Resources
When modeling risk events, it is common that several events could affect the same cost element of a project. During the simulation, two or more risk events can occur at the same time. The question becomes how to calculate the total impact. This type of modeling technique is very common and often needed in project […]
An earlier blog on Best Practice Principles in .Excel Modelling generated quite some interest, as well as demand for more details on some of the points made, especially those concerning the use of named ranges risk assessment models in Microsoft Excel. In the earlier posting, I had simply stated that (in my opinion): “Named ranges should […]
The blog positing on best practices in Excel modelling could be thought of as providing a reasonable and robust set of principles for building static Excel models. When building simulation models for risk analysis in Excel (for instance, with @RISK Monte Carlo software), some other points are worthy of consideration: A risk model may need to be built […]
This blog briefly posts some fairly standard (but not fully accepted, and more often simply not implemented!) “best practice principles” in Excel modelling. A later blog discusses a related topic as to whether risk modelling (when building Monte Carlo simulation models using @RISK in Microsoft Excel) requires the same (or a modified) set of principles. The […]
In Part I, combining simulation and decision tree analysis techniques was introduced. But what does that actually give you? What meaningful results are created to justify the work? Obviously there are good things to come, or I wouldn’t be bringing it up! A regular spreadsheet model can produce a distribution of outcomes like this: But […]
First of all, what is Monte Carlo simulation? Monte Carlo simulation is a computerized mathematical technique that allows people to account for variability in their process to enhance quantitative analysis and decision making. The technique is used by professionals in such widely disparate fields as finance, project management, energy, manufacturing, engineering, research and development, insurance, […]
When using @RISK (risk analysis software for conducting Monte Carlo simulations in Microsoft Excel), one of the output graphs is a tornado graph. Such graphs have their most direct interpretation for linear models with independent input distributions, such as in most typical cost budgeting models. In these cases, the regression coefficients provide a measure of […]
@RISK is used to assess the relative benefits of different drilling strategies for Lundin, a small exploration company, for field development. Click here to read the article.
A discussion of how modelling software is having an impact on monitoring the spread of global diseases. An example using @RISK to combat avian flu is included. Click here to read the article.
Researchers at the University of the Pacific’s Thomas J. Long School of Pharmacy and Health Sciences used Palisade’s NeuralTools to assist in the evaluation of both the impact of pathological processes and their implications for drug therapy. Click here to read the article.
According to a recent Harvard Business School study, higher IT capability directly correlates with superior revenue growth. Nearly all IT managers (at more than 150 large enterprises) surveyed for the study said they plan to increase outsourcing, particularly in the areas of application development and IT infrastructure. This article features PrecisionTree to evaluate outsourcing decisions. […]
This is the first in a series of articles that will examine some of the most commonly used decision-making methods for the selection or rejection of individual projects throughout the project portfolio management process. These methods determine whether or not a given project (either proposed or in process) should be included in your next capital […]
