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The Road most Travelled: The Impact of Urban Road Infrastructure on Supply Chain Network Vulnerability

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Abstract

Making a supply chain more resilient and making it more efficient are often diametrically opposed objectives. Managers have to make informed trade-offs when designing their supply chain networks. There are many methods available to quantify and optimise efficiency. Unfortunately the same cannot be said for vulnerability and resilience. We propose a method to quantify the impact that a supply chain’s dependence on the underlying transport infrastructure has on its vulnerability. The dependence relationship is modelled using multilayered complex network theory. We develop two metrics relating to the unique collection of shortest path sets namely redundancy and overlap. To test the relationships between these metrics and supply chain vulnerability we simulate progressive random link disruption of the urban road network and assess the impact this has on Fully Connected, Single Hub and Double Hub network archetypes. The results show that redundancy and overlap of the collection of shortest paths are significantly related to supply chain resilience, however under a purely random disturbance regime they hold no predictive power. This paper builds a foundation for a new field of inquiry into supply chain vulnerability by presenting a flexible mathematical formulation of the multilayered network and defining and testing two novel metrics that could be incorporated into supply chain network design decisions.

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Notes

  1. In this article kurtosis refers to Pearson’s measure of kurtosis which is the unadjusted fourth standardised moment of a distribution. This is not to be confused with excess kurtosis.

References

  • Bartholdi, Jarumaneeroj P, Ramudhin A (2014) A new connectivity index for container ports Technical report. The Supply Chain & Logistics Institute, Georgia Institute of Technology

  • Bellamy M, Basole C (2013) Network analysis of supply chain systems: a systematic review and future research. Syst Eng 16(2):235–249

    Article  Google Scholar 

  • Bhatia G, Lane C, Wain A (2013) Building resilience in supply chains. Risk response network. World Economic Forum

  • Boccaletti S, Bianconi G, Criado R, Del Genio CI, Gómez-Gardeñes J, Romance M, Señdina Nadal I, Wang Z, Zanin M (2014) The structure and dynamics of multilayer networks. Phys Rep 544(1):1–122

    Article  Google Scholar 

  • Bóta A, Gardner L, Khani A (2017) Identifying critical components of a public transit systems for outbreak control. Networks and Spatial Economics. Available online from https://doi.org/10.1007/s11067-017-9361-2

  • Cardillo A, Zanin M, Gómez-Gardeñes J, Romance M, García del Amo A, Boccaletti S (2015) Modeling the multi-layer nature of the European air transport network: Resilience and passengers re-scheduling under random failures. Eur Phys J Special Topics 215(1):23–33

    Article  Google Scholar 

  • Chacon N, Doherty S, Hayashi C, Green R, Lever I (2012) New models for addressing supply chain and transport risk. Risk response network. World Economic Forum

  • Choi T, Dooley K, Rungtusanatham M (2001) Supply networks and complex adaptive systems: control versus emergence. J Oper Manag 19:351–366

    Article  Google Scholar 

  • Choi TY, Wu Z (2009) Taking the leap from dyads to triads: Buyer-supplier relationships in supply networks. J Purch Supply Manag 15:263–266

    Article  Google Scholar 

  • Cohen R, Erez K, Avraham D, Havlin S (2000) Resilience of the internet to random breakdowns. Phys Rev Lett 85:4626–4628

    Article  Google Scholar 

  • Cohen R, Erez K, Avraham D, Havlin S (2001) Breakdown of the internet under intentional attack. Phys Rev Lett 86(16):3682–3685

    Article  Google Scholar 

  • Criado R, Hernández-Bermejo B, Romance M (2007) Efficiency, vulnerability and cost: an overview with applications to subway networks worldwide. Int J Bifurcation Chaos 17:2289–2301

    Article  Google Scholar 

  • Crucitti P, Latora V, Marchiori M, Rapisarda A (2004) Error and attack tolerance of complex networks. Phys A 340:388–394

    Article  Google Scholar 

  • Crucitti P, Latora V, Porta S (2006) Centrality measures in spatial networks of urban streets. Phys Rev E 73:036125

    Article  Google Scholar 

  • Danziger MM, Bashan A, Berezin Y, Shekhtman LM, Havlin S (2014) An introduction to interdependent networks. In: International conference on nonlinear dynamics of electronic systems, CCIS, vol 438, pp 189–202

  • Darayi M, Barker K, Santos J (2017) Component importance measures for multi-industry vulnerability of a freight transportation network. Network and Spatial Economics. Available online from https://doi.org/10.1007/s11067-017-9359-9

