Hide
Show all
MODEL FOR PRODUCT STRATEGY ANALYSIS OF AN IT COMPANY USING FUZZY INFERENCE
Abstract
The subject of study is the strategic analysis process of product strategy based on an IT company’s key performance indicators. The objective is to improve the product strategy’s analysis process by implementing a model for evaluating and analyzing the product strategy based on the IT company’s key performance indicators system. The tasks are to investigate methods and tools for strategic analysis of IT product strategy; to develop a model for evaluating an IT company’s product strategy based on a system of key performance indicators, which characterizes the results of strategy implementation in three aspects: financial results, level of customer orientation of the product; effectiveness of the sales team and marketing activities; and to investigate the results of implementing the model for improving the product strategy to achieve the IT company’s strategic goals. Methods of data normalization, fuzzy logic, and the Mamdani method for constructing a fuzzy inference system were used. The following results were obtained. Unlike existing models, the developed model of strategic analysis of product strategy is the result of a hierarchical combination of fuzzy inference systems for evaluating product strategy in three aspects: financial result, level of customer-orientation of IT product, and effectiveness of sales and marketing activities. As a result of the model implementation, an analysis of the product strategy is conducted regarding the possibility of its further implementation to achieve the IT company’s strategic objective. The use of a strategic analysis model based on fuzzy inference systems enables the product manager to obtain product strategy assessments and analyze KPIs, which generally represent the financial and market results of strategy implementation. Ways to improve the product strategy, considering the IT company’s goals, are identified based on the analysis results. Conclusions. The scientific novelty of the results obtained is as follows: a model for evaluating and analyzing product strategy, based on the KPI system of the IT company, has been developed for the first time. Implementing this model in the strategic analysis of an IT product will enable the development of a product strategy aligned with the IT company's strategic goals. Unlike existing models, the developed model of strategic analysis of product strategy is the result of a hierarchical combination of fuzzy inference systems for evaluating product strategy across three aspects: financial results, the level of customer orientation of the IT product, and the effectiveness of sales and marketing activities. As a result of the model’s implementation, an analysis of the product strategy is conducted to assess the feasibility of its further implementation to achieve the IT company’s strategic objective.
Keywords
References
Moskalenko, V., Fonta, N., & Grinchenko, M. The Method of Forming a Dynamic Projects Portfolio of IT Companies: CEUR Workshop Proceedings of the 1st International Workshop IT Project Management, Kharkiv, Ukraine, NTU “KhPI”, 2020, vol. 1, no. 2565, pp. 152-161. Available at: https://www.scopus.com/pages/publications/85082127337, https://ceur-ws.org/Vol-2565/paper14.pdf (аccessed 10.05.2025).
Rohovyi, M., & Grinchenko, M. Towards the improvement of project team performance based on large language models. Radioelectronic and computer systems, 2024, no. 4 (112), pp. 229-247. DOI: https://doi.org/10.32620/reks.2024.4.19
Akhiiezer, O., Holotaistrova, H., Gomozov, Y., Mats, V., & Rogovyi, A. Strategic Brand Portfolio Management. Bulletin of the National Technical University "KhPI". Series: Strategic Management, Portfolio, Program and Project Management, 2022, no. 2(6), pp. 3-6. DOI: https://doi.org/10.20998/2413-3000.2022.6.1.
Kuzmynchuk, N., Mardus, N., Davydov, O., Yevtushenko, V. Melen, O., & Kharchenko, A. Devising the policy of funds allocation in the decision-making process under the budget and tax management. Eastern-European Journal of Enterprise Technologies. Transfer of technologies: industry, energy, nanotechnology, 2022, vol. 1, no. 13(115), pp. 69-79. DOI: https://doi.org/10.15587/1729-4061.2022.253072.
Romanenkov, Y., Mukhin, Kosenko, V., Revenko, D, Lobach, O., Kosenko, N., & Yakovleva, A. Criterion for Ranking Interval Alternatives in a Decision-Making Task. International Journal of Modern Education and Computer ScienceThis, 2024, no. 16 (2), pp. 72–82. DOI: https://doi.org/10.5815/ijmecs.2024.02.06.
Grinchenko, M., & Moskalenko, V. Information Technology for Strategic Analysis of IT Company Projects. CEUR Workshop Proceedings of the 4th International Workshop IT Project Management, Kharkiv, Ukraine, NTU “KhPI”, 2023, no. 3453, pp. 128-138. Available at: https://www.scopus.com/record/display.uri?eid=2-s2.0-85170571566&origin=resultslist (accessed 23.05.2025).
Milsom, J. VRIO, SWOT, Value Chain, RBV, PESTEL. Available at: https://blog.i-nexus.com/vrio-v-swot-v-value-chain-v-rbv-v-pestel (accessed 23.05.2025).
