Practical Use of Monte Carlo Simulation for Risk Management within the International Construction Industry.

Summary

1 Introduction

1.1  Theoretical Model of Risk Management Circle

Within this theoretical model and risk management circle, risks can be seen as controllable and assessable. According to the author, Dr. Tilo Nemuth, “risk identification at an early stage and an integrated in-house risk management is therefore an indispensable requirement for a monetarily positive result of a project.” The author uses the following risk management circle as depicted in figure 1, for an overall guideline of a risk management system. “Risk” is also defined in this article as “Risk = probability of risk occuring  x  impact of risk occurring.” 

Figure 1: Stempowski’s Risk Management Circle.

1.2  Objectives for Risk Management of Project Cost 

a.     Project risks must be identified early on in the tender and acquisition phase.

b.     Monetary analysis of risk impacts must be conducted.

c.     Display of the impact of failures.

d.     Improved risk awareness.

e.     Filtering of high risk projects and implementation of knock-out-criteria for projects in the early stages of growth.

2 Implementation of Risk Assessment in Estimation Procedure and Tender Process

2.1 Two-stage system and comprehension of Monte Carlo Simulation

In this section, Dr. Nemuth claimed that project risks can be placed into categories for more of a organized process of evaluation. This section also introduces the implementation of a two-stage system meant for the “aggregation of project risks.” The first stage is an analysis of all risks and the second stage is a detailed evaluation of the critical risks found from that first analysis. Emphasis is then placed on a Monte Carlo Simulation due to its superiority when compared to other risk analysis methods and techniques.

With reference to the risk management circle presented earlier, the two-stage process is further explained by the following example illustrated in this article. 

Stage 1 = Phase 1 + 2 (identify and analyze the project risks)

Stage 2 = Phase 3 (evaluate the risks with MCS) and preparation for Phase 4 (monitoring)

The results of a Monte Carlo Simulation can be seen as a probability distribution. Below in figure 2 is a probability density, while figure 3 is an example of the results displayed in a cumulative ascending chart. 

Figure 2: Probability Density for Monte Carlo Simulation.

Figure 3: An example of the cumulative ascending chart.

 

3 Conclusion 

The purpose of this article was to illustrate that risks for projects are capable of being analyzed and evaluated. This simulation provides decision makers with a better scope of understanding regarding the risks that are present but also the results as well, whether positive or negative. The Monte Carlo Simulation allows for a more concentrated focus on the critical risks at play. Filtering high risks at an early stage can assist the decision maker with avoiding failure later on.

Source:

Nemuth, T. (2008). Practical Use of Monte Carlo Simulation for Risk Management within the International Construction Industry. International Probabilistic Workshop, 1-12.

What is Monte Carlo Simulation? – RiskAMP

Summary

Despite the multitude of studies on Monte Carlo simulation that are highly sophisticated in their nature, Risk AMP a company which specializes in designing and developing statistical and stochastic models over a variety of test platforms, devised a concise article outlining directly what Monte Carlo simulations contribute to analysts and decision makers. Namely, a “Monte Carlo simulation, or probability simulation, is a technique used to understand the impact of risk and uncertainty in financial, project management, cost, and other forecasting models.”

Monte Carlo simulations are designed to assess uncertainty within a specific problem to give early warning to decision makers. In doing so, quantitative models are created to project into the future to give the analyst and decision makers the “best” estimate of the expected values or outcomes. Although on the same side of the coin, estimates generated by Monte Carlo simulations contain, “inherent uncertainty and risk, because it’s an estimate of an unknown value.” RiskAMP assess Monte Carlo simulations to be different from standard forecasting models since these simulations provide ranges of outcomes which present upper bound and lower bound limits to future possible outcomes.

The Monte Carlo simulation runs a randomized set of tasks which calculates those independently to the further calculate hundreds or thousands of randomly-selected values in the model against each other. This process of calculating outcomes is done until the model has reached the primary results in the model.

The paper discusses a basic example through job tasks over a series of months. To do this the model generates three distinct estimates to evaluate the frequency of a particular job taking place and rates of those jobs as what the probability is that they will occur frequently in the same time interval. Further, the Monte Carlo simulation, “will randomly generate values for each of the task, then calculate the total time to completion.

To the aforementioned end, it is “extremely unlikely,” that the simulation will assess that the absolute outcomes will be observed in reality to at the minimum or maximum total values delineated in the model. For instance, it is unlike something will have 100% certainty or 0% certainty. However, risk is built into the model due to the overall ability of an outcome to take place over instances which are assessed by the model to have a low probability.

Finally, like all forecasting models, RiskAMP assesses that the Monte Carlo simulation is only as good as the “assumptions” the analysts develop and build into the simulation. Additionally, they make the distinction that, “it’s important to remember that the simulation only represents probabilities and not certainty.” Regardless, RiskAMP contributes that Monte Carlo simulations can be “valuable tool[s]” when forecasting into the unknown.

Critique

As stated at the outset, this article has an effective way of portraying the complex ideas of Monte Carlo simulations with concision and without getting down in the technical mathematical jargon. That said, the article hit on a touchstone of how Monte Carlo simulations could be effective for forecasting into the future with accuracy, but did not completely flesh out those concepts. In the end, however, this article is beneficial or anyone who is looking to understand at a basic level how Monte Carlo simulations can better help develop insights and inform decision making about the future.

Source

What is Monte Carlo Simulation?, RiskAMP.