Using Monte Carlo simulation to create a ranked check list of risks in a portfolio of railway construction projects

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.

Tydens die bekendstelling van gekwantifiseerde risikowaardebepalings by ’n Suid-Afrikaanse vervoermaatskappy in staatsbesit, is daar ’n tekort aan vaardighede gevind tydens risiko-identifisering en ‘n gekwantifiseerde risiko-analise. Om hierdie probleem te beredder, is ’n lys van spoorbaankonstruksie risikos ontwikkel. Die basis van hierdie lys was 38 individuele spoorbaankonstruksie projek risiko registers wat opgeneem is in ’n enkele risiko register. Na afloop hiervan is die risiko register skoongemaak, geklassifiseer, en ’n Monte Carlo-simulasie met behulp @Risk sagteware gedoen wat ’n ranglys van risikos geproduseer het. Hierdie ranglys wys dat die projek omvang ’n invloed het op die posisie van risikos in die ranglys.