Multi criteria Decision Aid Methods (MCDA) applied to performance management.

The decision-making process is often a complex activity as it involves consideration of several objectives and alternatives. Applying MCDA methods is recommended in these situations for they are able to handle the need of satisfying multiple objectives and analyzing different alternatives when deciding. In organizational context, where most of the decisions need to contemplate different interests from areas or departments involved, applying MCDA methods enable managers with adequate support to their decision-maker activities and offer more consistent answers to specific organizational demands. These methods encompass different problematic and may be applied, for instance, to select or rank alternatives of action accordingly to some criteria, or to classify alternatives into pre-defined categories.

Group Decision Making (GDM) applied to performance management.

Group decision making consists of a process in which there are two or more experts with different preferences and knowledge involved in judging some alternatives to obtain a decision that represents a collective solution. Group decision-making problems usually involve several alternatives that are evaluated by judgments of various decision-makers regarding the performance on the criteria. Thus, multicriteria group decision-making techniques (MCGDM) have been proposed and applied in the literature to deal with this process. The process of group decision-making can be done in two ways: consensus or aggregation of individual judgments. Consensus-based techniques seek to minimize divergences of opinion among decision makers; usually, optimization techniques are applied to achieve a solution of agreement among decision makers. Otherwise, the techniques based on the aggregation of the judgments of the decision makers, use mathematical aggregation operators in order to unify the judgments in a aggregated matrix of group decision making. Aggregation approaches are usually used to model the different judgments and views of those involved in the process.

Soft computing techniques applied to performance management.

Soft computing deals with approximate models and gives solutions to complex real-life problems. Unlike hard computing, soft computing is tolerant of imprecision, partial truth, and approximations. In effect, the role model for soft computing is the human mind. Soft computing is based on techniques such as fuzzy logic, genetic algorithms, artificial neural networks, machine learning, and expert systems. Performance management is a process of linked activities that aim to ensure goals are being met in the most efficient and productive way possible. Within organisations, performance management attempts to drive efficiency of operations by aligning internal and external activities with the company’s objectives. Therefore, this research subject encompasses the application of soft computing techniques to enhance, promote, assess and support operations performance management in cases in which it would not be possible, such as when there is presence of hesitation, uncertainty, small and incomplete data sets.