» Download zip file of presentations from the Palisade Risk Conference in Las Vegas
(Individual presentations are also linked below.)
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Conference Kickoff and Palisade Update Sam McLafferty Palisade President Sam McLafferty reviews goals of this year’s conference and ways you can benefit from the event before giving some context and background about Palisade as a company. He will also review important partnerships with leading global corporations in fields such as banking, energy, engineering, and manufacturing. Then, Sam will touch on key new features in the recent 6.0 release of @RISK and DecisionTools Suite software, as well as give a sneak peek at what is coming next. There is so much to see, Sam will highlight just a few innovations to demonstrate the breadth and power of this new standard in risk and decision analysis. More in-depth exploration of @RISK and DecisionTools Suite 6.0 can be found in the Software Presentation track.
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Anatomy of a Rollout: Real-World Stories of How Risk and Decision Analysis is Implemented Across an Enterprise Randy Heffernan Entrenched attitudes, IT policies, lack of quantitative training — there are many barriers that can get in the way of implementing an effective risk analysis solution. Yet the benefits of such a challenge are worth the effort: fewer surprises, new opportunities, and better decisions. Building on Sam's discussion of successful Palisade customers, Vice President Randy Heffernan will expand on some key corporate adoptions of @RISK and DecisionTools Suite software with a view to understanding important steps each took on their path to implementation. Randy will explore some of the most successful rollouts we've seen, examining milestones passed and challenges met along the way. At the end of the talk, you will come away with an outline for your own roadmap to better risk management.
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Holistic Decision-Making at Intel: How One Decision Affects Another within a Company Portfolio Making decisions with uncertain outcomes can be difficult. Understanding how various decisions interact in the context of a portfolio of investments can be even harder, and just as important. At Intel they are working with the concept of “Incremental Value” for major decisions - what would our business look like with this project, and what would our business look like without it. This presentation will work through the concept of incremental value and a simple example of how to model typical decisions in an uncertain world using @RISK.
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Round Table Discussion: Challenges in Your Risk Analysis Journey
Panel of Experts, including Patrick Murray, HDR; Basil Stumborg, British Columbia Hydro; Moderated by Randy Heffernan In wrapping up this year's conference, Palisade Vice President Randy Heffernan will lead a discussion with leaders in the field of risk and decision analysis from a variety of industries. Each has a story to tell about his own organization's implementation of risk analysis, and we will share anecdotes and lessons learned about what went right and what to avoid. Audience participation is strongly encouraged. It's a great chance to learn from your peers and influence the direction of your own company's evolution toward better, smarter decisions.
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Cisco Case Study – Manage Risks of Supply Chain Disruptions For a company with a 100 Billion market cap, more than 25,000 product ID’s, 1,000 suppliers and 68,000 active components, designing a resilient supply chain to mitigate potential risks is absolutely critical. Equally daunting, is the task of managing costs associated with these risks optimally in an interconnected global market place that is more dynamic than ever. This business case will show how Cisco's Network and Design Management Team (ND&M) employed Palisade's @RISK, RISKOptimizer and StatTools to conduct a risk analysis across its entire supply chain process model towards the design of one that is more resilient against risk yet able to perform at optimal costs. Key takeaways from this business case:
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Cost & Schedule Risk Assessment Timothy J. Havranek Risk management is becoming increasingly important in the world of capital project management. Traditional risk management focuses primarily on evaluating and managing cost risk; while schedule risk is seldom or only marginally addressed. Schedule delays increase project carrying costs and in some cases introduce contract penalties and reduced project return on investment. In addition, there are many projects that are schedule driven rather than cost driven, such as having a major project complete in time for a scheduled event. Since both cost and schedule risk are important and often correlated, the ability to conduct both assessments concurrently using one model, as is possible with @RISK 6.0, is ideal. This case study involves the cost and schedule risk assessment (CSRA) of a combined demolition / remediation project to prepare an industrial property for mixed commercial / residential redevelopment. The CSRA model includes the use of probabilistic branching to address uncertainties associated with pre-demolition permitting activities and probability distributions to address uncertainties associated with the number of units of material such as contaminated soil or asbestos containing material to be removed from the site. It also includes the use of use of a risk register to address events that could impact the cost and durations of given tasks or introduce entirely new tasks. In addition to presenting a model's structure and results; best practices for gathering useful data from project stakeholders and for creating dynamic network schedules will be described. Lastly, methods for tracking the project and updating risk through project execution will be presented.
