Abstract
The complexity of modern-day supply chains makes logistics operations more vulnerable towards disturbances, which endangers sustainability goals in the short-term. Local disturbances might effect logistics at large, as we typically see in congested urban areas. As a consequence, the Internet of Things (IoT) is gaining attention as a novel paradigm that promotes interconnected networks of context-aware electronic devices used for remote monitoring and control. These capabilities may stimulate anticipatory behaviour and more resilient supply chains, but a clear framework prescribing which objects to empower with electronic devices is still lacking. In this paper, we aim to semantically bridge the resilience and IoT paradigms in logistics environments. The ontology is developed by means of a bibliometric- and systematic literature study in search of essential concepts, and a field study to evaluate the ontology’s effectiveness. Our ontology can form the basis to enhance resilience by replacing risk assessments with condition-based control mechanisms, resulting in better cooperation between human and software agents to resolve disturbances quicker, and more accurate training of machine learning algorithms in favour of autonomous decision making.
This work is supported by the Netherlands Organization for Scientific Research (NWO) [grant number 628.0098.015].
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References
Agarwal, R., et al.: Unified IoT ontology to enable interoperability and federation of testbeds. In: 2016 IEEE 3rd World Forum on Internet of Things (WF-IoT), pp. 70–75. IEEE (2016)
Al-Fuqaha, A., Guizani, M., Mohammadi, M., Aledhari, M., Ayyash, M.: Internet of Things: a survey on enabling technologies, protocols, and applications. IEEE Commun. Surv. Tut. 17(4), 2347–2376 (2015). https://doi.org/10.1109/COMST.2015.2444095
Al-Talib, M., Melhem, W.Y., Anosike, A.I., Garza Reyes, J.A., Nadeem, S.P., kumar, A.: Achieving resilience in the supply chain by applying IoT technology. Procedia CIRP 91, 752–757 (2020). https://doi.org/10.1016/j.procir.2020.02.231
Ameri, F., Sormaz, D., Psarommatis, F., Kiritsis, D.: Industrial ontologies for interoperability in agile and resilient manufacturing. Int. J. Prod. Res. 60(2), 420–441 (2022). https://doi.org/10.1080/00207543.2021.1987553
Anand, N., Yang, M., van Duin, J.H.R., Tavasszy, L.: GenCLOn: an ontology for city logistics. Exp. Syst. Appl. 39(15), 11944–11960 (2012). https://doi.org/10.1016/j.eswa.2012.03.068
Association Supply Chain Management (ASCM): SCOR digital standard (2023). https://scor.ascm.org/. Accessed 24 Feb 2023
Atzori, L., Iera, A., Morabito, G.: The Internet of Things: a survey. Comput. Netw. 54(15), 2787–2805 (2010). https://doi.org/10.1016/j.comnet.2010.05.010
Bajaj, G., Agarwal, R., Singh, P., Georgantas, N., Issarny, V.: A study of existing ontologies in the IoT-domain. arXiv preprint arXiv:1707.00112 (2017)
Batlajery, B.V., Weal, M., Chapman, A., Moreau, L.: prFood: ontology principles for provenance and risk in the food domain. In: 2018 IEEE 12th International Conference on Semantic Computing (ICSC), pp. 17–24. IEEE (2018)
Benazzouz, T., Echchtabi, A., Charkaoui, A.: Ontology for risks in medicines supply chain: case of public hospitals in Morocco. MATEC Web Conf. 105, 00012 (2017). https://doi.org/10.1051/matecconf/201710500012
Bermudez-Edo, M., Elsaleh, T., Barnaghi, P., Taylor, K.: IoT-lite: a lightweight semantic model for the Internet of Things. In: 2016 INTL IEEE Conferences on Ubiquitous Intelligence & Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People, and Smart World Congress, pp. 90–97. IEEE (2016)
Bhamra, R., Dani, S., Burnard, K.: Resilience: the concept, a literature review and future directions. Int. J. Prod. Res. 49(18), 5375–5393 (2011). https://doi.org/10.1080/00207543.2011.563826