Lacaze, A.S., Ferreira, F.A.F., & Santos, M.R. Adding value to the VRIO framework using DEMATEL. Management Decision, 2025; vol. 63, no. 4, pp. 1154–1175. DOI: https://doi.org/10.1108/MD-10-2023-1935
Mai, T.L., Tran, V.T., Phan, T.T., Nguyen, K.N., & Nguyen, Q.T.T. Impact of macro factors on country-level entrepreneurship: evidence from an empirical panel data study. Humanit Soc Sci Commun, 2025, no. 12, article no. 778. DOI: https://doi.org/10.1057/s41599-025-05034-0.
Piddubna, L. I., Piddubnyi, I. O., Proskurnina, N. V., & Gorobinska, M. V. Peculiarities of Building the Value Chain of the IT-industry Outsourcing Enterprises. Financial and Credit Activity Problems of Theory and Practice, 2021, vol.1, no. 32., pp. 158–167. DOI: https://doi.org/10.18371/fcaptp.v1i32.200343.
Shao, D., Ishengoma, F., & Marwa, N. A PESTEL analysis of generative AI in the digital economy. Global Knowledge, Memory and Communication, 2025, article no. May 2025 DOI: https://doi.org/10.1108/GKMC-08-2024-0499.
Golianek P. How to Use HEART Framework to Make the Right Product Decisions Available at: https://www.netguru.com/blog/heart-framework (accessed 23.05.2025).
Key Product Management Metrics and KPIs. Available at: https://www.altexsoft.com/blog/15-key-product-management-metrics-and-kpis/ (accessed 23.05.2025).
AARRR Framework. The Definitive Guide to Pirate Metrics. Available at: https://www.design-withvalue.com/aarrr-framework (accessed 23.05.2025).
Moskalenko, V. Y., & Grinchenko, M. A. Modeling the process of forming a KPI system for evaluating a product strategy based on a fuzzy cognitive map. Scientific and technical journal "Bionics of intelligence", 2025, vol. 2, no. 103, pp. 72-81. DOI: https://doi.org/10.30837/bi.2025.2(103).10
Li, J., & Zhang, B. The Role of Interaction Design Based on Fuzzy Decision Support System in Improving User Experience. International Journal of Fuzzy Systems, 2025. DOI: 10.1007/s40815-024-01918-6
Zadeh, L.A, & Aliev, R.A. Fuzzy Logic Theory and Applications. Part I and Part II. World Scientific Publishing Company, 2018, 612 p. DOI: https://doi.org/10.1142/10936.
Gafa, C. Fuzzy Inference System implementation in Python. Available at: https://medium.com/data-science/fuzzy-inference-system-implementation-in-python-8af88d1f0a6e (accessed 23.05.2025).
IFIS - Python library for interval-valued fuzzy inference. Available at: https://codeocean.com/ capsule/2679012/tree/v2 (аccessed 10.05.2025).
Singh, H., & Lone, A. Y. Deep Neuro-Fuzzy Systems with Python. Publisher Apress Berkeley, CA. 2020, 260 p. ISBN: 978-1-4842-5360-1. DOI: https://doi.org/10.1007/978-1-4842-5361-8.
Martínez-González, A., Moreno-Hernández, D., Zamarripa-Ramírez, J.C.I., & Ortega-Martínez, O.F. Design and Implementation of a Fuzzy Logic Algorithm Embedded in a Microcontroller for Combustion Applications. International Journal of Fuzzy Systems, 2025, pp. 1–16. DOI: 10.1007/s40815-025-02052-7
Niazmandi, M. M., Sedaeesoula R., Lari S., & Moussavi, P. An integrated risk and productivity assessment model for public–private partnership projects using fuzzy inference system. Decision Analytics Journal, 2024, no. 10, article no. 100376. DOI: https://doi.org/10.1016/j.dajour.2023.100376.
Attanayake, A.M. Sakura, R.H., Ratnayake, Chadima, R. M., & Markeset, T. Development of a framework for safety and resilience in overhead power cables: Use of a fuzzy inference system for risk and reliability analysis, Journal of Safety Science and Resilience, 2025, In Press, Journal Pre-proof. DOI: https://doi.org/10.1016/j.jnlssr.2025.03.005.
Yılmaz, R., & Buzluca, F. A fuzzy logic-based quality model for identifying microservices with low maintainability. Journal of Systems and Software, 2024, no. 216, article no. 112143 DOI: https://doi.org/10.1016/j.jss.2024.112143.
Tüű-Szabó, B., & Kóczy, L.T. A highly accurate Mamdani fuzzy inference system for tennis match predictions. Fuzzy Optim Decis Making, 2025, no. 24, pp. 99–127. DOI: 10.1007/s10700-025-09440-6.
