Nothing Special   »   [go: up one dir, main page]

Skip to main content

AI-Enhanced Maintenance for Building Resilience and Viability in Supply Chains

  • Chapter
  • First Online:
Supply Network Dynamics and Control

Part of the book series: Springer Series in Supply Chain Management ((SSSCM,volume 20))

Abstract

In the era of Industry 4.0, supply chain management still faces the challenge of operating with increasingly complex networks under high uncertainty. These uncertainties influence decision-making processes and change the balance in the supply chain. Enterprise, therefore, strives to enable data-driven decision-making by increasing the digitalization and intelligentization of their processes. Artificial Intelligence (AI) approaches in particular can reinforce enterprises to proactively respond to changes and problems in the supply chain at an early stage and thus plan ahead. Utilizing predictive analytics and semantic modeling may improve target performance metrics, increases flexibility, and enables the development of a resilient and viable supply chain. This chapter provides an AI-enhanced approach for integrative modeling and analysis of related Key Performance Indicators (KPIs) toward building resilience and viability in manufacturing and supply chains, aided by Dynamic Bayesian Networks (DBN).

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  • acatech. (2014). Resilien-Tech; “Resilience by Design”: A strategy for the technology issues of the future. acatech – NATIONAL ACADEMY OF SCIENCE AND ENGINEERING. Accessed August 04, 2021, from https://www.acatech.de/projekt/resilien-tech-resilience-by-design-strategie-fuer-die-technologischen-zukunftsthemen/

  • Ansari, F., Khobreh, M., Seidenberg, U., & Sihn, W. (2018). A problem-solving ontology for human-centered cyber physical production systems. CIRP Journal of Manufacturing Science and Technology, 22C, 91–106.

    Article  Google Scholar 

  • Ansari, F., Glawar, R., & Nemeth, T. (2019). PriMa: A prescriptive maintenance model for cyber-physical production systems. International Journal of Computer Integrated Manufacturing, 32(4–5), 482–503.

    Article  Google Scholar 

  • Ansari, F., Glawar, R., & Sihn, W. (2020). Prescriptive maintenance of CPPS by integrating multimodal data with dynamic Bayesian networks. In J. Beyerer, A. Maier, & O. Niggemann (Eds.), Machine learning for cyber physical systems (pp. 1–8). Springer.

    Google Scholar 

  • Ansari, A., Kohl, L., Giner, J., & Meier, H. (2021). Text mining for AI enhanced failure detection and availability optimization in production systems. CIRP Annals – Manufacturing Technology, 40(1), 373–376.

    Article  Google Scholar 

  • Bai, J., Chang, X., Trivedi, K. S., & Han, Z. (2021). Resilience-driven quantitative analysis of vehicle platooning service. IEEE Transactions on Vehicular Technology, 70, 5378–5389. https://doi.org/10.1109/TVT.2021.3077118

    Article  Google Scholar 

  • Bauer, D., Böhm, M., Bauernhansl, T., & Sauer, A. (2021). Increased resilience for manufacturing systems in supply networks through data-based turbulence mitigation. Production Engineering and Research Development, 15, 385–395. https://doi.org/10.1007/s11740-021-01036-4

    Article  Google Scholar 

  • Bhatia, G., Lane, C., & Wain, A. (2013). Building resilience in supply chains; An initiative of the risk response network. World Economic Forum. Accessed August 03, 2021, from http://www3.weforum.org/docs/WEF_RRN_MO_BuildingResilienceSupplyChains_Report_2013.pdf

  • Bonde, H. (2018). 3 examples of reducing supply chain uncertainty – downstream. SAS. Accessed August 03, 2021, from https://blogs.sas.com/content/hiddeninsights/2018/07/12/reducing-supply-chain-uncertainty-downstream/

    Google Scholar 

  • Capgemini. (2020) Fast forward: Rethinking supply chain resilience for a post-COVID-19 world. Capgemini Research Institute. Accessed August 04, 2021, from https://www.capgemini.com/wp-content/uploads/2020/11/Fast-forward_Report.pdf

