scholarly journals Achieving ‘best value’ for the community by deployment of risk based cost estimation using Monte-Carlo Simulation to rate-payer-funded capital intensive road projects.

2014 ◽  
Vol 1 (1) ◽  
pp. 74
Author(s):  
Mahender Rao ◽  
Harshavardhan Vijay Ranade
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Cost Estimation ◽  
Best Value ◽  
Road Projects ◽  
Capital Intensive

scholarly journals Economics of alternative water resources with an emphasis on aquifer storage and recovery

2017 ◽  
Vol 18 (2) ◽  
pp. 612-621 ◽  
Author(s):  
Jae-ho Choi ◽  
Miroslaw Skibniewski ◽  
Young-Gyoo Shim
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Water Resources ◽  
Water Supply ◽  
Cost Estimation ◽  
Treatment Plant ◽  
Aquifer Storage And Recovery ◽  
Critical Question ◽  
Aquifer Storage ◽  
Alternative Water

Abstract This paper demonstrates a comprehensive methodology for assessing the comparison of unit water production cost (UWPC) between alternative water resources including desalination, freshwater reservoirs, single-purpose dams, underground dams and two indirect water in take technologies – riverbank filtration and aquifer storage and recovery (ASR). This study considers the Monte Carlo simulation as the only viable solution to tackle this critical question, which can be used to evaluate the economics of diverse water supply schemes incorporating those alternatives and prepare long-term water supply planning. Built upon actual and conceptual cost data for each alternative, total project cost and operation and management cost estimation models for each alternative were developed and used for generating mean UWPC information using the Monte Carlo simulation approach. The mean UWPC differences between alternative water supply schemes were found to be statistically significant and the simulation results revealed that ASR is the lowest-cost option to provide drinkable water for both cases when a conventional water treatment plant (WTP) and advanced WTP were used as a connected post-treatment process.

scholarly journals SUPPLY CHAIN RISK MANAGEMENT BY MONTE CARLO METHOD

2017 ◽  
Vol 7 (4) ◽  
pp. 20-23
Author(s):  
Tomasz Rymarczyk ◽  
Grzegorz Kłosowski
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Supply Chain ◽  
Cost Estimation ◽  
Historical Data ◽  
Supply Chain Risk Management ◽  
Supply Chain Risk ◽  
Chain Management

In this paper, the conceptual model of risk-based cost estimation for completing tasks within supply chain is presented. This model is a hybrid. Its main unit is based on Monte Carlo Simulation (MCS). Due to the fact that the important and difficult to evaluate input information is vector of risk-occur probabilities the use of artificial intelligence method was proposed. The model assumes the use of fuzzy logic or artificial neural networks – depending on the availability of historical data. The presented model could provide support to managers in making valuation decisions regarding various tasks in supply chain management.

scholarly journals Revolutionising cost structure for integrated project delivery: a BIM-based solution

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Faris Elghaish ◽  
Sepehr Abrishami ◽  
M. Reza Hosseini ◽  
Soliman Abu-Samra
Keyword(s):  
At Risk ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Cost Estimation ◽  
Real Life ◽  
Cost Structure ◽  
Project Delivery ◽  
Integrated Project Delivery ◽  
Content Type ◽  
Integrated Project

PurposeThe amalgamation of integrated project delivery (IPD) and building information modelling (BIM) is highly recommended for successful project delivery. However, IPD lacks an accurate cost estimation methodology at the “front-end” of projects, when little project information is available. This study aims to tackle this issue, through presenting analytical aspects, theoretical grounds and practical steps/procedures for integrating target value design (TVD), activity-based costing (ABC) and Monte Carlo simulation into the IPD cost structure, within a BIM-enabled platform.Design/methodology/approachA critical review was conducted to study the status of cost estimation within IPD, as well as exploring methods and tools that can enhance the cost estimation process for IPD. Thereafter, a framework is developed to present the proposed methodology of cost estimation for IPD throughout its entire stages. A case project is used to validate the practicality of the developed solution through comparing the profit-at-risk percentage for each party, using both traditional cost estimation and the proposed solution.FindingsAfter applying the proposed IPD's cost estimation framework, on a real-life case project, the findings demonstrated significant deviations in the profit-at-risk value for various work packages of the project (approximately 100% of the finishing package and 22% of openings package). By providing a precise allocation of overhead costs, the solution can be used in real-life projects to change the entire IPD cost structure and ensure a fair sharing of risk–rewards among the involved parties in IPD projects.Practical implicationsUsing the proposed methodology of cost estimation for IPD can enhance the relationship among IPD's core team members; all revealed financial deficiencies will be considered (i.e. compensation structure, profit pooling), hence enhancing the IPD performance.Originality/valueThis paper presents a comprehensive solution for integrating BIM and IPD in terms of cost estimation, offering three main contributions: (1) an innovate approach to utilise five-dimensional (5D) BIM capabilities with Monte Carlo simulation, hence providing reliable cost estimating during the conceptual TVD stage; (2) mathematical models that are developed through integrating ABC into the detailed 5D BIM to determine the three IPD's cost structure limbs; and (3) a novel mechanism of managing cost saving (rewards) through distinguishing between saved resources from organisation level, to daily task level, to increase trust among parties.

