» Download a zip file of the 2010 London presentations
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Number Cruncher or Decision Professional? Andrea Dickens and Dr Sven Roden Are quantitative analysts appropriately called upon by decision-makers? Certainly decision-makers like our ability to quantify complex situations that include huge amounts of uncertainty. But do they really view us as trusted advisors that they should automatically consult when they face difficult decisions? Or do they prefer to simply have us as back-office number crunchers? This presentation will discuss some of the ways that we could get ourselves more integrated into the decision-making process. We will also explore how we could act as a community to organise ourselves so we are seen as Decision Professionals and not just number crunchers.
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Palisade Overview, and Avoiding "The Number" Sam McLafferty and Randy Heffernan Having just celebrated its 25th anniversary, Palisade stands at the forefront of risk and decision software analytics. Sam will provide a bit of background on Palisade’s history and will describe what sets Palisade apart in the market. He will give an overview of Palisade’s best-selling @RISK and DecisionTools Suite of analytical tools for Excel, with a special emphasis on the just-released new language versions in French, German, Spanish, Portuguese, and Japanese. Sam will describe the latest enhancements and additions to the product line before providing a glimpse into what’s coming next from the company. There’s been a lot of talk about the need for “proper risk analysis” in the last couple of years. However, risk analysis can be both qualitative and quantitative. Any meaningful risk analysis must be done probabilistically, but what does that mean? Palisade Vice President Randy Heffernan will talk about probabilistic risk analysis: what it is, why it’s important, and how you can benefit. And why there’s no such thing as “the” number.
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Based on sophisticated analysis enabled through use of decision analysis and science application and supporting tools (@RISK) leadership is endowed with an increasing amount of insight that supports decision making. During the conference we have seen multiple examples and case studies that illustrate what is possible. This presentation explores how these insights can directly address the concerns from decision makers to help them make trade-offs that require thought, discussion and the reality of implementation. We see decision making not as an event – it is a process and ultimately a capability.
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Custom Software using Dr Javier Ordóñez @RISK and DecisionTools Suite software ship with full-featured development environments that allow you to create custom applications using Palisade technology directly in Excel. Palisade offers custom software development services to take full advantage of these Excel Developer Kits (XDKs), creating applications tailored to your needs right in your spreadsheet. We can also create custom applications using @RISK and other technology for any Windows-based application outside of Excel. Using a custom interface, we will show how to define uncertain elements in each model and how to interpret the simulation results. We will present several examples to reveal how @RISK can be used to plan investment strategies for retirement, manage a portfolio of assets, perform a cost risk analysis and assess the risks in prospecting for oil. These examples demonstrate how users can run a model tailored to their needs without learning how to use @RISK.
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Benefits from weather derivatives in agriculture: a portfolio optimisation using RISKOptimizer Crop farmers in Germany‘s federal state of Brandenburg have one of the highest volatilities of family income in Europe. The main reason is that levels of rainfall in Brandenburg are low, and the predominant sandy soils there retain water poorly. Although climatic circumstances create high production risks in Brandenburg, crop farming is the main line of agricultural production. It is expected that fluctuations in temperature and rainfall will be much higher in the future and this would again raise the fluctuation in yields, caused mainly by absence of water in the main growth period in the region of North-East Germany. In this paper we evaluate which kind of management strategy would be optimal for crop farmers in a specific county of Brandenburg and provide hints to insurers on how to design an effective instrument for this specific region. In a first step we create a put option for weather derivative speculation.
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Bushing blocks optimization for an external gear pump Maria Pia D’Ambrosio This project aims at the optimization of the design of the bushing blocks assembled into a specific type of external gear pump produced by Casappa, one of the world leader manufacturer of this kind of machinery. The bushing block is one of the key parts of a gear pump. Its optimization depends on a large amount of parameters and has to fulfil multiple economical, functional and mechanical requirements. The proper optimization of the blocks is a crucial element of the pump design. The presentation discusses how automation can be applied to the process measurements (using CMM - Coordinate Measuring Machine - data acquisition) of the block dimensions, in order to define the process natural variation and the type of distribution better approximating the real data. These evaluations are then used as input of a Monte Carlo simulation with multiple models and responses. The above mentioned techniques will lead to a combined optimization of the design parameters. This optimization will enable to achieve all the targets and to satisfy both economical requirements and process constraints.
