Mathematical model of data analysis of service requests and complaints at an enterprise

Purpose of research: improving the efficiency and control of production processes by identifying potential risks of incidents and product defects during post-production stage.

Methods. This article examines the analysis of service requests and complaints automated processing at an enterprise. This process includes the analysis of incoming data on product incidents and defects and the search for causeand-effect relationships with production processes at various stages of the product life cycle. Requests about defects and product incidents are pre-registered in the unified database through the CRM-system and are available for further analysis. To carry out the research, a mathematical model for analyzing service requests and complaints using factor analysis was developed.

Results. Based on the mathematical model, a practical analysis of the enterprise's data was carried out and the main factors have been identified. The calculations were performed using the STATISTICA data processing package. The physical interpretation of the obtained factors makes it possible to explain the cause of incidents and defects, provide an expert opinion and to take measures on further production processes adjustments. The practical application of the developed mathematical model is carried out as the software module, which is implemented into the CRM system at an enterprise as an additional component for automated data processing.

Conclusion. The pre-prepared data of service requests and complaints is sent from the CRM system for analysis. The developed software module performs an automated analysis of the received data. The results of the analysis are physically interpreted and used to make management decisions at the enterprise, which allows regular monitoring of current production processes of the enterprise and make appropriate adjustments to improve the efficiency of production processes.

1. Andreev V.N., Baranov V.V., Burdina A.A., Lukina S.V., Korshunova E.D., Okorakova A.A., Frolov E.B. Managing the sustainable development of industry in the context of digitalization. Moscow: Janus-K; 2022. 119 p. (In Russ.)

2. Kryuchkova E.V., Mozharovskaya A.A., Tohunc N.B.. Enterprise restructuring. Moscow: Yanus-K; 2023. 120 p. (In Russ.).

3. Koloshkina I.E., Kapitanov A.V., Feofanov A.N. The effectiveness of the automated intelligent system for the formation of technological documentation. Informatsionnye tekhnologii v proektirovanii i proizvodstve = Information technologies in design and production. 2024; (1): 50-57 (In Russ.)

4. Meshkov V.G. Identification of interrelations between processes in the context of automation of production systems of the enterprise. Vestnik MGTU "Stankin" = Bulletin of MSTU "Stankin". 2023; (2): 105-109. (In Russ.). https://doi.org/10.47617/2072-3172_2023_2_105

5. Egorov S., Kapitanov A., Kozlova A. Application problems of process capability evaluation methods in modern quality assurance systems. In: IOP Conference Series: Materials Science and Engineering: International Conference on Modern Trends in Manufacturing Technologies and Equipment 2019, ICMTME 2019. Sevastopol: Institute of Physics Publishing; 2020. 033054 p. https://doi.org/10.1088/1757-899X/709/3/033054

6. Bykova A.V., Kapitanov A.V. Principles of interaction of the automated customer relationship system and the company's automated control system at various stages of the product lifecycle. Vestnik MGTU «STANKIN» = Bulletin of MSTU "Stankin". 2023; (1): 15-21. (In Russ.)

7. Bykova A.V., Kapitanov A.V. Analysis of CRM systems and formation of the architectural solution structure of an automated CRM system for machine-building production. Vestnik MGTU «STANKIN» = Bulletin of MSTU "Stankin". 2024; (1): 129-137. (In Russ.).

8. Bykova A.V., Pozdnyak Y. Algorithmization of Automation and Service Management Processes of an Industrial Enterprise. In: Quality Management, Transport and Information Security, Information Technologies (QM&TIS&IT), 2024. P.155-159.

9. Korochkina S.V., Doljenkova A.V. Optimization of the Complaints Management business process in order to increase customer satisfaction. Vestnik Altajskoj Akademii Jekonomiki i Prava= Bulletin of the Altai Academy of Economics and Law. 2020; (6): 247-252. (In Russ.)

10. Bykova A.V., Bekmeshov A.Ju. Formalization of business processes of an enterprise when modeling an automated customer relationship management system. Avtomatizatsiya i modelirovanie v proektirovaniii i upravlenii = Automation and Modeling in Design and Management. 2024; (1): 33-41. (In Russ.). https://doi.org/10.30987/2658-6436-2024-1-33-41

11. Babaev R.N., Besschyotnova N.N., Besschyotnov V.P. A multiparametric analysis of the pigment composition of the leaf apparatus of representatives of the genus Betula L. Vestnik Povolzhskogo gosudarstvennogo tekhnologicheskogo universiteta. Ser.: Les. Ekologiya. Prirodopol'zovanie = Bulletin of the Volga State Technological University. Ser.: Les. Ecology. Environmental management. 2023; (3): 42-54. (In Russ.). https://doi.org/10.25686/2306-2827.2023.3.42

12. Goncharenko S.N., Lachihina A.B. Monitoring of security incidents of the geoinformation management and control system of an industrial enterprise. Gornyi informacionnoanaliticheskij byulleten = Mining Information and Analytical Bulletin. 2022; (3): 108-116. (In Russ.). https://doi.org/10.25018/0236_1493_2022_3_0_108

13. Chaki M.R., Guha Banhi, Biswas Sanjib, et al. Multivariate framework for introspecting the motivational factors for satisfaction in HR profession. Vojnotehnički glasnik. Military technical courier. 2024. 72(2): 651-675. https://doi.org/10.5937/vojtehg72-48661

14. Shnyakina E.A., Kostin V.N. Development of algorithmic support for decisionmaking on identification of typical violators of categorized objects. Izvestiya vysshih uchebnyh zavedenij. Povolzhkij region. Tekhnicheskie nauki = Izvestiya vysshikh uchebnykh zavedeniy. The Volga region. Technical sciences. 2023; (3): 72-82. (In Russ.). https://doi.org/10.21685/2072-3059-2023-3-5

15. Gavrilyev E.I., Avdeenko T.V. A structural model for assessing the qualifications of IT specialists based on data mining of information systems. Vestnik SibGUTI = Bulletin of SibGUT. 2023; 17(4): 15-33. (In Russ.). https://doi.org/10.55648/1998-6920-2023-17-4-15-33

16. Afonin P.N., Afonin D.N. Statistical analysis using modern software tools. St. Petersburg: IC "Intermedia"; 2017. 100 p. (In Russ.)

17. Kozlova A.V. Development of an algorithm for digital data processing in order to analyze the results obtained, predict quality and their subsequent implementation in other tasks with the necessary adaptation. Vestnik MGTU Stankin = Bulletin of MSTU Stankin. 2021; (1): 59-63 (In Russ.).

18. Baranova E.M., Baranov A.N., Borzenkova S.Yu., Kuleshova N.V. Research of information system performance indicators using the STATISTICA program. Izvestiya Tul'skogo gosudarstvennogo universiteta. Tekhnicheskie nauki = Proceedings of Tula State University. Technical science. 2022; (10): 199-205. (In Russ.). https://doi.org/10.24412/2071-6168-2022-10-199-205

19. Raiko D., Abdunurova A. Defining a selection procedure of CRM systems for the information-analytical support to the marketing activities at an enterprise. Eastern-European Journal of Enterprise Technologies. 2024; (13): 41-58. https://doi.org/10.15587/1729-4061.2024.298301

20. Bykova A.V., Karlova T.V. Identification of optimal parameters for evaluating the effectiveness of the company's implementation of a customer relationship management system. Kachestvo. Innovacii. Obrazovanie = Quality. Innovations. Education. 2023;(3): 25-32 (In Russ.).