The Dualism of Information Measurement Methods in Industrial Analytics Management

Abstract

In the economic space of dizzying changes, the growth of the analytical arsenal of industrial management shows the need to develop theoretical and methodological tools to study the quantitative and qualitative features of the functioning of production systems. The impact of random factors gives rise to the uncertainty of the system’s behavior and use of the statistical concept to measure their disorder, while non-statistical measures to assess current states and the information embedded in them are less prominent in research and industrial management. The hypothesis about the dualism of both — probabilistic and deterministic — methods becomes relevant in the face of increasing dynamics and chaos in the activities of industrial enterprises. The purpose of the study was to develop a deterministic approach to a generalized assessment of the variety of its discrete states, contained in their sequence of non-statistical information, and to argue for the complementarity of the proposed approach to the classical entropy doctrine of the disorder of system states and control information in it. The research methodology is system analysis, thermodynamics, statistical physics, synergetics, control and information theories, and production management. The information and empirical basis of the study was data on workload of production structures of a machine-building enterprise that manufactures a wide range of products. The objects of research are industrial enterprises that perform certain technological operations. Scientific novelty lies in the synthesis of statistical and non-statistical approaches to measure the order of the states and the amount of information embedded in their sequence for production systems. The conducted research provides industrial management with the analytics of the functioning of production systems to improve the efficiency and stability of enterprises in a highly volatile business environment.