METHODOLOGY FOR CALCULATING ROI TO ASSESS THE EFFECTIVENESS OF IMPLEMENTING DIGITAL SOLUTIONS IN PRODUCTION SYSTEMS TAKING INTO ACCOUNT OPERATING AND CAPITAL EXPENDITURES
DOI:
https://doi.org/10.46827/ejefr.v9i5.2061Ključne reči:
return on investment, digital technologies, capital expenditures, operating expenditures, net present value, internal rate of return, time considerationApstrakt
This article examines the methodology for calculating Return on Investment in relation to digital solutions in production systems, taking into account both capital and operating expenditures. The specifics of the structure of costs and benefits of digital projects are studied, including direct and indirect effects, as well as the temporal dynamics of their manifestation. The necessity of modifying the classical approach through the use of discounted indicators and integration with other investment analysis tools, such as Net Present Value, Internal Rate of Return, and Payback Period, is analyzed. Based on practical implementation examples, the applied significance of the methodology is demonstrated and the main drivers of the economic effect of digitalization are identified. Particular attention is paid to the limitations associated with the quality of initial data, the choice of discount rate, and the difficulties of accounting for intangible factors.
JEL: O33, M15, E22
Article visualizations:
Reference
Yang G, Jiang J, 2025. Cost-benefit tradeoff mediates the transition from rule-based to memory-based processing during practice. PLoS biology. 23(1). Retrieved from https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.3002987
Kovalenko A, 2025. Automation of monitoring and self-recovery mechanisms in backend architecture of financial systems. German International Journal of Modern Science. 103: 8-11.
Zharmagambetov Ye, 2025/1. Portfolio Immunization and Debt Maturity Management Strategies. Innovations and Investments. 1: 451-456.
Zorchenko NV, Tyupina TG, Parshutin ME, 2024. Technologies used by General Electric to create digital twins for energy industry. Power Technology and Engineering. 58(3): 521-526. https://doi.org/10.1007/s10749-024-01839-2
Shah JP, 2025. Advancing Smart Manufacturing through Industrial IoT: Enhancing Operational Efficiency and Predictive Maintenance. AIJR Proceedings. 7(4): 45-56. https://doi.org/10.21467/proceedings.7.4.5
Akinsanya A, 2025. Enhancing Process Efficiency and Security in the US Manufacturing Sector: Evidence from Industry Implementation. Iconic research and engineering journals. 8(8): 753-762. Retrieved from https://www.researchgate.net/publication/389434693_Enhancing_Process_Efficiency_and_Security_in_the_US_Manufacturing_Sector_Evidence_from_Industry_Implementation
Parapalli SL, 2025. Evolution of MES in Autonomous Factories: From Reactive to Predictive Systems. International journal of data science and machine learning. 5(01): 127-136. Retrieved from https://www.academicpublishers.org/journals/index.php/ijdsml/article/view/4098
Syed S, 2023. Advanced Manufacturing Analytics: Optimizing Engine Performance through Real-Time Data and Predictive Maintenance. Letters in High Energy Physics. 184-195. https://doi.org/10.2139/ssrn.5031293
George AS, 2024. AI-enabled intelligent manufacturing: A path to increased productivity, quality, and insights. Partners Universal Innovative Research Publication. 2(4): 50-63. https://doi.org/10.5281/zenodo.13338085
