scholarly journals Synergies between an Observed Port and a Logistic Company: Application of the Discounted Cash–Flow Model and the Monte Carlo Simulation

2017 ◽  
Vol 8 (1) ◽  
pp. 1-18 ◽  
Author(s):  
Dejan Dragan ◽  
Bojan Rosi ◽  
Toni Avžner
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Cash Flow ◽  
Flow Model ◽  
Strategic Partnership ◽  
Discounted Cash Flow ◽  
Strategic Cooperation ◽  
Simulation Based ◽  
Main Motive ◽  
Dcf Model

AbstractThe paper addresses an analysis of potential synergies in collaboration between an observed Port in the Mediterranean Sea and Central-European logistic railway-services based company. Both companies have established a strategic partnership. The main motive was cooperation in rail transport, with a particular emphasis on potential synergies that would a rail traffic have brought to a port’s business. For the purpose of synergies valuation under uncertain conditions, a Monte Carlo simulation-based framework with integrated discounted cash flow (DCF) model is applied. The possible values of future synergies are calculated via the DCF model by simultaneously changing values of different uncertain financial parameters at each repetition of a Monte Carlo scenario-playing mechanism. In this process, predicted forecasts of future synergetic throughputs are also used for various types of observed cargo. As it turned out, the generated synergies’ values follow the approximate normal distribution. Based on statistical inference and analysis of probability intervals it was discovered that there might indeed exist certain important synergies in the collaboration between both companies. This fact has convinced us into a belief in the correctness of companies′ decision to enter into such kind of strategic cooperation.

scholarly journals A Comparison of Monte Carlo Simulation and Discounted Cash Flow Investment Appraisal Techniques Using an Office Building in Akure, Nigeria

2017 ◽  
Vol 1 (2) ◽  
pp. 299-308
Author(s):  
S. P. Akinbogun ◽  
O. P. Binuyo ◽  
O. T. Akinbogun
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Cash Flow ◽  
Investment Decision ◽  
Single Point ◽  
Investment Analysis ◽  
Point Estimate ◽  
Discounted Cash Flow ◽  
Expected Return ◽  
Level Of Risk

Every rational investor aimed to secure an optimum return from an investment at certain level of risk. However, the factors that affect the realization of the expected return are not known with certainty. Therefore, the reliability of a single point estimate for investment decision is debatable particularly when the likelihood of realizing the expected return is crucial to the investor. This study aimed at examining a better method of analysing the uncertainty in property investment rather than the use of the single point estimate formula. We collected a set of historic data from a multi-tenanted office complex in Akure to carry out the investment analysis using Monte Carlo simulation and Discounted Cash Flow Technique. The results from the study were compared and revealed that the single point estimate accounted for a huge level of risk as probability of realizing the expected return is unknown. The Monte Carlo simulation offers a more robust opportunity for a measured investment decisions by providing a probability of realizing different level of the expected returns. The study concludes that it is difficult to make a smart investment decision on a single point estimate. It therefore recommends the use of Monte Carlo simulation for property investment analysis for a more realistic return.

Using Monte Carlo Simulation to Improve Ceramic R&D Investment Decisions

2010 ◽  
Vol 105-106 ◽  
pp. 798-801
Author(s):  
Bao Cheng He ◽  
Hong Tao Jiang ◽  
Shu Zhi Yao ◽  
Bao Yuan He
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Real Options ◽  
Cash Flow ◽  
Investment Decisions ◽  
Discounted Cash Flow ◽  
Valuation Model ◽  
Managerial Flexibility ◽  
D Investment

The success of ceramic companies is highly dependent on research and development (R&D). Thus, a pivotal aim of management is to allocate resources to the best scientific and financial R&D projects. But the valuation of ceramic R&D is a difficult task for managers. The conventional discounted cash flow (DCF) methods fail to consider the value of managerial flexibility provided by R&D projects. Real options Analysis (ROA) offers a superior way of capturing the value of flexibility. It enables decision-maker to value projects more accurately by incorporating managerial flexibilities into the valuation model. However, ROA can’t effectively deal with the volatility of parameters in itself under high uncertain circumstance. In view of the limitation of ROA, this paper uses Monte Carlo simulation to solve the parameters volatility problems. In the end, the case study proves that Monte Carlo simulation can improve R&D investment decisions, especially for highly unpredictable ceramic R&D projects.

