Effectiveness of Internet of Things migration into hybrid economic projects

Abstract

The study aimed to scientifically substantiate the economic feasibility of migrating hybrid economic projects to Internet of Things technology solutions and to identify the conditions under which such migration would deliver sustainable economic benefits. The methodological framework included comparative economic, structuraldynamic and scenario analysis, sensitivity assessment and financial modelling based on the calculation of net present value, return on investment, total cost of ownership and integral economic efficiency indicator. The empirical base covered data from Azerbaijan’s logistics, agricultural and industrial segments for 2021-2025. The results showed that digitalisation provides economic benefits and stabilises revenue streams in all sectors studied. In logistics, the introduction of Internet of Things technologies reduces operational delays by 43% and transaction costs by 17%, resulting in a significant increase in profitability. In the agricultural sector, the use of sensor systems reduces operating costs by 18.8% and risk costs by 17.4%, reducing the total cost of ownership by 12.7%. In the industry, modernisation increases average annual revenues to 162,000 manats while reducing total costs to 298,000 manats, generating maximum net present value in an optimistic scenario. The integrated economic efficiency model revealed cross-sector differences: logistics demonstrates the most stable results, while industry has the greatest potential but remains sensitive to the cost of capital and the level of digital risks. The results confirm that the economic efficiency of digital migration is achieved by reducing operating costs, mitigating risks, increasing benefits, and complying with the architectural compatibility requirements of digital systems. The practical significance of the study is determined by the fact that the identified dependencies make it possible to develop economically sound scenarios for the implementation of Internet of Things in key sectors of Azerbaijan and to more accurately predict the expected effects at different cost levels and technological conditions.