B2B supply chain performance enhancement road map using data mining techniques

Presently, modern B2B supply chain management (B2B-SCM) equipped with semi-automated data logging systems accumulate large volumes. However, each SC unit in B2B-SC still individually develops their performance. Besides, their linkages of performance attributes between SC units still lack vital infor...

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Main Authors: Pongsak Holimchayachotikul, Ridha Derrouiche, Komgrit Leksakul, Guido Guizzi
Format: Conference Proceeding
Published: 2018
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http://cmuir.cmu.ac.th/jspui/handle/6653943832/50780
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Institution: Chiang Mai University
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spelling th-cmuir.6653943832-507802018-09-04T04:49:12Z B2B supply chain performance enhancement road map using data mining techniques Pongsak Holimchayachotikul Ridha Derrouiche Komgrit Leksakul Guido Guizzi Engineering Mathematics Presently, modern B2B supply chain management (B2B-SCM) equipped with semi-automated data logging systems accumulate large volumes. However, each SC unit in B2B-SC still individually develops their performance. Besides, their linkages of performance attributes between SC units still lack vital information extraction to improve theirs. Therefore, this paper aims to propose an integrated framework between B2B supply chain (B2B-SC) performance evaluation systems and data mining techniques, developing relationship rules of collaborative performance attribute enhancement. The methodology is as follows. Firstly, B2B-SC performance evaluation questionnaires based on two levels able to characterize collaborative relation between two or more partners in their SC were gathered from the case study companies. The data set of relationships between enterprise and its direct customers of the case study companies in France was used for demonstration. Secondly, data cleaning and preparations for rule extraction were performed on the questionnaire database. The significance of attribute was calculated using attribute ranking algorithms by means of information gain based on ranker search. These results were used to choose the crucial attributes from each micro view. Thirdly, web graph analysis was performed on this data to confirm the strong attribute relationship. Next, association rule was deployed to extract performance attribute relationship rules grounded on support and confidence cross validation method. The quality of each recognized rule is tested and, from numerous rules, only those that are statistically very strong and contain vital information are selected. Last but not least, these rules are interpreted by domain experts and studied by domain engineers to build a collaborative performance attribute enhancement road map. Furthermore, the final rule set of extracted rules contains very interesting information relating to SCs and also point out the critical existing SC attribute improvement. Ultimately, companies in this SC are able to use this framework to design and adjust their units to conform with the exact customer needs. 2018-09-04T04:45:33Z 2018-09-04T04:45:33Z 2010-12-01 Conference Proceeding 1792507X 2-s2.0-79958747836 https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=79958747836&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/50780
institution Chiang Mai University
building Chiang Mai University Library
country Thailand
collection CMU Intellectual Repository
topic Engineering
Mathematics
spellingShingle Engineering
Mathematics
Pongsak Holimchayachotikul
Ridha Derrouiche
Komgrit Leksakul
Guido Guizzi
B2B supply chain performance enhancement road map using data mining techniques
description Presently, modern B2B supply chain management (B2B-SCM) equipped with semi-automated data logging systems accumulate large volumes. However, each SC unit in B2B-SC still individually develops their performance. Besides, their linkages of performance attributes between SC units still lack vital information extraction to improve theirs. Therefore, this paper aims to propose an integrated framework between B2B supply chain (B2B-SC) performance evaluation systems and data mining techniques, developing relationship rules of collaborative performance attribute enhancement. The methodology is as follows. Firstly, B2B-SC performance evaluation questionnaires based on two levels able to characterize collaborative relation between two or more partners in their SC were gathered from the case study companies. The data set of relationships between enterprise and its direct customers of the case study companies in France was used for demonstration. Secondly, data cleaning and preparations for rule extraction were performed on the questionnaire database. The significance of attribute was calculated using attribute ranking algorithms by means of information gain based on ranker search. These results were used to choose the crucial attributes from each micro view. Thirdly, web graph analysis was performed on this data to confirm the strong attribute relationship. Next, association rule was deployed to extract performance attribute relationship rules grounded on support and confidence cross validation method. The quality of each recognized rule is tested and, from numerous rules, only those that are statistically very strong and contain vital information are selected. Last but not least, these rules are interpreted by domain experts and studied by domain engineers to build a collaborative performance attribute enhancement road map. Furthermore, the final rule set of extracted rules contains very interesting information relating to SCs and also point out the critical existing SC attribute improvement. Ultimately, companies in this SC are able to use this framework to design and adjust their units to conform with the exact customer needs.
format Conference Proceeding
author Pongsak Holimchayachotikul
Ridha Derrouiche
Komgrit Leksakul
Guido Guizzi
author_facet Pongsak Holimchayachotikul
Ridha Derrouiche
Komgrit Leksakul
Guido Guizzi
author_sort Pongsak Holimchayachotikul
title B2B supply chain performance enhancement road map using data mining techniques
title_short B2B supply chain performance enhancement road map using data mining techniques
title_full B2B supply chain performance enhancement road map using data mining techniques
title_fullStr B2B supply chain performance enhancement road map using data mining techniques
title_full_unstemmed B2B supply chain performance enhancement road map using data mining techniques
title_sort b2b supply chain performance enhancement road map using data mining techniques
publishDate 2018
url https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=79958747836&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/50780
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