  • Chen, X., Xi, Z., & Jing, P. (2017). A unified framework for evaluating supply chain reliability and resilience. IEEE Transactions on Reliability, 66, 1144–1156. https://doi.org/10.1109/TR.2017.2737822

    Article  Google Scholar 

  • Christopher, M., & Peck, H. (2004). Building the resilient supply chain. International Journal of Logistics Management, 15, 1–14. https://doi.org/10.1108/09574090410700275

    Article  Google Scholar 

  • Esmaeel, R. I., Zakuan, N., Jamal, N. M., & Taherdoost, H. (2018). Understanding of business performance from the perspective of manufacturing strategies: Fit manufacturing and overall equipment effectiveness. Procedia Manufacturing, 22, 998–1006. https://doi.org/10.1016/j.promfg.2018.03.142

    Article  Google Scholar 

  • Giebler, C., Gröger, C., Hoos, E., Eichler, R., Schwarz, H., & Mitschang, B. (2020). Data Lakes auf den Grund gegangen. Datenbank-Spektrum, 20, 57–69. https://doi.org/10.1007/s13222-020-00332-0

    Article  Google Scholar 

  • Golan, M. S., Jernegan, L. H., & Linkov, I. (2020). Trends and applications of resilience analytics in supply chain modeling: Systematic literature review in the context of the COVID-19 pandemic. Environment Systems and Decisions, 40, 222–243. https://doi.org/10.1007/s10669-020-09777-w

    Article  Google Scholar 

  • Hosseini, S., & Ivanov, D. (2019). A new resilience measure for supply networks with the ripple effect considerations: A Bayesian network approach. Annals of Operations Research. https://doi.org/10.1007/s10479-019-03350-8

  • Hourbracq, M., Wuillemin, P. H., Gonzales, C., & Baumard, P. (2016). Real time learning of non-stationary processes with dynamic Bayesian networks. In Information processing and management of uncertainty in knowledge-based systems (pp. 338–350). Springer. https://doi.org/10.1007/978-3-319-40596-4_29

    Chapter  Google Scholar 

  • Ivanov, D. (2020). Viable supply chain model: Integrating agility, resilience and sustainability perspectives—lessons from and thinking beyond the COVID-19 pandemic. Annals of Operations Research. https://doi.org/10.1007/s10479-020-03640-6

  • Ivanov, D., & Dolgui, A. (2021). A digital supply chain twin for managing the disruption risks and resilience in the era of Industry 4.0. Production Planning & Control, 32(9), 775–788. https://doi.org/10.1080/09537287.2020.1768450

  • Ivanov, D., Sokolov, B., Chen, W., Dolgui, A., Werner, F., & Potryasaev, S. (2021a). A control approach to scheduling flexibly configurable jobs with dynamic structural-logical constraints. IISE Transactions, 53(1), 21–38. https://doi.org/10.1080/24725854.2020.1739787

    Article  Google Scholar 

  • Ivanov, D., Blackhurst, J., & Das, A. (2021b). Supply chain resilience and its interplay with digital technologies: Making innovations work in emergency situations. International Journal of Physical Distribution and Logistics Management, 51(2), 97–103. https://doi.org/10.1108/IJPDLM-03-2021-409

    Article  Google Scholar 

  • Ivanov, D. (2022). Digital supply chain management and technology to enhance resilience by building and using end-to-end visibility during the COVID-19 pandemic. IEEE Transactions on Engineering Management, 1–11. https://doi.org/10.1109/tem.2021.3095193

  • Karl, A. A., Micheluzzi, J., Leite, L. R., & Pereira, C. R. (2018). Supply chain resilience and key performance indicators: A systematic literature review. Production, 28. https://doi.org/10.1590/0103-6513.20180020

  • Klappich, D., & Muynck, B. (2020). Predicts 2021: Supply chain technology. Gartner. Accessed August 03, 2021, from https://www.gartner.com/en/documents/3993865/predicts-2021-supply-chain-technology

  • Knight, F. H. (2014). Risk, uncertainty and profit. Martino Publishing.

    Google Scholar 

  • Kohl, L., Ansari, F., & Sihn, W. (2021). A modular federated learning architecture for integration of AI-enhanced assistance in industrial maintenance. Academic Society for Work and Industrial Organization. (in Press).