Cao, S., Bryceson, K., Hine, D.: Improving supply chain risk visibility and communication with a multi-view risk ontology. Supply Chain Forum Int. J. 21(1), 1–15 (2020). https://doi.org/10.1080/16258312.2020.1717990
Cao, T., Mu, W., Montarnal, A., Barthe-Delanoë, A.-M.: A method of ontology evolution and concept evaluation based on knowledge discovery in the heavy haul railway risk system. In: Camarinha-Matos, L.M., Afsarmanesh, H., Antonelli, D. (eds.) PRO-VE 2019. IAICT, vol. 568, pp. 220–233. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-28464-0_20
Chiang, M., Zhang, T.: Fog and IoT: an overview of research opportunities. IEEE IoT J. 3(6), 854–864 (2016). https://doi.org/10.1109/JIOT.2016.2584538
Čolaković, A., Hadžialić, M.: Internet of Things (IoT): a review of enabling technologies, challenges, and open research issues. Comput. Netw. 144, 17–39 (2018). https://doi.org/10.1016/j.comnet.2018.07.017
Compton, M.: The SSN ontology of the W3C semantic sensor network incubator group. J. Web Semant. 17, 25–32 (2012). https://doi.org/10.1016/j.websem.2012.05.003
Crainic, T.G., Gendreau, M., Potvin, J.-Y.: Intelligent freight-transportation systems: assessment and the contribution of operations research. Transp. Res. Part C Emerg. Technol. 17(6), 541–557 (2009). https://doi.org/10.1016/j.trc.2008.07.002
Cullinane, K., Haralambides, H.: Global trends in maritime and port economics: the COVID-19 pandemic and beyond. Marit. Econ. Logist. 23(3), 369–380 (2021). https://doi.org/10.1057/s41278-021-00196-5
Dausch, M., Hsu, C.: Engineering service products: the case of mass-customising service agreements for heavy equipment industry. Int. J. Serv. Technol. Manage. 7(1), 32–51 (2006)
Defèr, F., Schuh, G., Stich, V.: Towards a unified reliability-centered information logistics model for production assets. In: Lalic, B., Majstorovic, V., Marjanovic, U., von Cieminski, G., Romero, D. (eds.) APMS 2020. IAICT, vol. 591, pp. 11–18. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-57993-7_2
Feng, F., Pang, Y., Lodewijks, G.: Towards context-aware supervision for logistics asset management: concept design and system implementation. In: Ziemba, E. (ed.) AITM/ISM -2016. LNBIP, vol. 277, pp. 3–19. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-53076-5_1
Fernández-López, M., Gómez-Pérez, A., Juristo, N.: Methontology: from ontological art towards ontological engineering (1997)
Geerts, G.L., O’Leary, D.E.: A supply chain of things: the EAGLET ontology for highly visible supply chains. Decis. Support Syst. 63, 3–22 (2014)
Ghanadbashi, S., Golpayegani, F.: Using ontology to guide reinforcement learning agents in unseen situations. Appl. Intell. 52(2), 1808–1824 (2021). https://doi.org/10.1007/s10489-021-02449-5
Ghiani, G., Laporte, G., Musmanno, R.: Introduction to Logistics Systems Planning and Control. Wiley, Hoboken (2005). https://doi.org/10.1002/0470014040
Gliem, D., Jessen, U., Wenzel, S., Kusturica, W., Laroque, C.: Ontology-based forecast of the duration of logistics processes in one-of-a-kind production in SME. Logist. Res. 15(1), 5 (2022)
Gonnet, S., Vegetti, M., Leone, H., Henning, G.: Scontology: a formal approach toward a unified and integrated view of the supply chain. In: Cruz-Cunha, M.M., Cortes, B.C., Putnik, G.D. (eds.) Adaptive Technologies and Business Integration: Social, Managerial and Organizational Dimension, pp. 137–158. IGI Global, Hershey, Pennsylvania, USA (2007)
Grandry, E., Feltus, C., Dubois, E.: Conceptual integration of enterprise architecture management and security risk management. In: 2013 17th IEEE International Enterprise Distributed Object Computing Conference Workshops, pp. 114–123. IEEE (2013)
Grubic, T., Fan, I.-S.: Supply chain ontology: review, analysis and synthesis. Comput. Ind. 61(8), 776–786 (2010)
Guizzardi, G.: Ontological foundations for structural conceptual models. Ph.D. thesis, University of Twente, October 2005
