Decision support systems as a tool for optimizing R&D costs in university laboratories: Economic model and scenario analysis

In the context of limited funding and increasing demands for research efficiency in the university environment, the task of cost optimization is becoming paramount. This article examines the use of decision support systems (DSS) as a key tool for improving the economic efficiency of scientific project management. The aim of the study is to develop and test an economic model integrated with DSS and designed to optimize resource allocation and minimize costs in university laboratories. A multicriteria model based on systems analysis and linear programming is presented, taking into account the main R&D cost items: labor costs, equipment depreciation, consumables, and overhead. To assess the practical applicability and economic impact of implementing a decision support system (DSS), a scenario analysis was conducted, including three possible development paths: “baseline” (unchanged), “conservative” (moderate impact), and “optimistic” (maximum efficiency). The modeling results demonstrate that implementing a DSS can reduce overall R&D costs by 25…30% over a five-year period, with a payback period of approximately three years. The paper also presents the results of a PESTLE and SWOT analysis, identifying the key external and internal factors influencing the implementation and operation of the system. The practical significance of the study lies in the proposal of a specific methodological approach and tools that can be adapted and used by the management of university laboratories and research centers to improve the transparency, validity, and effectiveness of financial decisions.