Matching Consignees/Shippers Recommendation System in Courier Service Using Data Analytics
The purpose of this research was to create a Matching Consignees/Shippers Recommendation System (MCSRS). We used the association rule to identify product associations, the clustering technique to group shippers and consignees according to behaviors when receiving goods from similar shipper groups, a...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-08-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/10/16/5585 |
| _version_ | 1797558646879551488 |
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| author | Jutamat Jintana Apichat Sopadang Sakgasem Ramingwong |
| author_facet | Jutamat Jintana Apichat Sopadang Sakgasem Ramingwong |
| author_sort | Jutamat Jintana |
| collection | DOAJ |
| description | The purpose of this research was to create a Matching Consignees/Shippers Recommendation System (MCSRS). We used the association rule to identify product associations, the clustering technique to group shippers and consignees according to behaviors when receiving goods from similar shipper groups, and the decision tree to identify possible matches between shippers and consignees. Finally, Monte Carlo simulation was used to estimate potential revenue. The case study is a courier company in Thailand. The results showed that garment products and clothes were the products with the highest association. Shippers and consignees of these products were segmented according to recency, frequency, monetary factors, number of customers, number of product items, weight, and day. Three rules are proposed that enabled the assignment of 8 consignees to 56 shippers with an estimated increase in revenue by 36%. This approach helps decision-makers to develop an effective cost-saving new marketing, inclusive strategy quickly. |
| first_indexed | 2024-03-10T17:34:16Z |
| format | Article |
| id | doaj.art-4209e6519fc34723b5258c0b4d7e5e7f |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T17:34:16Z |
| publishDate | 2020-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-4209e6519fc34723b5258c0b4d7e5e7f2023-11-20T09:54:18ZengMDPI AGApplied Sciences2076-34172020-08-011016558510.3390/app10165585Matching Consignees/Shippers Recommendation System in Courier Service Using Data AnalyticsJutamat Jintana0Apichat Sopadang1Sakgasem Ramingwong2Graduate Program in Industrial Engineering, Department of Industrial Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai 50200, ThailandExcellence Center in Logistics and Supply Chain Management, Chiang Mai University, Chiang Mai 50200, ThailandExcellence Center in Logistics and Supply Chain Management, Chiang Mai University, Chiang Mai 50200, ThailandThe purpose of this research was to create a Matching Consignees/Shippers Recommendation System (MCSRS). We used the association rule to identify product associations, the clustering technique to group shippers and consignees according to behaviors when receiving goods from similar shipper groups, and the decision tree to identify possible matches between shippers and consignees. Finally, Monte Carlo simulation was used to estimate potential revenue. The case study is a courier company in Thailand. The results showed that garment products and clothes were the products with the highest association. Shippers and consignees of these products were segmented according to recency, frequency, monetary factors, number of customers, number of product items, weight, and day. Three rules are proposed that enabled the assignment of 8 consignees to 56 shippers with an estimated increase in revenue by 36%. This approach helps decision-makers to develop an effective cost-saving new marketing, inclusive strategy quickly.https://www.mdpi.com/2076-3417/10/16/5585courier servicerecommendation systemassociation ruleclustering techniquedecision tree |
| spellingShingle | Jutamat Jintana Apichat Sopadang Sakgasem Ramingwong Matching Consignees/Shippers Recommendation System in Courier Service Using Data Analytics Applied Sciences courier service recommendation system association rule clustering technique decision tree |
| title | Matching Consignees/Shippers Recommendation System in Courier Service Using Data Analytics |
| title_full | Matching Consignees/Shippers Recommendation System in Courier Service Using Data Analytics |
| title_fullStr | Matching Consignees/Shippers Recommendation System in Courier Service Using Data Analytics |
| title_full_unstemmed | Matching Consignees/Shippers Recommendation System in Courier Service Using Data Analytics |
| title_short | Matching Consignees/Shippers Recommendation System in Courier Service Using Data Analytics |
| title_sort | matching consignees shippers recommendation system in courier service using data analytics |
| topic | courier service recommendation system association rule clustering technique decision tree |
| url | https://www.mdpi.com/2076-3417/10/16/5585 |
| work_keys_str_mv | AT jutamatjintana matchingconsigneesshippersrecommendationsystemincourierserviceusingdataanalytics AT apichatsopadang matchingconsigneesshippersrecommendationsystemincourierserviceusingdataanalytics AT sakgasemramingwong matchingconsigneesshippersrecommendationsystemincourierserviceusingdataanalytics |