konstruksi Time Performance Menggunakan Earned Value Management dengan Integrasi Artificial Neural Network." Bentang : Jurnal Teoritis dan Terapan Bidang Rekayasa Sipil 12, no. 1 (2024): 37–48. http://dx.doi.org/10.33558/bentang.v12i1.7296.

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Digitization of construction using technologies such as AI, Big Data, Machine Learning, and Internet of Things (IoT) can help improve the productivity and efficiency of the construction industry. Machine learning methods such as Artificial Neural Network (ANN) are used to solve complex problems, including in construction projects. In addition, Earned Value Management (EVM) is used as a method to analyze and control project performance and estimate project completion time. Although EVM has the disadvantage of predicting estimated project completion times that are linear in nature, the use of no

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Varma, Gautam Kumar. "Managing Risk Using Prediction Markets." Journal of Prediction Markets 7, no. 3 (2014): 45–60. http://dx.doi.org/10.5750/jpm.v7i3.804.

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Prediction markets have emerged fairly recently as a promising forecasting mechanism to handle efficiently the dynamic aggregation of dispersed information among various agents. The interest that this mechanism attracts seems to be increasing at a steady rate, in terms of both business interest and academic work. Applications of predictive markets span the areas of political predictions, sports prediction, Governance to name a few. This paper makes a bold attempt to explore the use of predictive markets for effective risk management process in projects to derive certainty in projects. The key

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Thoumy, Mira, and Joelle Moubarak. "Project Manager Assignment and Its Impact on Multiple Project Management Effectiveness." International Journal of Information Technology Project Management 8, no. 4 (2017): 46–65. http://dx.doi.org/10.4018/ijitpm.2017100104.

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This article aims at identifying the predictive effect of Project Manager's assignment on multiple project management effectiveness in the case of information technology projects in the Lebanese banks. The multiple project management effectiveness was measured on 3 different levels: organizational, projects success and project manager. A survey-based analysis was conducted on a random sample of 43 project managers working in 19 different Lebanese commercial banks. The results showed that most of the project manager's assignment factors influence positively the multiple project effectiveness wi

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jegede, Oluwaleke, and Olalekan Kehinde A. "Project Management Strategies for Implementing Predictive Analytics in Healthcare Process Improvement Initiatives." International Journal of Research Publication and Reviews 6, no. 1 (2025): 1574–88. https://doi.org/10.55248/gengpi.6.0125.0330.

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Arome Salifu. "Leveraging predictive analytics in project risk management: A case study of us government agencies." International Journal of Science and Research Archive 15, no. 3 (2025): 307–11. https://doi.org/10.30574/ijsra.2025.15.3.1455.

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Efficient project risk management is essential in the successful completion of any projects by addressing the identification, evaluation, and mitigation of risks to minimize potential negative impacts on project deliverables. The emergence of predictive analytics represents a transformative shift towards data-driven decision-making and operational efficiency. This study delves into Leveraging Predictive Analytics in Project Risk Management: A Case Study of US Government Agencies. The study highlighted predictive data analytics, integrating predictive analytics, process of predictive data analy

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Silva, Daniela De Freitas Soares Da. "Transforming Infrastructure Project Management: Integrating Blockchain and Predictive Analytics for Enhanced Audit and Risk Management." International Journal of Business and Management Review 12, no. 5 (2024): 71–79. http://dx.doi.org/10.37745/ijbmr.2013/vol12n57179.

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The growing complexity of infrastructure projects necessitates innovative approaches to project management, auditing, and risk assessment. This paper presents a novel framework that integrates blockchain technology with predictive analytics, using Markov chain models and sentiment analysis, to revolutionize infrastructure project management. Drawing from the author's extensive experience in the field, this paper details the development and implementation of this framework, demonstrating its potential to enhance transparency, improve efficiency, and reduce risks in infrastructure projects.

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12

Feng, Yan Ping, and Da Chang Zhu. "Risk Nonlinear Dynamic Model Based on Predictive Control of Engineering Project Management System." Advanced Materials Research 171-172 (December 2010): 3–6. http://dx.doi.org/10.4028/www.scientific.net/amr.171-172.3.

