Artificial Intelligence Enabled Project Management: A Systematic Literature Review
Source abstract
Abstract
In the Industry 5.0 era, companies are leveraging the potential of cutting-edge technologies such as artificial intelligence for more efficient and green human-centric production. In a similar approach, project management would benefit from artificial intelligence in order to achieve project goals by improving project performance, and consequently, reaching higher sustainable success. In this context, this paper examines the role of artificial intelligence in emerging project management through a systematic literature review; the applications of AI techniques in the project management performance domains are presented. The results show that the number of influential publications on artificial intelligence-enabled project management has increased significantly over the last decade. The findings indicate that artificial intelligence, predominantly machine learning, can be considerably useful in the management of construction and IT projects; it is notably encouraging for enhancing the planning, measurement, and uncertainty performance domains by providing promising forecasting and decision-making capabilities.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: BIM and Construction Integration · Construction Project Management and Performance · Occupational Health and Safety Research
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Engineering of Bilbao, University of the Basque Country—UPV/EHU, 48013 Bilbao, Spain
Victoria University Business School, Victoria University, Melbourne 3011, Australia
References (100+)
Show source-provided references
1. Breque, M., De Nul, L., Petrides, A., and European Commission (2021). Industry 5.0: Towards a Sustainable, Human-Centric and Resilient European Industry, European Commission, Directorate-General for Research and Innovation.
2. Mccarthy, J. (2022, February 15). What Is Artificial Intelligence?. Available online: http://www-formal.stanford.edu/jmc/whatisai/whatisai.html.
3. Artificial Intelligence (AI) Applications for COVID-19 Pandemic10.1016/j.dsx.2020.04.012 ↗
4. PMI (2019). AI @ Work: New Projects, New Thinking, Project Management Institute.
5. PMI (2021). PMBOK Guide, Project Management Institute.
6. Russell, S., and Norvig, P. (2010). Artificial Intelligence a Modern Approach, Pearson Education, Inc.. [3rd ed.].
7. Evaluation of Deep Learning-Based Artificial Intelligence Techniques for Breast Cancer Detection on Mammograms: Results from a Retrospective Study Using a BreastScreen Victoria Dataset10.1111/1754-9485.13278 ↗
8. Agarwala, N., and Chaudhary, R.D. (2021). International Political Economy Series, Springer International Publishing.
9. Warin, T., and Stojkov, A. (2021). Machine Learning in Finance: A Metadata-Based Systematic Review of the Literature. J. Risk Financ. Manag., 14.10.3390/jrfm14070302 ↗
10. A Survey on Intrusion Detection System: Feature Selection, Model, Performance Measures, Application Perspective, Challenges, and Future Research Directions10.1007/s10462-021-10037-9 ↗