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Creating CMMI® Process Performance Baselines and Models using @RISK Dr. Vladimir Savin EPAM Systems created Integrated Information Security and Quality Management System. It is based on the EPAM Quality Policy and Information Security Policy reflecting most of the requirements of ISO 9001:2008, CMMI® for Development and others. Latest CMMI ML4 appraisal SCAMPI Class A was successfully conducted in March, 2011. This article addresses the issues associated with effectively implementing statistical process control to manage and improve software development processes. The expected usage of statistical techniques in achieving CMMI Maturity Levels 4 and 5 is described. According to CMMI the purpose of Organizational Process Performance (OPP) is to establish and maintain a quantitative understanding of the performance of the organization’s set of standard processes. The purpose of the Quantitative Project Management (QPM) process area is to quantitatively manage the project’s defined process to achieve the project’s established quality and process-performance objectives. Quantitative process performances data support quality and process-performance objectives, and allow quantitatively manage the organization’s projects. For creating Organization’s (Process Performance Baseline) PPBs and (Process Performance Model) PPMs, EPAM is using @RISK, @RISK’s Project tool, Time Series Analysis with @RISK, Control Charts, Regression Analysis, and ANOVA with StatTools. Key practices of OPP and QPM are selecting sub-processes critical to evaluating performance and that help to achieve the project’s quality and process performance objectives. One approach is based on Confidence Intervals and Monte Carlo simulation with @RISK. EPAM is using sub-processes that allow predicting the latent defects in the delivered product using measurements of work product attributes such as complexity and process attributes such as preparation time for peer reviews and review effort. The following sub-processes are considered:
Outcomes of PPMs are prediction number of defects expected in each of the reviews, (classified by type - technical review and walkthrough - and work product - Requirements, Design, Code, and Test Cases). The predictions are represented by the upper control limit, the central value and the lower control limit and the confidence of achieving them. The model summarizes also the total number of defects expected at project level, represented too by the upper control limit, the central value and the lower control limit and the confidence of achieving them, and the associated confidence interval. Second approach is MS Project with @RISK usage for Quantitative Project Management that allows effectively implements Earn Value Management (EVM) methodology and Risk Analysis using Monte Carlo simulation. Using @RISK’s Project tool we can introduce uncertainty to the project and calculate needed risks by tasks regarding starting, duration, branching and so on. These project management techniques which measure the integration of technical performance, cost and schedule against planned performance within a given project. It brings significant benefits to project management. The result is a simple set of metrics providing early warnings of performance issues, allowing for timely and appropriate adjustments. Third approach is Time Series Analysis with @RISK for Forecasting Requirements Volatility. There are three groups of @RISK Time Series functions: ARMA (autoregressive moving average) processes, GBM (geometric Brownian motion) and its variations, and the ARCH (autoregressive conditional heteroskedasticity) process and its variations. After investigation we have accepted GBM with mean reversion (GBMMR) approach as most suitable for our goals.
