Failure to deliver on time represents a critical obstacle to the competitiveness of textile companies in Latin America. While traditional approaches have been applied to reduce the impact of the problem, unresolved operational gaps persist that warrant a new approach. This case study corresponds to a Peruvian textile company whose compliance rate is 82.3%, below the regional standard of 96%. In this context, a model is proposed aimed at increasing the punctuality of deliveries through the integration of three complementary tools: process standardization, data mining and Box-Behnken experimental design. The proposal is based on a diagnosis that identifies operational limitations associated with variability in the ensimage sub-process, determined by its low capacity indicator. The implementation of standard work is proposed as a basis to stabilize the operation. A preliminary data mining approach using historical data and descriptive dashboards identified patterns influencing treatment quality. A Box-Behnken experiment is then designed to optimize the most influential operating parameters. The validation is carried out through simulations that project an increase in delivery compliance to 89.15%, based on the reduction in the rework index derived from greater compliance with quality specifications. The results show that combining traditional and technological tools improves operational performance in a measurable way, constituting a replicable option to increase compliance in the textile sector.