Abstract
<jats:p>The article proposes a management mechanism for interpreting the results of three-circuit diagnostics of production systems in the coordinates «conditional entropy (H) — complexity (C) — thesaurus (T)» in the context of Industry 5.0. It is shown that traditional KPIs often record deviations after the fact and do not provide a reproducible choice of measures under multiple constraints. To translate information indicators into solutions, an octant classification of states and a universal matrix of management decisions have been developed, defining the transition «octant → goal priority (H↓/C↑/T↑) → package of measures». The package of measures is structured by levels: operational (flow and downtime management), organizational (roles, responsibilities, reaction regulations) and digital (data management, observability, AI analytics). A result control contour and target trajectories of the transition between octants have been introduced, which allows using the matrix as a regulated cycle of improvements and reduction of managerial uncertainty. The purpose of the research is to develop a universal matrix of management decisions based on the results of diagnostics of information indicators and formalize the rules for its application in a reproducible cycle of improvements in the implementation of the trajectory of the production system in the logic of Industry 5.0. The methodological basis of the research is an information-entropy approach to assessing the manageability and sustainability of production systems, methods for normalizing indicators, classifying the conditions of the production system and systematizing management measures by impact levels with subsequent control of the result. The results of the study consist in the development of an octant classification of states according to information parameters and a universal decision matrix that provides a transition from diagnosis to impact priorities, a package of measures and measurable control of the effect and target transition between states. The prospects for the study lie in the need for industry-wide calibration of min/max thresholds and a set of control indicators, testing of the matrix on empirical data from various types of production, and the development of a digital monitoring loop for data quality and reliability of AI models.</jats:p>