Service quality assessment in IT projects based on aggregate indicators - A. E. Krasnov; A. A. Sapogov (Российский технологический журнал, 2024)
Objectives. Due to the need for prompt and rational assessment of service quality within the framework of complex IT projects, including infrastructure servicing and maintenance, which often involve a large number of identical or similar iterations, it becomes necessary to develop novel analysis methods based on nonlinear aggregation of indicators. As a result of changes in the structure of the process, territorial remoteness, automation, informatization, and the emergence of big data, the use of existing assessment methods often becomes impossible or labor-intensive. The purpose of the present work is to develop an approach to assessing the quality of work (services) in the framework of IT projects based on nonlinear aggregation of indicators.Methods. The proposed approach to assessing service quality within IT projects is based on nonlinear aggregation of a number of indicators involving a preliminary decomposition of the system into private indicators. In order to meet the requirements of the decomposition process, service quality indicators must fully characterize the properties of the service as a whole at the different stages of its life cycle.Results. The application of the proposed nonlinear aggregation methodology to quality indicators obtained by decomposing the system is described with the further calculation of a single indicator that takes all the essential initial parametric indicators into account. The decomposition of complex systems to the level of elementary relationship subsystems more adequately reflects interrelated phenomena in a complex system.Conclusions. The practical application of the neural network parametric data aggregation model for assessing the quality of IT services is demonstrated. The use of an aggregated information and analytical indicator for assessing service quality increases the availability of analytical information for decision makers, reduces the dimension of analytical data, and improves the objectivity of the obtained generalized information.