(untitled) (Research, Society and Development, 2026)
Digital transformation has become a key factor in improving the competitiveness of small and medium-sized enterprises (SMEs) in the metal-mechanical sector. This study presents the application of a data analytics model focused on project-based production planning in an SME dedicated to the manufacturing of industrial silos and complementary components. The main objective was to improve the On-Time Delivery (OTD) rate through the use of accessible digital tools. The methodology was based on the analysis of simulated operational data corresponding to a six-month period, including variables related to production volume, workload, rework, logistics, and transportation availability. The predictive model was implemented in Google Colab using regression techniques to estimate the monthly performance of the production system. The results show that the multiple linear regression model achieved a coefficient of determination R² = 0.862, indicating strong explanatory capacity for the On-Time Delivery (OTD) indicator. The historical analysis showed an average compliance rate of 69.36%, while under an operational optimization scenario characterized by reduced rework, decreased finished goods accumulation, and improved transportation availability the projected OTD increased to 81.92%, representing an improvement of 12.56 percentage points. These findings highlight the potential of data analytics as a decision-support tool for production planning in metal-mechanical SMEs.