  • Duan Y, Lu F (2014) Robustness of city road networks at different granularities. Phys A 411:21–34

    Article  Google Scholar 

  • Ducruet C (2013) Network diversity and maritime flows. J Transp Geogr 30:77–88

    Article  Google Scholar 

  • Ducruet C (2016) The polarization of global container flows by interoceanic canals: geographic coverage and network vulnerability. Marit Policy Manag 43(2):242–260

    Article  Google Scholar 

  • Ducruet C, Lee S, Ng A (2010a) Centrality and vulnerability in liner shipping networks: revisiting the Northeast Asian port hierarchy. Marit Policy Manag 37:17–36

  • Ducruet C, Notteboom T (2012) The worldwide maritime network of container shipping: spatial structure and regional dynamics. Global Netw 12(3):395–423

    Article  Google Scholar 

  • Ducruet C, Rozenblat C, Zaidi F (2010b) Ports in multi-level maritime networks: evidence from the Atlantic (1996 – 2006). J Transp Geogr 18:508–518

  • Fraser D, Notteboom T, Ducruet C (2016) Peripherality in the global container shipping network: The case of the Southern African container port system. GeoJournal 81:139. https://doi-org.uplib.idm.oclc.org/10.1007/s10708-014-9610-6

    Article  Google Scholar 

  • Gallotti R, Barthelemy M (2014) Anatomy and efficiency of urban multimodal mobility. Sci Rep 4:6911

    Article  Google Scholar 

  • Gallotti R, Barthelemy M (2015) The multilayer temporal network of public transport in Great Britain. Sci Data 2:140056

    Article  Google Scholar 

  • González Laxe F, Freire Seoane M, Pais Montes C (2012) Maritime degree, centrality and vulnerability: port hierarchies and emerging areas in containerized transport (2008 – 2010). J Transp Geogr 24:33–44

    Article  Google Scholar 

  • Gudmundsson A, Mohajeri N (2013) Entropy and order in urban street networks. Sci Rep 3:3324

    Article  Google Scholar 

  • Háznagy A, Fi I, London A, Németh T (2015) Complex network analysis of public transportation networks: a comprehensive study. Models and Technologies for Intelligent Transportation Systems (MT-ITS)

  • Hearnshaw E, Wilson M (2013) A complex network approach to supply chain network theory. Int J Oper Prod Manag 33(4):442–469

    Article  Google Scholar 

  • Heckmann I, Comes T, Nickel S (2015) A critical review on supply chain risk — definition, measure and modeling. Omega 52:119–132

    Article  Google Scholar 

  • Holl A, Mariotti I (2017) The geography of logistics firm location, The role of accessibility. Networks and Spatial Economics. Available online from https://doi.org/10.1007/s11067-017-9347-0

  • Hu Y, Zhu D (2009) Empirical analysis of the worldwide maritime transportation network. Phys A 388:2061–2071

    Article  Google Scholar 

  • Jiang B (2007) A topological pattern of urban street networks: universality and peculiarity. Phys A 384:647–655

    Article  Google Scholar 

  • Jiang B, Claramunt C (2004) Topological analysis of urban street networks. Environ Plann B 31:151–162

    Article  Google Scholar 

  • Joubert J, Axhausen KW (2013) A complex network approach to understand commercial vehicle movement. Transportation 40(3):729–750

    Article  Google Scholar 

  • Joubert JW, Viljoen NM (2017) Multilayer complex networks, v3. Mendeley Data. Available online from https://doi.org/10.17632/268byhmvv5.3

  • Kaluza P, Kölzsch A, Gastner MT, Blasius B (2010) The complex network of global cargo ship movements. J R Soc Interface 7:1093–1103

    Article  Google Scholar 

  • Kivelä M, Arenas A, Barthelemy M, Gleeson JP, Moreno Y, Porter MA (2014) Multilayer networks. J Complex Netw 2:203–271

    Article  Google Scholar 

  • Kurant M, Thiran P (2006a) Extraction and analysis of traffic and topologies of transportation networks. Phys Rev E 74:036114

  • Kurant M, Thiran P (2006b) Layered complex networks. Phys Rev Lett 96:138701

  • Lee KM, Min B, Goh KI (2015) Towards real-world complexity: and introduction to multiplex networks. Eur Phys J B 88(2):48. https://doi-org.uplib.idm.oclc.org/10.1140/epjb/e2015-50742-1

