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Simulation Decomposition: New Approach for Better Simulation Analysis of Multi-Variable Investment Projects
Abstract and Figures
This paper presents a new method to enhance simulation-based analysis of complex investments that contain multi-variable uncertainty. The method is called "simulation decomposition". Typically the result of simulation-based investment analysis is in the form of histogram distributions - here we propose a method for first classifying the possible outcomes of selected uncertain variables into states and then using combinations of the created states in the decomposition of the simulated distribution into a number of sub-distributions. The sub-distributions that can be matched to state-combinations of the variables contain relevant actionable information that helps managers in decision-making with regards to the studied investments. A numerical illustration of a renewable energy investment is used to demonstrate the usability, the enhanced analytical power, and the intuitively understandable benefits that can be reached by using the simulation decomposition method. The proposed method is generally usable and can be utilized independent of the investment context.
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... The macro-level simulation-based IFEW model introduced in [10] to determine the surplus nitrogen in the state of Iowa is extended to include a crop-weather model using linear regression of historical weather parameters, which is based on a prior study [11]. Simulation decomposition (SD) [12,13] is used to visualize the effects of weather variability on the IFEW nitrogen export. Furthermore, SD analysis is used to distinguish the influences of different weather scenarios affecting the surplus nitrogen. ...
... The simulation decomposition (SD) [12] approach is an extension to the Monte Carlo simulation [21] that enhances the explanatory capability of the simulation results by exploiting the inherent cause-and-effect relationship between the input and output parameters [13]. ...
... The current section provides a brief description of SD from an application point of view. A detailed description of SD can be found in [12]. ...
The state of Iowa is known for its high-yield agriculture, supporting rising demands for food and fuel production. But this productivity is also a significant contributor of nitrogen loading to the Mississippi River basin causing the hypoxic zone in the Gulf of Mexico. The delivery of nutrients, especially nitrogen, from the upper Mississippi River basin, is a function, not only of agricultural activity, but also of hydrology. Thus, it is important to consider extreme weather conditions, such as drought and flooding, and understand the effects of weather variability on Iowa’s food-energy-water (IFEW) system and nitrogen loading to the Mississippi River from Iowa. In this work, the simulation decomposition approach is implemented using the extended IFEW model with a crop-weather model to better understand the cause-and-effect relationships of weather parameters on the nitrogen export from the state of Iowa. July temperature and precipitation are used as varying input weather parameters with normal and log normal distributions, respectively, and subdivided to generate regular and dry weather conditions. It is observed that most variation in the soil nitrogen surplus lies in the regular condition, while the dry condition produces the highest soil nitrogen surplus for the state of Iowa.
... In this vein, in this paper we select two modern analysis techniques used in the analysis and the valuation of flexibility, the pay-off method [21] and (Monte Carlo) simulation based analysis, called "simulation decomposition" [22] and use them to study incentivepolicies in the context of biofuels. The reason for selecting these two methods is the fit of these methods to the type of uncertainty that surrounds the context of biofuel-policies [23]. ...
... The procedure is based on (i) identifying the relevant variables that can be affected by the project owner, their relevant "states", and boundaries for each state; (ii) forming "groups" or scenarios by combining the states; (iii) running the simulation, while keeping track on the input-output "inference"; (iv) visualizing the results such that the outcome resulting from each input group (scenario) is separately visualized and allows better understanding of "what leads to what". The procedure is depicted in Figure 2. The detailed description of the procedure, how the results from it are visualized, and available implementation tools can be found in [22,47]. Simulation decomposition has demonstrated its value in renewable energy policy analysis [22,48], in other environmental policy issues [25,49], and can be generally applied to any problem modeled with Monte Carlo simulation independent of the context [47]. ...
... The procedure is depicted in Figure 2. The detailed description of the procedure, how the results from it are visualized, and available implementation tools can be found in [22,47]. Simulation decomposition has demonstrated its value in renewable energy policy analysis [22,48], in other environmental policy issues [25,49], and can be generally applied to any problem modeled with Monte Carlo simulation independent of the context [47]. ...
