Login / Register Submit Your Article
cover
Article Menu

Journal Browser
Vol. 6 (2024)
Vol. 5 (2023)
Vol. 4 (2022)
Vol. 3 (2021)
Vol. 2 (2020)
Vol. 1 (2019)
Open Access Journal Article

Predictive Models for Risk Assessment in Project Management

by Michael Taylor 1,*
1
Michael Taylor
*
Author to whom correspondence should be addressed.
MAD  2022 4(2):33; https://doi.org/10.69610/j.mad.20220930
Received: 29 July 2022 / Accepted: 20 August 2021 / Published Online: 30 September 2022
View Full-Text
Download PDF

Abstract

The field of project management has witnessed significant advancements in the development of predictive models for risk assessment. This paper delves into the significance and application of these models in enhancing the decision-making process within project environments. Predictive models for risk assessment in project management aim to forecast potential risks and their potential impact on project outcomes. By leveraging historical data, statistical techniques, and machine learning algorithms, these models are designed to provide insights into the likelihood and severity of various risks. The paper discusses the different types of predictive models, such as qualitative and quantitative approaches, and examines their advantages and limitations. Additionally, it highlights the integration of predictive models into project management frameworks and their role in proactive risk management. The study emphasizes the importance of selecting appropriate models based on project characteristics, risk context, and available data. Furthermore, the paper explores the challenges associated with the implementation of predictive models and suggests strategies for improving their accuracy and reliability. Overall, this paper provides a comprehensive overview of the current state of predictive models in risk assessment and their potential to optimize project management practices.

Keywords: predictive models; risk assessment; project management; decision-making; machine learning.;
Copyright: © 2022 by Taylor. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) (Creative Commons Attribution 4.0 International License). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Cite This Paper
APA Style
Taylor, M. (2022). Predictive Models for Risk Assessment in Project Management. Management Analytics and Decision, 4(2), 33. doi:10.69610/j.mad.20220930
ACS Style
Taylor, M. Predictive Models for Risk Assessment in Project Management. Management Analytics and Decision, 2022, 4, 33. doi:10.69610/j.mad.20220930
AMA Style
Taylor M. Predictive Models for Risk Assessment in Project Management. Management Analytics and Decision; 2022, 4(2):33. doi:10.69610/j.mad.20220930
Chicago/Turabian Style
Taylor, Michael 2022. "Predictive Models for Risk Assessment in Project Management" Management Analytics and Decision 4, no.2:33. doi:10.69610/j.mad.20220930

Share and Cite

ACS Style
Taylor, M. Predictive Models for Risk Assessment in Project Management. Management Analytics and Decision, 2022, 4, 33. doi:10.69610/j.mad.20220930
AMA Style
Taylor M. Predictive Models for Risk Assessment in Project Management. Management Analytics and Decision; 2022, 4(2):33. doi:10.69610/j.mad.20220930
Chicago/Turabian Style
Taylor, Michael 2022. "Predictive Models for Risk Assessment in Project Management" Management Analytics and Decision 4, no.2:33. doi:10.69610/j.mad.20220930
APA style
Taylor, M. (2022). Predictive Models for Risk Assessment in Project Management. Management Analytics and Decision, 4(2), 33. doi:10.69610/j.mad.20220930

Article Metrics

Article Access Statistics

References

  1. Burbules, N. C., & Callister, T. A. (2000). Watch IT: The Risks and Promises of Information Technologies for Education. Westview Press.
  2. Haslett, C. (1978). Risk, uncertainty, and decision-making in the design and operation of complex systems. In R. G. D'Amico, J. R. Birge, & J. L. Souder (Eds.), Risk Analysis in Engineering (pp. 37-56). Pergamon Press.
  3. Cleland, D. I., & Ireland, R. W. (2001). Project Management: Strategic Planning, Marketing, and Organizational Issues. McGraw-Hill.
  4. U.S. Department of Defense. (2001). DoD risk management framework. http://www.dtic.mil/doctrine/DoD_5015_01.html.
  5. Chakrabarti, G., & Chakrabarti, S. (2002). A fuzzy set-based approach to risk assessment. Journal of Loss Prevention in the Process Industries, 15(2), 93-101.
  6. Yager, R. R. (1999). Fuzzy logic, neural networks, and soft computing. IEEE Transactions on Neural Networks, 10(3), 679-691.
  7. Meersman, R., & Vanhoucke, M. (2003). A Monte Carlo simulation-based approach for risk assessment in project management. Information and Software Technology, 45(11), 739-745.
  8. Zeng, J., & Mao, Y. (2002). A Bayesian network model for risk assessment and decision-making. Expert Systems with Applications, 23(4), 317-325.
  9. Schwaber, K., & Sutherland, J. (2012). Scrum: The Art of Doing Twice the Work in Half the Time. Harvard Business Press.
  10. Skulmoski, G. J., Hartman, F. T., & Krahn, J. V. (2004). A meta-synthesis of risk-based software process improvement. IEEE Software, 21(6), 90-95.
  11. Shiva, V. R., & Mahapatra, M. K. (2001). A review of risk management literature: 1960-2000. Journal of Construction Engineering and Management, 127(4), 339-345.