This study examines the combined influence of social, quality, and delivery performance practices on the operational performance of Indian textile “small and medium-sized enterprises” (SMEs). In an increasingly competitive and sustainability-driven manufacturing environment, understanding how these factors interact to enhance operational outcomes is essential for both researchers and practitioners. To address this, the study uses a hybrid analytical framework integrating “Partial Least Squares Structural Equation Modelling” (PLS-SEM) and “Necessary Condition Analysis” (NCA). Data were collected from 40 Indian textile SMEs through a structured survey, and the relationships among constructs were empirically validated. The PLS-SEM results show that delivery performance exerts the most significant positive impact on operational performance, followed by social and quality performance. NCA further show that achieving high operational performance requires minimum threshold levels of these three practices, emphasizing their necessity rather than sufficiency. The integrated outcomes show that SMEs can improve operational efficiency by simultaneously optimizing social, quality, and delivery dimensions. This study provides actionable insights for SME managers into how delivery reliability, workforce well-being, and consistent quality management collectively enhance productivity and sustainability outcomes. This work contributes by integrating socio-technical and sustainability perspectives within an operational performance framework and by demonstrating the complementary use of PLS-SEM and NCA in SME performance research. This work provides a comprehensive understanding of performance enhancement pathways and offers a robust methodological foundation for future research in sustainable and lean-oriented manufacturing systems.

The manufacturing sector has grown significantly during the last two to three decades. Because of the intense competition, customers now place a higher priority on price, delivery time, and product quality. This study employs lean methods and methodologies, including root-cause analysis, kaizen ideas, evaluation of kaizen ideas, and before-and-after comparison, to evaluate the operational performance of the organization. It accomplishes this by using kaizen analysis to assess the level of lean implementation in Indian manufacturing companies. A questionnaire was developed for the goal of collecting data, and industry experts were contacted personally and questioned. According to this study, common implementation of lean practices leads to a reduction of 68% in setup loss, 70% in setup time, 17% in energy consumption, 11% in electricity consumption, 51% in lead time, and an increase of 8.5 times in kaizen per team annually.

This study explores key factors such as process product and social as independent variables to examine the level of adoption of digital technology as a dependent variable in Indian automotive small and medium enterprises (SMEs). Partial least squares structural equation modelling (PLS-SEM) data were collected from 40 small and medium enterprises in automotive sector. The analysis tested the six hypotheses and found that five were statistically significant indicating that factors related to process (H1b and H1a), product (H2b and H3a), and social (H2a) are positively associated with industry digitalization. Notably, 78.9% of the variance in the dependent variable is explained by the independent variable for small enterprises whereas 85.9% of the variance in the dependent variable is explained by the independent variable for medium enterprises. These findings highlight the need to achieve or strengthen the remaining factors or practices regarding social performance (H3b) for effective adoption of industry digitalization effectively in small and medium enterprises in India. This research contributes to a deeper understanding of these practices and offers practical as well as theoretical implications for enhancing these practices especially in small and medium enterprises in India.