A variety of policy types are available to foster the transition to a low-carbon economy. In every sector, including transportation, heat and power production, policymakers face the choice of what type of policy to adopt. For this choice, it is crucial to understand how different mechanisms incentivize investments in terms of improving their profitability, shaping the flexibility available for investors, and how they are affected by the surrounding uncertainty. This paper focuses on transportation-biofuel policies, particularly on the financial incentives put on the bio-component of fuel and the combination of using penalties and tax-relief. Delivery of vital policymaking insights by using two modern simple-to-use profitability analysis methods, the pay-off method and the simulation decomposition method, is illustrated. Both methods enable the incorporation of uncertainty into the profitability analyses, and thus generate insight about the flexibilities involved, and the factors affecting the results. The results show that the combination of penalties and tax-relief is a way to steer fuel-production towards sustainability. The two methods used for analysis complement each other and provide important insights for analysis and decision-making beyond what the commonly used profitability analysis methods typically provide.
... SimDec is generalizable to any simulation method without the need for any significant additional computing resources. SimDec's innovative visual analytics capabilities have already been considered in a diverse range of environmental decision-making problems (Kozlova et al., 2016;Kozlova and Yeomans, 2019;Deviatkin et al., 2020;Hietanen, 2020;Sadyhova, 2020). Although not commercially available, downloadable versions of SimDec code are readily accessible in Matlab (Kozlova et al., 2018a(Kozlova et al., , 2018b, VBA for Excel (Kozlova and Yeomans, 2020), Python, and R (Sadyhova, 2020). ...
... Recently, the visual analytics capabilities of SimDec have been used to produce novel insights into such diverse environmental decision-making problems as renewable energy policy analysis (Kozlova et al., 2016;Hietanen, 2020), environmental policy planning (Kozlova and Yeomans, 2019), carbon footprint analysis (Deviatkin et al., 2020), and green construction (Sadyhova, 2020). Hietanen (2020) demonstrated how performing a decomposition of renewable energy policy with different sets of factors can uncover additional insights and provide a comprehensive understanding of the problem's underlying complexities. ...
... Hietanen (2020) demonstrated how performing a decomposition of renewable energy policy with different sets of factors can uncover additional insights and provide a comprehensive understanding of the problem's underlying complexities. Figure 4 provides a summary of SimDec's visualization output from these various complex, multifaceted environmental decision-making problems (Kozlova et al., 2016;Kozlova and Yeomans, 2019;Deviatkin et al., 2020;Hietanen, 2020). ...
... Simulation decomposition (SimDec) has recently provided a simple, but powerful, advancement to the standard Monte Carlo approach (Kozlova et al. 2016). The basic concept behind SimDec is to decompose (and color-code) the final output distribution into groups of outcomes resulting from multivariable combinations of different states of the input variables. ...
... Because the contribution of the state combinations to the overall output is easy to portray visually, SimDec can reveal previously unidentified connections between the multivariable combinations of inputs on the outputs. The decomposition has been shown to provide deeper insights into the uncertainty surrounding the problem, to assist decision making when actionable variables are chosen for decomposition, and to better understand the interplay of different sources of uncertainty on the distribution of outcomes (Kozlova et al. 2016, Kozlova andYeomans 2019). The SimDec approach is completely generalizable to any Monte Carlo model with negligible additional computational overhead. ...
... Although stacked histograms and bar charts have been widely used across all domains of academia and industry, the idea of decomposing Monte Carlo simulation results has remained largely overlooked. To the best of our knowledge, multivariable simulation decomposition has only been applied to a couple of investment cases (Kozlova et al. 2016, Kozlova andYeomans 2019); single-factor decomposition has been used in memristor performance analysis (García-Redondo et al. 2012) and has been alluded to sparingly in some very narrowly focused commercial software products. ...
Monte Carlo (MC) simulation is widely used in many different disciplines in order to analyze problems that involve uncertainty. Simulation decomposition has recently provided a simple, but powerful, advancement to the standard Monte Carlo approach. Its value for better informing decision making has been previously shown in the investment-analysis field. In this paper, we demonstrate that simulation decomposition can enhance problem analysis in a wide array of domains by applying it to three very different disciplines: geology, business, and environmental science. Further extensions to such disciplines as engineering, natural sciences, and social sciences are discussed. We propose that by incorporating simulation decomposition into pedagogical practices, we expect students to significantly advance their problem-understanding and problem-solving skills.
