New Approaches to Project Risk Assessment Utilizing the Monte Carlo Method

Seňová, Andrea; Tobisová, Alica; Rozenberg, Róbert

doi:10.3390/su15021006

Cited by 12 publications

(5 citation statements)

References 49 publications

(60 reference statements)

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“…The same judgment-based and distribution-based grouping exists in conventional and non-AI-based RM methods, classifying them into deterministic and stochastic (probabilistic) models [36]. Deterministic models, such as the Probability-Impact matrix [37] or Pareto analysis [38], predict a fixed value and mostly follow a frequentist statistic.…”

Section: Figure 2 Ai-based Risk Management Frameworkmentioning

confidence: 99%

“…On the other hand, the stochastic models represent the random behavior of risk factors through various types of distributions that emerge from data (frequentist) or expert opinion (Bayesian) and provide a probability distribution of each outcome. For instance, the Monte Carlo method runs multiple simulations on the model to reach a frequentist distribution of possible outcomes with an objective and data-based judgment [36], or Program Evaluation Review Technique (PERT) is a probabilistic method based on the assumption that the duration of a single activity can be described by a probability density function [39]. However, a main difference between these methods and AI-based algorithms is that they predict outcomes based on some rules, distributions, and formulas set by the model, whereas AI algorithms learn these rules by observing many samples of input and output data and detecting the patterns between them.…”

Section: Figure 2 Ai-based Risk Management Frameworkmentioning

confidence: 99%

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Deterministic and Probabilistic Risk Management Approaches in Construction Projects: A Systematic Literature Review and Comparative Analysis

2023

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Risks and uncertainties are inevitable in construction projects and can drastically change the expected outcome, negatively impacting the project’s success. However, risk management (RM) is still conducted in a manual, largely ineffective, and experience-based fashion, hindering automation and knowledge transfer in projects. The construction industry is benefitting from the recent Industry 4.0 revolution and the advancements in data science branches, such as artificial intelligence (AI), for the digitalization and optimization of processes. Data-driven methods, e.g., AI and machine learning algorithms, Bayesian inference, and fuzzy logic, are being widely explored as possible solutions to RM domain shortcomings. These methods use deterministic or probabilistic risk reasoning approaches, the first of which proposes a fixed predicted value, and the latter embraces the notion of uncertainty, causal dependencies, and inferences between variables affecting projects’ risk in the predicted value. This research used a systematic literature review method with the objective of investigating and comparatively analyzing the main deterministic and probabilistic methods applied to construction RM in respect of scope, primary applications, advantages, disadvantages, limitations, and proven accuracy. The findings established recommendations for optimum AI-based frameworks for different management levels—enterprise, project, and operational—for large or small data sets.

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Section: Figure 2 Ai-based Risk Management Frameworkmentioning

confidence: 99%

Section: Figure 2 Ai-based Risk Management Frameworkmentioning

confidence: 99%

Deterministic and Probabilistic Risk Management Approaches in Construction Projects: A Systematic Literature Review and Comparative Analysis

2023

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“…The purpose of sensitivity analysis is to assess the effects of factors such as production volume, selling prices, costs, investment expenses, and interest rates on economic indicators like net present value, profit, and return on investment. The process involves analyzing the correlation between variations in these factors and their impact on the selected economic parameters of the project [15].…”

Section: Payback Periodmentioning

confidence: 99%

A Financial Feasibility Study to Determine the Best Funding Structure for a Total Renovation Project of the Karebosi Field in Makassar

