Over the last 3 months, we have seen 3 of the 4 major players in the Excel Monte-Carlo Simulation arena introduce new releases. We hear a lot of talk about which tool is best and the truth is there is no perfect answer – it’s a personal thing dictated by user skill, preference and need.

For this reason, we have created a series of videos showing comparing how each tool is used to apply Monte-Carlo simulation to a model / spreadsheet. Our focus will be on :

• Oracle Crystal Ball 11.1.2.1
• Palisade @RISK 5.7
• Vose ModelRisk Standard 4.0
• Frontline Risk Solver 11.0
• Comparison

To keep the playing field level, we have used a simple additive model, which is simply defining a series of distributions (i.e. costs, budget items…), summing them up and analyzing the resulting sensitivity analysis. We have kept things simple, so we are not correlating any of the variables nor using any fancy math.

As you will see, there are definite differences AND similarities regarding how these packages tackle building a model. We are going to focus on those relating to inserting and copying input distributions as well as defining and analyzing model outputs. The objective is to compare the ease, usability and efficiency of each tool and give people the opportunity to choose for themselves which tool reflects their needs and preferences better.

Is there a winner in this battle between Crystal Ball and ModelRisk? To quote that way-too-often-quoted reply: It depends. Some users will value certain technical capabilities over others. Some users will value user-friendliness over accuracy. If there is to be a group deployment of a MCA spreadsheet package, usability may trump technical capabilities overall. Does it matter if one package has more distributions to choose from if there are only three that are of interest for your particular class of stochastic problems? Would it matter what kind of correlation enforcement method is used if, as in many manufactured assemblies, there is practically no correlation between separate components? Probably not. But if they do (as in financial and insurance applications), there will be a clear winner.

Correlation behavior in ModelRisk is enforced with the use of copulas. Copulas offer more flexibility in accurately simulating real data scatter-plot patterns than do single-value correlation coefficients. While this advantage is clear for financial and insurance applications, its implementation in an MCA spreadsheet simulator can make the difference between universal adoption and rejection by a majority of the intended user group. Let us now use ModelRisk (MR) to enforce the correlation behavior between Duke Basketball offense scores and their opponents' scores, based on the '09/'10 historical data.

In our quest to simulate future Duke Basketball scores, we have taken past historical data of individual games during the '09/'10 season and fitted probability distributions to that data. Two PDFs are generated; one for Duke's scores (offense) and one for their opponents' scores (defense). We have used both Crystal Ball and ModelRisk to perform this task. Is there something missing in our PDF formulations?

How should you look at processes when designing a virtual organization?

In order to effectively manage activities across organizations' it becomes critical to have certain processes in place to manage as well as measure performance and quality (Hammer, 2001; Champy, 2002; P-CMM v2.00, July 2001). According to Venkatraman (1994), organizations seeking to effectively integrate with business partners must first get their house in order through the use of BPR – Business Process Redesign/Re-engineering. Hammer (2001) says that for those who have re-engineered their internal business processes and extracted most of the value available internally, must now look at integrating and re-engineering externally to yield the next gains in value and profitability.

This article highlights and discusses Venkatraman and Henderson's take on the various vectors of Virtual Strategy.

What is a virtual strategy? What does it cover within the company? Who is affected? Who benefits? Venkatraman & Henderson (1998) reflect these questions in a 3 vector and 3 stages model (presented in Figure 5) that is aligned in spirit with Chesbrough & Teece's view that no one formally defined structure will ensure the success of a virtual organization.

This article covers the competencies, resources and assets that must be developped when making the transition to virtual organizing

A variety of authors have generated lists of firm capabilities and resources that may enable firms to conceive of and implement value-creating strategies (Barney, 1991; Bharadwaj, 2000).

In order to better define Venkatraman & Henderson's virtual organization, we have broken it down into components that are inspired by Barneys' (1991) three generic resource types and Nitin Nohria's (2003) model of organizational competencies; competencies that have characterized firms who performed exceptionally and developed or maintained a leadership position over the last 10 years. Nohria also suggests that to achieve overall success you need to excel in all 4 primary competencies and at least 2 of the secondary (See Figure 3). However Nohria emphasizes execution as primary means of success and differentiation.

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:

 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.