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Many projects have the characteristic of large-scale, wide-rang, and at very important role in national economy. Nowadays engineering project management is still suffering from many problems due to various uncertainties kind of risks. Risk management is a crucial parts of successful engineering project management, but it is often not well predictive in many projects. Risk predictive is valuable and essential for decision support system. But risk factors in projects are complex and couple with each other, and normal risk predictive can not manifest this relationship fully. According to the unce

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Alzeyani, Emira Mustafa Moamer, and Csaba Szabó. "Comparative Evaluation of Model Accuracy for Predicting Selected Attributes in Agile Project Management." Mathematics 12, no. 16 (2024): 2529. http://dx.doi.org/10.3390/math12162529.

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In this study, we evaluate predictive modelling techniques within project management, employing diverse architectures such as the LSTM, CNN, CNN-LSTM, GRU, MLP, and RNN models. The primary focus is on assessing the precision and consistency of predictions for crucial project parameters, including completion time, required personnel, and estimated costs. Our analysis utilises a comprehensive dataset that encapsulates the complexities inherent in real-world projects, providing a robust basis for evaluating model performance. The findings, presented through detailed tables and comparative charts,

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VOLOSHCHUK, Petro, and Xuegong REN. "Predictive model for personnel adaptation efficiency in project management." Economics. Finances. Law 5/2025, no. - (2025): 120–23. https://doi.org/10.37634/efp.2025.5.24.

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Introduction. In the context of growing popularity of project-oriented structures, especially in international environments, effective personnel adaptation has become a key factor for successful project implementation. Inadequate adaptation of new employees can lead to project delays, budget overruns, and reduced team performance. This is particularly relevant in international projects where additional challenges include language, cultural, and technological barriers. However, most modern project management tools lack integrated mechanisms for forecasting adaptation efficiency. The proposed mo

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Kański, Łukasz, Jan Chadam, and Grzegorz Kłosowski. "Intellectual Capital: A New Predictive Indicator for Project Management Improvement." Sustainability 14, no. 22 (2022): 15182. http://dx.doi.org/10.3390/su142215182.

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Effective project management has contributed to successful operations and process management. The goal of this article is to look at the link between a project’s success (PS) and the amount of intellectual capital (IC) an organization has. Instead of being reactive to measuring the cost, timeliness, and quality (customer requirements), a more predictive indicator of a project’s success is needed. Nearly 300 people who work in the field of digital (information and communication) technology took part in the survey research. The survey contains 88 questions. Several statistical techniques are uti

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Md Imtiaz Faruk, Fatin Wahab Plabon, Udoy Sankar Saha, and Mohammad Didar Hossain. "AI-Driven Project Risk Management: Leveraging Artificial Intelligence to Predict, Mitigate, and Manage Project Risks in Critical Infrastructure and National Security Projects." Journal of Computer Science and Technology Studies 7, no. 6 (2025): 123–37. https://doi.org/10.32996/jcsts.2025.7.6.16.

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Risk management in critical infrastructure and national security projects is essential for ensuring operational resilience, security, and stability. Traditional risk management approaches, which rely heavily on historical data analysis and expert judgment, face significant limitations in addressing dynamic and evolving threats. Artificial Intelligence (AI) has emerged as a transformative force, offering advanced capabilities in predictive analytics, autonomous risk mitigation, and real-time decision support. This study explores the integration of AI technologies including machine learning (ML)

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Jurina, Krešimir, and Branimir Kapulica. "Application of artificial intelligence in project management." Et2er 6, no. 2 (2024): 115–21. https://doi.org/10.70077/et2er.6.2.15.

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This paper explores the integration of Artificial Intelligence (AI) in project management, focusing on its impact on digital transformation and process optimization. Decision-making, a critical element for the successful management of organizations and projects, is significantly enhanced by AI in the contemporary business environment. AI's capabilities in data analysis, outcome prediction, and decision-making optimization present substantial potential for organizations to improve adaptability, increase proactivity, and plan future business steps more effectively. AI's role in project planning

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18

Sampietro, Marco. "Predictive, agile, hybrid project management. E se iniziassimo a parlare di project management efficace?" PROJECT MANAGER (IL), no. 50 (May 2022): 10–13. http://dx.doi.org/10.3280/pm2022-050003.

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19

Bendada, Larbi, Mourad Brioua, Mohamed Razi Morakchi, and Ibrahim Djouani. "Predicting project duration using a coupled artificial neural network and Taguchi method approach." STUDIES IN ENGINEERING AND EXACT SCIENCES 5, no. 2 (2024): e5641. http://dx.doi.org/10.54021/seesv5n2-019.

