Evaluation and optimization of heterogeneous data systems based on performance indicators using an integrated algorithm

The study presents an integrated algorithm for evaluating and optimizing systems with heterogeneous data, taking into account managerial and organizational performance indicators. The proposed algorithm consists of data coverage analysis (DCA), fuzzy data analysis (FDA), and a set of statistical methods for evaluating the likelihood of the obtained results. An integrated algorithm has been developed for determining the most effective heterogeneous performance indicators, which differs in its method of selecting reliable indicators and allows for the formulation of strategies for improving organizational systems. A set of 12 criteria indicating the application of an integrated method was selected for verification. The results showed that the AOD results have a lower mean absolute percentage error (MAPE) than the fuzzy AOD results. The study also analyzes and weighs indicators, and the results showed that the indicators "investments in research and development relative to production costs" and "investments in education and retraining per employee" are the most effective. The study presents a unique algorithm for taking into account heterogeneous managerial and organizational factors. It can handle data uncertainty due to the presence of fuzzy inference mechanisms in the algorithm. The weights of the indicators are determined using a set of reliable statistical algorithms.

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