... Optimization tools are required to quickly generate alternatives and evaluate corresponding outcomes. Other research has adopted decomposition approaches; for example, the simulation decomposition proposed by Kozlova et al. (2016) enables decision-makers to check the influence of any multivariable combination of inputs on a system's outcome, providing an insightful simulation. Babic et al. (2020) Humans are responsible for ensuring a problem's suitability (Linkov et al. 2020). ...
... Optimization tools are required to quickly generate alternatives and evaluate corresponding outcomes. Other research has adopted decomposition approaches; for example, the simulation decomposition proposed by Kozlova et al. (2016) enables decision-makers to check the influence of any multivariable combination of inputs on a system's outcome, providing an insightful simulation. Babic et al. (2020) Humans are responsible for ensuring a problem's suitability (Linkov et al. 2020). ...
The concept of a Digital Twin (DT) has stood out among the emerging digitization technologies and been embraced by U.S. and EU governments and companies. Practitioners and scholars recognize the closeness between DT and Operations Research (OR) and seek cooperation between the two fields. Driven by the question of how OR can help implement DTs, we aim to (i) identify key properties and functions of a DT from the lens of OR, (ii) evaluate the importance and urgency of OR methods in DT applications, (iii) suggest specific OR research opportunities to realize key functions of a DT, and (iv) summarizing non-OR factors that may become OR-related and influence future DT implementations. We survey the literature and show that OR and DT can contribute to each other in the areas of real-time decisions, digital models, and data integration.
For effective management of an investment project, it is necessary to correctly identify and estimate possible risks. Currently, a number of works are devoted to research on the methodological foundations of risk management in the implementation of investment projects, but they all depend on the specifics of the projects themselves. The purpose of this work is to study the existing methods of risk assessment of an investment project and determine the appropriate method for future research and the formation of descriptions of modern technological ideas and techniques. Risk assessment is certainly important in modern realities. The changing situation forces us to consider each project in detail. Technology does not stand still and comes to the rescue to assess the risks was not only easier, but also better. After all, it is very important to work out the problem and assess the risks of the project using various methods. This article will demonstrate basic information about risk assessment using the Simulation Decomposition method.KeywordsSimulation decompositionInvestment risksRisk calculation methods
The development of aviation and materials for their manufacture, the transition to electric motors and chemical current sources in ground transport require the study of electrochemical processes in contact with dissimilar metals, both in the form of individual structural elements and in the form of dissimilar phases of metal alloys. The article presents the results of a study of galvanic corrosion of aluminum alloys in atmospheric conditions using the developed method for studying changes in the electrochemical parameters of the aluminum surface during galvanic contact with metals such as zinc, copper, cobalt, magnesium, manganese. It is shown that during galvanic contact of dissimilar metals, the stationary potential of aluminum and the rate of its corrosion in a corrosive medium change, and the change in potential depends on the type of metal, and does not depend on the ratio of the areas of aluminum and metal in contact with the corrosive medium.KeywordsAviation materialsElectric transportAluminum alloyAlloying elementCorrosionElectroplatingCorrosion potentialCalculation of the corrosion potential value
This paper focuses on the analysis of agricultural and engineering processes using simulation decomposition (SD). SD is a technique that utilizes Monte Carlo simulations and distribution decomposition to visually evaluate the source and the outcome of different portions of data. Here, SD is applied to three distinct processes: a model problem, a nondestructive evaluation testing system, and an agricultural food-water-energy system. The results demonstrate successful implementations of SD for the different systems, and the illustrate the potential of SD to support new understanding of cause and effect relationships in complex systems.
This thesis presents an analysis of recently enacted Russian renewable energy policy based on capacity mechanism. Considering its novelty and poor coverage by academic literature, the aim of the thesis is to analyze capacity mechanism influence on investors’ decision-making process.
The current research introduces a number of approaches to investment analysis. Firstly, classical financial model was built with Microsoft Excel® and crisp efficiency indicators such as net present value were determined. Secondly, sensitivity analysis was performed to understand different factors influence on project profitability. Thirdly, Datar-Mathews method was applied that by means of Monte Carlo simulation realized with Matlab Simulink®, disclosed all possible outcomes of investment project and enabled real option thinking. Fourthly, previous analysis was duplicated by fuzzy pay-off method with Microsoft Excel®. Finally, decision-making process under capacity mechanism was illustrated with decision tree.