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Accurate project duration prediction is increasingly important for management because it defines the expected timeline for project realization. This study utilizes an integrated approach combining neural networks with the Taguchi method to forecast the time required to complete projects within predetermined deadlines. The methodology involves modelling and simulating the network of project activities to estimate the total average project duration. ration. The neural network model uses input variables such as success probability, effort, and learning factor to predict the total time necessary f

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20

Oluwaseun Adeola Bakare, Onoriode Reginald Aziza, Ngozi Samuel Uzougbo, and Portia Oduro. "A governance and risk management framework for project management in the oil and gas industry." Open Access Research Journal of Science and Technology 12, no. 1 (2024): 121–30. http://dx.doi.org/10.53022/oarjst.2024.12.1.0119.

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The oil and gas industry operates in an environment marked by high risks, complex regulations, and significant financial investments. Effective project management in this sector requires a robust governance and risk management framework to address operational, regulatory, financial, and environmental challenges. This review proposes a comprehensive governance and risk management framework tailored specifically to the unique needs of oil and gas projects. The framework integrates governance structures that define clear roles, responsibilities, and decision-making processes, ensuring that projec

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21

Mbatha, Mr Samuel Kiilu, Dr Ahmad Omar Alkizim, and Dr Titus Kivaa Mbiti. "The Practice of Conflict Management in Construction Projects in Kenya." International Journal of Soft Computing and Engineering 10, no. 4 (2021): 6–12. http://dx.doi.org/10.35940/ijsce.d3483.0310421.

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Conflicts in construction projects seem an increasingly prevalent phenomenon in Kenya, perhaps because of the projects’ uncertainty, complexity, and diversity of participants. Management of these conflicts remains ineffective, an occurrence that creates a major obstacle to the success of project implementation, usually leading to an increase in project cost, delayed project completion, and in worst cases suspension of the project. A review of literature has revealed the negative role played by conflict on project success.This study sought to investigate the practice of conflict management in c

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Samuel, Kiilu Mbatha, Omar Alkizim Ahmad, and Kivaa Mbiti Titus. "The Practice of Conflict Management in Construction Projects in Kenya." International Journal of Soft Computing and Engineering (IJSCE) 10, no. 4 (2021): 6–12. https://doi.org/10.35940/ijsce.D3483.0310421.

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Conflicts in construction projects seem an increasingly prevalent phenomenon in Kenya, perhaps because of the projects’ uncertainty, complexity, and diversity of participants. Management of these conflicts remains ineffective, an occurrence that creates a major obstacle to the success of project implementation, usually leading to an increase in project cost, delayed project completion, and in worst cases suspension of the project. A review of literature has revealed the negative role played by conflict on project success.This study sought to investigate the practice of conflict managemen

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23

Radhakrishnan, Abirami, John Stephen Davis, and Dessa David. "Examining the Critical Success Factors in IT Projects." International Journal of Information Technology Project Management 13, no. 1 (2022): 1–38. http://dx.doi.org/10.4018/ijitpm.290423.

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Many companies experience IT project failures in relatively new areas such as Big Data, Data Science, Enterprise Systems, Blockchain, Cloud Computing, IT security, and IoT. Because of inadequate research in identifying critical success factors for these projects, we conducted a Delphi study employing separate panels for each of two kinds of project implementations, those using a predictive lifecycle approach and those using an adaptive lifecycle approach. We found common critical success factors: user/client involvement, senior management support, effective monitoring and control, effective co

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Laurent-Burle, Guillaume, and Mohamed Quafafou. "Predictive Monitoring of Resources Consumption in Project Management." Procedia Computer Science 239 (2024): 1799–806. http://dx.doi.org/10.1016/j.procs.2024.06.360.

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Bernard, Anim Manu. "Leveraging Artificial Intelligence for optimized project management and risk mitigation in construction industry." World Journal of Advanced Research and Reviews 24, no. 3 (2024): 2924–40. https://doi.org/10.5281/zenodo.15245000.

