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Data Mining with Clustering Algorithms to Reduce Packaging Costs : A Case Study

Zhao, Chuan; Johnsson, Mats LU and He, Mingke (2017) In Packaging Technology and Science 30(5). p.173-193
Abstract

Reducing package-related cost is essential for various companies and institutions. Different packages are usually designed separately for each and every product, which results in less cost-effective packaging systems. In this study, a data mining model with three clustering algorithms was developed to modularize a packaging system by reducing the variety of packaging sizes. The three algorithms were k-means clustering, agglomerative hierarchical clustering and self-organizing feature map. The package models with similar shapes and sizes were clustered automatically and replaced by one package model with a size that suited them all. The study also analysed the financial effects including the purchasing and inventory costs of the package... (More)

Reducing package-related cost is essential for various companies and institutions. Different packages are usually designed separately for each and every product, which results in less cost-effective packaging systems. In this study, a data mining model with three clustering algorithms was developed to modularize a packaging system by reducing the variety of packaging sizes. The three algorithms were k-means clustering, agglomerative hierarchical clustering and self-organizing feature map. The package models with similar shapes and sizes were clustered automatically and replaced by one package model with a size that suited them all. The study also analysed the financial effects including the purchasing and inventory costs of the package material and the transportation cost of the packaged products. The case study was carried out at Ericsson to select the best clustering algorithm of the three and to test the effectiveness and applicability of the proposed model. The results show that the packaging system modularized by the agglomerative hierarchical clustering algorithm is more cost-effective in this case compared with the ones modularized by the other two clustering algorithms and with the one without modularization. Copyright © 2017 John Wiley & Sons, Ltd.

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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
agglomerative hierarchical clustering, case study, k-means clustering, package size, SOFM clustering
in
Packaging Technology and Science
volume
30
issue
5
pages
21 pages
publisher
John Wiley & Sons
external identifiers
  • scopus:85017193359
  • wos:000398221900001
ISSN
0894-3214
DOI
10.1002/pts.2286
language
English
LU publication?
yes
id
8ce740ef-ab79-4aa8-b6b6-330e84fafa7a
date added to LUP
2017-04-26 11:41:24
date last changed
2017-09-18 13:33:15
@article{8ce740ef-ab79-4aa8-b6b6-330e84fafa7a,
  abstract     = {<p>Reducing package-related cost is essential for various companies and institutions. Different packages are usually designed separately for each and every product, which results in less cost-effective packaging systems. In this study, a data mining model with three clustering algorithms was developed to modularize a packaging system by reducing the variety of packaging sizes. The three algorithms were k-means clustering, agglomerative hierarchical clustering and self-organizing feature map. The package models with similar shapes and sizes were clustered automatically and replaced by one package model with a size that suited them all. The study also analysed the financial effects including the purchasing and inventory costs of the package material and the transportation cost of the packaged products. The case study was carried out at Ericsson to select the best clustering algorithm of the three and to test the effectiveness and applicability of the proposed model. The results show that the packaging system modularized by the agglomerative hierarchical clustering algorithm is more cost-effective in this case compared with the ones modularized by the other two clustering algorithms and with the one without modularization. Copyright © 2017 John Wiley &amp; Sons, Ltd.</p>},
  author       = {Zhao, Chuan and Johnsson, Mats and He, Mingke},
  issn         = {0894-3214},
  keyword      = {agglomerative hierarchical clustering,case study,k-means clustering,package size,SOFM clustering},
  language     = {eng},
  month        = {05},
  number       = {5},
  pages        = {173--193},
  publisher    = {John Wiley & Sons},
  series       = {Packaging Technology and Science},
  title        = {Data Mining with Clustering Algorithms to Reduce Packaging Costs : A Case Study},
  url          = {http://dx.doi.org/10.1002/pts.2286},
  volume       = {30},
  year         = {2017},
}