Multicriteria techniques for sustainable supply chain management

• Autores: Iván Felipe Barrera Jiménez
• Directores de la Tesis: Marina Segura Maroto (dir. tes.) , Concepción Maroto Álvarez (dir. tes.)
• Lectura: En la Universitat Politècnica de València ( España ) en 2024
• Idioma: inglés
• Tribunal Calificador de la Tesis: Joaquín Sicilia Rodríguez (presid.) , Ana Isabel Sánchez Galdón (secret.) , Alfonso Mateos Caballero (voc.)
• Enlaces
  • Tesis en acceso abierto en: RiuNet
• Resumen

  • Multicriteria methods provide an analytical and structured approach to decision making in supply chain management. These techniques allow multicriteria evaluations, which are essential for choosing and managing sustainable business partners. The aim of this thesis is to contribute to sustainable supply chain management by developing new multicriteria models and techniques to assess suppliers and customers. Models have been designed in order to incorporate business preferences to make collaborative decisions in the transparent selection and ranking of alternatives based on sustainable criteria. New methods have also been developed to classify alternatives into ordered groups and to assess their quality. Both models and methods have been validated using empirical cases and compared with alternative approaches.

    The methodology is based on an in-depth literature review, as well as the expertise of supply chain professionals. The proposed multicriteria models integrate techniques such as the Analytical Hierarchical Process (AHP), Multi-Attribute Utility Theory (MAUT) and the PROMETHEE method. Three new algorithms have also been developed for classifying alternatives into nominal and ordered groups (sorting problem).

    Firstly, a hybrid multicriteria model has been proposed and validated with real data for technology supplier qualifying, selection and ranking. This model integrates compensatory (AHP, MAUT) and non-compensatory (PROMETHEE, FlowSort) methods in a hierarchy with sustainability criteria to evaluate products, suppliers and manufacturers. Validation of the model in a real context and its comparison with an alternative model has demonstrated its ability to provide relevant and transparent information for decision making in the sustainable evaluation of technology suppliers in the banking sector.

    Secondly, a new algorithm has been designed, called Global Local Net Flow sorting (GLNF sorting), which classifies alternatives into ordered groups based on the net flows generated in global and local searches with PROMETHEE. In addition, the SILhouette for Sorting (SILS) algorithm has been designed to calculate a quality index in the classifications. Both algorithms have been empirically validated in supplier segmentation and their results compared with other published methods. On the one hand, the GLNF sorting algorithm excels in improving the discrimination between suppliers close to the limiting profiles by exploiting the level of preference similarity between alternatives. On the other, SILS improves the quality of alternative assignments to groups, allows for a detailed analysis of suppliers and facilitates decision making.

    Thirdly, a customer segmentation model based on transactions and collaboration has been proposed in the Business to Business context, applying AHP and GLNF sorting. Validated with 8,157 customers of a multinational company, it has been assessed by SILS and descriptive statistics. This model generates more homogeneous and robust groups than the K-means cluster method. This tool enables companies to automate decisions and perform detailed analysis to improve customer relationships, aligning with their collaboration strategies and market approaches.

    Fourthly, global and local searches have been used to propose an algorithm for nominal classification based on two dimensions, which provides a very useful strategic matrix for supply chain managers.

    Finally, the PrometheeTools software package has been developed in R, which automates the implementation of PROMETHEE, GLNF sorting and SILS to solve multicriteria problems of alternatives ranking and classification. This package has been successfully validated and stands out for the efficiency in PROMETHEE and especially when solving problems with thousands of alternatives. It is available by open access in the CRAN repository for use by researchers and practitioners interested in multicriteria decision making.