Integrating Linear Regression and Multi-Criteria Decision Making for Assessing Financial Statement Risks in Manufacturing Firms

TL;DR

This paper introduces an integrated framework combining economic discounting and linear regression to evaluate financial risks in manufacturing firms, addressing limitations of traditional methods by considering multiple decision criteria and complex cost structures.

Contribution

It develops a theoretical discounting model and applies linear regression to empirically assess economic performance considering multiple criteria in manufacturing financial risk assessment.

Findings

The model effectively converts multi-time costs into present value.

Linear regression accurately approximates economic performance influenced by multiple factors.

The framework improves risk assessment accuracy over traditional methods.

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.