Hachicha, M., Fahad, M., Moalla, N., Ouzrout, Y.: Performance assessment architecture for collaborative business processes in BPM-SOA-based environment. Data Knowl. Eng. 105, 73–89 (2016)
Hu, S., Wang, H., She, C., Wang, J.: AgOnt: ontology for agriculture Internet of Things. In: Li, D., Liu, Y., Chen, Y. (eds.) CCTA 2010. IAICT, vol. 344, pp. 131–137. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-18333-1_18
Inoue, H., Todo, Y.: Firm-level propagation of shocks through supply-chain networks. Nat. Sustain. 2(9), 841–847 (2019)
Ivanov, D., Dolgui, A.: A digital supply chain twin for managing the disruption risks and resilience in the era of industry 4.0. Prod. Plan. Control 32(9), 775–788 (2021)
Khezaz, A., Hina, M.D., Guan, H., Ramdane-Cherif, A.: Driving context detection and validation using knowledge-based reasoning. In: KEOD, pp. 219–226 (2020)
Lambert, D.M.: Supply chain management, chap. 1. In: Lambert, D.M. (ed.) Supply Chain Management: Processes, Partnerships, Performance, pp. 1–22. Supply Chain Management Institute, Sarasota (2008)
Lee, J.M., Wong, E.Y.: Suez Canal blockage: an analysis of legal impact, risks and liabilities to the global supply chain. In: MATEC Web of Conferences, vol. 339. EDP Sciences (2021)
Li, J., Gou, J., Mu, W., Peng, L.: Modeling of railway risk inter-relation based on the study of accident context. In: Tina Comes, F.B., Hanachi, C., Lauras, M., Montarnal, A. (eds.) Proceedings of the 14th International Conference on Information Systems for Crisis Response And Management, pp. 328–340. Iscram, Albi (2017)
Li, S., Xu, L.D., Zhao, S.: The Internet of Things: a survey. Inf. Syst. Front. 17(2), 243–259 (2015)
Liu, Y., et al.: Digital twin-driven approach for smart city logistics: the case of freight parking management. In: Dolgui, A., Bernard, A., Lemoine, D., von Cieminski, G., Romero, D. (eds.) APMS 2021. IAICT, vol. 633, pp. 237–246. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-85910-7_25
Lyu, M., Biennier, F., Ghodous, P.: Integration of ontologies to support control as a service in an industry 4.0 context. Serv. Oriented Comput. Appl. 15(2), 127–140 (2021)
Madni, A.M., Jackson, S.: Towards a conceptual framework for resilience engineering. IEEE Syst. J. 3(2), 181–191 (2009)
Madni, A.M., Lin, W., Madni, C.C.: IDEON\(^\text{ TM }\): an extensible ontology for designing, integrating, and managing collaborative distributed enterprises. Syst. Eng. 4(1), 35–48 (2001)
Mazouni, M.H., Aubry, J.F.: A PHA based on a systemic and generic ontology. In: 2007 IEEE International Conference on Service Operations and Logistics, and Informatics, pp. 1–6. IEEE (2007)
Miorandi, D., Sicari, S., De Pellegrini, F., Chlamtac, I.: Internet of Things: vision, applications and research challenges. Ad Hoc Netw. 10(7), 1497–1516 (2012)
Nagowah, S.D., Sta, H.B., Gobin-Rahimbux, B.A.: An overview of semantic interoperability ontologies and frameworks for IoT. In: 2018 Sixth International Conference on Enterprise Systems (ES), pp. 82–89. IEEE (2018)
Palmer, C., Urwin, E.N., Niknejad, A., Petrovic, D., Popplewell, K., Young, R.I.: An ontology supported risk assessment approach for the intelligent configuration of supply networks. J. Intell. Manuf. 29, 1005–1030 (2018)
Pattar, S., et al.: Ontology based service discovery for intelligent transport systems using Internet of Things. In: 2018 Fourteenth International Conference on Information Processing (ICINPRO), pp. 1–3 (2018)
Ponomarov, S.Y., Holcomb, M.C.: Understanding the concept of supply chain resilience. Int. J. Logist. Manage. 20, 124–143 (2009)
Protalinsky, O., Khanova, A., Shcherbatov, I.: Simulation of power assets management process. In: Dolinina, O., Brovko, A., Pechenkin, V., Lvov, A., Zhmud, V., Kreinovich, V. (eds.) ICIT 2019. SSDC, vol. 199, pp. 488–501. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-12072-6_40
Ramezani, J., Camarinha-Matos, L.M.: Approaches for resilience and antifragility in collaborative business ecosystems. Technol. Forecast. Soc. Chang. 151, 119846 (2020)