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Educational Effectiveness: Fact, Fantasy and Fraud Clayton Graham Dr. Glenn "Max" McGee Few venues in the social sciences generate more discussion and conjecture than education in general and elementary education in particular. Each state is entrusted with the responsibility of measuring and establishing standards of academic achievement. A couple of fundamental questions arise:
Identifying achievement (or lack thereof) by school districts mandates a deeper examination
Educational performance history will be covered and the effectiveness of top down governmental mandates and ground up district leadership will be addressed. The objective is to demonstrate how proper quantitative information coupled with locally based leadership can actually become a beacon of direction for our faltering educational system. Out of the melee of uncertainty by governmental entities making progress has fallen to school districts by default. Some innovative school districts utilizing sound analytical tools have blazed a trail to achieve remarkable improvement in not only academics, but financial responsibility, community relations, and defined expectations. The path to educational improvement is a process, including and not limited to: testing, normalizing data, and transforming it to quality information, resulting in a prioritized game plan. Actual case studies of school districts will be directed towards:
StatTools, Evolver and elements of @RISK are used throughout the analysis.
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Evaluating Risk Management Tools for Agribusiness: A California Perspective Dr. Mark Manfredo California is unique among agricultural production regions of the United States, namely due to the tremendous diversity and value of its agricultural production. Because of this, the risk environment faced by California agribusinesses is often difficult for one to fully comprehend, and thus leads to challenges in effectively managing risks. Indeed, many of the talks presented at this conference have helped to provide a better understanding of the current risk environment faced by California agribusinesses. Risk management is challenging, and there is no “cookie cutter” approach. This is especially true for California agribusinesses given the risk management tools available are somewhat limited in their applicability (Blank and McDonald, 1995). What is probably more important than the use of any particular risk management tool, however, is a proper assessment and ultimately measurement of risk. Only after risks are assessed and measured can the proper risk management tool(s) and/or strategies be implemented. Given this, the objective of this paper and accompanying presentation is to provide an overview of various risk management tools available to diverse California agribusinesses. Prior to the examination of these specific tools, a primer on risk measurement is provided that helps the reader understand common risk measures as well as the overarching objectives of risk management - to reduce uncertainty and downside risk impacting financial performance. The specific risk management tools then explored are exchange traded futures and options, forward contracts, and crop insurance. To keep the paper and presentation tractable, focus is placed on evaluating the applicability, as well as the pros and cons, of each of these tools. Next, a set of available risk management strategies are simulated and evaluated in the context of a hypothetical San Joaquin Valley navel orange farm. In doing this, special attention is paid to the performance of the new specialty crop revenue insurance policies (Actual Revenue History - ARH) currently under pilot testing by the USDA - Risk Management Agency (RMA). A summary and final thoughts are then presented.
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Forecasting Airport Aviation Activity: Integrating Risk Analysis Using Monte Carlo Simulation Dr. Sharon Sarmiento Vernon Kinley Forecasts of aviation activity are key input to airport planning and financial analyses. The authors present the development of aviation activity forecasts using a hybrid modeling approach that: (1) utilizes available information on scheduled airline service for the near term; and (2) employs multivariate time series regression analysis that links long-term growth trends in airport activity to key demand drivers such as economic trends, yield trends, industry structural changes, and changes in airline service at the airport. The application of the modeling approach to the development of rental car demand projections is also presented. All forecasts are subject to risks and uncertainty, and the market volatility that the aviation industry has faced over the past decade and the significant uncertainty in market outlook call for a more comprehensive and systematic assessment of forecast risk. The authors perform comprehensive risk analysis using Monte Carlo simulation—using @RISK software—to develop a range of possible outcomes. Along with the hybrid forecast modeling approach using multivariate time series regression analysis, risk analysis using Monte Carlo simulation presents a more rigorous and comprehensive approach to developing airport traffic and rental car demand forecast scenarios.
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Good Practices and Common Mistakes Dr. Huybert Groenendaal An increasing number of organizations are using analytical techniques such as quantitative risk analysis, value at risk (VaR), and risked NPV to help them improve decision making. However, all too often, these techniques may not be used optimally or accurately and their full value may not be realized. During this presentation, Dr. Groenendaal will share his hands-on experience through hundreds of projects, and will discuss the following topics:
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It’s not all Rocket Science; Very Simple and Highly Effective uses of @RISK from the World of Corporate Finance and Investment Management J. Todd Larson Todd Larson spent 5 years with Procter & Gamble and 7 years with Amazon.com in a wide variety of finance roles, from Treasury to M&A to Retail management. In every role, he utilized @RISK extensively to help solve a myriad of problems. Today he will discuss his experiences in using @RISK in the corporate environment, with a particular focus on how beneficial even simple simulation models can be in guiding proper corporate behavior.
