A fuzzy best-worst multi-criteria decision-making method for third-party logistics provider selection

Abstract

In recent years, the outsourcing of logistics functions to a third-party has been a major alternative to vertical integration. Third-party logistics provider can serve as a significant source of competitive advantage for firms aiming to focus on their core competencies. In selecting a strategic third-party logistics partner, there are many criteria and potential providers that must be carefully evaluated. Hence, third-party logistics provider selection is a multi-criteria decision-making problem; and it is extremely important that decision makers have a reliable decision support tool to select the best partner. Several multi-criteria decision making methods have been proposed. Some of these methods like Analytical Hierarchy Process (AHP) and Analytic Network Process (ANP) require decision-makers to use pairwise comparisons in order to determine their preferences. However, due to the large number of criteria and potential providers associated with third-party logistics selection decision, these pairwise comparisons might lead to a reduction in the overall consistency. This thesis addresses this issue by extending the newly proposed best-worst method to incorporate decision-makers' uncertainty and vagueness while requiring fewer comparisons as compared to a method like Fuzzy AHP. The aim of this thesis is twofold: first, a fuzzy best-worst multi-criteria decision-making method is proposed to handle the issue of larger number of comparisons and uncertainty in judgements. Secondly, the proposed method is applied to a third-party logistics selection problem at a medium-sized company in Turkey. The results of the study show that the proposed method efficiently handles decision maker's inherent uncertainty while requiring fewer number of comparisons.

In recent years, the outsourcing of logistics functions to a third-party has been a major alternative to vertical integration. Third-party logistics provider can serve as a significant source of competitive advantage for firms aiming to focus on their core competencies. In selecting a strategic third-party logistics partner, there are many criteria and potential providers that must be carefully evaluated. Hence, third-party logistics provider selection is a multi-criteria decision-making problem; and it is extremely important that decision makers have a reliable decision support tool to select the best partner. Several multi-criteria decision making methods have been proposed. Some of these methods like Analytical Hierarchy Process (AHP) and Analytic Network Process (ANP) require decision-makers to use pairwise comparisons in order to determine their preferences. However, due to the large number of criteria and potential providers associated with third-party logistics selection decision, these pairwise comparisons might lead to a reduction in the overall consistency. This thesis addresses this issue by extending the newly proposed best-worst method to incorporate decision-makers' uncertainty and vagueness while requiring fewer comparisons as compared to a method like Fuzzy AHP. The aim of this thesis is twofold: first, a fuzzy best-worst multi-criteria decision-making method is proposed to handle the issue of larger number of comparisons and uncertainty in judgements. Secondly, the proposed method is applied to a third-party logistics selection problem at a medium-sized company in Turkey. The results of the study show that the proposed method efficiently handles decision maker's inherent uncertainty while requiring fewer number of comparisons.