Abstract
When introducing quantified risk assessments at a South African state-owned freight company, a skills gap was found during the identification and quantified risk analysis. To help with risk identification, a checklist of risks for railway construction projects was developed. The basis of this checklist was 38 individual railway construction project risk registers that were collated into a single risk register. After the risks had been cleaned up and classified, a Monte Carlo simulation using @Risk software was done that produced a ranked check list of risks. This list of risks is valuable because subject matter experts developed it, and can be used as a risk identification checklist by stakeholders in similar projects. The simulation results also showed that project scope is an influencing factor on the ranking of risks.
Highlights
This article presents, as a case study, a way in which quantified risk registers are used to identify and rank the risks in a portfolio of railway construction projects
The methodology followed to create the simulation results as part of the research process in this paper is described in terms of (i) the model used, (ii) the creation of a combined risk register (CRR), (iii) projects divided into various categories, (iv) the importance of cleaning up and consolidating risk names, (v) the creation of named ranges, (vi) the creation of reports, and (vii) error checking and model validation
The simulation results for the case study rail portfolios are presented in terms of (i) descriptive statistics, (ii) the risks causing uncertainty in the project portfolio, (iii) the effect of scope on the ranking or risks in a specific project category, and (iv) the application of these results in the project management of rail projects
Summary
This article presents, as a case study, a way in which quantified risk registers are used to identify and rank the risks in a portfolio of railway construction projects. It starts by presenting the context in which the case study risk registers were collected. It continues with a concise literature survey on risks that can be found on railway construction projects. The article continues with the simulation results, where a list of ranked risks found on rail construction projects is presented. The simulation results provide some evidence that project scope is a factor determining the ranking of risk in -scoped projects
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