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Calculation of Construction Costs for Building Projects – Application of the Monte Carlo Method Construction time is of crucial importance when it comes to utilizing the production factors in an effective and efficient way. Construction periods that are too short usually result in higher cost, poorer quality and a larger number of disputes. This presentation sets out to demonstrate the calculation of construction time and cost whilst considering key construction management parameters. Beyond a simple, deterministic method, other options for calculation are shown that rely on probability calculus. The approaches described to determine construction time and cost are illustrated by a building project example. The deterministic method results in one value per each calculation process (calculation mode 1). In calculation mode 2, probability calculus is applied in a simple fashion. Both range and probability of occurrence can be considered for the relevant input variables. For the third calculation mode, the Monte Carlo method is applied using Palisade’s @RISK software. For each of the parameters to be determined, this method shows a probability distribution. Using a high-rise building project, the application of the Monte Carlo method (calculation mode 3) to determine construction time and costs is demonstrated. Weighted triangles are used as distribution functions, which makes it possible to consider minimum and maximum values, as well as expected values. The correlation between probability of occurrence and construction times is reflected by a probability distribution.
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Cellulosic Bioethanol Plant Project Risk Management via Simulation Cellulosic bioethanol has garnered much interest as a promising low-carbon-footprint, alternative fuel which does not consume edible foodstuffs (as traditional corn and sugar-based biofuels controversially do). However, the science and systems behind producing cellulosic bioethanol profitably on a large scale have yet to be definitively and consistently implemented. As such, entrepreneurs interested in this compelling new approach to energy are eager to better map risks and unknowns before committing investment capital to such ventures. Many use the traditional, static Net Present Value (NPV) model to chart project risks in spreadsheets. However, given the multiple, varied, overlapping, and dynamically fluctuating unknowns (commodity prices, exchange rates, energy costs, manufacturing costs, productivity rates, market dynamics, variable tax benefits, etc.), static NPV models are limited if not dangerously misleading to prospective investors. Using Palisade @RISK, a robust, dynamic cellulosic ethanol plant is simulated to give broad insight into risks and unknowns, the better to chart and control these factors. The simulated model includes dynamic economic variables, expense, market, revenue, and productivity elements. The model offers flexibility to tailor the virtual plant to specific configurations, and thus has promise as a due-diligence tool for prospective bioethanol entrepreneurs, investors, project managers, and corporations seeking to become involved in this compelling new field. At a more basic level, the model is an interesting example of using @RISK to model NPV for a dynamic manufacturing business, taking into account economic factors, sales projections, revenue predictions, varying expenses, etc., and thus is of potential interest to finance generalists.
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Decisions, Decisions At the most basic level, making a decision involves choosing between two alternatives, where the outcomes and probabilities of each choice are known. Making a decision becomes complicated when the outcomes are uncertain and/or the probabilities are unknown. Risk analysis in its broadest sense finds solutions to these difficult problems. It is not sufficiently well recognised that decision outcomes can be influenced by cognitive bias. Although it has been well established that these cognitive biases do exist, it is not straightforward to either demonstrate them, or convince decision makers of their existence.
This presentation will use @RISK models to demonstrate these cognitive biases among the audience present.
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The economics of Supply Chain Risk Management using @RISK Supply chain risk management is an emerging field which has been growing significantly in importance because of modern management concepts such as lean, globalization and outsourcing. The mutual dependencies and close collaboration in modern supply chains create unique risks and challenges. Supply chain risk management is an economic process and choosing the elements and amount of risk mitigations should be based on economic measures. The lecture will give an overview of the concepts and process of supply chain risk management and will demonstrate how using Monte Carlo simulation techniques with @RISK adds value to the decision making processes and enable us to purchase the most cost effective mitigations.
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Faldo's folly or Monty's Carlo - The Ryder Cup & Monte Carlo Simulation The Ryder Cup is arguably the most prestigious and most exciting golf tournament in the World. It is a team event contested once every 2 years between 12 golfers from Europe and 12 golfers from the United States of America. For the 12 singles matches on the final Sunday, each captain selects the order in which his players tee off. In 2008, after an eventual US victory, the sporting press was hugely critical of Nick Faldo’s (the European captain) slate selection. This article looks to explore the justification of such criticism. First, existing academic results are reviewed and, where necessary, updated for 2008. Second, using Monte Carlo simulation, we consider the scheduling of players who react differently under pressure. This simple sporting example illustrates how Monte Carlo simulation can be used to analyse a range of potential scenarios enabling better, more informed decisions. Within a business context, where a winning outcome is essential, non-operational research practitioners must understand how operational research techniques can be used to make better, more informed decisions. This presentation concludes by discussing how the Ryder Cup model, together with a related example analysing interdependent project risks, was successfully used within a consultancy environment to introduce non-OR practitioners to the theory behind and the potential of Monte Carlo simulation.