    Article  Google Scholar 

  • Lordan O (2014) Study of the full-service and low-cost carriers network configuration. J Indust Eng Manag 7(5):1112–1123

    Article  Google Scholar 

  • Lordan O, Sallan JEN, Gonzalez-Prieto D (2016) Robustness of airline route networks. Phys A 445:18– 26

    Article  Google Scholar 

  • Lordan O, Sallan J, Simo P, Gonzalez-Prieto D (2014a) Robustness of the air transport network. Transp Res E 68:155–163

  • Lordan O, Sallan J, Simo P, Gonzalez-Prieto D (2015) Robustness of airline alliance route networks. Commun Nonlinear Sci Numer Simul 22(1–3):587–595

    Article  Google Scholar 

  • Lordan O, Sallan JM, Simo P (2014b) Study of the topology and robustness of airline route networks from the complex network approach: a survey and research agenda. J Transp Geogr 37:112–120

  • Lotero L, Cadillo A, Hurtado R, Gómez-Gardeñes J (2016) Several multiplexes in the Same City: The role of socioeconomic differences in urban mobility. In: Garas A (eds) Interconnected networks. Understanding complex systems. Springer, Cham

  • Mattsson L-G, Jenelius E (2015) Vulnerability and resilience of transport systems — a discussion of recent research. Transportation 81:16–34

    Google Scholar 

  • Mohamed-Chérif F, Ducruet C (2016) Regional integration and maritime connectivity across the Maghreb seaport system. J Transp Geogr 51:280–293

    Article  Google Scholar 

  • Mouronte ML, Benito RM (2012) Structural properties of urban bus and subway networks of Madrid. Netw Hetero Media 7:415–428

    Article  Google Scholar 

  • O’Kelly M (2015) Network hub structure and resilience. Netw Spatial Econ 15 (4):235–251

    Article  Google Scholar 

  • Ortigosa J, Menendez M (2014) Traffic performance on quasi-grid urban structures. Cities 36:18–27

    Article  Google Scholar 

  • Ouyang M, Zhao L, Hong L, Pan Z (2014) Comparisons of complex networks based models and real train flow model to analyze Chinese railway vulnerability. Reliab Eng Syst Saf 123:38–46

    Article  Google Scholar 

  • Pais Montes C, Freire Seoane M, González Laxe F (2012) General cargo and containership emergent routes: a complex networks description. Transp Policy 24:126–140

    Article  Google Scholar 

  • Parshani R, Rozenblat C, Ietri D, Ducruet C, Havlin S (2011) Inter-similarity between coupled networks. Europhys Lett 92(6):68002

    Article  Google Scholar 

  • Pastoras-Satorras R, Vespignani A (2001) Epidemic spreading in scale-free networks. Phys Rev Lett 86(14):3200

    Article  Google Scholar 

  • Pathak S, Day J, Nair A, Sawaya W, Kristal M (2007) Complex and adaptivity in supply networks: Building supply network theory using a complex adaptive systems perspective. Decis Sci 38(4):547–580

    Article  Google Scholar 

  • Peck H (2006) Reconciling supply chain vulnerability, risk and supply chain management. Int J Log Res Appl 9(2):127–142

    Article  Google Scholar 

  • Porta S, Latora V, Wang F, Rueda S, Strano E, Scellato S, Cardillo A, Belli E, Cárdenas F, Cormenzana B, Latora L (2012) Street centrality and the location of economic activities in Barcelona. Urban Stud 49(7):1471–1488

    Article  Google Scholar 

  • R Core Team (2013) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna

    Google Scholar 

  • Rao S, Goldsby T (2009) Supply chain risk: a review and typology. Int J Logist Manag 20(1):97–123

    Article  Google Scholar 

  • Reggiani A, Bucci P, Russo G (2011) Accessibility and network structures in german commuting. Netw Spatial Econ 11(4):621–641

    Article  Google Scholar 

  • Rodríguez-Núñez E, García-Palomares JC (2014) Measuring the vulnerability of public transport networks. J Transp Geogr 35:50–63

    Article  Google Scholar 

  • Rupi F, Bernardi S, Rossi G, Danesi A (2015) The evaluation of road network vulnerability in mountainous areas: a case study. Netw Spatial Econ 15(2):397–411

    Article  Google Scholar 

  • Salehi M, Sharma R, Marzolla M, Magnani M, Siyari P, Montesi D (2015) Spreading processes in multilayer networks. IEEE Trans Netw Sci Eng 2 (2):65–83