scholarly journals Economic and Risk Analysis to Deep Water Operations

2020 ◽  
pp. 11-16
Author(s):  
C. E. Ubani ◽  
A. O. Oluobaju
Keyword(s):  
Sensitivity Analysis ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Cash Flow ◽  
Net Present Value ◽  
Oil And Gas ◽  
Risk Model ◽  
Flow Analysis ◽  
Discounted Cash Flow ◽  
Exploration And Production

The exploration and production (E&P) operations of oil and gas project in deep waters, is associated with risks. These risks affects return on investment if they are not identified and analyzed to reduce their impact on the project. This study seek to apply Discounted Cash Flow (DCF) analysis, Monte Carlo Simulation and Sensitivity analysis, to an existing field in the Niger Delta region in Nigeria, to ascertain the viability of deepwater project when it is affected by government fiscal terms and technical terms. Economic and risk models were developed to determine profitability indicators and risk associated with the project. Risk simulator software was used to carry out Monte Carlo simulation and the sensitivity analysis. Results obtained showed that the project was economically viable with a Net Present Value (NPV) of $1,621.8 million and Internal Rate of Return (IRR) of 34%. The Monte Carlo Simulation and the sensitivity analysis showed that the Contractor’s NPV and percentage take were most sensitive to tax (under the fiscal terms) with an range of $639.27 million for a variation of by +/- 10% and crude price (under the technical term). The model developed can easily be applied in investment selections and decision makers should make decision based on the outcome of both economic model (cash flow analysis) and the risk model (Monte Carlo Simulation).

scholarly journals Voronoi diagram and Monte-Carlo simulation based finite element optimization for cost-effective 3D printing

2021 ◽  
Vol 50 ◽  
pp. 101301
Author(s):  
A.Z. Zheng ◽  
S.J. Bian ◽  
E. Chaudhry ◽  
J. Chang ◽  
H. Haron ◽  
...  
Keyword(s):  
Monte Carlo Simulation ◽  
Finite Element ◽  
Monte Carlo ◽  
3D Printing ◽  
Voronoi Diagram ◽  
Cost Effective ◽  
Simulation Based

Monte Carlo Simulation of Texture and Microstructure Transformation during Annealing of Steel

2007 ◽  
Vol 129 ◽  
pp. 83-87
Author(s):  
Hua Long Li ◽  
Jong Tae Park ◽  
Jerzy A. Szpunar
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Grain Boundary ◽  
Computer Model ◽  
Graphical Representation ◽  
Structural Characteristics ◽  
Polycrystalline Materials ◽  
Virtual Experiments ◽  
Grain Boundary Character ◽  
Simulation Based

Controlling texture and microstructure evolution during annealing processes is very important for optimizing properties of steels. Theories used to explain annealing processes are complicated and always case dependent. An recently developed Monte Carlo simulation based model offers an effective tool for studying annealing process and can be used to verify the arbitrarily defined theories that govern such processes. The computer model takes Orientation Image Microscope (OIM) measurements as an input. The abundant information contained in OIM measurement allows the computer model to incorporate many structural characteristics of polycrystalline materials such as, texture, grain boundary character, grain shape and size, phase composition, chemical composition, stored elastic energy, and the residual stress. The outputs include various texture functions, grain boundary and grain size statistics that can be verified by experimental results. Graphical representation allows us to perform virtual experiments to monitor each step of the structural transformation. An example of applying this simulation to Si steel is given.

scholarly journals A Novel Monte-Carlo Simulation-Based Model for Malware Detection (eRBCM)

Electronics ◽  
2021 ◽  
Vol 10 (22) ◽  
pp. 2881
Author(s):  
Muath Alrammal ◽  
Munir Naveed ◽  
Georgios Tsaramirsis
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Information Systems ◽  
Malware Detection ◽  
New Model ◽  
Malicious Activity ◽  
Model Based ◽  
Simulation Based ◽  
Computer Based ◽  
Reinforcement Model

The use of innovative and sophisticated malware definitions poses a serious threat to computer-based information systems. Such malware is adaptive to the existing security solutions and often works without detection. Once malware completes its malicious activity, it self-destructs and leaves no obvious signature for detection and forensic purposes. The detection of such sophisticated malware is very challenging and a non-trivial task because of the malware’s new patterns of exploiting vulnerabilities. Any security solutions require an equal level of sophistication to counter such attacks. In this paper, a novel reinforcement model based on Monte-Carlo simulation called eRBCM is explored to develop a security solution that can detect new and sophisticated network malware definitions. The new model is trained on several kinds of malware and can generalize the malware detection functionality. The model is evaluated using a benchmark set of malware. The results prove that eRBCM can identify a variety of malware with immense accuracy.

Sign in / Sign up

Export Citation Format

Share Document