Level: Advanced
TCM Section(s):
7.6. Risk Management
4.1. Project Implementation
Venue: 2022 AACE International Conference & Expo

Abstract: Affordable, powerful computing and the widespread availability of quantitative risk analysis (QRA) software has helped democratize data analytics in project delivery. This, in itself, is great for the project controls profession and industry at large. However, problems arise when:

1. project simulations are created without a thorough appreciation of the need for ongoing or continuing project risk management (CPRM),
2. risk analysts embark on risk computing before establishing the foundation in a competent SRA-ready schedule,
3. identities and parameters about risks are collected within an environment where individual SMEs cannot or will not provide their candid opinions, or more simply,
4. risk terminology or processes are invoked that generate bias or noise, undermining risk data quality, potentially, creating more harm than good.

This paper will describe the means for ensuring a mathematically unambiguous and traceable rationale for establishing contingency, with relevance that extends into and throughout project delivery, providing protection against known risk and resilience in the face of emerging risk. This approach is underpinned by integrated cost and schedule risk analysis as the primary method of modeling before project approval and, conducted in such a way that minimizes motivated reasoning and establishes a data feedback loop to support:

1. quantitative risk management (QRM),
2. increasingly predictable projects outcomes and, ultimately,
3. a risk-based competitive advantage.