# RESEARCH ARTICLES | RISK + CRYSTAL BALL + ANALYTICS

## Defining the Virtual Organization (Part 1/5)

Traditional definitions of the Virtual Organization have mostly taken a commodity-based, view of the interactions among partners (Kanter, 1994; Chesborough & Teece, 1996). One of the most notable examples of this type of virtual strategy to produce and deliver a product is the IBM PC. The early success of this venture was based on the same principals as those presented by Chesbrough & Teece's (1996) definition of a virtual organization:

## Overview and objectives of collaboration

In this blog entry, we look at the ideas and forces shaping modern collaboration. The new success factors are presented as well as diffentent schools of thought regarding collaboration and alliances.

## Correlation and Impact on Monte Carlo Analysis Results (5/8)

All the top dogs in the Monte Carlo Analysis spreadsheet universe have distribution-fitting capabilities. Their interfaces have common elements, of course, since they rely on (for the most part) the same PDFs in their arsenal of distribution-fitters. There are important differences, to be sure. It is hoped this comparison will illustrate pros and cons from a practical standpoint. Before going over our scorecard between Crystal Ball and ModelRisk, there is one more very important capability category begging for review: Correlation.

## Discrete Distribution Fitting to Duke Basketball Scores, in ModelRisk (4/8)

Let the battle begin anew. We continue our journey in uncertainty modeling, having understood how to fit distributions to data using Crystal Ball (CB). How does that experience compare to what ModelRisk (MR) has to offer?

Open the Duke 09_10 Scores spreadsheet with ModelRisk loaded in the Excel environment. We will first create the MR Objects representing the fitted PDFs. (Just as with the CB exercise, it is good practice to examine a variety of best-fitting distributions, rather than blindly accepting the top dog.) Then, in distinctly separate cells, we will create the VoseSimulate functions that behave as sampled values from the PDFs modeled by the MR Objects.

## Distributions in ModelRisk as Objects (3/8)

As with Crystal Ball, ModelRisk has the ability to fit distributions to historical data. The analyst looking to describe the variation of a Monte Carlo Analysis input can use "fitting" windows to select data and manipulate other options. How does the ModelRisk (MR) fitting experience stack up against the Crystal Ball (CB) methods and options? There are some important differences one should understand about MR before fitting PDFs to the Duke 09_10 Scores spreadsheet.