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Managing Risk within Capital Program Budgets Korey Campbell Korey's presentation will show how the incorporation of risk analysis can assist airports develop budgets for major capital improvement programs. Specifically, Korey will provide examples that demonstrate how @RISK can be used to establish project contingency, and RISKOptimizer can help determine the most advantageous mix of funding sources.
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Margin Risk: A Different Approach to Risk Analysis and Risk Management in Agriculture Steven Slezak Dr. Jay Noel In agribusiness operations, risk management practices have focused historically on managing the volatility of production prices. The emphasis on revenue risk - market prices for agricultural commodities - was not practiced consistently throughout the various production levels in the industry. Large, sophisticated agribusiness concerns were more likely to devote resources to risk analysis and risk management than were smaller operations, such as independent farms and ranches. Regardless of size, ag risk analysis was about understanding the impact of the volatility of output prices, not that of input cost volatility. Over the last decade, agriculture in the US and around the world has been struggling to deal with increasing volatility in the prices of ag commodities, and in the costs of basic ag inputs - energy, fertilizer, water, and land. This realization is changing risk management and risk analysis practices at all levels of agricultural production - large and small. Ongoing domestic and global economic stagnation has forced agribusiness and, perhaps more importantly, the institutions that provide debt and equity investment to agriculture to expand the operational definition of risk management. Agribusiness is coming to understand that managing revenue risk alone is no longer a practical approach. The industry is turning its attention to managing the risks of ag production costs. Risk management practices focused on margin risk - strategies to minimize the volatility of profits by simultaneously minimizing the volatility in revenues and costs - are beginning to take shape. This may not be news to other industries, but it represents a real breakthrough in thinking about ag risk and in conceptualizing the risk and return characteristics of global and domestic ag operations. This case involves a lettuce farming operation in California that is learning the importance of managing margin risk and, as a result, is beginning to see positive results operationally and financially. The case will compare and contrast the farm operation’s situation under traditional revenue risk management methods with the results it has experienced using a strategy of margin risk management.
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Portfolio Management & Financial Planning using @RISK Matthew H. Rosenberg Matthew Rosenberg's presentation will cover the use of Monte Carlo simulation and optimization to create customized personal financial plans for individuals. He will also cover how the results of a personal financial plan are translated into a portfolio management strategy that will give an investor the highest probability of achieving their financial goal.
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Pricing Energy Efficiency and Renewable Energy Investments Under Risk and Uncertainty Anthony Sclafani Decisions to invest in energy efficiency and renewable energy are made based on both the technical and financial merits of the investments; both aspects include risk and uncertainty. The technical/performance benefits of energy efficiency and renewable energy are subject to variations in operating conditions. For example, the performance of a solar photovoltaic array is related to the incident solar radiation - which varies continuously throughout the year. The performance of upgraded HVAC equipment is related to the local weather - which also varies continuously throughout the year. Traditional methods of simulating the technical performance of these systems have been based on best-case/worst-case scenarios and, more recently, on layers of point estimates. This presentation describes the application of probabilistic methods to these analyses to clarify the range of possible performance outcomes and reduce over-reliance on point estimates. The expected performance of energy efficiency and renewable energy investments are normally input into financial models that are used to price the investments based on some constraints. Many variables in the models have some degree of uncertainty where probabilistic methods are currently applied such as utility rate forecasts, estimates of construction costs, project contingency, etc. This presentation discusses the effect of a performance guarantee on the performance risk and pricing of the investment. The goal of the presentation is to demonstrate how a probabilistic evaluation of the technical uncertainty and financial risks improves decision-making in guaranteed energy savings performance contracts.
