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Multi-factor association dependence modelling for project risk analysis.

Byung-Cheol Kim1

  • 1Project and Supply Chain Management, Black School of Business, Penn State Erie, the Behrend College, 5101 Jordan Road, Erie, PA, 16563-1400 USA.

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|August 25, 2021
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Summary

This study introduces the Multi-Factor Association Model (MFAM), an analytic tool for quantitative project risk analysis. The MFAM offers efficient and scalable risk assessment without complex simulations, providing reliable estimates comparable to traditional methods.

Keywords:
Multi-factor dependence modelProject risk analysisSecond-moment approachStructured association

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Area of Science:

  • Project Management
  • Quantitative Risk Analysis
  • Operations Research

Background:

  • Quantitative project risk analysis often relies on simulation methods, which can be computationally intensive.
  • Existing models may not fully capture the complex interdependencies between multiple risk factors in projects.
  • There is a need for efficient, scalable, and mathematically consistent methods for project risk assessment.

Purpose of the Study:

  • To present an analytic (non-simulation) dependence model for quantitative project risk analysis.
  • To introduce the Multi-Factor Association Model (MFAM) and demonstrate its application and efficiency.
  • To highlight the MFAM's potential as a robust and scalable quantitative risk analysis tool.

Main Methods:

  • Developed the Multi-Factor Association Model (MFAM) to account for multiple association factors in project settings.
  • Established a hierarchical tree of association factors from standardized project plans (WBS, resource allocation, risk register).
  • Encoded the hierarchical structure into an analytic model providing a closed-form solution for a correlation matrix.

Main Results:

  • The MFAM was programmed in Microsoft Excel using basic cell functions for accessibility.
  • Demonstrated computational efficiency using two alternative schedules with distinct resource utilizations.
  • MFAM provides reliable risk estimates comparable to Monte Carlo simulation under mild assumptions.

Conclusions:

  • The MFAM offers enhanced robustness in dealing with multiple risk factors in projects.
  • The non-simulation, second-moment approach ensures computational efficiency for large-scale problems.
  • The MFAM is scalable to high-dimensional risk problems with an affordable computational burden, offering flexibility.