The article examines the problems of learning and adaptation in control of production resource efficiency. The minimum consumption of resources leads to the greatest efficiency. The possibilities of decreasing resource consumption depend on random factors. In the face of such uncertainty, learning and adaptation can be used to control to reduce resource consumption. In this case, it is necessary to take into account the activity of the elements of manufacturing system associated with the presence of their own goals, which do not necessarily coincide with the goal of the system as a whole. These elements can influence adaptation and learning procedures to achieve their goals. The research is carried out on a three-level model of manufacturing. At its top level is a boss who evaluates the foreman who is at the middle level of the system. At the bottom of the system is the worker. The boss must to control the efficiency of the foreman in such a way as to save resources to a minimum. But the foreman knows own capabilities better than the boss. In turn, the worker knows his own resource-saving potential better than the foreman. So both the foreman and the worker can manipulate their resources consumption to gain more stimuli. To avoid such manipulation under conditions of uncertainty, mechanism of intelligent tracking and decision-making for resource efficiency of manufacturing is proposed. This mechanism includes procedure for digital self-adaptation of boss and procedure for assessing of the foreman. Sufficient conditions of the optimality of this mechanism are found, in which random opportunities of decreasing resource consumption are fully used. With such a mechanism, the foreman is interested in improving the worker's efficiency. It is proved that for this it is sufficient to use linear procedures of adaptive planning and stimulation of worker. The proposed approach to intelligent tracking and decision-making for resource efficiency is illustrated by the example of water consumption at the refit of locomotives in the depot of the Russian Railways company.