This study focuses on estimating the lead times of various processes in wind tower factories. Accurate estimation of these times allows for more efficient sequencing of activities, proper allocation of resources, and setting of realistic delivery dates, thus avoiding delays and bottlenecks in the production flow and improving process quality and efficiency. In addition, accurate estimation of these times contributes to a proper assessment of costs, overcoming the limitations of traditional techniques; this allows for the establishment of tighter quotations. The data used in this study were collected at wind tower manufacturing facilities in Spain and Brazil. Data preprocessing was conducted rigorously, encompassing cleaning, transformation, and feature selection processes. Following preprocessing, machine learning regression analysis was performed to estimate lead times. Nine algorithms were employed: decision trees, random forest, Ridge regression, Lasso regression, Elastic Net, support vector regression, gradient boosting, XGBoost, LightGBM, and multilayer perceptron. Additionally, the performance of two deep learning models, TabNet and NODE, designed specifically for tabular data, was evaluated. The results showed that gradient boosting-based algorithms were the most effective in predicting processing times and optimizing resource allocation. The system is designed to retrain models as new information becomes available.

The escalating complexity of B2C e-commerce logistics, particularly in last-mile delivery, coupled with the growing consumer preference for home delivery, presents a significant challenge: the need to reduce disruptions during the delivery process. These disruptions not only diminish perceived service quality but also escalate distribution costs and environmental emissions. This paper introduces a novel initiative to tackle delivery failures resulting from customer absences by enabling users to generate and share location statistics with delivery companies, contingent upon their explicit consent. This feature allows users to define a list of preferred locations for tracking, thereby enhancing coordination between customers and delivery providers. By fostering this collaborative approach, users gain greater control over their data, contributing to a more efficient and customer-centric e-commerce logistics ecosystem. Preliminary results demonstrate the application’s potential to reduce delivery failures by up to 45%. This strategy not only mitigates disruptions and lowers costs but also helps minimize environmental impact.