    Article  Google Scholar 

  • Sen P, Dasgupta S, Chatterjee A, Sreeram P, Mukherjee G, Manna S (2003) Small-world properties of the Indian railway network. Phys Rev E 67:036106

    Article  Google Scholar 

  • Shekhtman LM, Bagrow JP, Brockmann D (2014) Robustness of skeletons and salient features in networks. J Complex Netw 2(2):110–120

    Article  Google Scholar 

  • Solé-Ribalta A, Gómez S, Arenas A (2016) Congestion induced by the structure of multiplex networks. Phys Rev Lett 116(10):108701

    Article  Google Scholar 

  • Stauffer D, Aharony A (1991) Introduction to percolation theory, 2nd edn. Taylor & Francis, London

    Google Scholar 

  • Strano E, Cardillo A, Iacoviello V, Latora V, Messora R, Porta S, Scellato S (2009) Street centrality vs. commerce and service locations in cities: A kernel density correlation case study in Bologna, Italy. Environ Plann B 36:450–465

    Article  Google Scholar 

  • Strano E, Shai S, Dobson S, Barthelemy M (2015) Multiplex networks in metropolitan areas: generic features and local effects. J R Soc Interface 12(111):20150651

    Article  Google Scholar 

  • Sun X, Wandelt S, Cao X (2017) On node criticality in air transportation networks. Networks and Spatial Economics. Available online from https://doi.org/10.1007/s11067-017-9342-5

  • Taylor MA (2012) Susilawati Remoteness and accessibility in the vulnerability analysis of regional road networks. Transp Res A 46:761–771

    Google Scholar 

  • Thadakamalla H, Raghavan U, Kumara S, Albert R (2004) Survivability of multiagent-based supply networks: a topological perspective. IEEE Intell Syst 19 (5):24–31

    Article  Google Scholar 

  • Tsiostas D, Polyzos S (2015) Analyzing the maritime transportation system in Greece: a complex network approach. Netw Spatial Econ 15(4):981–1010

    Article  Google Scholar 

  • Tsiostas D, Polyzos S (2017) The complexity in the study of spatial networks: An epistemological approach. Network and Spatial Economics. Available online from https://doi.org/10.1007/s11067-017-9354-1

  • Tsiotas D, Polyzos S (2015) Decomposing multilayer transportation networks using complex network analysis: a case study for the Greek aviation network. J Complex Networks 3(4):642–670

    Article  Google Scholar 

  • Viljoen NM, Joubert J (2017) Multilayered complex network datasets for three supply chain layouts on an urban road grid Working paper 062. Centre for Transport Development, University of Pretoria

    Google Scholar 

  • Viljoen NM, Joubert JW (2016) The vulnerability of the global container shipping network to targeted link disruption. Phys A 462:396–409

    Article  Google Scholar 

  • Wagner S, Bode C (2006) An empirical investigation into supply chain vulnerability. J Purch Supply Manag 12:301–312

    Article  Google Scholar 

  • Wagner S, Neshat N (2010) Assessing the vulnerability of supply chains using graph theory. Int J Prod Econ 126:121–129

    Article  Google Scholar 

  • Zadeh ASM, Rajabi MA (2013) Analyzing the effect of the street network configuration on the efficiency of an urban transport system. Cities 31:285–297

    Article  Google Scholar 

  • Zanin M, Lacasa L, Cea M, Crístobal S (2009) A dynamical model for the air transportation network. In: European conference on modelling and simulation. Madrid, Spain, 9-12 June 2009, pp 256–261

  • Zanin M, Lillo F (2013) Modelling the air transport with complex networks: a short review. Eur Phys J Special Topics 215(5):5–21

    Article  Google Scholar 

  • Zhuo Y, Peng Y, Liu C, Liu Y, Long K (2011) Traffic dynamics on layered complex networks. Phys A 390:2401–2407

    Article  Google Scholar 

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Acknowledgements

The authors gratefully acknowledge the financial support received from the National Research Fund of South Africa (grant number 105519) during the completion of the research that was foundational to this paper.

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  1. Centre for Transport Development, University of Pretoria, Lynnwood Rd, Hatfield, Pretoria, 0002, South Africa

    Nadia M. Viljoen & Johan W. Joubert

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  1. Nadia M. Viljoen
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Correspondence to Nadia M. Viljoen.

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Viljoen, N., Joubert, J. The Road most Travelled: The Impact of Urban Road Infrastructure on Supply Chain Network Vulnerability. Netw Spat Econ 18, 85–113 (2018). https://doi.org/10.1007/s11067-017-9370-1

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