Abstract
Small and Medium (SME) companies are facing growing challenges while trying to implement globalized business strategies. Contemporary business models need to account for spatial-temporal changeable environments, where lack of confidence and uncertainty in data are a reality. Further, SMEs are finding it increasingly difficult to include all required competences in their internal structures; therefore, they need to rely on reliable business and supplier partnerships to be successful. In this paper we discuss a spatial-temporal decision approach capable of handling lack of confidence and imprecision on current and/or forecast data. An illustrative case study of business' partner selection demonstrates the approach suitability, which is complemented by a statistical analysis with different levels of uncertainty to assess its robustness in uncertain environments.
Highlights
When a company decides to extend its competences by establishing business partnerships, it needs decision support tools and methods to select the best partners or suppliers, in today’s spatial-temporal changeable global environments and several interesting contributions have been put forward during the last years [1,2,3]
Classical multi-criteria decision making MCDM models assume that criteria ratings and weights are known a priori and they are precise
The authors propose a Data Fusion process, based on fuzzy multi-criteria decision making concepts and techniques, such as: fuzzy sets to normalize the variables; an uncertainty mechanism to handle data imprecision; and mixture operators with weighting functions to fuse the information into a composite of candidate alternatives
Summary
When a company decides to extend its competences by establishing business partnerships, it needs decision support tools and methods to select the best partners or suppliers, in today’s spatial-temporal changeable global environments and several interesting contributions have been put forward during the last years [1,2,3]. Businesses need a suitable evaluation approach capable of supporting spatial-temporal selection processes within uncertain environments. After computing a possible decision for each temporal process (past, present and future data), the three evaluations are fused to obtain a ranked list of partners and/or suppliers. This will result on more informed and robust decisions to be taken, based on the procurement management strategy the buyer company finds appropriate. Afterwards, to demonstrate the flexibility and robustness of the method, we perform a statistical analysis over 30 similar problems – with data from past, future and present information – which are tested within three uncertain contexts: low confidence, average and high confidence on data.
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