This paper presents a comparative analysis of the outcomes achieved when two widely applied methods for supplier selection—the ‘technique for order of preference by similarity to ideal solution’ (TOPSIS) and ‘data envelopment analysis’—are applied to the problem of identifying the most preferred sustainable suppliers. Both fuzzy DEA and fuzzy TOPSIS are applied to a common dataset of logistics service providers in Sweden. The results reveal that TOPSIS outperforms DEA in terms of both calculation complexity and sensitivity to changes in the number of suppliers. However, output rankings from the two models are found to be less than perfectly correlated. The paper concludes that utilizing both methods, as applied to just a small number of evaluation criteria and a relatively low level of detail in the data, produces a useful pooled shortlist of potential sustainable suppliers. This can then form the basis for a second stage application where either of the methods may be applied to a greater number of criteria that are specified to a higher level of detail. Even more critically, the results also have the potential to point to specific aspects for discussion when negotiating price and service quality commitments with potential sustainable suppliers.