"background": "The efficiency of manufacturing systems is a critical determinant of industrial productivity and economic development. In many developing economies, including Rwanda, there is a paucity of longitudinal, plant-level studies that rigorously quantify technical efficiency within the engineering domain, limiting evidence-based policy and investment. ", "purpose and objectives": "This study aims to methodologically evaluate the operational systems of manufacturing plants and to estimate longitudinal efficiency gains. The primary objective is to provide a robust, panel-data-based measure of technical efficiency change over time. ", "methodology": "A stochastic frontier analysis (SFA) framework is applied to an unbalanced panel dataset of manufacturing plants. The core model is specified as \ Y{it = \0 + \\ Xit + (Vit - Uit), where Uit represents time-varying technical inefficiency. Estimation uses maximum likelihood with robust standard errors clustered at the plant level to account for heteroskedasticity and within-unit correlation. ", "findings": "The analysis reveals a positive trend in mean technical efficiency, with an estimated annual average gain of 2. 3% (95% CI: 1. 7% to 2. 9%). This improvement is strongly associated with capital deepening and the adoption of integrated production management systems, whereas plant age showed a statistically insignificant relationship. ", "conclusion": "The methodological approach confirms its utility for tracking manufacturing systems performance, demonstrating measurable and significant efficiency improvements within the Rwandan industrial sector over the study period. ", "recommendations": "Industrial policy should prioritise mechanisms that support sustained capital investment and the integration of modern production engineering systems. Future research should incorporate granular data on technology adoption and workforce skills. ", "key words": "technical efficiency, stochastic frontier analysis, panel data, manufacturing systems, industrial engineering, economic development", "contribution statement": "This paper provides a novel longitudinal dataset and application of SFA to Rwandan manufacturing, establishing a benchmark for efficiency measurement and revealing the significant role
Habimana et al. (Mon,) studied this question.