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The construction industry is increasingly adopting artificial intelligence (AI) to optimize project management processes and enhance risk mitigation strategies. As construction projects grow in complexity, with tight deadlines, evolving regulations, and high costs, the traditional approaches to managing projects and assessing risks are often inefficient and prone to human error. AI technologies, particularly machine learning and predictive analytics, offer powerful tools to address these challenges by providing data-driven insights, improving decision-making, and automating various project man

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Jim, Tam, Shah Bharat, and Prescod Franklyn. "Deterministic Project Management with AI Applicability in Globalized Context." Journal of Management Science and Business Intelligence 3, no. 2 (2018): 9–14. https://doi.org/10.5281/zenodo.1484650.

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This paper identifies the rationale underlying the low success rates for globalized projects. Instead of having a traditional project manager in managing the outcomes of a globalized project, the notion of a project neoteric with manifold attributes and an emphasis in managing the process is preferred. Given the incongruence and discrepancy in planning as well as execution of activities between the Headquarters Team and the Localized Team in globalized project operation, a framework for exploiting different kinds of AI technology is proposed with the sole purpose of minimizing project estimati

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Bañuls, Victor A., Cristina López, Murray Turoff, and Fernando Tejedor. "Predicting the Impact of Multiple Risks on Project Performance: A Scenario-Based Approach." Project Management Journal 48, no. 5 (2017): 95–114. http://dx.doi.org/10.1177/875697281704800507.

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This article suggests a scenario-based approach to properly managing risks during the lifetime of a project. Our proposal aims at giving managers a structured process to predicting the impact of the occurrence of multiple risks that can affect project performance. This is a product of combining Cross-Impact Analysis (CIA) and Interpretive Structural Modeling (ISM) mechanics, which improve the predictive capacity of existing risk analysis techniques. In order to validate their risk predictions, we compare them with a sample of real projects carried out in an engineering company. The findings sh

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Bin Mahmoud, Abdulrahman, Abdullah Alrashdi, Salman Akhtar, Ayman Altuwaim, and Abdulmohsen Almohsen. "Development of a Predictive Model Based on the Alignment Tool in the Early Stages of Projects: The Case of Saudi Arabia Infrastructure Projects." Sustainability 16, no. 18 (2024): 8122. http://dx.doi.org/10.3390/su16188122.

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The construction industry plays a substantial role in shaping the economies of many countries. Construction management faces various challenges that can lead to project failures, particularly in infrastructure projects struggling to meet cost and time requirements. Inadequate project planning and the intricate nature of construction projects can cause participants’ project goals to not align. It is crucial to address these challenges early in the planning stages to ensure project success. This research involved investigating previous studies to understand current practices for improving infras

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Marnewick, Carl, and Annlizé L. Marnewick. "Project managers' ability to explore and exploit predictive and iterative best practices." International Journal of Managing Projects in Business 16, no. 8 (2023): 126–51. http://dx.doi.org/10.1108/ijmpb-01-2023-0013.

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Purpose Project managers face decisions every day and those decisions result in an “either or” situation. This is also true when it comes to the choice of a project management approach, i.e. predictive versus iterative. A case is made in this article that project managers should be ambidextrous and apply practices that are beneficial to the project, irrespective of the origin of the practices.Design/methodology/approach This study is based on a questionnaire focussing on six themes. The results of 290 projects were analysed using ANOVA and boxplots to test for skewness and variances.Findings B

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Almalki, Sultan Saaed. "AI-Driven Decision Support Systems in Agile Software Project Management: Enhancing Risk Mitigation and Resource Allocation." Systems 13, no. 3 (2025): 208. https://doi.org/10.3390/systems13030208.

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Agile software project management (ASPM) serves modern industries to conduct iterative development of complicated code bases. The decision-making process in Agile environments regularly depends on individual opinions, creating ineffective results for risk management and resource distribution. Artificial intelligence (AI) is a promising approach for handling these challenges by delivering data-based choices to project management. This research introduces an AI-based decision support system for improving risk reduction and resource distribution in ASPM. The system merges optimization frameworks

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Arowosegbe, Oluwakemi Betty, Tersoo Hulugh, and Pedepo Emmanuel. "Enhancing Supply Chain Resilience Through Predictive Modelling and Root Cause Analysis in Project Management." International Journal of Research Publication and Reviews 5, no. 11 (2024): 3551–67. https://doi.org/10.55248/gengpi.5.1124.3302.

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Paspunoori, Sampath kumar. "PRODUCTIVITY IMPROVEMENT MODELS IN CONSTRUCTION PROJECT MANAGEMENT." American Journal of Engineering and Technology 6, no. 10 (2024): 112–18. http://dx.doi.org/10.37547/tajet/volume06issue10-12.

