This paper develops a new approach to select a common projection direction for performance evaluation of decision-making units (DMUs) using the data envelopment analysis (DEA) directional distance function. First, we define the concept of directional extensibility of a specific projection direction with respect to a set of inefficient DMUs. The concept shows the projection direction’s ability in reducing inefficiencies, simultaneously, for all inefficient DMUs. Using this concept, we propose a model which identifies the common projection direction that has the maximum directional extensibility. However, this common projection direction may contain zero elements. To avoid this problem and to select the final common projection direction, an algorithm is developed to select the common projection direction which is resulted in a trade-off between the directional extensibility and the similarity among the elements of the common projection direction. Our developments contribute by formally defining a concept (i.e., the directional extensibility) which can well reflect a common projection direction’s ability in reducing the DMUs’ inefficiencies. Moreover, the selected final common projection direction has both good directional extensibility and no zero elements, which helps to guide the inefficient DMUs to develop in a balanced way because the direction suggests the DMUs to improve in all inputs and outputs. Finally, we demonstrate the usefulness of our proposed approach by using an empirical case of 18 logistics companies listed among China’s top 500 enterprises in 2018.