International Journal of Multidisciplinary Research and Growth Evaluation  |  ISSN (Online): 2582-7138  |  Double-Blind Peer Review  |  Open Access  |  CC BY 4.0

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     2026:7/3

International Journal of Multidisciplinary Research and Growth Evaluation

ISSN (Online): 2582-7138 | Open Access

Leading with Lean Six Sigma and RPA in High-Volume Distribution: A Comprehensive Framework for Operational Excellence

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Abstract

This paper explores the integration of Lean Six Sigma and Robotic Process Automation (RPA) in high-volume distribution environments, offering a comprehensive framework for operational excellence. With the increasing demands of global supply chains, organizations face the need to optimize distribution processes to remain competitive. Lean Six Sigma, with its focus on waste reduction, process optimization, and continuous improvement, provides a robust methodology for enhancing efficiency. When combined with RPA, which automates repetitive and time-consuming tasks, distribution processes become faster, more accurate, and scalable. This paper presents a detailed theoretical foundation, real-world case studies, and a synergistic framework for integrating Lean Six Sigma and RPA to achieve significant improvements in distribution operations. The findings demonstrate that organizations can realize substantial cost savings, improved order accuracy, and faster processing times by implementing these strategies. Additionally, this paper outlines the strategic implications for distribution managers and offers directions for future research to further explore the potential of these methodologies in transforming high-volume distribution systems.

How to Cite This Article

Oluwafunmilayo Janet Esan, Ogechi Thelma Uzozie, Osazee Onaghinor, Grace Omotunde Osho, Julius Olatunde Omisola (2023). Leading with Lean Six Sigma and RPA in High-Volume Distribution: A Comprehensive Framework for Operational Excellence . International Journal of Multidisciplinary Research and Growth Evaluation (IJMRGE), 4(1), 1158-1164. DOI: https://doi.org/10.54660/.IJMRGE.2023.4.1.1158-1164

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