Ramzy, N., Auer, S., Ehm, H., Chamanara, J.: MARE: semantic supply chain disruption management and resilience evaluation framework (2022)
Rao, J., Gao, S., Miller, M., Morales, A.: Measuring network resilience via geospatial knowledge graph: a case study of the US multi-commodity flow network. In: Proceedings of the 1st ACM SIGSPATIAL International Workshop on Geospatial Knowledge Graphs, pp. 17–25 (2022)
Russomanno, D.J., Kothari, C.R., Thomas, O.A.: Building a sensor ontology: a practical approach leveraging ISO and OGC models. In: IC-AI, pp. 637–643 (2005)
Scheuermann, A., Hoxha, J.: Ontologies for intelligent provision of logistics services, pp. 106–111 (2012)
Scheuermann, A., Leukel, J.: Supply chain management ontology from an ontology engineering perspective. Comput. Ind. 65(6), 913–923 (2014)
Seydoux, N., Drira, K., Hernandez, N., Monteil, T.: IoT-O, a core-domain IoT ontology to represent connected devices networks. In: Blomqvist, E., Ciancarini, P., Poggi, F., Vitali, F. (eds.) EKAW 2016. LNCS (LNAI), vol. 10024, pp. 561–576. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-49004-5_36
Singh, S., Ghosh, S., Jayaram, J., Tiwari, M.K.: Enhancing supply chain resilience using ontology-based decision support system. Int. J. Comput. Integr. Manuf. 32(7), 642–657 (2019)
Sinha, D., Roy Chowdhury, S.: A framework for ensuring zero defects and sustainable operations in major Indian ports. Int. J. Qual. Reliab. Manage. 39(8), 1896–1936 (2022)
Suherman, A.G., Simatupang, T.M.: The network business model of cloud computing for end-to-end supply chain visibility. Int. J. Value Chain Manage. 8(1), 22–39 (2017)
Suri, K., Gaaloul, W., Cuccuru, A., Gerard, S.: Semantic framework for internet of things-aware business process development. In: 2017 IEEE 26th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE), pp. 214–219. IEEE (2017)
Uschold, M., King, M., Moralee, S., Zorgios, Y.: The enterprise ontology. Knowl. Eng. Rev. 13(1), 31–89 (1998)
Xu, L.D., He, W., Li, S.: Internet of things in industries: a survey. IEEE Trans. Industr. Inf. 10(4), 2233–2243 (2014)
Ye, Y., Yang, D., Jiang, Z., Tong, L.: An ontology-based architecture for implementing semantic integration of supply chain management. Int. J. Comput. Integr. Manuf. 21(1), 1–18 (2008)
Yeboah-Ofori, A., Mouratidis, H., Ismai, U., Islam, S., Papastergiou, S.: Cyber supply chain threat analysis and prediction using machine learning and ontology. In: Maglogiannis, I., Macintyre, J., Iliadis, L. (eds.) AIAI 2021. IAICT, vol. 627, pp. 518–530. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-79150-6_41
Yodo, N., Wang, P.: Engineering resilience quantification and system design implications: a literature survey. J. Mech. Des. 138(11), 111408 (2016)
Zdravković, M., Panetto, H., Trajanović, M., Aubry, A.: An approach for formalising the supply chain operations. Enterp. Inf. Syst. 5(4), 401–421 (2011)
Zekhnini, K., Cherrafi, A., Bouhaddou, I., Benabdellah, A.C.: Suppliers selection ontology for viable digital supply chain performance. In: Dolgui, A., Bernard, A., Lemoine, D., von Cieminski, G., Romero, D. (eds.) APMS 2021. IAICT, vol. 633, pp. 622–631. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-85910-7_66
Zhou, F., He, Y., Ma, P., Mahto, R.V.: Knowledge management practice of medical cloud logistics industry: transportation resource semantic discovery based on ontology modelling. J. Intellect. Cap. 22(2), 360–383 (2021)
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Koot, M., Mes, M.R.K., Iacob, M.E. (2024). Building an Ontological Bridge Between Supply Chain Resilience and IoT Applications. In: Proper, H.A., Pufahl, L., Karastoyanova, D., van Sinderen, M., Moreira, J. (eds) Enterprise Design, Operations, and Computing. EDOC 2023. Lecture Notes in Computer Science, vol 14367. Springer, Cham. https://doi.org/10.1007/978-3-031-46587-1_5
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