How can you use outranking methods to improve MCDM?

The method does provide an multidimensional objective view on choosing the best alternative or outcome to choose as a baseline but it also discounts the progress towards development of any single outcome which might carry more weight than the current outcome being used for decision making depending upon the scenario.

MCDM is a very usefull tool when dealing with complex projects. Complex projects which usually need evaluation of multiple aspects (or dimensions) where sometimes some of them may lacks of information or based on uncertainity, finds in the MCDM an structured way to present it. It is very important, in this case, to be able to weigh them out. This helps to balance and focus the analysis. At the end the final outcome will be a blended based criteria.

As I mentioned before, when constructing the evaluation matrix is very important to select the different dimensions, even if we already know that some may lack of complete information, or are based in qualitative or subjective information. The second most important process is to weigh each dimension. Sometime helps to create a side column to describe the assumptions taken. Use of methods like TOPSIS can be done from mathematical approach applying a formula or as pragmatic aproximation defining a criteria, (yes, that works). There are other methods but you will need to find the best that will fit your analysis.

For simple decision making few dimensions are enough. In complex scenarios with multidimensional approach, there is a need to find proper ways to get to a decision. Outranking methods helps in this process, but "one size dosen't fit all". Proper selection of methods will depend on the type of dimension selected for analysis. I.e, deciding which "software to acquire" where the nich is very restricted there will be dimensions like "support language availability" (restriction), is important to be assesed and determined. Once again weighing is very important, in this case applying a transitivity weighing process could help. In general, MCDM goal is to determine a preference ordering among a number of available alternatives.

The decision-maker's preferences is a typical limitation. Usually the one developing the MCDM matrix for a project is not done by the decision maker. For the case of dimensions that can be completely determined by computational methods may lead on a result that is directly related to the quality of data and the variables used. In some cases methods tends to simplify the process of data-análisis up the hierarchy defined in the evaluation matrix. In mathematical terms , propagating the credibility matrices from lower levels through the intermediate levels up to the root node may lead to ambiguous conclusions.

The key limitation of outranking methods is low interpretability and re-use of the decision information. Outranking methods focus on revealing relative differences between the alternatives, often in comparison to each other or some 'ideal' - this makes the decision evaluation 'local', i.e., tight to a specific decision situation. In contrast, the methods based on multi-attribute value theory and expected utility usually enable 'global' assessment of alternatives, e.g., estimating alternative's outcomes independent of the other alternatives. This information, while used for further ranking, provides additional insights into the decision problem itself and can be reused in related problems or monitoring of the decision results.

Always from a practical point of view and using the general approach of the MCDM, andn the outranking methods, can be use for example for ERP Solution selection. I personally did it but consider the my following conclussions: 1) Create your adapted own approach. 2) Be as methodological as possible. 3) Select the appropiate outranking methods that best suits your needs. Nevertheless, no unified or agreed method was identified or accepted by the merket, industry or companies. I ussually use Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methods to aid with the software selection problem.