Analysis of Unfulfilled Orders in a Mass Consumption Distribution Center and Innovative Proposal Through Methods Engineering and Industry 4.0

This article proposes an operational improvement in a mass consumption distribution center to reduce unfulfilled orders that affect customer service. Root causes include the absence of standardized inventory procedures, limited visibility in product reception, undefined picking locations, and disorganized order picking. These issues lead to operational errors, warehouse congestion, and a high rate of order rejections. The implementation of standardization of inventory registration, Digital Kanban, Slotting and Machine Learning is proposed to predict order patterns and allocate resources efficiently. Compared to the existing literature, which addresses the identification of the problem by various authors. The proposed solution seeks to increase operational efficiency, optimize inventory control and reduce the percentage of unfulfilled orders, contributing to the level of service and competitiveness of the distribution center. This study provides an adaptable model for distribution centers in Latin America that wish to modernize their logistics processes by combining traditional tools with Industry 4.0 technologies.