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. OPSOMMINGTydens die bekendstelling van gekwantifiseerde risikowaardebepalings by 'n Suid-Afrikaanse vervoermaatskappy in staatsbesit, is daar 'n tekort aan vaardighede gevind tydens risikoidentifisering 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. INTRODUCTIONThis 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 model used during the simulation is then broadly discussed, referring in more detail to the use of MS Excel and to Monte Carlo simulation software such as @Risk. 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 similarly-scoped projects.

Assessment on the Current and Future Performance of Addis Ababa Light Rail Transit Service Using Mathematical Modeling

“…e previous study demonstrated how the queuing theory was applied to optimize a small businesses [9], healthcare [10], supermarket checkout [11], subway stations [12], ticket windows [13], multi-stage production lines [14], and utilization of concreting equipment in construction [15]. e optimization of call center staffing [16], transportation of sugar cane to mills [17], and ranked checklist of risks [18] all used the Monte Carlo simulation method. One of the many pieces of literature examined in this field is the application of queuing theory to automated teller machine (ATM) facilities using Monte Carlo Simulation [19].…”

Use of DELPHI-AHP Method to Identify annd Analyze Risks in Seaport Dry Port System

“…Based on the 204 selected articles, we identified all the risk factors found, and we tried to classify them into different categories of risk factors: 1/ risk factors that can be caused by human factors, such as the lack of professional skills (Hsu, 2015), unsuitable reaction to errors (Pallis, 2017) or not following some procedures (Klockner and Toft, 2015); 2/ risk factors that can cause negative influence on the environment (Langella et al, 2016); 3/ risk factors that can be classified in the frame of security, such as smuggling (Loh et al, 2017;Pallis, 2017), terrorism attacks (A John et al, 2014;John et al, 2016;Loh et al, 2017) or other crimes (Joubert and Pretorius, 2017); 4/ natural risk factors such as floods (Pallis, 2017;Gou and Lam, 2018), strong cross wind (Yan et al, 2018) or earthquakes (A John et al, 2014;John et al, 2016;Pallis, 2017); 5/ organizational factors caused by the lack of good organization procedures like berth congestion (John et al, 2016;A John et al, 2014;Zhao et al, 2017;Wang and Guo, 2018) and storage area congestion (A John et al, 2014) (John et al, 2016) (Loh et al, 2017) (A. Kadir et al, 2020Zhao et al, 2017); 6/ operational risk factors, which contain factors related to the operations of the seaport dry port system, such as failure of equipment (Klockner and Toft, 2015) or transportation good spillage (John et al, 2016); 7/ technical risk factors such as lack of equipment maintenance (John et al, 2014), and 8/ economic risk factors like commercial fraud (Tseng et al, 2015) oil price rise (Zhao et al, 2017) (Hsu, 2015), (Loh et al, 2017), (Bergheim et al, 2015), …”

“…(John et al, 2014), (John et al, 2016), (Pallis, 2017), (Pak et al, 2015), (Gou and Lam, 2018;McIntosh and Becker, 2020;Zhang and Lam, 2015), (Lam et al, 2017), (Chang et al, 2020;Zhu et al, 2020), (Yan et al, 2018), (Klockner and Toft, 2015), (Misnevs et al, 2015), (Wang et al, 2017), (Lagadec et al, 2018), (Binti Sa'adin et al, 2016), (Dindar et al, 2017), (Sanchis et al, 2020 ;Zaili Yang et al, 2018), (Bubeck et al, 2019), (Macciotta et al, 2017;Wang et al, 2020), (Alyami et al, 2019). (John et al, 2014), (John et al, 2016), (Loh et al, 2017), (Kadir et al, 2020;Zhao et al, 2017), (Pallis, 2017), (Zhisen Yang et al, 2018), (Hsu, 2015), (Morgan et al, 2016), (Filtness and Naweed, 2017), (Gong and Liu, 2020), (Wang and Guo, 2018), (Başhan et al, 2020), (Klockner and Toft, 2015), (Tsao et al, 2017), (Joubert and Pretorius, 2017), (Alyami et al, 2019), (Gong and Liu, 2020), (Sunaryo and Hamka, 2017), (Bentaleb et al, 2015b (John et...…”

Monte Carlo Simulation in a Peruvian Highway

“…Also, another study employed a portfolio of railway construction projects to estimate their risk by Monte Carlo simulation. They encountered that equipment availability and placement issues have been the riskiest factors in all the project stages [19].…”