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How can we help decision makers feel more comfortable with Monte Carlo? A black box is any device whose workings are not understood by or accessible to its user. This definition fits exactly what some decision makers in the Oil & Gas industry think about Monte Carlo simulation, especially if they come from an engineering background. It is not only a question of training, there is a genuine need for those who bear the responsibility of important decisions to fully grasp the information that is presented to them, and exercise their judgment on concrete cases. However, without a proper model of risks and uncertainties, the selection of a few deterministic scenarios remains subjective and there is no way to know how representative they are. For complex projects, such as a large offshore development or an LNG plant, whose resource base includes many fields and prospects, Monte Carlo simulation is the only way to correctly explore the space of possible outcomes and optimize decisions. The challenge is to be both correct and understood. A number of principles and techniques can be proposed to achieve this goal and will be discussed. An important rule is to distinguish the analysis itself, which should have the level of complexity required to get correct results, from the presentation of the results which must be kept as simple as possible. All too often technical people want to share the beauties and intricacies of their models… and only manage to puzzle their audience. The “Collapse and Expand Child Branches” of PrecisionTree is a good model for introducing complexity only as needed. A technique that proved useful to dispel the black-box image is to collect all simulated values in @RISK and store them in an Excel™ worksheet, which is presented as a database of scenarios. These are traceable, they can be inspected individually, and impossible ones can be deleted. Any decision or strategy may be tested against the database by using appropriate filters. Acceptability is greatly improved if the analysis provides deterministic validation points that decision makers can relate to and helps them gain confidence in the results. Application specific examples related to the optimization of an appraisal strategy will be presented.
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Internal Modelling under Solvency II Under upcoming regulations for the Insurance Industry in Europe, insurers will be given the opportunity to submit results from their own internal models to the regulatory authorities for the assessment of Solvency Requirements. In this presentation we will explore how Monte Carlo simulation techniques can be used to comply with the new regulations, and how insurers can use these techniques to their own advantage.A case study will be developed of an insurance company with multiple lines of business, a reinsurance program, and a varied asset portfolio. We will show how @RISK models can be used to:
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New Approaches to Transport Project Assessment: Reference Scenario Forecasting and Quantitative Risk Analysis This presentation sets out a new methodology for examining the uncertainties relating to transport decision making based on infrastructure appraisals. Traditional transport infrastructure projects are based upon cost-benefit analyses in order to appraise the projects feasibility. Recent research however has proved that the point estimates derived from such analyses are embedded with a large degree of uncertainty. Thus, a new scheme was proposed in terms of applying quantitative risk analysis (QRA) and Monte Carlo simulation in order to represent the uncertainties within the cost-benefit analysis. Additionally, the handling of uncertainties is supplemented by making use of the principle of Optimism Bias, which depicts the historical tendency of overestimating transport related benefits (user demands i.e. travel time savings) and underestimating investment costs.
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Pricewaterhouse Coopers and Palisade: An insight into how Pricewaterhouse Coopers have used Palisade solutions over the years in their consulting projects. As a provider of advisory services to companies in a wide selection of industries, all of whom are faced with different issues and challenges, this presentation provides an overview of how one of the world's leading consultancies has utilised the flexibility and versatility of @RISK.