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Reliability-based Design of Foundations for Oil and Gas Elevated Platform Dr. Ok-Youn Yu The oil and gas industry endeavors to minimize the environmental impact during oil and gas drilling operations. For instance, by reducing the footprint during drilling operations using a reusable modular platform and a small mobile rig, in 2003 and 2004 Anadarko and Maurer Technology Inc. demonstrated a new foundation concept in the Arctic. Their objective was to drill in an ecologically sensitive area without disturbing the ground surface. Use of an elevated platform in environmentally sensitive areas requires the use of piles to support the elevated deck instead of gravel pads used in a conventional drilling system. The aim of this presentation is to introduce an environmentally friendly foundation method for onshore drilling systems, and to conduct a parametric study of different foundations to improve the understanding of these types of foundation designs, by introducing uncertainty quantification for various rig weights and soil conditions in environmentally sensitive areas (e.g., desert environments and wetland applications). This presentation introduces a case study on pile capacity calculations for onshore elevated platforms depending on various soil types and pile types, and sets the basis for a full risk analysis on this type of foundation.
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Mark S. Rudd The renewable energy industry is growing rapidly. New energy alternatives exist today that were not even considered just a few years ago. But, just because a fuel is renewable, it is not constant. The ability to use Monte Carlo simulation of key variables can determine which projects are built and which are not. This presentation will walk through a typical biomass gasification project, noting potential areas of variance and how they might be modeled. The review will ultimately flow into a project financial analysis using a Discounted Cash Flow analysis and Return On Investment forecast. Finally, a review of project drivers, risks and strengths will be done using a standard “tornado diagram” and possible solutions.
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Risk Analyses in Support of Risk Management Decisions during Project Delivery This session will discuss the importance and interpretation of risk analysis for risk management decisions. Project examples will discuss a variety of risk analyses that include: (a) business case evaluations of alternatives; (b) refinement of program budget and schedule; (c) risk-based management of contingency; (d) forecasting of escalation and risk-reducing contracting strategies; and (f) evaluation of procurement and alternative delivery strategies.
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Risk Analysis for Effective Airport Financial Planning in an Uncertain Business Environment Brian Drake Brian's presentation focuses on incorporating risk analysis in airport financial modeling. Performing Monte Carlo simulation using @RISK software, he shows how risk analysis can help airports develop more effective financial plans for their capital investment programs in an uncertain business environment.
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Risk-Based Lifecycle Costing Analysis Gregory Brink Life cycle costing (LCC) is a method of analysis deployed by facility owners tasked with maintaining large infrastructure systems such as highways, hospitals, and subway systems; however its application to the design development process is fraught with a myriad of assumptions. Assumptions affecting the cost of future repairs can greatly impact the initial design decisions and enhanced methods are needed to evaluate future cash flows, judge the accuracy of future cost projections, and ascertain the schedule (When) these repairs may be needed in the future. By expanding the LCC evaluation process to include risk analysis to better model these assumptions, future costs can be analyzed using present worth analysis, cost and schedule distributions, probabilistic ranges, anticipated repair cycles, and even consider event risks over the facility life. More robust decision making through the use of @RISK allows for enhanced comparisons between design alternatives with improved accuracy in calculating the total cost of ownership while allowing for improved projections for the scheduling of required maintenance events. Armed with improved information concerning life cycle cost and the true present value costs of ownership, owners can balance initial and future cost to optimize designs, improve the value of their programs, and support their design decisions with defensible cost analysis. Improved information at the hands of enhanced modeling of facility and ownership costs ultimately leads to a higher probability of net value improvement and enhanced long-term development and investment decisions. Overall, this presentation will discuss the use of risk modeling in application for LCC analysis and the outcomes and benefits that can be derived.
