One of the biggest challenges in designing a logistics network is predicting thedemand flows between all pairs of points in the network. Until recently, thegravity model was mainly used for estimating the demand flow between points.However, the gravity model uses historical data to estimate values for its multi-ple parameters and distance between pairs to forecast the demand flow. Distancevalues close to zero and unprecedented changes in demand flow data create nu-merical instability for the gravity model’s output. Hence, the proximity factor,a single parameter model that uses the relative ordering of pairs instead of dis-tance, was developed. In this paper, we systematically compare the proximityfactor and the gravity model. It is shown that the proximity factor is a robustand competitive alternative to the gravity model. According to our analysis,the proximity factor model can replace the gravity model in some applicationswhen no historical data is available to adjust the parameters of the latter.