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Article
Publication date: 8 February 2013

Ofir Ben‐Assuli and Moshe Leshno

Although very significant and applicable, there have been no formal justifications for the use of MonteCarlo models and Markov chains in evaluating hospital admission…

Abstract

Purpose

Although very significant and applicable, there have been no formal justifications for the use of MonteCarlo models and Markov chains in evaluating hospital admission decisions or concrete data supporting their use. For these reasons, this research was designed to provide a deeper understanding of these models. The purpose of this paper is to examine the usefulness of a computerized MonteCarlo simulation of admission decisions under the constraints of emergency departments.

Design/methodology/approach

The authors construct a simple decision tree using the expected utility method to represent the complex admission decision process terms of quality adjusted life years (QALY) then show the advantages of using a MonteCarlo simulation in evaluating admission decisions in a cohort simulation, using a decision tree and a Markov chain.

Findings

After showing that the MonteCarlo simulation outperforms an expected utility method without a simulation, the authors develop a decision tree with such a model. real cohort simulation data are used to demonstrate that the integration of a MonteCarlo simulation shows which patients should be admitted.

Research limitations/implications

This paper may encourage researchers to use MonteCarlo simulation in evaluating admission decision implications. The authors also propose applying the model when using a computer simulation that deals with various CVD symptoms in clinical cohorts.

Originality/value

Aside from demonstrating the value of a MonteCarlo simulation as a powerful analysis tool, the paper's findings may prompt researchers to conduct a decision analysis with a MonteCarlo simulation in the healthcare environment.

Details

Journal of Enterprise Information Management, vol. 26 no. 1/2
Type: Research Article
ISSN: 1741-0398

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Article
Publication date: 2 March 2012

Pavlos Loizou and Nick French

The purpose of this paper is to deal with the appropriateness of using the Monte Carlo simulation as a technique to calculate risk in real estate development.

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4392

Abstract

Purpose

The purpose of this paper is to deal with the appropriateness of using the Monte Carlo simulation as a technique to calculate risk in real estate development.

Design/methodology/approach

The paper is divided into two interlinked segments. The first segment examines the general definition of risk and Monte Carlo simulation methodology as a tool to estimate risk. The second outlines the appropriateness of using Monte Carlo as a tool to model real estate development, given the lack of data quality and its inability to account for human relationships in the development process.

Findings

It is important that the Monte Carlo Simulation model is used as prescriptive model that builds on the original elicitation procedures; produces initial results; allows for detailed sensitivity analysis and then remodels as required. In short, to be fully effective, the Monte Carlo Simulation model needs to be used in a complementary fashion with an understanding of human judgement and decision making.

Research limitations/implications

A fuller analysis may include an examination of the uncertainties in each of the components of the appraisal and account for the appropriate distributions of each of these variables. This is generally referred to as a Monte Carlo simulation. The argument in favour of a Monte Carlo simulation is that it helps the appraiser have a better understanding of the possible outcomes for the development and the relative impact of each input in the pricing of the project.

Practical implications

A lot of work has been done looking at scenario modelling with probabilities and the results that ensue. However, it is important that these quantitative results are placed in the context of the heuristic and cognitive approaches adopted by the decision maker. In other words, the behaviour of the decision maker is as influential in the interpretation of the results as the numbers themselves. This paper looks at the advantages and disadvantages of using Monte Carlo simulation in this context.

Originality/value

This study contributes significantly to the practical application of probability‐based models to development appraisal. The findings of the study are useful for users of feasibility studies to understand the context in which a development feasibility is carried out, and for appraisers to extend the scope of their analysis when carrying them out.

Details

Journal of Property Investment & Finance, vol. 30 no. 2
Type: Research Article
ISSN: 1463-578X

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Article
Publication date: 3 August 2012

Anand Prakash, Sanjay Kumar Jha and Rajendra Prasad Mohanty

The purpose of this paper is to propose the idea of linking the use of the Monte Carlo simulation with scenario planning to assist strategy makers in formulating strategy…

Abstract

Purpose

The purpose of this paper is to propose the idea of linking the use of the Monte Carlo simulation with scenario planning to assist strategy makers in formulating strategy in the face of uncertainty relating to service quality gaps for life insurance business, where discontinuities always remain for need‐based selling.