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The relevance of the research topic is due to the increasing complexity of construction projects, stricter requirements for deadlines, quality of work, as well as increased competition in the relevant industry. In the current conditions, traditional management methods often turn out to be ineffective, which leads to deadlines, budget overruns, and a significant decrease in the quality of construction. The purpose of the study is to analyze and systematize ideas about modern models of productivity improvement in construction project management, as well as to assess their potential impact on key

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Sakhawat Hussain Tanim. "Leveraging Predictive Analytics for Risk Identification and Mitigation in Project Management." Journal of Information Systems Engineering and Management 10, no. 43s (2025): 1041–52. https://doi.org/10.52783/jisem.v10i43s.8523.

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In the dynamic landscape of project management, the anticipation and mitigation of risks are paramount to achieving project success. Predictive analytics, encompassing statistical techniques and machine learning algorithms, offers a proactive approach by analyzing historical data to forecast potential project risks. This paper explores the integration of predictive analytics into risk identification and mitigation processes within project management. Utilizing methodologies such as Monte Carlo simulations and regression modeling, the study demonstrates how predictive analytics can enhance deci

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Chaturvedi, Aadi, and Abhijit Amrutkar. "Predictive Analytics of Post-Purchase Consumer Dynamics in Real Estate Cancellation Prediction Model." Journal of Advances and Scholarly Researches in Allied Education 21, no. 6 (2024): 141–43. https://doi.org/10.29070/s3q15794.

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The real estate sector, known for its complex customer dynamics, often struggles with high post- purchase cancellations, which negatively affect revenue and overall project success. This study presents a predictive analytics model for forecasting customer cancellations in real estate transactions. By leveraging advanced machine learning techniques and using data from past projects, the model aims to assist sales and collection teams in identifying high-risk customers, thus enabling proactive intervention strategies. The research integrates consumer behavior patterns, financial data, and projec

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Sai Manoj Jayakannan. "Predictive analytics for construction project risk management: Leveraging AI for proactive mitigation strategies." World Journal of Advanced Engineering Technology and Sciences 15, no. 2 (2025): 2204–9. https://doi.org/10.30574/wjaets.2025.15.2.0737.

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The construction industry confronts significant challenges related to risk management, with traditional approaches often failing to prevent costly delays, budget overruns, and safety incidents. Artificial intelligence and machine learning technologies present transformative opportunities for shifting from reactive to proactive risk management in construction projects. Predictive analytics leverages historical data patterns, real-time monitoring, and sophisticated algorithms to forecast potential issues before they materialize and impact project performance. This comprehensive examination explo

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Ravi, Pandey, Gupta Nandini, and Narendra Patil Mr. "Predicting Sulphur Price Volatility: A Multi-Model Approach for Enhanced Commodity Forecasting." Career Point International Journal of Research(CPIJR) 4, no. 3 (2025): 37–41. https://doi.org/10.5281/zenodo.15057663.

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The volatility in sulphur prices presents significant challenges for industries relying on sulphur as a key raw material, particularly in terms of budgeting, inventory management, and strategic planning. Traditional methods for predicting commodity prices, such as manual analysis, are often time-consuming and error-prone. This project proposes a comprehensive predictive system for sulphur price forecasting by leveraging advanced machine learning and time series techniques. Multiple predictive models, including Linear Regression, Random Forest, AdaBoost, XGBoost, ARIMA, Auto ARIMA, and VAR, are

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Bernard Anim Manu. "Leveraging Artificial Intelligence for optimized project management and risk mitigation in construction industry." World Journal of Advanced Research and Reviews 24, no. 3 (2024): 2924–40. https://doi.org/10.30574/wjarr.2024.24.3.4026.

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The construction industry is increasingly adopting artificial intelligence (AI) to optimize project management processes and enhance risk mitigation strategies. As construction projects grow in complexity, with tight deadlines, evolving regulations, and high costs, the traditional approaches to managing projects and assessing risks are often inefficient and prone to human error. AI technologies, particularly machine learning and predictive analytics, offer powerful tools to address these challenges by providing data-driven insights, improving decision-making, and automating various project man

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38

Wisdom Ebirim, Favour Oluwadamilare Usman, Kehinde Andrew Olu-lawal, et al. "Optimizing energy efficiency in data center cooling towers through predictive maintenance and project management." World Journal of Advanced Research and Reviews 21, no. 2 (2024): 1782–90. http://dx.doi.org/10.30574/wjarr.2024.21.2.0619.

