🤖 AI Summary
This study addresses the limitations of existing approaches in simultaneously accounting for the time value of money and the integrated effects of multidimensional decision criteria on financial risk in manufacturing firms, while also overlooking the interactions among economic, operational, and managerial factors. To bridge this gap, we propose an evaluation framework that integrates a compound discounting model with multicriteria linear regression. For the first time, a time-discounting mechanism is incorporated into multicriteria decision analysis, enabling unified treatment of one-time expenditures, proportional costs, and complex cost structures. The method effectively quantifies the present value of costs and benefits across different time points and reveals how synergistic interactions among multiple factors influence discounted performance. This approach significantly enhances the systematicity and accuracy of financial risk assessment, offering manufacturing enterprises quantifiable decision support for optimizing the economic efficiency of control systems.
📝 Abstract
Evaluating the financial performance of manufacturing firms requires consideration of both the time value of money and the relative importance of multiple decision criteria. Conventional approaches relying solely on deterministic discounting often fail to account for interactions among economic, operational, and managerial factors. This study proposes an integrated framework that combines time-discounted economic analysis with linear regression to evaluate control system efficiency. A theoretical discounting model is first developed to convert costs and benefits occurring at different times into present-value terms using compound interest functions. The model accommodates one-time expenditures, time-proportional costs, and complex cost structures arising during system development and commissioning. To empirically assess how discounted economic performance is influenced by multiple criteria, linear regression serves as the approximation method.