Design/methodology/approach

The paper reviews briefly some applications of scenario planning. Scenario planning emphasizes the development of a strategic plan that is robust across different scenarios. The paper provides considerable evidence to suggest a new strategic approach using Monte Carlo simulation for making scenario planning.

Findings

The paper highlights which particular service quality gap attribute as risk impacts most and least for the possibility of occurrences as best case, worst case, and most likely case.

Research limitations/implications

This study suffers from methodological limitations associated with convenience sampling and anonymous survey‐based research.

Practical implications

The approach using Monte Carlo simulation increases the credibility of the scenario to an acceptable level, so that it will be used by managers and other decision makers.

Social implications

The paper provides a thorough documentation on scenario planning upon studying the impact of risk and uncertainty in service quality gap for making rational decisions in management of services such that managers make better justification and communication for their arguments.

Originality/value

The paper offers empirical understanding of the application of Monte Carlo simulation to scenario planning and identifies key drivers which impact most and least on service quality gap.

Details

Journal of Strategy and Management, vol. 5 no. 3
Type: Research Article
ISSN: 1755-425X

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Article
Publication date: 25 January 2011

A.J. Thomas, J. Chard, E. John, A. Davies and M. Francis

The purpose of this paper is to propose a bearing replacement strategy which employs the Monte Carlo simulation method. In this contribution the method is used to estimate…

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1063

Abstract

Purpose

The purpose of this paper is to propose a bearing replacement strategy which employs the Monte Carlo simulation method. In this contribution the method is used to estimate the economic impact on the selection of a particular bearing change strategy. The simulation demonstrates that it is possible to identify the most cost‐effective approach and thus suggests a suitable bearing replacement policy, which in turn allows engineers to develop the appropriate maintenance schedules for their company.

Design/methodology/approach

The paper develops the Monte Carlo method through a case study approach. Three case studies are presented. The first study is detailed and chronicles the design, development and implementation of the Monte Carlo method as a means of defining a bearing replacement strategy within a subject company. The second and third cases outline the application of the Monte Carlo method in two different environments. These applications made it possible to obtain proof of concept and also to further prove the effectiveness of the Monte Carlo simulation approach.

Findings

An effective development of the Monte Carlo approach is proposed and the effectiveness of the method is subsequently evaluated, highlighting the benefits to the host organization and how the approach led to significant improvement in machinery reliability through a bearing replacement strategy.

Practical implications

The design, development and implementation of a bearing replacement strategy provide a simple yet effective approach to achieving significant improvements in system reliability and performance through less downtime and greater cost savings. The paper offers practising maintenance managers and engineers a strategic framework for increasing productive efficiency and output.

Originality/value

The proposed bearing replacement strategy contributes to the existing knowledge base on maintenance systems and subsequently disseminates this information in order to provide impetus, guidance and support towards increasing the development companies in an attempt to move the UK manufacturing sector towards world‐class manufacturing performance.

Details

International Journal of Quality & Reliability Management, vol. 28 no. 2
Type: Research Article
ISSN: 0265-671X

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Article
Publication date: 21 May 2020

Osman Hürol Türkakın, Ekrem Manisalı and David Arditi

In smaller projects with limited resources, schedule updates are often not performed. In these situations, traditional delay analysis methods cannot be used as they all…

Abstract

Purpose

In smaller projects with limited resources, schedule updates are often not performed. In these situations, traditional delay analysis methods cannot be used as they all require updated schedules. The objective of this study is to develop a model that performs delay analysis by using only an as-planned schedule and the expense records kept on site.

Design/methodology/approach

This study starts out by developing an approach that estimates activity duration ranges in a network schedule by using as-planned and as-built s-curves. Monte Carlo simulation is performed to generate candidate as-built schedules using these activity duration ranges. If necessary, the duration ranges are refined by a follow-up procedure that systematically relaxes the ranges and develops new as-built schedules. The candidate schedule that has the closest s-curve to the actual s-curve is considered to be the most realistic as-built schedule. Finally, the as-planned vs. as-built delay analysis method is performed to determine which activity(ies) caused project delay. This process is automated using Matlab. A test case is used to demonstrate that the proposed automated method can work well.

Findings

The automated process developed in this study has the capability to develop activity duration ranges, perform Monte Carlo simulation, generate a large number of candidate as-built schedules, build s-curves for each of the candidate schedules and identify the most realistic one that has an s-curve that is closest to the actual as-built s-curve. The test case confirmed that the proposed automated system works well as it resulted in an as-built schedule that has an s-curve that is identical to the actual as-built s-curve. To develop an as-built schedule using this method is a reasonable way to make a case in or out of a court of law.

