Data Mining with Clustering Algorithms to Reduce Packaging Costs : A Case Study
(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.
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- author
- Zhao, Chuan ; Johnsson, Mats LU and He, Mingke
- organization
- publishing date
- 2017-05-01
- 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 Inc.
- 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
- 2024-04-14 10:13:24
@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.</p>}},
author = {{Zhao, Chuan and Johnsson, Mats and He, Mingke}},
issn = {{0894-3214}},
keywords = {{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 Inc.}},
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}},
doi = {{10.1002/pts.2286}},
volume = {{30}},
year = {{2017}},
}