Capacity remuneration paid within 15 years is calculated individually for each RE project as variable annuity that guarantees a particular return on investment adjusted on changes in national interest rates. Analysis results indicate that capacity mechanism creates a real option to invest in renewable energy project by ensuring project profitability regardless of market conditions if project-internal factors are managed properly. The latter includes keeping capital expenditures within set limits, production performance higher than 75% of target indicators, and fulfilling localization requirement, implying producing equipment and services within the country. Occurrence of real option shapes decision-making process in the following way. Initially, investor should define appropriate location for a planned power plant where high production performance can be achieved, and lock in this location in case of competition. After, investor should wait until capital cost limit and localization requirement can be met, after that decision to invest can be made without any risk to project profitability. With respect to technology kind, investment into solar PV power plant is more attractive than into wind or small hydro power, since it has higher weighted net present value and lower standard deviation. However, it does not change decision-making strategy that remains the same for each technology type.
Fuzzy pay-method proved its ability to disclose the same patterns of information as Monte Carlo simulation. Being effective in investment analysis under uncertainty and easy in use, it can be recommended as sufficient analytical tool to investors and researchers.
Apart from described results, this thesis contributes to the academic literature by detailed description of capacity price calculation for renewable energy that was not available in English before. With respect to methodology novelty, such advanced approaches as Datar-Mathews method and fuzzy pay-off method are applied on the top of investment profitability model that incorporates capacity remuneration calculation as well. Comparison of effects of two different RE supporting schemes, namely Russian capacity mechanism and feed-in premium, contributes to policy comparative studies and exhibits useful inferences for researchers and policymakers.
Limitations of this research are simplification of assumptions to country-average level that restricts our ability to analyze renewable energy investment region wise and existing limitation of the studying policy to the wholesale power market that leaves retail markets and remote areas without our attention, taking away medium and small investment into renewable energy from the research focus. Elimination of these limitations would allow creating the full picture of Russian renewable energy investment profile.
The paper is designed to compare two real option valuation techniques, Datar-Mathews method based on the probabilistic approach and a fuzzy pay-off method based on the possibilistic theory. These approaches comprise similar logic, recognizing the whole investment project as a real option, if investment can be terminated in case of loss forecast. Real option value is defined as a risk adjusted expected mean of the positive side of the resulting outcome distribution. Simple intuition makes these methods attractive for investment analysis. However, being relatively young they have not spread deeply to business practice and academic research. Possessing identic logic but utilizing different theoretical foundations these techniques are especially interesting to compare. In general, results obtained from applying these methods to real option analysis are consistent. Simple triangular possibilistic distribution appears to overly simplify an investment case with complex interaction of uncertain factors. However, possibilistic theory provides grounds for further method extension. Fuzzy inference rules applied to outcomes resulting from different combinations of uncertain factors create an aggregate possibilistic distribution that joins features of real option and sensitivity analyses. This enables to trace interconnections of uncertain factors to particular ranges of investment pay-off, facilitating and deepening investment analysis.
Context:
The pathology of classic Burkitt lymphoma (BL) remains a challenge despite being a well-defined entity, in view of the significant overlap with atypical BL and B-cell lymphoma intermediate between DLBL (diffuse large B cell lymphoma) and BL. They are difficult to be segregated in resource-limited setups which lack molecular testing facilities. This is further affected by interobserver variability and experience of the reporting pathologist.
Aims:
The aim of our study was to quantitate variability among a group of pathologists with an interest in lymphomas (albeit with variable levels of experience) and quantitate the benefit of joint discussions as a tool to increase accuracy and reduce interobserver variability of pathologists, in the diagnosis of BL in a resource-limited setup.
Materials and Methods:
A set of 25 non-Hodgkin lymphoma cases in which a diagnosis of BL was entertained were circulated to 14 participating pathologist within the Mumbai lymphoma study group. A proforma recorded the morphologic and immunohistochemical features perceived during the initial independent diagnosis followed by a consensus meeting for discussion on morphology and additional information pertinent to the case.