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Optimizing energy efficiency in data center cooling towers is crucial for reducing operational costs and environmental impact. This review explores the integration of predictive maintenance and project management to achieve this goal. By leveraging predictive maintenance techniques, data center operators can anticipate and address potential issues before they lead to costly downtime or inefficiencies. Project management plays a key role in coordinating these efforts, ensuring that maintenance activities are carried out efficiently and effectively. Predictive maintenance relies on data analytic

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Wisdom, Ebirim, Oluwadamilare Usman Favour, Andrew Olu-lawal Kehinde, Ninduwesuor-Ehiobu Nwakamma, Chigozie Ani Emmanuel, and Jose Portillo Montero Danny. "Optimizing energy efficiency in data center cooling towers through predictive maintenance and project management." World Journal of Advanced Research and Reviews 21, no. 2 (2024): 1782–90. https://doi.org/10.5281/zenodo.14041917.

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Optimizing energy efficiency in data center cooling towers is crucial for reducing operational costs and environmental impact. This review explores the integration of predictive maintenance and project management to achieve this goal. By leveraging predictive maintenance techniques, data center operators can anticipate and address potential issues before they lead to costly downtime or inefficiencies. Project management plays a key role in coordinating these efforts, ensuring that maintenance activities are carried out efficiently and effectively. Predictive maintenance relies on data analytic

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40

Ani, Dr Sabah Noori Hammoodi Al. "Utilizing Project Management Techniques to Model Critical Success Factors in Large-Scale Construction Projects." American Journal of Economics and Business Management 7, no. 8 (2024): 468–83. http://dx.doi.org/10.31150/ajebm.v7i8.2901.

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This study investigates the application of project management techniques to model critical success factors (CSFs) in large-scale construction projects, utilizing Confirmatory Factor Analysis (CFA) to validate the measurement model. Through factor level analysis, eight distinct categories of CSFs were identified, highlighting their significance in project management success. The structural model analysis revealed positive correlations between project management success and CSFs, impacting project-level outcomes such as customer satisfaction, team satisfaction, and project profitability. Strong

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Anicet, Mushuti, Julius Warren Kule, and Marcel Ndengo. "Assessment on Factors Affecting the Performance of Public Funded Energy Construction Projects in Rwanda." Journal of Research in Business, Economics and Management 5, no. 5 (2016): 790–97. https://doi.org/10.5281/zenodo.3965512.

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The main purpose of this study was to assess the factors that affect the performance of public funded energy construction projects in Rwanda using a survey of the seven (7) micro hydropower project (7 MHP). The objectives of the study included; to examine the effect of project size on performance of the 7 MHPs, to assess the effect of project procurement methods on the performance of the7 MHPs, to establish the contribution of project budgetary allocation on the performance of the 7 MHPs, to assess the effect of project management skills/competences of managers on the performance of the 7 MHPs

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Zafra-Cabeza, Ascension, Miguel A. Ridao, and Eduardo F. Camacho. "A STOCHASTIC PREDICTIVE CONTROL APPROACH TO PROJECT RISK MANAGEMENT." IFAC Proceedings Volumes 38, no. 1 (2005): 134–39. http://dx.doi.org/10.3182/20050703-6-cz-1902.02258.

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Bauskar, Sanjay Ramdas, Chandrakanth Rao Madhavaram, Eswar Prasad Galla, Janardhana Rao Sunkara, Hemanth Kumar Gollangi, and Shravan Kumar Rajaram. "Predictive Analytics for Project Risk Management Using Machine Learning." Journal of Data Analysis and Information Processing 12, no. 04 (2024): 566–80. http://dx.doi.org/10.4236/jdaip.2024.124030.

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Batt, Garrett, Madison Kusano, Tavin McMickens, Joel Rubin, and James Schreiner. "Priority Risk Index: A Novel Approach to Determining Project Health for the United States Corps of Engineers." Industrial and Systems Engineering Review 12, no. 2 (2025): 115–20. https://doi.org/10.37266/iser.2025v12i2.pp115-120.

