RESEARCH ARTICLES | RISK + CRYSTAL BALL + ANALYTICS

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.

IBM PC - A virtual EnterpriseTraditional 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.

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.

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