Research limitations/implications

Practitioners specifying activity ranges to perform Monte Carlo simulation can be characterized as subjective and perhaps arbitrary. To minimize the effects of this limitation, this study proposes a method that determines duration ranges by comparing as-built and as-planned cash-flows, and then by systematically modifying the search space. Another limitation is the assumption that the precedence logic in the as-planned network remains the same throughout construction. Since updated schedules are not available in the scenario considered in this study, and since in small projects the logic relationships are fairly stable over the short project duration, the assumption of a stable logic throughout construction may be reasonable, but this issue needs to be explored further in future research.

Practical implications

Delays are common in construction projects regardless of the size of the project. The critical path method (CPM) schedules of many smaller projects, especially in developing countries, are not updated during construction. In case updated schedules are not available, the method presented in this paper represents an automated, practical and easy-to-use tool that allows parties to a contract to perform delay analysis with only an as-planned schedule and the expense logs kept on site.

Originality/value

Since an as-built schedule cannot be built without updated schedules, and since the absence of an as-built schedule precludes the use of any delay analysis method that is acceptable in courts of law, using the method presented in this paper may very well be the only solution to the problem.

Details

Engineering, Construction and Architectural Management, vol. 27 no. 10
Type: Research Article
ISSN: 0969-9988

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Article
Publication date: 21 March 2019

Mark Taylor, Vince Kwasnica, Denis Reilly and Somasundaram Ravindran

The purpose of this paper is to use the game theory combined with Monte Carlo simulation modelling to support the analysis of different retail marketing strategies, in…

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1026

Abstract

Purpose

The purpose of this paper is to use the game theory combined with Monte Carlo simulation modelling to support the analysis of different retail marketing strategies, in particular, using payoff matrices for modelling the likely outcomes from different retail marketing strategies.

Design/methodology/approach

Theoretical research was utilised to develop a practical approach for applying game theory to retail marketing strategies via payoff matrices combined with Monte Carlo simulation modelling.

Findings

Game theory combined with Monte Carlo simulation modelling can provide a formal approach to understanding consumer decision making in a retail environment, which can support the development of retail marketing strategies.

Research limitations/implications

Game theory combined with Monte Carlo simulation modelling can support the modelling of the interaction between retail marketing actions and consumer responses in a practical formal probabilistic manner, which can inform marketing strategies used by retail companies in a practical manner.

Practical implications

Game theory combined with Monte Carlo simulation modelling can provide a formalised mechanism for examining how consumers may respond to different retail marketing strategies.

Originality/value

The originality of this research is the practical application of game theory to retail marketing, in particular the use of payoff matrices combined with Monte Carlo simulation modelling to examine likely consumer behaviour in response to different retail marketing approaches.

Details

Marketing Intelligence & Planning, vol. 37 no. 5
Type: Research Article
ISSN: 0263-4503

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Article
Publication date: 5 June 2007

Adolfo Crespo Marquez and Benoît Iung

This paper proposes a method to model and assess the availability and reliability of a system when numerous factors such as system complexity, wide range of failure modes…

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1661

Abstract

Purpose

This paper proposes a method to model and assess the availability and reliability of a system when numerous factors such as system complexity, wide range of failure modes, environment, and sustainability may influence system behaviour.

Design/methodology/approach

The approach for reliability/availability study is using continuous time stochastic simulation (Monte Carlo simulation) and is based on seven steps for covering logical phases from system description to simulation result discussion. The feasibility and benefits of this approach are shown in a case study on cogeneration plant.

Findings

Owing to the factors influencing the system behaviour, the opportunity to carry out system availability/reliability assessment through analytical models will be many times very restrictive. Thus a general approach to this problem is proposed based on Monte Carlo (stochastic) simulation. The simulation of the system's life process will be carried out in the computer, and estimates will be made for the desired measures of performance. The simulation will then be treated as a series of real experiments, and statistical inference will then be used to estimate confidence intervals for the performance metrics.