scholarly journals Analyzing Large Workers’ Compensation Claims Using Generalized Linear Models and Monte Carlo Simulation

Safety ◽  
2018 ◽  
Vol 4 (4) ◽  
pp. 57 ◽  
Author(s):  
Fatemeh Davoudi Kakhki ◽  
Steven Freeman ◽  
Gretchen Mosher
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Generalized Linear Models ◽  
Cost Estimation ◽  
Financial Risk ◽  
Linear Models ◽  
Simulation Models ◽  
Workers Compensation ◽  
The Usa ◽  
Compensation Claims

Insurance practitioners rely on statistical models to predict future claims in order to provide financial protection. Proper predictive statistical modeling is more challenging when analyzing claims with lower frequency, but high costs. The paper investigated the use of predictive generalized linear models (GLMs) to address this challenge. Workers’ compensation claims with costs equal to or more than US$100,000 were analyzed in agribusiness industries in the Midwest of the USA from 2008 to 2016. Predictive GLMs were built with gamma, Weibull, and lognormal distributions using the lasso penalization method. Monte Carlo simulation models were developed to check the performance of predictive models in cost estimation. The results show that the GLM with gamma distribution has the highest predictivity power (R2 = 0.79). Injury characteristics and worker’s occupation were predictive of large claims’ occurrence and costs. The conclusions of this study are useful in modifying and estimating insurance pricing within high-risk agribusiness industries. The approach of this study can be used as a framework to forecast workers’ compensation claims amounts with rare, high-cost events in other industries. This work is useful for insurance practitioners concerned with statistical and predictive modeling in financial risk analysis.

Cost estimation through Monte Carlo simulation in architectural early design stage

2020 ◽  
pp. 002072092092330
Author(s):  
Nan Wu ◽  
Shen-Guan Shih
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Cost Estimation ◽  
Architectural Design ◽  
Construction Cost ◽  
Design Stage ◽  
Design Decision ◽  
Affecting Factors ◽  
Early Design ◽  
Early Design Stage

Architectural design can be considered an information-adding process, and within this process, each design decision provides information that uncovers some uncertainty regarding what is to be constructed. In early design stages, cost estimation is indispensable for subsequent decision making, but it cannot be accurate owing to the uncertainty associated with decisions that have not been made. This study proposes a Monte Carlo simulation–based method for designers to estimate potential trends in the construction cost of future design developments. The simulation reveals the probability distribution of construction cost via massive sampling over acceptable ranges of cost-affecting factors that have not yet been uncovered in the current design stage. The simulation result can serve as a part of a dashboard that provides guidance to designers for more optimally controlling construction cost.

scholarly journals Using Risk Simulation to Reduce the Capital Cost Requirement for a Programme of Capital Projects

2017 ◽  
Vol 15 (1) ◽  
pp. 1-13
Author(s):  
Francois Joubert ◽  
Leon Pretorius
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Cost Estimation ◽  
Research Result ◽  
Simulation Software ◽  
Capital Projects ◽  
Risk Simulation ◽  
The Individual ◽  
The Relationship

This paper combines various concepts related to (i) project risk management, (ii) Monte Carlo simulation, (iii) project contingency cost estimation, and (iv) the relationship between project and programme risks, to illustrate that the contingency requirements are lower when simulating all the risks in the programme when comparing it with the individual project contingency requirement. A case study organisation provided 86 quantified risk registers related to port and rail capital projects. For each of these risk registers, the project contingency was estimated using a prescribed risk register template and Monte Carlo simulation software. The same 86 quantified risk registers were then used to simulate the programme contingency. The simulation results indicated that the programme contingency requirement was approximately 8% points lower than that of the sum of the individual projects. The first implication of this research result is that, should borrowed capital be used to fund the projects, the interest bill would be higher when calculating project contingency on a project-by-project basis. The second is that regularly appearing low probability, high impact risks, should be identified and these risks should be quantified not in the projects themselves, but in a centrally managed, programme cost contingency fund.

An Extended Monte Carlo Method on Simulating the Development Cost Uncertainties of Aircraft

2010 ◽  
Vol 118-120 ◽  
pp. 810-814 ◽  
Author(s):  
Zhao Dong Huang ◽  
Wen Bing Chang ◽  
Yi Yong Xiao ◽  
Rui Liu
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Risk Analysis ◽  
Cost Estimation ◽  
Deterministic Model ◽  
Extended Model ◽  
Development Cost ◽  
Building Model ◽  
The Cost ◽  
Cost Risk

Monte Carlo Simulation is a general method for evaluating a deterministic model by iteratively generating inputs so as to get the natural distribution of outputs, which has often been employed for risk analysis of development cost estimation under uncertain environment. However, the traditional way of implementing Monte Carlo Simulation on cost risk analysis is always based on deterministic Cost Estimation Relation (CER) model and does not take the uncertainty of history cost data used to build CER into account, which will considerably affect the cost risk analysis. In this paper, we extend Monte Carlo Simulation model to make its simulating process cover the stage of building model so that not only the inputs are iteratively generated but also the model is iteratively rebuilt. An example is carried out to compare the extended model to the traditional one on analyzing aircraft development cost risk, which shows that the risk distribution gotten by Extended Monte Carlo Simulation is considerably different to that gotten by traditional one.

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