11. Zhang, X.-D. (2020). A Matrix Algebra Approach to Artificial Intelligence, Springer.10.1007/978-981-15-2770-8 ↗
12. Deep Learning10.1038/nature14539 ↗
13. Yegnanarayana, B. (2005). Artificial Neural Networks, Prentice-Hall of India Private Limited.
14. Natural Language Processing10.1002/aris.1440370103 ↗
15. Fuzzy Sets10.1016/S0019-9958(65)90241-X ↗
16. Jackson, P. (1986). Introduction to Expert Systems, Addison-Wesley Pub. Co.
17. Genetic Algorithm: Review And Application
18. Ant Colony Optimization10.1109/MCI.2006.329691 ↗
19. Towards a Methodology for Developing Evidence-Informed Management Knowledge by Means of Systematic Review10.1111/1467-8551.00375 ↗
20. Project Sustainability Strategies: A Systematic Literature Review10.1016/j.ijproman.2016.11.006 ↗
21. The Strategic Use of Artificial Intelligence in the Digital Era: Systematic Literature Review and Future Research Directions10.1016/j.ijinfomgt.2020.102225 ↗
22. Understanding 5G Technology for Future Supply Chain Management10.1080/13675567.2020.1762850 ↗
23. Google Scholar, Web of Science, and Scopus: A Systematic Comparison of Citations in 252 Subject Categories
24. Litigation Outcome Prediction of Differing Site Condition Disputes through Machine Learning Models10.1061/(ASCE)CP.1943-5487.0000148 ↗
25. Predicting the Litigation Outcome of PPP Project Disputes between Public Authority and Private Partner Using an Ensemble Model10.3846/jbem.2021.13219 ↗
26. Stakeholders Classification System Based on Clustering Techniques
27. Guo, J., Li, Z., Ju, S., Manoharan, M., and Knight, A. (2020, January 17–20). DLS Magician: Promoting Early-Stage Collaboration by Automating Ui Design Process in an EandP Environment. Proceedings of the International Conference on Intelligent User Interfaces, Cagliari, Italy.10.1145/3379336.3381462 ↗
28. Use of Artificial Intelligence in a Regulated Design Environment—A Beam Design Example10.1007/978-3-030-51295-8_2 ↗
29. Miller, G. (2021, January 26). Artificial Intelligence Project Success Factors: Moral Decision-Making with Algorithms. Proceedings of the 16th Conference on Computer Science and Intelligence Systems, Sofia, Bulgaria.10.15439/2021F26 ↗
30. Knowledge-Based System for Resolving Design Clashes in Building Information Models10.1016/j.autcon.2019.103001 ↗
31. Comparison of Machine Learning Algorithms for Software Project Time Prediction
32. An Effective Approach for Software Project Effort and Duration Estimation with Machine Learning Algorithms10.1016/j.jss.2017.11.066 ↗
33. Estimating Construction Duration of Diaphragm Wall Using Firefly-Tuned Least Squares Support Vector Machine10.1007/s00521-017-2840-z ↗
34. Automation in Construction Scheduling: A Review of the Literature10.1007/s00170-015-7339-0 ↗
35. Aljebory, K.M., and QaisIssam, M. (2019, January 21–24). Developing AI Based Scheme for Project Planning by Expert Merging Revit and Primavera Software. Proceedings of the 16th International Multi-Conference on Systems, Signals and Devices, SSD 2019, Istanbul, Turkey.10.1109/SSD.2019.8893274 ↗
36. Firefly Algorithm to Solve a Project Scheduling Problem10.1007/978-3-319-33625-1_40 ↗
37. Extended Learning Method for Designation of Co-Operation
38. Rachman, V., and Ma’sum, M.A. (2017, January 23–24). Comparative Analysis of Ant Colony Extended and Mix-Min Ant System in Software Project Scheduling Problem. Proceedings of the WBIS 2017: 2017 International Workshop on Big Data and Information Security, Jakarta, Indonesia.10.1109/IWBIS.2017.8275107 ↗
39. Hamada, M.A., Abdallah, A., Kasem, M., and Abokhalil, M. (2021, January 28–30). Neural Network Estimation Model to Optimize Timing and Schedule of Software Projects. Proceedings of the SIST 2021—2021 IEEE International Conference on Smart Information Systems and Technologies, Nur-Sultan, Kazakhstan.10.1109/SIST50301.2021.9465887 ↗
40. Analytical Structure of a Fuzzy Logic Controller for Software Development Effort Estimation10.1007/978-81-322-2734-2_22 ↗
41. Improving Efficiency of Fuzzy Models for Effort Estimation by Cascading & Clustering Techniques10.1016/j.procs.2016.05.234 ↗
42. Algorithmic Based and Non-Algorithmic Based Approaches to Estimate the Software Effort10.14257/ijmue.2015.10.4.15 ↗
43. SEffEst: Effort Estimation in Software Projects Using Fuzzy Logic and Neural Networks10.1080/18756891.2012.718118 ↗
44. Soltanveis, F., and Alizadeh, S.H. (2016, January 9). Using Parametric Regression and KNN Algorithm with Missing Handling for Software Effort Prediction. Proceedings of the 2016 Artificial Intelligence and Robotics, IRANOPEN 2016, Qazvin, Iran.10.1109/RIOS.2016.7529494 ↗
45. Neural Network Models for Software Development Effort Estimation: A Comparative Study10.1007/s00521-015-2127-1 ↗
46. Abulalqader, F.A., and Ali, A.W. (2018, January 20–21). Comparing Different Estimation Methods for Software Effort. Proceedings of the 2018 1st Annual International Conference on Information and Sciences, AiCIS 2018, Fallujah, Iraq.10.1109/AiCIS.2018.00016 ↗
