Whitepapers & Briefs

Palisade Thought Leadership in Risk and Decision Analysis

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Schedule Risk Analysis Simplified

This presentation was given by David Hulett of Hulett and Associates at the Project Management Institute annual meeting. It is a brief overview showing the benefits of accounting for uncertainty in your project models.

by: David Hulett, Hulett and Associates

Industries: Construction / Engineering

Project Cost Risk Analysis: The Risk Driver Approach Prioritizing Project Risks and Evaluating Risk Responses

This presentation outlines limitations of the traditional three-point estimate approach to quantitative risk analysis. The authors introduce the Risk Driver method to cost risk analysis in projects. The approach uses @RISK, and a sample spreadsheet model with risk registers is included so you can try it yourself. A simple refinery construction example is included.

by: David T. Hulett, Keith Hornbacher, and Waylon T. Whitehead; Hulett Associates, LLC

Industries: Construction / Engineering

Unknown Unknowns in Project Probabilistic Cost and Schedule Risk Models

Practitioners recognize a requirement to consider unknown unknowns in project risk management. Same time, clear and consistent recommendations on incorporating of unknown unknowns into risk models have yet to be proposed. This paper outlines thinking process and comes up with practical recipes on handling unknown unknowns.

by: Yuri Raydugin, Risk Services & Solutions Inc.

Industries: Construction / Engineering

Optimizing Global Clinical Trial Investments

Advanced risk modeling for patient enrollment forecasting in 23 country phase III trials saves millions for pharmaceutical company.

by: Todd D. Clark – President, Value of Insight Consulting, Inc.

Industries: Healthcare / Pharmaceuticals

Schedule Risk and Contingency using @RISK and Probabilistic Analysis

Project overruns and failures are all too commonplace. As many of the underlying problems are difficult to address in the short term, it is clear that project managers need some help in obtaining sufficient schedule and cost contingency to avoid overruns. This paper suggests ways of using probabilistic analysis and Monte Carlo simulation so that managers can visualize and quantify the uncertainty in their projects and make thought-provoking predictions of the likelihood of being on-time and on-budget. With this new information, it will be possible to make more informed decisions about target dates, pricing, budgeting and risk management, as well as manage customer and stakeholder expectations more effectively.

by: Ian Wallace, Project Risk Consultant

Industries: Construction / Engineering

Put More Science into Quantitative Risk Analyses

Zhao argues that risk practitioners are responsible for putting more science and Monte Carlo simulation into risk analyses to foster and bolster the credibility of risk quantification.

by: John Zhao, Quality and Risk Manager, Statoil ASA

Industries: Energy / Utilities

Simulating the Financial Consequences of the Subprime Mortgage Crisis

Roy Nersesian describes how the selling of mortgages as investments (collateralized mortgage obligations, or CMOs), coupled with lax governmental regulation and the greed of house flippers fueled the flames of the home buying and building frenzy. He uses an @RISK simulation to show exactly how and where the investors lost in the ensuing housing market meltdown.

by: Roy Nersesian, Department of Management and Leadership, Monmouth University

Industries: Finance / Banking

Using Simulation to Support The Reinsurance Decision of a Medical Stop-Loss Provider

This paper illustrates how simulation modeling can be employed to support the reinsurance decision of a medical insurer. We do this in the context of a simplified but realistic example, where a medical insurer is evaluating a request for proposal to provide stop-loss coverage for a trust, which provides comprehensive medical coverage to employees of a major conglomerate. Simulation is employed to evaluate alternative reinsurance options for the stop-loss provider. We incorporate uncertainty about the true loss distribution through the use of alternative distributions to model total claims.

by: Lina S. Chan, FSA, MAAA, FCA Managing Partner, CP Risk Solutions, LLC, and Domingo Castelo Joaquin, Illinois State University, Insurance Markets and Companies: Analyses and Actuarial Computations, Volume 1, Issue 2

Industries: Insurance / Reinsurance

The Effectiveness of Using Project Management Tools and Techniques for Delivering Projects

The outcome of this work is to demonstrate how the adoption of project management tools and specific risk management software, if successfully implemented, can be important tools in enabling project managers to achieve higher levels of success. The authors use @RISK in the study.

by: Mufeed Hajjaji, Paul Denton, Steve Jackson, Computing and Engineering Researchers' Conference, University of Huddersfield

Industries: Construction / Engineering

Modeling the competitive market efficiency of Egyptian companies: A probabilistic neural network analysis

Understanding efficiency levels is crucial for understanding the competitive structure of a market and/or segments of a market. This study uses two artificial neural networks (NN) and a traditional statistical classification method to classify the relative efficiency of top listed Egyptian companies. “Because of its extensive capabilities for building networks based on a variety of training and learning methods, NeuralTools was chosen in this study.” Results indicate that the neural network models are superior to the traditional statistical methods. The study shows that the neural networks have a great potential for the classification of companies’ relative efficiency due to their robustness and flexibility of modeling algorithms.

by: Dr. Mohamed M. Mostafa, Expert Systems with Applications

Ceramic coatings for jet engine turbine blades

Ceramic thermal barrier coatings (TBCs) are applied to jet turbine blades to protect them from the high temperature gases leaving the combustion chamber and to increase the efficiency of the engine. @RISK was used to estimate emissions reductions due to improved coatings.

by: David Parsons and Julia Chatterton, Carbon Brainpoint Case Study, Cranfield University

Estimation of Uncertainty Distributions for Internal Flood Initiators Using Parametric Sensitivity Study

To help understand the effect on the uncertainty associated with the initiating frequency for an internal flood scenario based on contributions from various water systems, a parametric study was performed in which each of the system piping failure rates was fitted to a cumulative distribution. A parametric analysis was performed using @RISK.

by: Robert J. Wolfgang, Consultant, ERIN Engineering and Research, Inc.

Industries: Construction / Engineering

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