A Data-Driven Lean Manufacturing Framework for Enhancing Productivity in Textile Micro-Enterprises †

The textile sector plays a crucial role in Peru’s economy. This case study examines a Micro and Small Enterprise (MSE) in the Peruvian textile sector, which experienced a productivity decline to 0.085 units per sol in 2023, compared to the sector average of 0.13 units per sol. This productivity gap resulted in a 22.45% reduction in the company’s income. Previous studies addressing similar productivity issues have achieved only marginal improvements. This study aims to achieve more significant results by implementing 5S, Total Productive Maintenance (TPM), digitization, and advanced data analytics to enhance data recording and overall productivity. Data analytics is utilized to transform raw data into actionable insights, optimize maintenance, and improve quality control. The methodology was tested through a pilot project in the company’s apparel division, resulting in a productivity increase of 0.10 sol/unit. The study concludes that the applied methodology, supported by data analytics, effectively addresses the productivity issues and optimizes the processes within the case study. In a textile sector MSE, which has a problem with the low productivity present during the past year of 2023, i.e., of 0.085 und/sol whereas is at 0.13 und/sol on the side of the sector, it thus generates a negative economic impact of 22.45% of the company’s income and a presenting a gap of 0.085 und/sol while the sector is at 0.13 und/sol. Previously, studies have been presented, seeking to solve similar problems and obtaining minimally positive results, which is why the motivation to achieve favorable results to ensure that the MSEs in the sector can develop optimally with the support of tools such as 5S, TPM, and innovative technologies such as digitization, thus allowing better recording of their data. The application of this methodology is designed through a pilot in the apparel area of the company, allowing it to achieve a positive result by increasing productivity by 0.10 sol/unit. It can be concluded that this methodology allows solving the problems addressed and optimizing the processes of the case study.