This paper revisits and revitalizes Nerlove's (1965) efficiency measure in a modern framework that allows for multiple inputs and outputs. Nerlove's efficiency measure is based on profit maximization and decomposes overall efficiency into two components: price or allocative efficiency and technical efficiency. Unlike most efficiency measures, Nerlove's measure is expressed in difference form rather than ratio form, which may explain its delayed recognition. The paper introduces the directional technology distance function, which generalizes Luenberger's shortage function and Blackorby and Donaldson's translation function. This function measures the distance from a given input-output vector to the technology frontier in a preassigned direction. It is shown to be a complete function representation of a technology with free disposal of inputs and outputs. The directional technology distance function is related to the profit function through a dual correspondence. This dual relationship is demonstrated, and it is shown that all previous dual correspondences are special cases of this relationship. The paper also shows how the directional technology distance function can be used to represent the Nerlovian efficiency measure. Efficiency measures are discussed, and it is shown that the directional technology distance function can be used to measure technical efficiency. The paper introduces new measures of profit efficiency and relates them to Nerlove's measures of relative efficiency. These measures are derived from the inequality (3.4), which relates the profit function to the directional technology distance function. The paper concludes that the directional technology distance function provides a natural and comprehensive measure of efficiency that encompasses both technical and allocative efficiency. It also addresses the issue of linear homogeneity in Nerlove's measure, which is resolved through the price normalization inherent in the dual relationship between the profit function and the directional technology distance function.This paper revisits and revitalizes Nerlove's (1965) efficiency measure in a modern framework that allows for multiple inputs and outputs. Nerlove's efficiency measure is based on profit maximization and decomposes overall efficiency into two components: price or allocative efficiency and technical efficiency. Unlike most efficiency measures, Nerlove's measure is expressed in difference form rather than ratio form, which may explain its delayed recognition. The paper introduces the directional technology distance function, which generalizes Luenberger's shortage function and Blackorby and Donaldson's translation function. This function measures the distance from a given input-output vector to the technology frontier in a preassigned direction. It is shown to be a complete function representation of a technology with free disposal of inputs and outputs. The directional technology distance function is related to the profit function through a dual correspondence. This dual relationship is demonstrated, and it is shown that all previous dual correspondences are special cases of this relationship. The paper also shows how the directional technology distance function can be used to represent the Nerlovian efficiency measure. Efficiency measures are discussed, and it is shown that the directional technology distance function can be used to measure technical efficiency. The paper introduces new measures of profit efficiency and relates them to Nerlove's measures of relative efficiency. These measures are derived from the inequality (3.4), which relates the profit function to the directional technology distance function. The paper concludes that the directional technology distance function provides a natural and comprehensive measure of efficiency that encompasses both technical and allocative efficiency. It also addresses the issue of linear homogeneity in Nerlove's measure, which is resolved through the price normalization inherent in the dual relationship between the profit function and the directional technology distance function.