Enhanced Community Detection Based on Cross Time for Higher Visibility In Supply Chain: A Six-Steps Model Framework
Zuraida Abal Abas1, Nurul Hafizah Mohd Zaki2, Siti Azirah Asmai3, Ahmad Fadzli Nizam Abdul Rahman4, Zaheera Zainal Abidin5
1Zuraida Abal Abas, (OptiMAS), Faculty of Information Communication and Technology, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, Durian Tunggal, Melaka Malaysia.
2Nurul Hafizah Mohd Zaki, Faculty of Information Communication and Technology, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, Durian Tunggal, Melaka Malaysia.
3Siti Azirah Asmai, (OptiMAS), Faculty of Information Communication and Technology, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, Durian Tunggal, Melaka Malaysia.
4Ahmad Fadzli Nizam Abdul, (OptiMAS), Faculty of Information Communication and Technology, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, Durian Tunggal, Melaka Malaysia.
5Zaheera Zainal Abidin, (INSFORNET), Faculty of Information Communication and Technology, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, Durian Tunggal, Melaka Malaysia.
Manuscript received on October 12, 2019. | Revised Manuscript received on 21 October, 2019. | Manuscript published on November 10, 2019. | PP: 4509-4513 | Volume-9 Issue-1, November 2019. | Retrieval Number: A4019119119/2019©BEIESP | DOI: 10.35940/ijitee.A4019.119119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Increasing the visibility in supply chain network had decrease the risk in industries. However, the current Cross-Time approach for temporal community detection algorithm in the visibility has fix number of communities and lack of operation such as split or merge. Therefore, improving temporal community detection algorithm to represent the relationship in supply chain network for higher visibility is significant. This paper proposed six steps model framework that aim: (1) To construct the nodes and vertices for temporal graph representing the relationship in supply chain network; (2) To propose an enhanced temporal community detection algorithm in graph analytics based on Cross-time approach and (3) To evaluate the enhanced temporal community detection algorithm in graph analytics for representing relationship in supply chain network based on external and internal quality analysis. The proposed framework utilizes the Cross-Time approach for enhancing temporal community detection algorithm. The expected result shows that the Enhanced Temporal Community Detection Algorithm based on Cross Time approach for higher visibility in supply chain network is the major finding when implementing this proposed framework. The impact advances industrialization through efficient supply chain in industry leading to urbanization.
Keywords: Supply Chain Network, Temporal Community Detection, Graph Analytics, Internet of Things, Risk.
Scope of the Article: Internet of Things