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Throughput Analysis using Monte Carlo Simulation Britt Calloway During large distribution center designs, it is critical to understand the variability in throughput without doing complex and time consuming simulation. Especially for consulting or proof of concept simulations, Monte Carlo is a powerful tool to kickstart the project into a risk aware mode and develop a high level sensitivity to different system options. In this seminar, Britt Calloway will present previous projects that have used Monte Carlo to conduct sensitivity analysis to throughput. In addition, he will present a Six Sigma Optimization that was used to minimize variability and reduce cost in an academic problem and how it could be implemented in a real world environment.
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USACE/Greenup Locks and Dam – Engineering Risk & Reliability Analysis using @RISK Greenup Locks and Dam is a U.S. Army Corps of Engineers (USACE) project, located on the Ohio River, near Greenup, Kentucky. Construction of the project was completed in 1967. The project consists of two lock chambers with a 30 foot lift, a dam consisting of flow regulating gates, a bridge that connects U.S. Route 52 in Ohio to U.S. Route 23 in Kentucky, and a 70 MW hydroelectric power plant. Internal stability of the Greenup Lock Middle Wall monoliths was evaluated to estimate the probability of unsatisfactory performance throughout the planning horizon. The planning horizon spans from initial construction, 1967, to the end of the benefit accrual period, 2070. The @RISK analysis was performed using the USACE requirements for the Planning and Design of Navigation Locks, EM 2602. Probability distribution parameters were developed for the time-independent variables of concrete strength and geomaterial properties. The reinforcing steel yield strength and anchor rod tensile strength properties were time-dependent based on a model of steel deterioration. Expert Opinion Elicitation was used to attain consensus on the distribution parameters. Event trees, with Hazard Functions, were used to evaluate rehabilitation strategies as failure of the Middle Wall would result in disruption of navigation causing loss of revenue. The model was calibrated to match the Middle Wall performance to date. |
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Using DecisionTools to Mitigate Project Risk for Large Scale Wood Pellet Export Projects Dr. William Strauss Over the next 5 to 8 years more than 4.5 billion dollars in capital costs for new capacity in the wood pellet export sector will be invested in North America. This will increase North American export capacity from the current 6 million tons per year to a forecast 45 million tons per year by 2020. These are large sums that demand careful due diligence prior to investment. FutureMetrics has forecast the growth of the pellet export market using the new @RISK time series functions. They have also used these new functions to quantify the uncertainty in the market growth. FutureMetrics has also stochastically modeled how the prices for pellets imported into Europe and the raw material costs in North America will react to the growth in capacity and demand. They use @RISK Monte Carlo simulation to show the likely distribution of the projects' returns on investment and to pinpoint key leverage points in mitigating project risk.
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Using Decision Trees and Uncertainty Analysis to Inform Negotiation Strategy Negotiating strategy contains elements that lend themselves well to an application of risk analysis using decision trees; there are complex choices, outcomes are uncertain, and decision makers are risk averse. As a crown corporation supplying 90% of the electricity to the province of British Columbia, Canada, such decisions are complicated by the fact that the corporation has multiple objectives: keep costs low, minimize impacts to its reputation, and enhance environmental values. This presentation will walk through a simplified version of a decision faced by BC Hydro recently. It will highlight the application of decision trees and uncertainty analysis paying particular attention to how unique elements came to the fore: assessing levels of risk aversion, understanding value tradeoffs among multiple items in a negotiation package (monetary and non-monetary), and how this information can be used to derive a “walk away” level for negotiation.
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Using PrecisionTree to Evaluate Clinical Development Options in Pharmaceuticals Taking a pharmaceutical compound from investigation and discovery through to commercialization is a time-consuming and expensive process with many potential pitfalls and complicating factors along the way. Using PrecisionTree to model different scenarios for the development of a product has helped Ikaria manage the complexity of this process and develop insights to help scientific and commercial decision-makers better understand the options available to them. This presentation will walk through an example of a PrecisionTree model similar to one that was used in a recent clinical trial design decision at Ikaria. Integration with @RISK will also be demonstrated.
