Multi-Criteria Decision Making (MCDM) methods use normalization techniques to allow aggregation of criteria with numerical and comparable data. With the advent of Cyber Physical Systems, where big data is collected from heterogeneous sensors and other data sources, finding a suitable normalization technique is also a challenge to enable data fusion (integration). Therefore, data fusion and aggregation of criteria are similar processes of combining values either from criteria or from sensors to obtain a common score. In this study, our aim is to discuss metrics for assessing which are the most appropriate normalization techniques in decision problems, specifically for the Analytical Hierarchy Process (AHP) multi-criteria method. AHP uses a pairwise approach to evaluate the alternatives regarding a set of criteria and then fuses (aggregation) the evaluations to determine the final ratings (scores).

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Purpose 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. Design/methodology/approach Following the proposed approach, the authors compare six well-known normalization techniques applied to a case study of selecting suppliers in collaborative networks. Findings 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. Originality/value This is the first study about comparing normalization techniques for selecting the best normalization in dynamic multiple-criteria decision-making models in collaborative networks.

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