47. Reasoning with Noisy Software Effort Data
48. Software Project Scheduling Using the Hyper-Cube Ant Colony Optimization Algorithm
49. An Optimized Resolution for Software Project Planning with Improved Max-Min Ant System Algorithm10.14257/ijmue.2015.10.6.04 ↗
50. Management of Resources in Multiunit Construction Projects with the Use of a Tabu Search Algorithm10.3846/13923730.2015.1073616 ↗
51. Decision Support for Project Rescheduling to Reduce Software Development Delays Based on Ant Colony Optimization10.2991/ijcis.11.1.68 ↗
52. Discovering Software Developer’s Coding Expertise through Deep Learning10.1049/iet-sen.2019.0290 ↗
53. Gaitanidis, A., Vassiliadis, V., Kyriklidis, C., and Dounias, G. (2016, January 18–20). Hybrid Evolutionary Algorithms in Resource Leveling Optimization: Application in a Large Real Construction Project of a 50,000 DWT Ship. Proceedings of the ACM International Conference Proceeding Series, Thessaloniki, Greece.10.1145/2903220.2903227 ↗
54. Tzanetos, A., Kyriklidis, C., Papamichail, A., Dimoulakis, A., and Dounias, G. (2018, January 9–12). A Nature Inspired Metaheuristic for Optimal Leveling of Resources in Project Management. Proceedings of the ACM International Conference Proceeding Series, Patras, Greece.10.1145/3200947.3201014 ↗
55. Construction Resource Allocation and Leveling Using a Threshold Accepting–Based Hyperheuristic Algorithm10.1061/(ASCE)CO.1943-7862.0000492 ↗
56. An Ant Colony-Based Optimization Model for Resource-Leveling Problem10.1007/978-981-16-5839-6_29 ↗
57. Intelligent Planning of Road Pavement Rehabilitation Processes through Optimization Systems10.1016/j.treng.2021.100081 ↗
58. Predicting Construction Cost and Schedule Success Using Artificial Neural Networks Ensemble and Support Vector Machines Classification Models10.1016/j.ijproman.2011.09.002 ↗
59. Cash Flow Prediction for Construction Project Using a Novel Adaptive Time-Dependent Least Squares Support Vector Machine Inference Model
60. Evolutionary Fuzzy Decision Model for Cash Flow Prediction Using Time-Dependent Support Vector Machines10.1016/j.ijproman.2010.01.004 ↗
61. Symbiotic Organisms Search-Optimized Deep Learning Technique for Mapping Construction Cash Flow Considering Complexity of Project10.1016/j.chaos.2020.109869 ↗
62. Adaptation of Evolutionary Algorithms for Decision Making on Building Construction Engineering (TSP Problem)10.2478/eletel-2014-0015 ↗
63. Optimizing the Schedule of Dispatching Construction Machines through Artificial Intelligence10.1016/j.cej.2016.06.104 ↗
64. Exploration and Research on Project Engineering Management Mode Based on Bim10.1007/978-3-030-51556-0_27 ↗
65. Optimized Artificial Intelligence Models for Predicting Project Award Price10.1016/j.autcon.2015.02.006 ↗
66. A Support Vector Machine Method for Bid/No Bid Decision Making
67. An Intelligent Fuzzy-Based Hybrid Metaheuristic Algorithm for Analysis the Strength, Energy and Cost Optimization of Building Material in Construction Management10.1007/s00366-021-01420-9 ↗
68. Gerogiannis, V.C., Fitsilis, P., and Kameas, A.D. (2011). EANN/AIAI (2), Springer.
69. Proposal of a Framework and Integration of Artificial Intelligence to Succeed IT Project Planning10.30534/ijatcse/2019/114862019 ↗
70. Application of Machine Learning Technology to Analyze the Probability of Winning a Tender for a Project10.15514/ISPRAS-2020-32(2)-3 ↗
71. Marchinares, A.H., and Aguilar-Alonso, I. (2020, January 18–20). Project Portfolio Management Studies Based on Machine Learning and Critical Success Factors. Proceedings of the 2020 IEEE International Conference on Progress in Informatics and Computing, PIC 2020, Shanghai, China.10.1109/PIC50277.2020.9350787 ↗
72. Big Data Analytics in Agile Software Development: A Systematic Mapping Study10.1016/j.infsof.2020.106448 ↗
73. Dam, H.K., Tran, T., Grundy, J., Ghose, A., and Kamei, Y. (2019, January 25–31). Towards Effective AI-Powered Agile Project Management. Proceedings of the 2019 IEEE/ACM 41st International Conference on Software Engineering: New Ideas and Emerging Results, ICSE-NIER 2019, Montreal, QC, Canada.10.1109/ICSE-NIER.2019.00019 ↗
74. A Decision Support System for Contractor Prequalification for Surety Bonding10.1016/j.autcon.2011.05.017 ↗
75. Prequalification of Egyptian Construction Contractors Using Fuzzy-AHP Models10.1108/ECAM-09-2011-0088 ↗
76. Forecasting Contractor’s Deviation from the Client Objectives in Prequalification Model Using Support Vector Regression10.1016/j.ijproman.2012.11.002 ↗
77. Use of Chatbots in Project Management
78. Investigation of Forecasting Methods of the State of Complex IT-Projects with the Use of Deep Learning Neural Networks10.1007/978-3-030-26474-1_19 ↗
79. Kowalski, M., Zelewski, S., Bergenrodt, D., and Klüpfel, H. (2012, January 22–24). Application of New Techniques of Artificial Intelligence in Logistics: An Ontology-Driven Case-Based Reasoning Approach. Proceedings of the ESM, Essen, Germany.