Setyandri,

Aswin Rahadi

2023

ijcsrr

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The objective of this study is to determine the best debt-equity combination to fund the Public-Private Partnership (PPP) project applying the Build-Operate-Transfer (B-O-T) scheme for the total renovation of Karebosi Field in Makassar City, Indonesia. To assess the feasibility of the project, the financial feasibility study methodology is conducted. The study covers an analysis of both external and internal analysis. The external analysis covers the analysis of macroeconomic factors and microeconomic factors utilizing the PESTEL and Porter’s Five Forces framework. The internal analysis is focused on the resources of the project. The SWOT analysis presents the outcomes of both external and internal factors. In addition, the author presents three funding structure scenarios as potential alternatives to fund the project. The funding scenarios consist of three alternatives: scenario 1 of full equity funding, scenario 2 of hybrid funding with a proportion of 50% debt and 50% equity, lastly, scenario 3 of hybrid funding with a proportion of 70% debt and 30% equity. Along with that, the author also considers the operational occupancy scenarios that include worst-case, base-case, and best-case scenarios. The percentages of each of the three scenarios are 40%, 60%, and 80%. Following that, the financial projections are calculated for each scenario, resulting in investment measurements such as Net Present Value (NPV), Internal Rate of Return (IRR), and Payback Period. Finally, a risk analysis is performed to assess the sensitivity of the best funding structure and to identify potential risks. Additionally, the evaluation of risk mitigation is added to enhance the overall effectiveness of the best funding structure. The findings indicate that the project is feasible, and scenario 3 of hybrid funding with the combination of 70% debt and 30% equity is the best funding structure for the project. In addition, the author performs a sensitivity analysis on the best funding scenario, considering ten input variables. Furthermore, the sensitivity analysis indicates that five variables, namely WACC, assumption of operational occupancy per day, interest rate, soccer field rental rate, and the ratio of operating expenses to revenue, significantly impact the net present value (NPV). Strategies to reduce potential risks are effective marketing and operations, managing occupancy rate volatility using backup strategies, developing new revenue streams, and negotiating long-term rate fixes with lenders. Therefore, future research can explore the dynamic nature of the external factors that influence the Internal Rate of Return (IRR), Net Present Value (NPV), Payback Period, and Weighted Average Cost of Capital (WACC). Determining the best funding may require ongoing research to assess the effects of market conditions, interest rate fluctuations, and industry-specific factors. Furthermore, explore how input variables like WACC, operational occupancy per day, interest rate, soccer field rental rate, and operating expense ratio to revenue affect NPV.

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“…A recent study by Senova et al (2023) proposed a novel approach for assessing the risk of a project using the Monte Carlo method and the Crystal Ball software tool [29]. The study mentioned the challenges faced by companies in making effective investment decisions under market turbulence and increasing inflation, which can negatively affect managerial decisions.…”

Section: The Concept Of Optimizing Capital Structure In the Literatur...mentioning

confidence: 99%

Modelling Optimal Capital Structure in Gas and Oil Sector by Applying Simulation Theory and Programming Language of Python (Qatar Gas Transport Company)

Kulikov,

Alabed Alkader,

Panaedova

et al. 2023

Energies

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The goal of this study is to optimize a company’s capital structure by modelling and programming the required equations in the components of weighted average cost of capital. This process will be carried out in the programming language Python, and then Monte Carlo simulation will be applied to increase the number of combinations of debt and equity in the capital structure and obtain more accurate results on the Qatar gas transport company. Throughout various scenarios and outcomes, the study seeks to determine the ideal capital structure that reduces both the firm’s weighted average cost of capital and the risk to which the Qatar gas transport company will be exposed. The suggested method offers a practical instrument for managers and investors to make well-informed decisions when they apply financial strategies to optimize the company’s financial performance. Debt and equity are considered the main parts in the capital structure in every company, and if we can solve the problems connected to these two parts, we can optimize performance. The code of the programming language (Python) used in this study depends on logical discussion to compare different results. This research also will pay attention to the whole probabilities of debt and equity ratios in the Qatar Gas Transport Company. This research considers more variables in the analysis to obtain more accurate results about the firm’s financial status. The results in this research show that the cost of capital in the Qatar Gas Transport Company will be reduced to 5% (currently 6%) if the percentage of debt and equity in the capital structure is changed to 79% and 21%, respectively. In comparison with the other 9999 components of capital structure, this ratio (79%, 21%) will be the optimal one. The scatterplot reflects that the more repeated values of weighted average cost of capital were located around the value of 5%. This fact can enable us to conclude that the minimum value of WACC is 5%, even though there are lower values of WACC than 5%, but at a lower frequency than that of values of 5%. The study’s results are anticipated to introduce additional findings to help other authors to understand the mechanism of optimizing the capital structure and reduce the risk in the gas and oil sector.

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New Approaches to Project Risk Assessment Utilizing the Monte Carlo Method

Cited by 12 publications

References 49 publications

Deterministic and Probabilistic Risk Management Approaches in Construction Projects: A Systematic Literature Review and Comparative Analysis

Deterministic and Probabilistic Risk Management Approaches in Construction Projects: A Systematic Literature Review and Comparative Analysis

A Financial Feasibility Study to Determine the Best Funding Structure for a Total Renovation Project of the Karebosi Field in Makassar

Modelling Optimal Capital Structure in Gas and Oil Sector by Applying Simulation Theory and Programming Language of Python (Qatar Gas Transport Company)

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