    Google Scholar 

  • Kulkarni, C. S., Corbetta, M., & Robinson, E. I. (2021). Systems health monitoring: Integrating FMEA into Bayesian Networks 2021 IEEE Aerospace Conference (50100) (pp. 1–11). IEEE.

    Google Scholar 

  • Li, C., Mahadevan, S., Ling, Y., Choze, S., & Wang, L. (2017). Dynamic Bayesian network for aircraft wing health monitoring digital twin. AIAA Journal, 55, 930–941. https://doi.org/10.2514/1.J055201

    Article  Google Scholar 

  • Liang, H., Ganeshbabu, U., & Thorne, T. (2020). A dynamic Bayesian network approach for analysing topic-sentiment evolution. IEEE Access, 8, 54164–54174. https://doi.org/10.1109/ACCESS.2020.2979012

    Article  Google Scholar 

  • McCloskey, S. (2000). Probabilistic reasoning and Bayesian networks. In Proceedings of Neural Networks and Machine Learning (ICSG).

    Google Scholar 

  • Meng, Q., Wang, Y., An, J., Wang, Z., Zhang, B., & Liu, L. (2019). Learning non-stationary dynamic Bayesian network structure from data stream. In 2019 IEEE Fourth International Conference on Data Science in Cyberspace (DSC) (pp. 128–134). IEEE.

    Chapter  Google Scholar 

  • Mihajlovic, V., & Petkovic, M. (2001). Dynamic Bayesian networks: A state of the art. University of Twente Document Repository.

    Google Scholar 

  • Monostori, L., Kádár, B., Bauernhansl, T., Kondoh, S., Kumara, S., Reinhart, G., … Ueda, K. (2016). Cyber-physical systems in manufacturing. CIRP Annals, 65(2), 621–641.

    Article  Google Scholar 

  • Panetto, H., Iung, B., Ivanov, D., Weichhart, G., & Wang, X. (2019). Challenges for the cyber-physical manufacturing enterprises of the future. Annual Reviews in Control, 47, 200–213. https://doi.org/10.1016/j.arcontrol.2019.02.002

    Article  Google Scholar 

  • Passath, T., Huber, C., Kohl, L., Biedermann, H., & Ansari, F. (2021). A knowledge-based digital lifecycle-oriented asset optimisation. Tehnički glasnik (Online), 15, 226–334. https://doi.org/10.31803/tg-20210504111400

    Article  Google Scholar 

  • Quesada, D., Valverde, G., Larrañaga, P., & Bielza, C. (2021). Long-term forecasting of multivariate time series in industrial furnaces with dynamic Gaussian Bayesian networks. Engineering Applications of Artificial Intelligence, 103, 104301. https://doi.org/10.1016/j.engappai.2021.104301

    Article  Google Scholar 

  • Rastayesh, S., Bahrebar, S., Blaabjerg, F., Zhou, D., Wang, H., & Dalsgaard Sørensen, J. (2020). A system engineering approach using FMEA and Bayesian network for risk analysis—A case study. Sustainability, 12, 77. https://doi.org/10.3390/su12010077

    Article  Google Scholar 

  • Riester, R., Ansari, A., Foerster, M., & Matyas, K. (2020). A procedural model for utilizing case-based reasoning in after-sales management. 18th International Scientific Conference on Industrial Systems – Industrial Innovation in Digital Age, October 7–9, 2020, Novi Sad, Serbia.

    Google Scholar 

  • Rölli, M. (2021). Der Supply Chain Control Tower zur Steuerung des Transport-Managements. Wirtsch Inform Manag, 13, 20–29. https://doi.org/10.1365/s35764-020-00313-8

    Article  Google Scholar 

  • Schenkelberg, K., Seidenberg, U., & Ansari, F. (2020a). Analyzing the impact of maintenance on profitability using dynamic Bayesian networks. 13th CIRP Conference on Intelligent Computation in Manufacturing Engineering, Procedia CIRP, 88, 42–47.