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When is Quantitative Project Risk Analysis Necessary? How Effective Leaders Deploy @RISK for Excel/Project Keith D. Hornbacher “When is the right time to deploy quantitative analysis of project risk and uncertainty?” This presentation answers that important question. Mr. Hornbacher draws on his experience in the private and public sectors using @RISK for Excel / Project. In addition, feedback will be shared from seminars he conducts in the University of Pennsylvania’s graduate project leadership concentration (Dynamics of Projects, Programs, and Portfolios). Participants will learn what conditions enable as well as those that inhibit organizations when they implement non-deterministic project management systems, methods, and tools. This event highlights crucial elements in organizations and traits contributing to their leaders’ success in the transition from “old school” single value estimates to systems capable of handling uncertainty and risk. The discussion is framed by different perspectives gained during independent reviews of NASA programs and audits of GAO projects. Mr. Hornbacher draws from his direct experiences in project-program risk management and education. Key points participants will learn to recognize:
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Customized Software Applications 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 (Excel Developer Kits or XDKs). You can customize the application interface to include only what the users need, hiding unused @RISK functionality and preventing user access to the underlying model logic. You can also automate processes like reporting, generating only the charts and data you want. The result is a perfectly tailored application ready to roll out to your workgroup. And because the application is in Excel, the training required for users is minimal. Palisade Custom Development has written applications for cost estimation, asset management, retirement planning, oil and gas prospecting, and more – all utilizing @RISK technology in Excel. In this presentation, we will cover as many examples of custom applications as time allows.
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Decision Modeling with PrecisionTree Erik Westwig PrecisionTree is a powerful visual and analytical tool for mapping out complex, sequential decisions using decision trees directly in Excel. Using nodes, branches, and probabilities, you can represent and organize decisions ranging from oil prospecting to site development to options analysis. PrecisionTree can also be combined with @RISK to incorporate uncertainty and risk in tree models. This presentation combines an introduction to the PrecisionTree interface with demonstrations of how PrecisionTree can be used to analyze various problems in decision analysis.
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Finding Optimal Solutions with Thompson Terry RISKOptimizer 6.0 and Evolver 6.0 use powerful algorithms to perform optimization in Microsoft Excel. RISKOptimizer – an advanced analytical tool that comes with @RISK Industrial – builds on traditional optimization by adding Monte Carlo simulation to account for uncertain (stochastic), uncontrollable factors in your optimization problem. RISKOptimizer and Evolver have long used genetic algorithms to arrive at solutions that are impossible to find using most traditional methods. New version 6.0 introduces new optimization methods that can find optimal solutions even faster than genetic algorithms. This session introduces you to these powerful tools, showing you how to set up a model, define constraints within the model, and ultimately arrive at the optimal outcome. These steps will be illustrated by means of a detailed retirement portfolio optimization example.
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Rafael Hartke This introduction to @RISK will walk you through a risk analysis using various example models. Key features of @RISK will be highlighted, and new interface enhancements in version 6.0 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 and reporting options. There’s so much to see, we’ll cover as much as time permits. Please note this session is not an overview of the new features in version 6.0. That is covered in the “New Features of DecisionTools Suite 6.0 and @RISK 6.0” presentation.
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Introduction to the DecisionTools Suite Erik Westwig This session will show you how to use the elements of the DecisionTools Suite as a comprehensive risk analysis, decision-making, and statistical analysis toolkit. Each of the products in the Suite — @RISK, RISKOptimizer, Evolver, PrecisionTree, TopRank, StatTools, and NeuralTools — will be presented as time allows, showing how they can be used to solve practical problems in the real world. Pick up hints and tips for using the products together. We’ll also point out interface improvements along the way that can save time and enhance ease-of-use. Please note this session is not an overview of the new features in version 6.0. That is covered in the “New Features of DecisionTools Suite 6.0 and @RISK 6.0” presentation.