Liu, L., Yang, S., & Jiang, K. Fuzzy Decision Tree Optimization for Personalized Hybrid English Teaching in Smart Classrooms. Journal of Circuits, Sys-tems and Computers, 2025, vol. 34, no. 11, article no. 2550251. DOI: 10.1142/S0218126625502512
Fazlollahtabar, H. A fuzzy two-phase model for renewable energy system optimization under uncertainty: from operational to strategic scenario planning. Energy Conversion and Management: X., 2026, vol.30, article no. 101600. DOI: 10.1016/j.ecmx.2026.101600
Moradizadeh, H, Khan, S, & Kabir, G. Fuzzy inference systems for probabilistic failure assessment in asset-centric environments". Journal of Quality in Maintenance Engineering, 2026, vol. 32, no. 1, pp. 205–233. DOI: https://doi.org/10.1108/JQME-07-2025-0083
Rivero-Iglesias, J.M., Puente, J., Fernandez, I., & León, O. A Novel Combined Hybrid Group Multi-Criteria Decision-Making Model for the Selection of Power Generation Technologies. Systems, 2025, no. 13, article no. 742. DOI: https://doi.org/10.3390/systems13090742
Peykani, P., Emrouznejad, A., & Nouri, M. Best-Worst Multi-Criteria Decision-Making Method: A Review of the Literature. Socio-Economic Planning Sciences, 2026, no. 104, article no. 102345. DOI: https://doi.org/10.1016/j.seps.2025.102345
FIS Trees. Available at: https://www.mathworks.com/help/fuzzy/fuzzy-trees.html (аccessed 10.05.2025).
Peralta, A, Olivas, JA, & Navarro-Illana, P. A Fuzzy Logic Framework for Text-Based Incident Prioritization: Mathematical Modeling and Case Study Evaluation. Mathematics, 2025, vol. 13, no. 12, article no. 2014. DOI: https://doi.org/10.3390/math13122014
Fedorovich, O., Uruskiy, O., Pronchakov, Y., Lukhanin, M. Method and information technology to research the component architecture of products to justify investments of high-tech enterprise. Radioelectronic and Computer Systems, 2021, no. 1, pp.150-157. DOI: https://doi.org/10.32620/reks.2021.1.13
Fedorovych, O., & Malieiev, L.. Ontological model of structure and parameters of components in high-tech product modernization projects. Innovative Technologies and Scientific Solutions for Industries, 2024, no. 1 (27), pp. 179–191. DOI: https://doi.org/10.30837/ITSSI.2024.27.179
Carrasco-Garrido, C, Moreno-Cabezali, BM, & Martínez Raya, A. New perspectives on university quality assessment: A Mamdani Fuzzy Inference System approach. PLoS One, 2025, no. 20(5), article no. e0321013. DOI: https://doi.org/10.1371/journal.pone.0321013
Palaniappan, U., & Suganthi, L. Reckoning the performance of management institutions – A Mamdani fuzzy inference system approach. International Journal of Productivity and Performance Management, 2024, no. 73 (8), pp. 2441–2479. DOI: https://doi.org/10.1108/IJPPM-05-2023-0214
Cho, H.-C., Lee, D. H., Ju, H., Park, H.-C., Kim, H.-Y., & Kim, K. S. Fire Damage Assessment of Reinforced Concrete Structures Using Fuzzy Theory. Applied Sciences, 2017, vol. 7, no. 5, article no. 518. DOI: https://doi.org/10.3390/app7050518.
Keerthika, R., Niranjan, S. P., & Vlase, S. Fuzzy-Based Control System for Solar-Powered Bulk Service Queueing Model with Vacation. Applied Sciences, 2025, no. 15(13), article no. 7547. DOI: https://doi.org/10.3390/app15137547.
Khan, J, Fayaz, M, Zaman, U, Lee, E, Balobaid, AS, Bilal, M & Kim, K. Optimizing alpha–beta filter for enhanced predictions accuracy in industrial applications using Mamdani fuzzy inference system. Alexandria Engineering Journal, 2025, no. 119, pp. 598-608. DOI: https://doi.org/10.1016/j.aej.2025.01.116.
Tune Fuzzy Inference System Using Fuzzy Logic Designer. Available at: https://www.mathworks.com/help/fuzzy/tune-fuzzy-inference-system-using-fuzzy-logic-designer.html (аccessed 03.04.2026)
Moskalenko, V.Yu. Decision Support Model for the Product Strategy of an IT Company. Science and technology today, 2026, no. 3(57). pp. 1880-1893. DOI: 10.52058/2786-6025-2026-3(57)-1880-1893
DOI: https://doi.org/10.32620/reks.2026.2.09
Refbacks
- There are currently no refbacks.