80. Jallow, H., Renukappa, S., and Suresh, S. (2020, January 2–4). Knowledge Management and Artificial Intelligence (AI). Proceedings of the 21st European Conference on Knowledge Management, Online.
81. Flexible Management of Repetitive Construction Processes by an Intelligent Support System10.1016/j.eswa.2013.06.063 ↗
82. Mills, C., Escobar-Avila, J., and Haiduc, S. (2018, January 23–29). Automatic Traceability Maintenance via Machine Learning Classification. Proceedings of the 2018 IEEE International Conference on Software Maintenance and Evolution, ICSME 2018, Madrid, Spain.10.1109/ICSME.2018.00045 ↗
83. Francois, R., Nada, M., and Hassan, A. (2015, January 23–27). How to Extract Knowledge from Professional E-Mails. Proceedings of the 11th International Conference on Signal-Image Technology and Internet-Based Systems, SITIS 2015, Bangkok, Thailand.10.1109/SITIS.2015.113 ↗
84. Intelligent Purchasing: How Artificial Intelligence Can Redefine the Purchasing Function10.1016/j.jbusres.2020.11.050 ↗
85. Automated Compliance Checking of Construction Operation Plans Using a Deontology for the Construction Domain10.1061/(ASCE)CP.1943-5487.0000298 ↗
86. Semantic NLP-Based Information Extraction from Construction Regulatory Documents for Automated Compliance Checking10.1061/(ASCE)CP.1943-5487.0000346 ↗
87. Integrating Semantic NLP and Logic Reasoning into a Unified System for Fully-Automated Code Checking10.1016/j.autcon.2016.08.027 ↗
88. Kang, S., and Haas, C.T. (2018, January 20–25). Evaluating Artificial Intelligence Tools for Automated Practice Conformance Checking. Proceedings of the ISARC 2018—35th International Symposium on Automation and Robotics in Construction and International AEC/FM Hackathon: The Future of Building Things, Berlin, Germany.10.22260/ISARC2018/0015 ↗
89. Quality Insights: Artificial Neural Network and Taxonomical Analysis of Activity Networks in Quality Engineering10.1504/IJQET.2018.097334 ↗
90. Combining Techniques for Software Quality Classification: An Integrated Decision Network Approach10.1016/j.eswa.2010.09.136 ↗
91. Domain-Specific Hierarchical Text Classification for Supporting Automated Environmental Compliance Checking10.1061/(ASCE)CP.1943-5487.0000513 ↗
92. Dai, J., Wang, D., Yang, X., and Wei, X. (2016, January 23–25). Design and Implementation of a Group Decision Support System for University Innovation Projects Evaluation. Proceedings of the ICCSE 2016—11th International Conference on Computer Science and Education, Nagoya, Japan.10.1109/ICCSE.2016.7581571 ↗
93. A Fuzzy Decision Support System for Sustainable Construction Project Selection: An Integrated FPP-FIS Model10.3846/jcem.2020.12183 ↗
94. Building a Rough Sets-Based Prediction Model for Classifying Large-Scale Construction Projects Based on Sustainable Success Index10.1108/ECAM-05-2016-0110 ↗
95. The Application of Machine Learning to Consolidate Critical Success Factors of Lean Six Sigma10.1109/ACCESS.2021.3103931 ↗
96. Predicting the Success of Projects Using Evolutionary Hybrid Fuzzy Neural Network Method in Early Stages
97. A New Decision Support System at Estimation of Project Completion Time Considering the Combination of Artificial Intelligence Methods Based on Earn Value Management Framework
98. A Nearest Neighbour Extension to Project Duration Forecasting with Artificial Intelligence10.1016/j.ejor.2016.11.018 ↗
99. A Comparative Study of Artificial Intelligence Methods for Project Duration Forecasting10.1016/j.eswa.2015.10.008 ↗
100. Support Vector Machine Regression for Project Control Forecasting10.1016/j.autcon.2014.07.014 ↗