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Introduction to Project Risk Management Gustavo Vinueza The aim of this seminar is to provide a basic understanding of how the new @RISK 6.0 can help you manage uncertainty in your Microsoft Project schedules. Using Monte Carlo simulation, you will learn how to account for schedule and costs risks in a quick and comprehensive way. At last, here is a way to answer the question “What is the probability that my project will come in on time and within budget?” And with new version 6.0, risk modeling of your Project schedules is much more flexible and powerful than ever before. We will show you how to set up and run simulations, and how to interpret the results. You will learn how to use @RISK step-by-step, and become familiar with basic concepts and terminology. We’ll demonstrate powerful graphing and reporting that pinpoints where your risks lie and what their impact may be. You will see how using @RISK for your projects enables you to:
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Oil, Gas, and Utility Models in DecisionTools Suite Rafael Hartke @RISK and DecisionTools Suite software are widely used in the oil, gas, and utilities industries. There are many upstream applications, such as estimating volumetric reserves and production with Monte Carlo simulation in @RISK, and wildcat drilling using decision trees in PrecisionTree. Furthermore, @RISK can be used for valuation of wells and projects, and RISKOptimizer is well-suited to optimizing portfolios of different potential exploration projects. Downstream, refineries use @RISK to assess expansion projects and analyze supply chains. In the utilities sector, applications include demand forecasting, pricing, and load planning. @RISK 6.0’s new time series feature can be used to model uncertain demand and oil prices, and RISKOptimizer can help optimize load balancing problems. In this presentation, a sampling of simple Excel example models will be used to illustrate how powerful these tools can be for anyone working in the energy sector.
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Predictive and Data Analysis Dr. Chris Albright In this session you 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. This session will demonstrate, using easy-to-understand examples, applications of NeuralTools predictions.
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New Features of DecisionTools Suite 6.0
Sam McLafferty Erik Westwig Dr. Javier Ordóñez Dr. Chris Albright The 6.0 release of the DecisionTools Suite and @RISK marks an exciting advancement in quantitative risk and decision modeling. Powerful new analytical tools and robust ease of use features will appeal to both new users and seasoned experts alike. In this session, we will present a brief overview of major new features in version 6.0, such as time-series simulation modeling, new distribution fitting features, and integration with Microsoft Project. The floor will be open to questions and input on which features or applications you’d like to see most. Feel free to join in the discussion! Please note that time-series modeling and project management are also covered in detail in their own sessions, and that distribution fitting features will also be touched upon in the "Selecting the Right Distribution in @RISK 6.0" session.
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Schedule Risk Analysis with @RISK Gustavo Vinueza @RISK has long been used for schedule risk analysis and cost estimation across a variety of industry sectors. Now, the new integration of @RISK 6.0 with Microsoft Project schedules brings an entirely new dimension to schedule risk analysis. Through example models in construction, IT rollouts, and defense projects, we will see how to account for uncertainty in project schedules and determine the probability of finishing on time and in budget. You will learn to quantify the uncertainty inherent in task durations, dates, and resources, and how to uncover the risks driving your bottom line. Not only that, but through the @RISK for Excel interface, we will show how you can link delays and added costs in your models directly to risk registers, and how to combine cost estimation models with your schedules - analyses never before possible.
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Selecting the Right Distribution in @RISK 6.0 Thompson Terry 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 version 6.0 are shown.
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Time Series Analysis in @RISK 6.0 Dr. Chris Albright In statistics, economics and mathematical finance, a time series is a sequence of data points, measured typically at successive times spaced at uniform time intervals. Examples of time series are weekly currency exchange rates, the daily closing value of the NASDAQ Composite index or monthly crude oil prices. In traditional time series analysis, the past performance of the process is used as the basis for a single projected new path in the future. In reality, of course, there are an infinite number of possible future paths in any time series process. To address this, @RISK 6.0 now includes a time series analysis tool. This new functionality will let you simulate different possible future paths your time series process could take. You can construct these stochastic time series models directly or use historical data to fit time series functions to your data. You can then simulate many different possible future time series events quickly and easily, thus more accurately representing the uncertain future.
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