SmithKline Beecham method for project evaluation (Sharpe and Keelin, 1998) requires a lot of data to be generated, so this three phased approach seems to be a very time consuming process.
However, I quite like the idea of generating alternatives under these given aspects (current, buy-up, buy-down, minimal). From the ranking methods I preferred the scoring and comparative approaches. I could imagine combining this ?alternative evaluation? and the aforementioned ranking methods.
But also, I like the approach described by Asosheh, Nalchigar and Jamporazmey (2010) for IT projects, which is my area of work. The authors propose to extend the original balanced scorecard (BSC) perspective with a fifth one, ?uncertainty?, for processes, HR and technology risks and combine this BSC with data envelopment analysis (DEA). I believe these results would provide a good basis for project selection.
As it makes sense to combine several approaches in order to have a sound ranking (Morris and Pinto, 2007, p. 99), these above would be my preferred ones.
How would you see this fit for your area of work? Discuss
Respond on the statement below, you should choose examples from your own experience or find appropriate cases on the Web which you can discuss
The two more prominent methods for ranking portfolios & evaluating are real options vs optimisation models; when dealing with real options, Luehrmann (1998) states that the ROI of a project is comparable with future option as they can be analysed as investment options in context to benefits to expenditure. A decision tree (Copeland & Tufano, 2004) shows this by using a decision tree to calculate values in reverse to find the selection point of the portfolio process. Though beneficial when the correct point is chosen, the reliance on a potentially flawed value can lead to the actual benefit of the project within the portfolio being overrated, as the uncertainty over time is a high risk factor (and we do not have a crystal ball ? alas!). The fluctuations of an uncontrollable external environment (and other factors), makes the long-term accuracy of the model void as it will need constant updating (when done too often, the original valuation model looses validity), which is a drain on resources. Real Options do not cater for a realistic range; it uses ROI for decision making and ROI is not optimal for the ranking process as sales forecasts are not secure indicators (Rothman, 2009 & Sharpe & Keelin, 1998). Single point forecasts can be avoided, but replacing these with range forecasts present other challenges as it complicates the numerical equations, again increasing uncertainty. Real options also focus only on financial returns rather than the entire organizational strategy, further jeopardizing the attainment of achieving a balanced portfolio. The uncertainty (and complexity) makes it more difficult to attain a buy-in. Furthermore, the numbers are not trusted by those who work with them, meaning that the correct actions are not always taken (ie projects killed), so therefore the portfolio is run on personal opinions rather than numerical data, which can shift the focus from the holistic approach that is needed (Carr, 2002).
The optimization model is able to incorporate more factors than maximum return (Bodley-Scott & Branche, 2009), such as OPP (Optimal Project Portfolio) that allows more flexibility when weighing criteria to maximize benefits (such as dependencies and limitations ? Morris & Pinto, 2007). The evaluation criteria of OPP can cover a large spectrum according to the specifications and/or limitations of a project, while still being intrinsically simple through clustering criteria under one of the following; objectives, constraints and metric (Prieto, 2011). The ability to align the varying criteria easily with changing strategy is a major advantage when balancing a portfolio. The disadvantage of this is not having a full set of data (and therefore not making the right decision), as well as the time involved in data collection.
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