Oracle Crystal Ball has a complete set of functions that allows a modeler to extract information from both inputs (assumptions) and outputs (forecast). Used the right way, these special Crystal Ball functions can enable a whole new level of analytics that can feed other models (or subcomponents of the major model).

Understanding these is a must for anybody who is looking to use the developer kit.

Since the mid-1990s academics and business leaders have been striving to focus their businesses on what is profitable and either partnering or outsourcing the rest. I have assembled a long list of quotes that define what a virtual organization is and why it's different than conventional organizations. The point of looking at these quotes is to demonstrate that none of these models or definitions can adequately be achieved without some heavy analytics and integration of both IT (the wire, the boxes and now the cloud's virtual machines) and IS - Information Systems (Applications) with other stakeholder systems and processes. Up till recently it could be argued that these things can and could be done because we had the technology. But the reality is, unless you were an Amazon, e-Bay or Dell, most firms did not necessarily have the money or the know-how to invest in these types of inovations.

With the proliferation of cloud services, we are finding new and cheaper ways to do things that put these strategies in the reach of more managers and smaller organizations. Everything is game... even the phone system can be handled by the cloud. Ok, I digress, Check out the following quotes and imagine being able to pull these off without analytics.

The next posts will treat some of the tools and technologies that are available to make these business strategies viable.

Many speak of organizational alignment, but how many tell you how to do it? Others present only the financial aspects of portfolio optimization but abstract from how this enables the organization to meets its business objectives.  We are going to present a practical method that enables organizations to quickly build and optimize a portfolio of initiatives based on multiple quantitative and qualitative dimensions: Revenue Potential, Value of Information, Financial & Operational Viability and Strategic Fit. 
                  
This webinar is going to present these approaches and how they can be combined to improve both tactical and strategic decision making. We will also cover how this approach can dramatically improve organizational focus and overall business performance.

We will discuss these topics as well as present practical models and applications using @RISK.

It is a well-known fact that many projects fail to meet some or all of their objectives because some risks were either: underestimated, not quantified or unaccounted for. It is the objective of every project manager and risk analysis to ensure that the project that is delivered is the one that was expected. With the right know-how and the right tools, this can easily be achieved on projects of almost any size. We are going to present a quick primer on project risk analysis and how it can positively impact the bottom line. We are also going to show you how Primavera Risk Analysis can quickly identify risks and performance drivers that if managed correctly will enable organizations to meet or exceed project delivery expectations.

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Making decisions for the future is becoming harder and harder because of the ever increasing sources and rate of uncertainty that can impact the final outcome of a project or investment. Several tools have proven instrumental in assisting managers and decision makers tackle this: Time Series Forecasting, Judgmental Forecasting and Simulation.  

This webinar is going to present these approaches and how they can be combined to improve both tactical and strategic decision making. We will also cover the role of analytics in the organization and how it has evolved over time to give participants strategies to mobilize analytics talent within the firm.  

We will discuss these topics as well as present practical models and applications using @RISK.

A detailed comparison of the top Monte-Carlo Simulation Tools for Microsoft Excel

There are very few performance comparisons available when considering the acquisition of an Excel-based Monte Carlo solution. It is with this in mind and a bit of intellectual curiosity that we decided to evaluate Oracle Crystal Ball, Palisade @Risk, Vose ModelRisk and Frontline Risk Solver in terms of speed, accuracy and precision. We ran over 20 individual tests and 64 million trials to prepare comprehensive comparison of the top Monte-Carlo Tools.

Modeling in Excel or with any other tool for that matter is defined as the visual and/or mathematical representation of a set of relationships. Correlation is about defining the strength of a relationship. Between a model and correlation analysis, we are able to come much closer in replicating the true behavior and potential outcomes of the problem / question we are analyzing. Correlation is the bread and butter of any serious analyst seeking to analyze risk or gain insight into the future.

Given that correlation has such a big impact on the answers and analysis we are conducting, it therefore makes a lot of sense to cover how to apply correlation in the various simulation tools. Correlation is also a key tenement of time series forecasting…but that is another story.

In this article, we are going to build a simple correlated returns model using our usual suspects (Oracle Crystal Ball, Palisade @RISK , Vose ModelRisk and RiskSolver). The objective of the correlated returns model is to take into account the relationship (correlation) of how the selected asset classes move together. Does asset B go up or down when asset A goes up – and by how much? At the end of the day, correlating variables ensures your model will behave correctly and within the realm of the possible.

Copulas and Rank Order Correlation are two ways to model and/or explain the dependence between 2 or more variables. Historically used in biology and epidemiology, copulas have gained acceptance and prominence in the financial services sector.

In this article we are going to untangle what correlation and copulas are and how they relate to each other. In order to prepare a summary overview, I had to read painfully dry material… but the results is a practical guide to understanding copulas and when you should consider them. I lay no claim to being a stats expert or mathematician… just a risk analysis professional. So my approach to this will be pragmatic. Tools used for the article and demo models are Oracle Crystal Ball 11.1.2.1. and ModelRisk Industrial 4.0

One of the cool things about professional Monte-Carlo Simulation tools is that they offer the ability to fit data. Fitting enables a modeler to condensate large data sets into representative distributions by estimating the parameters and shape of the data as well as suggest which distributions (using these estimated parameters) replicates the data set best.

Fitting data is a delicate and very math intensive process, especially when you get into larger data sets. As usual, the presence of automation has made us drop our guard on the seriousness of the process and the implications of a poorly executed fitting process/decision. The other consequence of automating distribution fitting is that the importance of sound judgment when validating and selecting fit recommendations (using the Goodness-of-fit statistics) is forsaken for blind trust in the results of a fitting tool.

Now that I have given you the caveat emptor regarding fitting, we are going to see how each tools offers the support for modelers to make the right decisions. For this reason, we have created a series of videos showing comparing how each tool is used to fit historical data to a model / spreadsheet. Our focus will be on :

• Oracle Crystal Ball 11.1.2.1 Standard
• Palisade @RISK 5.7 Professional
• Vose ModelRisk 4.0 Professional
• Frontline Risk Solver 11.01 Professional

The goal of this comparison is to see how each tool handles this critical modeling feature.  We have not concerned ourselves with the relative precision of fitting engines because that would lead us down a rabbit hole very quickly – particularly when you want to be empirically fair.