TY - GEN
T1 - Analysis of Unfulfilled Orders in a Mass Consumption Distribution Center and Innovative Proposal Through Methods Engineering and Industry 4.0
AU - Soto-Choquecahua, Angie
AU - Pinedo-Ramirez, Milagros
AU - Salas-Castro, Rosa
AU - Alvarez, José C.
AU - Demirkesen, Sevilay
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.
PY - 2026
Y1 - 2026
N2 - 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.
AB - 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.
KW - digital Kanban
KW - distribution center
KW - Industry 4.0
KW - inventory management
KW - machine learning
KW - Methods Engineering
KW - slotting
KW - unfulfilled orders
UR - https://www.scopus.com/pages/publications/105030926722
U2 - 10.1007/978-3-032-11791-5_40
DO - 10.1007/978-3-032-11791-5_40
M3 - Contribución a la conferencia
AN - SCOPUS:105030926722
SN - 9783032117908
T3 - Mechanisms and Machine Science
SP - 431
EP - 440
BT - Advances in Sustainable Mechanical Manufacturing - Selected Contributions from MMIE 2025
A2 - Tsukamoto, Hideaki
PB - Springer Science and Business Media B.V.
T2 - 8th International Conference on Mechanical Manufacturing and Industrial Engineering, MMIE 2025
Y2 - 26 August 2025 through 29 August 2025
ER -