Topological Structure of Manufacturing Industry Supply Chain Networks
Empirical analyses of supply chain networks (SCNs) in extant literature have been rare due to scarcity of data. As a result, theoretical research have relied on arbitrary growth models to generate network topologies supposedly representative of real-world SCNs. Our study is aimed at filling the abov...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2018-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2018/3924361 |
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| author | Supun S. Perera Michael G. H. Bell Mahendrarajah Piraveenan Dharshana Kasthurirathna Mamata Parhi |
| author_facet | Supun S. Perera Michael G. H. Bell Mahendrarajah Piraveenan Dharshana Kasthurirathna Mamata Parhi |
| author_sort | Supun S. Perera |
| collection | DOAJ |
| description | Empirical analyses of supply chain networks (SCNs) in extant literature have been rare due to scarcity of data. As a result, theoretical research have relied on arbitrary growth models to generate network topologies supposedly representative of real-world SCNs. Our study is aimed at filling the above gap by systematically analysing a set of manufacturing sector SCNs to establish their topological characteristics. In particular, we compare the differences in topologies of undirected contractual relationships (UCR) and directed material flow (DMF) SCNs. The DMF SCNs are different from the typical UCR SCNs since they are characterised by a strictly tiered and an acyclic structure which does not permit clustering. Additionally, we investigate the SCNs for any self-organized topological features. We find that most SCNs indicate disassortative mixing and power law distribution in terms of interfirm connections. Furthermore, compared to randomised ensembles, self-organized topological features were evident in some SCNs in the form of either overrepresented regimes of moderate betweenness firms or underrepresented regimes of low betweenness firms. Finally, we introduce a simple and intuitive method for estimating the robustness of DMF SCNs, considering the loss of demand due to firm disruptions. Our work could be used as a benchmark for any future analyses of SCNs. |
| format | Article |
| id | doaj-art-9b015b0423264f3ea103fadfef0841e4 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-9b015b0423264f3ea103fadfef0841e42025-02-03T06:12:55ZengWileyComplexity1076-27871099-05262018-01-01201810.1155/2018/39243613924361Topological Structure of Manufacturing Industry Supply Chain NetworksSupun S. Perera0Michael G. H. Bell1Mahendrarajah Piraveenan2Dharshana Kasthurirathna3Mamata Parhi4Business School Institute of Transport and Logistics Studies (ITLS), University of Sydney, AustraliaBusiness School Institute of Transport and Logistics Studies (ITLS), University of Sydney, AustraliaSchool of Civil Engineering Complex Systems Research Group (CSRG), University of Sydney, AustraliaFaculty of Computing, Sri Lanka Institute of Information Technology (SLIIT), Sri LankaRoehampton Business School, University of Roehampton, UKEmpirical analyses of supply chain networks (SCNs) in extant literature have been rare due to scarcity of data. As a result, theoretical research have relied on arbitrary growth models to generate network topologies supposedly representative of real-world SCNs. Our study is aimed at filling the above gap by systematically analysing a set of manufacturing sector SCNs to establish their topological characteristics. In particular, we compare the differences in topologies of undirected contractual relationships (UCR) and directed material flow (DMF) SCNs. The DMF SCNs are different from the typical UCR SCNs since they are characterised by a strictly tiered and an acyclic structure which does not permit clustering. Additionally, we investigate the SCNs for any self-organized topological features. We find that most SCNs indicate disassortative mixing and power law distribution in terms of interfirm connections. Furthermore, compared to randomised ensembles, self-organized topological features were evident in some SCNs in the form of either overrepresented regimes of moderate betweenness firms or underrepresented regimes of low betweenness firms. Finally, we introduce a simple and intuitive method for estimating the robustness of DMF SCNs, considering the loss of demand due to firm disruptions. Our work could be used as a benchmark for any future analyses of SCNs.http://dx.doi.org/10.1155/2018/3924361 |
| spellingShingle | Supun S. Perera Michael G. H. Bell Mahendrarajah Piraveenan Dharshana Kasthurirathna Mamata Parhi Topological Structure of Manufacturing Industry Supply Chain Networks Complexity |
| title | Topological Structure of Manufacturing Industry Supply Chain Networks |
| title_full | Topological Structure of Manufacturing Industry Supply Chain Networks |
| title_fullStr | Topological Structure of Manufacturing Industry Supply Chain Networks |
| title_full_unstemmed | Topological Structure of Manufacturing Industry Supply Chain Networks |
| title_short | Topological Structure of Manufacturing Industry Supply Chain Networks |
| title_sort | topological structure of manufacturing industry supply chain networks |
| url | http://dx.doi.org/10.1155/2018/3924361 |
| work_keys_str_mv | AT supunsperera topologicalstructureofmanufacturingindustrysupplychainnetworks AT michaelghbell topologicalstructureofmanufacturingindustrysupplychainnetworks AT mahendrarajahpiraveenan topologicalstructureofmanufacturingindustrysupplychainnetworks AT dharshanakasthurirathna topologicalstructureofmanufacturingindustrysupplychainnetworks AT mamataparhi topologicalstructureofmanufacturingindustrysupplychainnetworks |