Supplier evaluation has become a significant topic over the past few decades, as companies have become more outsourced oriented. However, previous research on this topic has not paid adequate attention to the limitations associated with the availability of accurate and reliable data relating to the performance of potential suppliers. In an attempt to address this issue, this paper proposes a novel supplier evaluation model that can handle imprecise quantitative and qualitative data. Additionally, Decision Maker’s judgement regarding both qualitative and quantitative criteria are incorporated into this model so that a more comprehensive and realistic assessment of supplier performance can be achieved. The model combines five separate methods that have specific capabilities to handle multiple limitations in the existing methods: first, Fuzzy Analytical Hierarchy Process and Fuzzy TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method are used to analyse qualitative criteria/data; second, Analytical Hierarchy Process and Axiomatic Design are used to analyse quantitative criteria/data, with a particular focus on handling variability in performance data; and third, Data Envelopment Analysis is used to integrate the results of the two approaches above to arrive at a comparative assessment of supplier performance. The proposed integrated model is verified using a numerical example.