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Improving the operational efficiency of outbound retail logistics using clustering of retailers and consumers

Improving the operational efficiency of outbound retail logistics using clustering of retailers... The purpose of this paper is to investigate ways to improve operational efficiency of outbound retail logistics considering retailers and consumers by using clustering approach. The retailers are allocated to serve a cluster of consumers. This study demonstrates economic and environment benefits that are achieved in terms of reduced delivery time, transportation cost and carbon emissions.Design/methodology/approachThis study is based on modeling the outbound logistics of a retail chain by using Kohonen self-organizing map (KSOM). KSOM is an unsupervised learning and data analysis method for vector quantization, which is based on Euclidean distance method to form clusters.FindingsAppropriate clustering of retailers and consumers provides efficient locations of retailers that are identified using the KSOM training algorithm. It provides optimum distance with lesser delivery time, transportation cost and carbon emissions.Research limitations/implicationsThe implication of research includes modeling of operational procedures in a retail supply chain, which is a crucial task for a business. These operations positively affect the reduction in inventory and distribution costs, improvement in customer service and responsiveness to the ever-changing markets of consumer durables. Overall results are insightful and practical in the sense that implementation would result in consumer convenience, eco-friendly environment, etc.Originality/valueThere is not enough research available on outbound retail logistics considering retailers and consumers using clustering approach. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Modelling in Management Emerald Publishing

Improving the operational efficiency of outbound retail logistics using clustering of retailers and consumers

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References (34)

Publisher
Emerald Publishing
Copyright
© Emerald Publishing Limited
ISSN
1746-5664
DOI
10.1108/jm2-12-2016-0137
Publisher site
See Article on Publisher Site

Abstract

The purpose of this paper is to investigate ways to improve operational efficiency of outbound retail logistics considering retailers and consumers by using clustering approach. The retailers are allocated to serve a cluster of consumers. This study demonstrates economic and environment benefits that are achieved in terms of reduced delivery time, transportation cost and carbon emissions.Design/methodology/approachThis study is based on modeling the outbound logistics of a retail chain by using Kohonen self-organizing map (KSOM). KSOM is an unsupervised learning and data analysis method for vector quantization, which is based on Euclidean distance method to form clusters.FindingsAppropriate clustering of retailers and consumers provides efficient locations of retailers that are identified using the KSOM training algorithm. It provides optimum distance with lesser delivery time, transportation cost and carbon emissions.Research limitations/implicationsThe implication of research includes modeling of operational procedures in a retail supply chain, which is a crucial task for a business. These operations positively affect the reduction in inventory and distribution costs, improvement in customer service and responsiveness to the ever-changing markets of consumer durables. Overall results are insightful and practical in the sense that implementation would result in consumer convenience, eco-friendly environment, etc.Originality/valueThere is not enough research available on outbound retail logistics considering retailers and consumers using clustering approach.

Journal

Journal of Modelling in Management – Emerald Publishing

Published: Oct 15, 2018

Keywords: Clustering; Operational efficiency; Retail logistics; Consumer durables; Kohenon self-organizing map; Training algorithm

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