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Put More Science into Cost Risk Analyses Economists have nicknamed next “te(e)n” years as ”debtcade” due to the unexpected but already anticipated naughty financial behaviour. The crisis had been forecasted by those quant specialists but was ignored by those management generals. Quantitative analyses have the merits to quantify measurable risk probability and to forecast foreseeable risk consequences. The application of risk analyses using @RISK is pervasive in many industries, and its use has already been proliferated. In energy industry, oil price drives profitability in many ways; higher-than-budget costs of capital projects and operating facilities however effectively erode such profit margins. Whilst the futuristic oil price may be hard to predict due to its low manageability, it is absolutely possible to scientifically forecast the sizes of risks that companies are willing to take, and such risks may include the probabilistic volumes of newly discovered reserves, probability of meeting a project development schedule, chances of project cost overruns, and the likelihood of eroding entire project profitability. To achieve these goals, @RISk has lent a helping hand to business analysts for easier operation of complicated mathematical modelling. Statoil, an international oil company, takes risk management seriously and has applied Monte Carlo simulation techniques in their core and support businesses using @RISK package. Such applications not only include the solo use of individual application but integrated combinations from drilling and well completion to cost and schedule controls at project execution. Besides the widespread uses of the package, a specific application of @RISK to convincingly simulate required capital project contingency is worth discussing in details. A simplistic line-item ranging exercise using @RISK Monte Carlo simulation is no longer adequate to derive large capital project contingency, as empirical data confirmed that many disastrous cost overrunning projects were lack of contingency to cover the covert risks. To show the management complete risk picture on a project, both systemic risks that empirical history indicate the likelihood of occurrence, and specific risks that have discrete probabilistic characteristics, should be included in the overall project risk analysis. Therefore the combination of continuous PDF for project cost estimates and discrete PDF for project risk register may prevail and provide management with more convincing project cost contingency. This task is easy said than done as many oil companies currently neither use such approach, nor willingly collect empirical data to support such combination, they unfortunately continue with ranging line-items. To reach the climax of best in class quantitative risk analysis, research in theories and trials on pragmatic frontier realities are necessary. The author’s self interested manipulation of a mock cost estimate contingency model using @RISK simulation functions indicated that the integrated qualitative risk assessment and quantitative risk analysis can yield a more realistic project contingency, as the postulated estimate contingency percentages can no longer represent today’s economy. More over, incorporating potential delays of project execution schedule into cost risk analyses reflect the industry reality more often than not, generating a realistic project contingency. Therefore, such a model demonstrates a capability to better mimic project overall risk scenarios in a comprehensive manner, hence deriving a risk contingency that can sustain the contests of project vulnerability over the time. This approach also aligns with the AACEi’s Recommended Practices (RP 2009) for cost risk analyses.
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Risk Based Water Distribution Rehabilitation Planning The performance of deteriorating water distribution systems can be managed by replacing or rehabilitating pipes which is funded with capital maintenance expenditure. Capital maintenance investments in UK water company assets are justified on the basis of risk to customer service. It is important for water companies to achieve good capital efficiency by getting the best return from each £ invested. Halcrow worked with Bournemouth and West Hampshire Water to develop an innovative risk-based approach to targeting capital maintenance investment with economically efficient rehabilitation schemes. A spatial optimisation tool was developed to identify areas of the pipe network with the highest failure rates. The failure statistics of the ‘clusters’ of events were used to drive quantitative consequential impact models using Palisade @RISK tools to estimate the potential service risk that could mitigated through capital maintenance expenditure. Potential schemes for targeted investment were benchmarked against the company’s operational experience. The results suggest the clustering approach has the potential to provide significant improvement in terms of capital efficiency, when compared to the models used for long-term business planning.
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Risk Sharing in Waste Management Projects The waste management industry in the UK is undergoing a major shift in its approach to managing waste with the introduction of the European Commission’s Landfill Directive. The industry is set for significant expansion in the coming years. A large number of new treatment facilities are required to implement these changes and this will inevitably require huge investments. However, there is some reluctance in investing in the waste sector due to a lack of understanding of the potential risks associated with integrated waste management projects. This reluctance is intensified further by the current economic downturn, which has often had inadequate risk management cited as a key cause. Therefore, a transparent and auditable assessment of the performance, costs and risks of new treatment technologies is required to resolve the bottleneck in financing waste management facilities. In this paper, a risk assessment model for integrated waste management projects is presented. The model assesses the performance and cost related risks and uncertainties of a variety of mechanical, biological and thermal waste treatment technologies. In this paper, the cost and performance related uncertainties that can significantly influence the net cost of treating residual waste in a mechanical biological treatment facility and energy from waste facility are reported and discussed. The bespoke model utilises Palisade’s @RISK software and uses Monte Carlo simulations in order to report the highly sensitive parameters in the form of tornado graphs and net costs as probability distribution graphs. To date, most decision support models available and applied in the waste management sector assess impacts of uncertainties by modelling best and worst case scenarios. Most of these applications are incapable of handling uncertainties associated with a wide range of parameters as considered in the risk model discussed in this paper and therefore have limited ability to inform risk sharing in waste management projects. The model and the results discussed here act as a powerful decision support tool to inform and influence the risk sharing process on large integrated waste management projects.