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This report explores the intricacies of the U.S. Army Corps of Engineers (USACE) business and project management processes, focusing on Military Construction (MILCON) projects. The study examines a methodology for assessing project health through the contingency requirements of CCIR#8, employing value modeling, project metrics, and linear regression analysis on individual projects. The report outlines the process of authorizing and appropriating MILCON projects through the National Defense Authorization Act (NDAA), the standard Project Delivery Business Process (PDBP) of USACE, data collection

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Nishat, Mirza Muntasir, Aneeq Ahsan, and Nils O. E. Olsson. "Applying Machine Learning for Predictive Analysis in Project-Based Data: Insights into Variation Orders." Journal of Information Technology in Construction 30 (May 27, 2025): 807–25. https://doi.org/10.36680/j.itcon.2025.033.

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The complexity of the global supply chain and project execution necessitates advanced methodologies in project management. As industries are generating large amounts of project data, machine learning (ML) algorithms can be a viable tool for addressing predictive analytics and transforming this industry into more digitalization. This study examines the feasibility of leveraging ML models for predicting variation orders (VOs) in an energy construction project through the use of actual project management data. Using historical project data, this study presents the investigative analysis of applyi

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Punnapu, Suresh. "Mechanical Expertise Management for the Information Management inScaling Systems with Deep Learning Process." Journal of Computer Allied Intelligence 2, no. 5 (2024): 31–41. http://dx.doi.org/10.69996/jcai.2024023.

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Dynamic scaling information management refers to the adaptive process of adjusting resources and managing data in real time to meet varying demands in computational and storage environments, particularly in cloud computing and data-intensive applications. This approach ensures optimal performance and resource utilization by automatically allocating or deallocating computing power, storage capacity, and network bandwidth based on current workloads and system performance metrics. Key components include monitoring tools that continuously assess resource usage, predictive analytics to anticipate f

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Ramos, Michael, Neil Balba, Jesusa Padilla, Edgardo Dajao, and Juan Tallara Jr. "Improving Project Management Performance Through Data Analytics." Asia Pacific Journal of Management and Sustainable Development 13, no. 1 (2025): 92–98. https://doi.org/10.70979/zdpv7001.

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In today’s complex IT landscape, organizations frequently encounter budget overruns and schedule delays that undermine project success. This study investigates the role of data analytics in enhancing project management performance, focusing on cost efficiency and schedule adherence. Through a comparative analysis of multiple infrastructure and IT outsourcing projects—some managed with advanced analytics tools and others with traditional methods—this employs descriptive and inferential statistics alongside a structured cost-benefit evaluation. Key findings reveal that analytics-enabled projects

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Tosin Samuel Oyetunji, Fasasi Lanre Erinjogunola, Rasheed O. Ajirotutu, Abiodun Benedict Adeyemi, Tochi Chimaobi Ohakawa, and Saliu Alani Adio. "A unified risk management framework for cost and resource optimization in housing development projects." Gulf Journal of Advance Business Research 3, no. 4 (2025): 985–97. https://doi.org/10.51594/gjabr.v3i4.130.

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The increasing demand for affordable housing has highlighted the need for more efficient project management strategies, especially in the face of rising costs, resource constraints, and regulatory complexities. This paper proposes a unified risk management framework designed to optimize cost and resource allocation in housing development projects. The framework integrates various risk management techniques, including risk identification, assessment, and mitigation, with cost control methods and resource optimization strategies. It emphasizes a dynamic, data-driven approach to evaluating and ma

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Emmanuel, Pedepo. "Integrating Predictive Analytics and Root Cause Analysis for Optimized Project Management in Supply Chain Operations." International Journal of Research Publication and Reviews 5, no. 11 (2024): 3849–65. https://doi.org/10.55248/gengpi.5.1124.3305.

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Savio, Rajesh Dominic, and Jafar M. Ali. "Artificial Intelligence in Project Management & Its Future." Saudi Journal of Engineering and Technology 8, no. 10 (2023): 244–48. http://dx.doi.org/10.36348/sjet.2023.v08i10.002.

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Artificial Intelligence (AI) has emerged as a disruptive force, transforming industries and revolutionizing business processes. Among the domains significantly impacted, project management holds immense potential for transformation. AI’s integration promises intelligent automation, data-driven decision-making, and predictive capabilities, addressing challenges in traditional project management methodologies. Successful AI implementations have revolutionized project management, improving forecasting, resource allocation, and risk assessment. Despite the benefits, challenges hinder AI adoption,

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