Statistical analysis and Results:
The concordance was poor for independent diagnosis among all the pathologists with kappa statistics (±SE) of 0.168 (±0.018). Expert lymphoma pathologists had the highest (albeit only fair) concordance (kappa = 0.373 ± 0.071) and general pathologists the lowest concordance (kappa = 0.138 ± 0.035). Concordance for morphological diagnosis was highest among expert lymphoma pathologists (kappa = 0.356 ± 0.127). Revision of diagnoses after consensus meeting was highest for B-cell lymphoma intermediate between DLB and BL. To conclude, interobserver variation is a significant problem in BL in the post WHO 2008 classification era. Experience with a larger number of cases and joint discussion exercises such as the one we conducted are needed as they represent a simple and effective way of improving diagnostic accuracy of pathologists working in a resource-limited setup.
Russian renewable energy policy, introduced in May 2013, is a capacity mechanism-based approach to support wind, solar, and small hydro power development in Russia. This paper explores the effect of the new mechanism on the profitability of new renewable energy investments with a numerical example. The sensitivity of project profitability to selected factors is studied and the results are compared ceteris paribus to results from a generic feed-in premium case. Furthermore, the paper gives a complete and detailed presentation of the capacity price calculation procedure tied to the support mechanism.
This paper presents a detailed system dynamic (SD) model of a metal mining investment that is usable in ex ante profitability and operations management analysis. We show how the SD model can be used to analyze the profitability effect of three operational real options: the option to temporarily close production, the option to abandon production, and the option to increase production through cut-off grade change. The used SD model allows for intuitive modeling of the multiple interactive real options and arriving at results that are difficult, or impossible, to reach with commonly used spread-sheet software. We also analyze the effect of mining project debt-ratio to the project value and show that choosing the debt-ratio correctly affects project profitability. The effect on the project value of using three different future metal price scenarios with two different stochastic processes is illustrated to highlight the importance of correct process selection in modeling future metal price paths. A realistic case of a high cost nickel (Ni) metal mine is used as a basis for the presented numerical illustration of the model.
The inability of classic NPV analysis to capture the future value of options in a capital budgeting analysis is now well documented by Trigeorgis (199312.
Trigeorgis , L. 1993. Topics in real options and applications. Financial Management, 22(3): 202–223. View all references, 200514.
Trigeorgis , L. 2005. Making use of real options simple: An overview and applications in flexible/modular decision making. The Engineering Economist, 50(1): 25–53. View all references), Copeland and Antikarov (2001)4.
Copeland , T. and
Antikarov , V. 2001. Real options: A practitioner's guide, New York: TEXERE LLC. View all references, and others. In spite of this, traditional NPV analysis continues to be described as a normative approach. The author surveys Fortune 1,000 companies to see if they have picked up on the use of real options to complement traditional analysis. Out of 279 respondents, 40 were currently using real options (14.3%). While the percentage is small, the number is higher than in previous studies. The author goes on to describe in what manner real options are being used and, of equal importance, why they are resisted by many. Somewhat encouraging is the intent of well over half the nonusers to consider the use of real options in the future.
Russian renewable energy policy has undergone changes following an establishment of targets for installed capacity and power production using renewable energy sources and the introduction of new capacity based support scheme for renewable energy. The forecasted amount of future renewable power will not provide enough power production to meet growing demand for renewable energy; although, it will help with modernization of the energy sector and development of renewable technology and innovation. At the same time, the capacity support scheme for renewable energy may adversely affect capacity prices and become an additional burden for industrial consumers, who are already paying the cost of capacity support for conventional power plants, so-called Capacity Delivery Agreements (CDAs). This work assesses the impact of the new capacity based support scheme on capacity and electricity price formation. Modeling results show that the impact of capacity support for renewable energy is small compared to that of capacity support for conventional energy, suggesting that the Russian energy production mix will continue to be dominated by fossil fuel based generation.
Facility location problems have become a more strategic decision than just finding the lowest cost space to house a company's product. When choosing the placement of a distribution center, a company must weigh the new freight costs and the cost of a new or leased structure against its necessary service levels, as well as several other factors. Companies can also save on logistics costs in choosing the correct location by incorporating the inherent risk and variability that are involved in a facility location decision. However, in reality, most companies do not analytically consider risk and variability in choosing a location. This article presents a methodology to help determine candidate locations and then conduct a financial risk analysis to determine the ideal location of a new facility.