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Blockchain-based business process management (BPM) framework for service composition in industry 4.0

Author

Listed:
  • Wattana Viriyasitavat

    (Chulalongkorn University)

  • Li Xu

    (Old Dominion University)

  • Zhuming Bi

    (Purdue University Fort Wayne)

  • Assadaporn Sapsomboon

    (Chulalongkorn University)

Abstract

Business process management (BPM) aims to optimize business processes to achieve better system performance such as higher profit, quicker response, and better services. BPM systems in Industry 4.0 are required to digitize and automate business process workflows and support the transparent interoperations of service vendors. The critical bottleneck to advance BPM systems is the evaluation, verification, and transformation of trustworthiness and digitized assets. Most of BPM systems rely heavily on domain experts or third parties to deal with trustworthiness. In this paper, an automated BPM solution is investigated to select and compose services in open business environment, Blockchain technology (BCT) is explored and proposed to transfer and verify the trustiness of businesses and partners, and a BPM framework is developed to illustrate how BCT can be integrated to support prompt, reliable, and cost-effective evaluation and transferring of Quality of Services in the workflow composition and management.

Suggested Citation

  • Wattana Viriyasitavat & Li Xu & Zhuming Bi & Assadaporn Sapsomboon, 2020. "Blockchain-based business process management (BPM) framework for service composition in industry 4.0," Journal of Intelligent Manufacturing, Springer, vol. 31(7), pages 1737-1748, October.
    • Handle: RePEc:spr:joinma:v:31:y:2020:i:7:d:10.1007_s10845-018-1422-y
    DOI: 10.1007/s10845-018-1422-y
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    References listed on IDEAS

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    1. Carlos Coutinho & Adina Cretan & Catarina Ferreira Silva & Parisa Ghodous & Ricardo Jardim-Goncalves, 2016. "Service-based negotiation for advanced collaboration in enterprise networks," Journal of Intelligent Manufacturing, Springer, vol. 27(1), pages 201-216, February.
    2. Sophea Chhun & Néjib Moalla & Yacine Ouzrout, 2016. "QoS ontology for service selection and reuse," Journal of Intelligent Manufacturing, Springer, vol. 27(1), pages 187-199, February.
    3. Libin Han & Keyi Xing & Xiao Chen & Fuli Xiong, 2018. "A Petri net-based particle swarm optimization approach for scheduling deadlock-prone flexible manufacturing systems," Journal of Intelligent Manufacturing, Springer, vol. 29(5), pages 1083-1096, June.
    4. Yingfeng Zhang & Geng Zhang & Yang Liu & Di Hu, 2017. "Research on services encapsulation and virtualization access model of machine for cloud manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 28(5), pages 1109-1123, June.
    5. Hyesung Seok & Shimon Y. Nof, 2018. "Intelligent information sharing among manufacturers in supply networks: supplier selection case," Journal of Intelligent Manufacturing, Springer, vol. 29(5), pages 1097-1113, June.
    6. Cristian Danila & Georgiana Stegaru & Aurelian Mihai Stanescu & Cristina Serbanescu, 2016. "Web-service based architecture to support SCM context-awareness and interoperability," Journal of Intelligent Manufacturing, Springer, vol. 27(1), pages 73-82, February.
    7. Dariush Khezrimotlagh & Yao Chen, 2018. "The Optimization Approach," International Series in Operations Research & Management Science, in: Decision Making and Performance Evaluation Using Data Envelopment Analysis, chapter 0, pages 107-134, Springer.
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    Cited by:

    1. Su, Dan & Zhang, Lijun & Peng, Hua & Saeidi, Parvaneh & Tirkolaee, Erfan Babaee, 2023. "Technical challenges of blockchain technology for sustainable manufacturing paradigm in Industry 4.0 era using a fuzzy decision support system," Technological Forecasting and Social Change, Elsevier, vol. 188(C).
    2. Thomas Kitsantas, 2022. "Exploring Blockchain Technology and Enterprise Resource Planning System: Business and Technical Aspects, Current Problems, and Future Perspectives," Sustainability, MDPI, vol. 14(13), pages 1-17, June.
    3. Vineet Paliwal & Shalini Chandra & Suneel Sharma, 2020. "Blockchain Technology for Sustainable Supply Chain Management: A Systematic Literature Review and a Classification Framework," Sustainability, MDPI, vol. 12(18), pages 1-39, September.
    4. Sachin S. Kamble & Angappa Gunasekaran & Nachiappan Subramanian & Abhijeet Ghadge & Amine Belhadi & Mani Venkatesh, 2023. "Blockchain technology’s impact on supply chain integration and sustainable supply chain performance: evidence from the automotive industry," Annals of Operations Research, Springer, vol. 327(1), pages 575-600, August.
    5. Ana María Sánchez Pérez & Jorge Tarifa Fernández & Salvador Cruz Rambaud, 2020. "Assessing Blockchain Investments through the Learning Option: An Application to the Automotive and Aerospace Industry," Mathematics, MDPI, vol. 8(12), pages 1-13, December.

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