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Normalization is a crucial step in all decision models, to produce comparable and dimensionless data from heterogeneous data. As such, various normalization techniques are available but their performance depends on a number of characteristics of the problem at hand. Thus, this study aims to introduce a recommendation framework for supporting users to select data normalization techniques that better fit the requirements in different application scenarios, based on multi-criteria decision methods.

Following the proposed approach, the authors compare six well-known normalization techniques applied to a case study of selecting suppliers in collaborative networks.

With this recommendation framework, the authors expect to contribute to improving the normalization of criteria in the evaluation and selection of suppliers and business partners in dynamic networked collaborative systems.

This is the first study about comparing normalization techniques for selecting the best normalization in dynamic multiple-criteria decision-making models in collaborative networks.