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Securing of supply chain in aeronautic industry More than in other industry, aeronautic industry needs a particular attention from industrial suppliers regarding the supply in time of the on-board requested equipment. Consequences of a delay may lead to huge penalties from the aircraft manufacturer client, or even worse, order cancellation. On the other hand, for the electronics manufacturer, keeping in stock quantities of parts or equipment may represent a significant cost.
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A Six Sigma & Simulation Approach to Software Quality Risk Management In today’s competitive business environment, software quality and customer satisfaction are more important than ever. Achieving software quality goals is a major objective for software development organizations as it is a critical constraint on their projects. Software Quality is a phenomenon with significant uncertainty involving a substantial risk. Consequently, managing the software quality risk is an important challenge for software projects. The conventional approach to Software Quality Risk Management is based on analytic models and statistical analysis. The analytic models are static, so they don’t account for the inherent variability and uncertainty of software quality processes, which is an apparent deficiency. This paper presents an application of Six Sigma and Simulation in Software Quality Risk Management. DMAIC and simulation are applied to a quality process, such as testing, to assess and mitigate the risk in order to deliver the product on time, whilst achieving the quality goals. DMAIC is used to improve the process and achieve required (higher) capability. Simulation is used to predict the quality (reliability) considering the uncertainty and variability, which, in comparison with the analytic models, more accurately models the process. Presented experiments are applied on a real project using published data. Compared with the actual data, the experimental results are very satisfactorily verified. This enhanced approach is compliant with CMMI® and provides for substantial Software Quality performance-driven improvements. Simulation and Neural Networks Applications Achieving software reliability goals is a major objective for software development organizations as it is a critical constraint on their projects. Predicting the software reliability at some point in the future based on data already available, is an important challenge for software projects. The conventional approach to Software Reliability prediction is based on analytic models. These models don’t account for software processes’ inherent variability and uncertainty, and require estimation of parameters and unrealistic/oversimplified assumptions – apparent deficiencies. This paper presents applications of Simulation and Neural Networks in Software Reliability prediction from the practitioners’ perspective. Different simulation and neural networks models are used to predict the reliability of a real software system using published data. The predictive capability of the models is evaluated using the actual data. Comparison of simulation and neural networks models versus analytic models is provided. Simulation and Neural Network models offer a superior alternative to conventional analytic models.
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Succeeding in DecisionTools Suite 5 rollout – Unilever’s story Unilever is present in 150 countries, employs 175,000 people and has an annual turnover in excess of €40 bn. A pivotal question for any organisation of this size is how to embed Decision Analysis and ensure that the right people have the appropriate skills, tools and support to ensure that high quality strategic decisions are made wherever and whenever required. Software has a key part to play in the solution, but by itself it can never be the entire answer. Unilever has put into place a range of interventions to ensure the successful adoption of DecisionTools Suite (DTS) as part of an overall Decision Analysis embedding programme. A self-reinforcing cycle of interventions is ensuring the successful adoption of DTS within Unilever. This cycle starts with the use of a flexible licensing system (FLEXnet) which enables company-wide installation of DTS, and effective monitoring of users. A wide range of training interventions - from 20 minute e-learning modules to a 6-month advanced programme - ensures that users have the skills to use the software and other tools to apply Decision Analysis. Following training, users are supported in their use of DTS through a network of global and local communities of practice, a network of Advanced Practitioners, and a rapid response help service known internally as ‘Model Solutions’. Best practice Decision Analysis is recognised and celebrated throughout the organisation by the publishing of a best practice database and an annual awards event which is sponsored by members of the Unilever Finance Leadership Team. The cycle is completed through external validation of training programmes by Stanford University, which includes an examination of course participants to ensure they have the skills needed to analyse complex decisions. The potential to obtain professional certification provides an incentive to practitioners to use DTS as part of the Decision Analysis programme and ultimately to enable Decision Analysis to become ‘the way we do things’ at Unilever. |
Use of Monte Carlo Simulations for Risk Management in Pharmaceuticals - A Case Study Barry (Bir) Gujral
The risk based regulatory approaches recognize the level of scientific understanding of different factors affecting the product and quality performance and the capability of process control strategies to prevent and mitigate the risk of producing a poor quality product in Pharmaceuticals. A probability distribution function is assigned to the unknown variables and then the Monte Carlo Simulations using @RISK are run to determine the combined effect of multiple variables. The Simulation envisions process variances, uncertainties and interdependencies for continuous improvement. It also helps to control random and non-random variability for better consistency. The risk analysis approach selects values for independent variables as a function of a probability distribution function for each variable. Thus for a given data the variability in effectiveness is viewed as a probability distribution. A standard sensitivity study shows us the sensitivity of the resulting improvements from the range of outputs from a single variable.