    Google Scholar 

  • Schenkelberg, K., Seidenberg, U., & Ansari, F. (2020b). Supervised machine learning for knowledge-based analysis of maintenance impact on profitability, 21st IFAC World Congress, July 12–17, 2020, Berlin. IFAC-PapersOnLine, 53(2), 10651–10657.

    Article  Google Scholar 

  • Schenkelberg, K., Seidenberg, U., & Ansari, F. (2020c). A simulation-based process model for analyzing impact of maintenance on profitability. In 25th IEEE International Conference on Emerging Technologies and Factory Automation (IEEE ETFA), September 8–11, Vienna (pp. 805–812).

    Google Scholar 

  • Scholten, K., Stevenson, M., & van Donk, D. P. (2020). Dealing with the unpredictable: Supply chain resilience. IJOPM, 40, 1–10. https://doi.org/10.1108/IJOPM-01-2020-789

    Article  Google Scholar 

  • Serras, J. L., Vinga, S., & Carvalho, A. M. (2021). Outlier detection for multivariate time series using dynamic Bayesian networks. Applied Sciences, 11, 1955. https://doi.org/10.3390/app11041955

    Article  Google Scholar 

  • Stavropoulos, P., Papacharalampopoulos, A., Tzimanis, K., & Lianos, A. (2020). Manufacturing resilience during the coronavirus pandemic: On the investigation manufacturing processes agility. European Journal of Social Impact and Circular Economy, 1(3), 2–57.

    Book  Google Scholar 

  • Russell, S. J., Norvig, P., & Davis, E. (2010). Artificial intelligence: A modern approach (Prentice Hall series in artificial intelligence) (3rd ed.). Prentice Hall.

    Google Scholar 

  • Tong, Q., Yang, M., & Zinetullina, A. (2020). A dynamic Bayesian network-based approach to resilience assessment of engineered systems. Journal of Loss Prevention in the Process Industries, 65, 104152. https://doi.org/10.1016/j.jlp.2020.104152

    Article  Google Scholar 

  • Wang, M. (2018). Impacts of supply chain uncertainty and risk on the logistics performance. APJML, 30, 689–704. https://doi.org/10.1108/APJML-04-2017-0065

    Article  Google Scholar 

  • Weichhart, G., Mangler, J., Raschendorfer, A., Mayr-Dorn, C., Huemer, C., Hämmerle, A., & Pichler, A. (2021). An adaptive system-of-systems approach for resilient manufacturing. Elektrotechnik und Informationstechnik. https://doi.org/10.1007/s00502-021-00912-2

  • Werner, M. J. E., Yamada, A. P. L., Domingos, E. G. N., Leite, L. R., & Pereira, C. R. (2021). Exploring organizational resilience through key performance indicators. Journal of Industrial and Production Engineering, 38, 51–65. https://doi.org/10.1080/21681015.2020.1839582

    Article  Google Scholar 

  • World Economic Forum. (2017). The Global Risks Report 2017. World Economic Forum. Accessed August 04, 2021, from http://www3.weforum.org/docs/GRR17_Report_web.pdf

  • Yang, S., Bian, C., Li, X., Tan, L., & Tang, D. (2018). Optimized fault diagnosis based on FMEA-style CBR and BN for embedded software system. International Journal of Advanced Manufacturing Technology, 94, 3441–3453. https://doi.org/10.1007/s00170-017-0110-y

    Article  Google Scholar 

  • Zhang, L., Pan, Y., Wu, X., & Skibniewski, M. J. (2021). Dynamic Bayesian networks. In L. Zhang, Y. Pan, X. Wu, & M. J. Skibniewski (Eds.), Artificial intelligence in construction engineering and management (pp. 125–146). Springer Singapore.

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Fazel Ansari .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Ansari, F., Kohl, L. (2022). AI-Enhanced Maintenance for Building Resilience and Viability in Supply Chains. In: Dolgui, A., Ivanov, D., Sokolov, B. (eds) Supply Network Dynamics and Control. Springer Series in Supply Chain Management, vol 20. Springer, Cham. https://doi.org/10.1007/978-3-031-09179-7_8

Download citation

Publish with us

Policies and ethics