Practical implications

Individuals, companies as well as society in general are becoming more and more dependent on increasingly complex technical systems. Moreover, failure of these complex systems often causes a major loss of service with potentially serious consequences (i.e. critical risk). Thus their dependability with its facets such as reliability, availability, safety has become an important issue. For example, the ability of reliability/availability assessment of such systems is invaluable in industrial domains. Indeed reliability/availability assessment is used for various purposes such as maintenance strategy selection, maintenance planning, production planning, risk and cost evaluations. To face with this complexity, the existing analytical models are not well adapted to carry out system modelling and assessment due mainly to assumptions that are difficult to validate. This paper looks into this issue by proposing a generic approach based on Monte Carlo (stochastic) simulation.

Originality/value

The Monte Carlo simulation method allows one to consider various relevant aspects of systems operation that cannot be easily captured by analytical models. The utilisation of this method is growing for the assessment of overall plants availability and the monetary value of plant operation.

Details

Journal of Quality in Maintenance Engineering, vol. 13 no. 2
Type: Research Article
ISSN: 1355-2511

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Book part
Publication date: 19 December 2012

Lee C. Adkins and Mary N. Gade

Monte Carlo simulations are a very powerful way to demonstrate the basic sampling properties of various statistics in econometrics. The commercial software package Stata…

Abstract

Monte Carlo simulations are a very powerful way to demonstrate the basic sampling properties of various statistics in econometrics. The commercial software package Stata makes these methods accessible to a wide audience of students and practitioners. The purpose of this chapter is to present a self-contained primer for conducting Monte Carlo exercises as part of an introductory econometrics course. More experienced econometricians that are new to Stata may find this useful as well. Many examples are given that can be used as templates for various exercises. Examples include linear regression, confidence intervals, the size and power of t-tests, lagged dependent variable models, heteroskedastic and autocorrelated regression models, instrumental variables estimators, binary choice, censored regression, and nonlinear regression models. Stata do-files for all examples are available from the authors' website http://learneconometrics.com/pdf/MCstata/.

Details

30th Anniversary Edition
Type: Book
ISBN: 978-1-78190-309-4

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Article
Publication date: 5 July 2013

Charles‐Olivier Amédée‐Manesme, Fabrice Barthélémy, Michel Baroni and Etienne Dupuy

This paper aims to show that the accuracy of real estate portfolio valuations and of real estate risk management can be improved through the simultaneous use of Monte Carlo

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1152

Abstract

Purpose

This paper aims to show that the accuracy of real estate portfolio valuations and of real estate risk management can be improved through the simultaneous use of Monte Carlo simulations and options theory.

Design/methodology/approach

The authors' method considers the options embedded in Continental European lease contracts drawn up with tenants who may move before the end of the contract. The authors combine Monte Carlo simulations for both market prices and rental values with an optional model that takes into account a rational tenant's behaviour. They analyze how the options significantly affect the owner's income.

Findings

The authors' main findings are that simulated cash flows which take account of such options are more reliable that those usually computed by the traditional method of discounted cash flow.

Research limitations/implications

Some limitations are inherent to the authors' model: these include the assumption of the rationality of tenant's decisions and the difficulty of calibrating the model given the lack of data in many markets.

Originality/value

The main contribution of the paper is both by accounting for market risk (Monte Carlo simulations for the prices and market rental values) and for accounting for the idiosyncratic risk (the leasing risk).

Details

Journal of Property Investment & Finance, vol. 31 no. 4
Type: Research Article
ISSN: 1463-578X

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Book part
Publication date: 13 December 2013

Todd E. Clark and Michael W. McCracken

This article surveys recent developments in the evaluation of point and density forecasts in the context of forecasts made by vector autoregressions. Specific emphasis is…

Abstract

This article surveys recent developments in the evaluation of point and density forecasts in the context of forecasts made by vector autoregressions. Specific emphasis is placed on highlighting those parts of the existing literature that are applicable to direct multistep forecasts and those parts that are applicable to iterated multistep forecasts. This literature includes advancements in the evaluation of forecasts in population (based on true, unknown model coefficients) and the evaluation of forecasts in the finite sample (based on estimated model coefficients). The article then examines in Monte Carlo experiments the finite-sample properties of some tests of equal forecast accuracy, focusing on the comparison of VAR forecasts to AR forecasts. These experiments show the tests to behave as should be expected given the theory. For example, using critical values obtained by bootstrap methods, tests of equal accuracy in population have empirical size about equal to nominal size.

Details

VAR Models in Macroeconomics – New Developments and Applications: Essays in Honor of Christopher A. Sims
Type: Book
ISBN: 978-1-78190-752-8

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