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Dr Michael Rees This session covers some ideas in modeling best practices, in both Excel models and @RISK models. Topics include issues in model design, structure, formatting, error-checking and a variety of tools related to sensitivity analysis. We also mention some uses of Palisade’s TopRank for model auditing and checking.
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Introduction to DecisionTools Suite 5.5.1 Dr Michael Rees This session will show you how to use the elements of the new DecisionTools Suite 5.5.1 as a comprehensive risk analysis, optimization, and statistical analysis toolkit. Each of the products in the Suite — @RISK, RISKOptimizer, Evolver, PrecisionTree, TopRank, StatTools, and NeuralTools — will be presented, showing how they can be used to solve practical problems in the real-world. Pick up hints and tips for using the products together. Also, we'll take a look at the completely-translated Suite in one of the international versions: Spanish, French, German, Portuguese, or Japanese.
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Dr Michael Rees This introduction to @RISK will walk you through a risk analysis using various example models. Key features of @RISK will be highlighted, and new enhancements in version 5.5 will be pointed out along the way. You will experience the intuitive interface of @RISK as you define distributions, correlations, and other model components. During simulation you will be able to see all charts, thumbnails, and reports update in real time. View results with a variety of graphing options, including new cumulative-histogram overlays, scatter plots in scenario analysis, and more. There’s so much to see, we’ll cover as much as time permits.
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Dr Mirek Janusz RISKOptimizer uses powerful genetic algorithms to perform optimization in Microsoft Excel. Moreover, RISKOptimizer builds on traditional optimization by adding Monte Carlo simulation to account for uncertain (stochastic), uncontrollable factors in your optimization problem. This session introduces you to this powerful tool, showing you how to set up a model, define constraints within the model, and ultimately arrive at the optimal outcome. Examples of resource allocation, budgeting, and scheduling will be included.
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Introduction to Project Risk Management using @RISK for Project Ian Wallace The aim of this seminar is to give people a basic understanding of how @RISK for Microsoft Project works, including hands-on experience for setting up and running simulations, and interpreting the results. Attendees will learn about the key functionality within @RISK for Project in step-by-step method, enabling them to quickly become familiar with basic concepts and terminology. In addition to graphing and quantifying the risk in a business plan, you will learn how @RISK for Project, using Monte Carlo simulation, enables you to:
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Introduction to StatTools and NeuralTools Dr Mirek Janusz In this session we will learn how to use Palisade’s two data analysis tools: StatTools and NeuralTools. StatTools is a Microsoft Excel statistics add-in. This session will cover how to perform the most common statistical tests, and will include topics such as: Statistical Inference, Forecasting, Data Management, Summary Analyses, and Regression Analysis. NeuralTools imitates brain functions in order to “learn” the structure of your data. Once NeuralTools understands the data, it can take new inputs and make intelligent predictions. The new predictions are based on the patterns in known data, and offer uncanny accuracy. NeuralTools can automatically update predictions when input data changes, and it can even be combined with Palisade’s Evolver or Excel’s Solver to optimize tough decisions and achieve desired goals.
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Real Options Modelling with Dr Michael Rees This session introduces the topic of real options modelling as an extension of risk modelling. The link to general decision making under uncertainty and financial market options is also discussed. A variety of examples using @RISK and PrecisionTree is presented. |
Selecting the Right Distribution in @RISK Dr Javier Ordóñez This session covers the choice of the appropriate distribution in @RISK. A variety of approaches are presented and compared, including pragmatic, theoretical and data-driven methods. The use of distributions to treat a variety of risk modeling situations is discussed, and some new distributions and features in v5.5 are shown.
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Using @RISK in Cost Risk Analysis Dr Javier Ordóñez In this session we will explore how @RISK can be used to model cost uncertainty and risk events that will affect the total project cost. We will show how to model cost ranges and risk registers through the use of probability distributions. We will discuss how to measure correlation between variables, how to add a correlation matrix into a model, and the impact of correlation in a result. Once our simulation model is run, we will learn how to assess the contingency required. We will also learn how to identify the key drivers that are needed for a mitigation analysis, and learn how to use of multiple simulations to compare the effectiveness of the different mitigation strategies. If time allows, we will present examples of VBA macros that allow automating the construction and reporting of risk models. |