Project Evaluation and Selection for the Formation of the Optimal Portfolio
The increasing intensity of competition and fast technology changes make firms look for the development of more innovative products. In order to keep up with market trends and to stand out from competitors, most of the companies in technological fields turn to the development of R&D projects. Generally, these are risky due to the uncertainty surrounding its technological feasibility and its future commercial success, which make their evaluation and selection difficult to achieve. Nevertheless, the survival of an organization is highly correlated with correct project selection and management (Ashrafi, 2012). A wide diversity of approaches have been developed and used throughout recent years in order to cope with this problem, each of them with its own advantages and disadvantages. The most common are quantitative methods, qualitative methods and hybrids methods (Ashrafi, 2012). Nonetheless, most of the times not only one method is used during the evaluation process.
Even if the projects are correctly evaluated separately, this does not guarantee that the best portfolio can be formed. The best portfolio is the one that maximizes the probabilities of achieving the goals set by the company (Chien,2002). Its formation is not just about evaluating independent projects; it is about evaluating the whole portfolio itself. Choosing the best projects based on financial or any other criteria may not result in the best portfolio due to dependencies or interrelations that may exist between the projects. The most common of them are related to cost, resources, financial return and technical factors (Blau, 2004), as well as outcome and impact dependencies. The problem of evaluating and selecting projects and scheduling them is complicated further by their presence. These interdependencies add complexity to the decision that needs to be taken, since the decision-making process becomes less straightforward. A good example is the impossibility of using some quantitative methods, like linear programming. A good example of this situation is given when investing an additional monetary unit, since it may have impact in more than one project, since the additive restriction does no longer exists under this perspective. In consequence, a difference between measuring the preference for the portfolio as a whole and measuring the preferences for projects in the portfolio (Chien, 2002), and it may be also the case where the objectives that are considered when evaluating portfolios are different than those used when selecting individual projects.
If a company decides to evaluate a portfolio as it would evaluate projects, the amount of time required would be unmanageable. With only a set of 10 projects, the decision-maker would need to evaluate 1024 portfolios (Ghapanchi, 2012), which would be impossible for most of the companies. In consequence, newer methods have been developed to assist with choosing the best portfolio within a shorter time. This article focuses on describing and analyzing these methods.
