Optimization of Working Capital for Financial Sustainability in Manufacturing Companies: A Statistical Model

Background: Working capital management plays a critical role in ensuring business liquidity and financial sustainability. However, few studies in developing economies have employed multivariate statistical techniques to optimize working capital decisions. This study addresses this gap by applying discriminant analysis to classify Ecuadorian manufacturing firms according to their financial sustainability and business continuity. Methods: A quantitative approach was applied to a sample of 112 manufacturing companies located in Zone 3 of Ecuador, covering the 2017–2020 period. The model incorporated working capital indicators and the Z-Score index as independent variables, while company size served as the categorical dependent variable. Results: The discriminant function retained two significant predictors—Working Capital (2019) and Z-Score (2017)—with an eigenvalue of 0.191, a canonical correlation of 0.400, and an overall classification accuracy of 71.4%. Box’s M test (p = 0.000) indicated unequal covariance matrices, suggesting cautious interpretation but acceptable robustness of the model. Conclusions: This study concludes that working capital and Z-Score are effective indicators for assessing financial sustainability and predicting firm continuity. The findings provide practical insights for managers and policymakers to enhance financial efficiency and resource allocation. The originality of this work lies in the application of discriminant analysis to model financial sustainability in Ecuador’s manufacturing sector, offering a statistical foundation for future optimization models.