IDEAS home Printed from https://ideas.repec.org/a/spr/infsem/v20y2022i1d10.1007_s10257-022-00551-w.html
   My bibliography  Save this article

SLA-aware operational efficiency in AI-enabled service chains: challenges ahead

Author

Listed:
  • Robert Engel

    (IBM Research - Almaden)

  • Pablo Fernandez

    (Universidad de Sevilla)

  • Antonio Ruiz-Cortes

    (Universidad de Sevilla)

  • Aly Megahed

    (IBM Research - Almaden)

  • Juan Ojeda-Perez

    (Universidad de Sevilla)

Abstract

Service providers compose services in service chains that require deep integration of core operational information systems across organizations. Additionally, advanced analytics inform data-driven decision-making in corresponding AI-enabled business processes in today’s complex environments. However, individual partner engagements with service consumers and providers often entail individually negotiated, highly customized Service Level Agreements (SLAs) comprising engagement-specific metrics that semantically differ from general KPIs utilized on a broader operational (i.e., cross-client) level. Furthermore, the number of unique SLAs to be managed increases with the size of such service chains. The resulting complexity pushes large organizations to employ dedicated SLA management systems, but such ‘siloed’ approaches make it difficult to leverage insights from SLA evaluations and predictions for decision-making in core business processes, and vice versa. Consequently, simultaneous optimization for both global operational process efficiency and engagement-specific SLA compliance is hampered. To address these shortcomings, we propose our vision of supplying online, AI-supported SLA analytics to data-driven, intelligent core workflows of the enterprise and discuss current research challenges arising from this vision. Exemplified by two scenarios derived from real use cases in industry and public administration, we demonstrate the need for improved semantic alignment of heavily customized SLAs with AI-enabled operational systems. Moreover, we discuss specific challenges of prescriptive SLA analytics under multi-engagement SLA awareness and how the dual role of AI in such scenarios demands bidirectional data exchange between operational processes and SLA management. Finally, we discuss the implications of federating AI-supported SLA analytics across organizations.

Suggested Citation

  • Robert Engel & Pablo Fernandez & Antonio Ruiz-Cortes & Aly Megahed & Juan Ojeda-Perez, 2022. "SLA-aware operational efficiency in AI-enabled service chains: challenges ahead," Information Systems and e-Business Management, Springer, vol. 20(1), pages 199-221, March.
    • Handle: RePEc:spr:infsem:v:20:y:2022:i:1:d:10.1007_s10257-022-00551-w
    DOI: 10.1007/s10257-022-00551-w
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10257-022-00551-w
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10257-022-00551-w?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Wang, Yulan & Wallace, Stein W. & Shen, Bin & Choi, Tsan-Ming, 2015. "Service supply chain management: A review of operational models," European Journal of Operational Research, Elsevier, vol. 247(3), pages 685-698.
    2. Yaoguang Zhong & Fangfang Guo & Huajun Tang & Xumei Chen, 2020. "Research on Coordination Complexity of E-Commerce Logistics Service Supply Chain," Complexity, Hindawi, vol. 2020, pages 1-21, April.
    3. Kavinesh J. Singh & Andy B. Philpott & R. Kevin Wood, 2009. "Dantzig-Wolfe Decomposition for Solving Multistage Stochastic Capacity-Planning Problems," Operations Research, INFORMS, vol. 57(5), pages 1271-1286, October.
    4. Tuncdan Baltacioglu & Erhan Ada & Melike D. Kaplan & Oznur Yurt And & Y. Cem Kaplan, 2007. "A New Framework for Service Supply Chains," The Service Industries Journal, Taylor & Francis Journals, vol. 27(2), pages 105-124, March.
    5. Aly Megahed & Taiga Nakamura & Mark Smith & Shubhi Asthana & Michael Rose & Maja Daczkowska & Sandeep Gopisetty, 2020. "Analytics and Operations Research Increases Win Rates for IBM’s Information Technology Service Deals," Interfaces, INFORMS, vol. 50(1), pages 50-63, January.
    6. Dimitris Bertsimas & Nathan Kallus, 2020. "From Predictive to Prescriptive Analytics," Management Science, INFORMS, vol. 66(3), pages 1025-1044, March.
    7. Caron, E.A.M. & Daniels, H.A.M., 2008. "Explanation of exceptional values in multi-dimensional business databases," European Journal of Operational Research, Elsevier, vol. 188(3), pages 884-897, August.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Boon-itt, Sakun & Wong, Chee Yew & Wong, Christina W.Y., 2017. "Service supply chain management process capabilities: Measurement development," International Journal of Production Economics, Elsevier, vol. 193(C), pages 1-11.
    2. Robert Suurmond & Larry J. Menor & Finn Wynstra, 2022. "Examining service triad operations: Formation, functioning, and feedback exchanges," Production and Operations Management, Production and Operations Management Society, vol. 31(8), pages 3352-3370, August.
    3. Wang, Wei & Feng, Lipan & Li, Yongjian & Xu, Fangchao & Deng, Qianzhou, 2020. "Role of financial leasing in a capital-constrained service supply chain," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 143(C).
    4. Sarat K. Jena & Purushottam L. Meena, 2019. "Price and Service Competition in a Tourism Supply Chain," Service Science, INFORMS, vol. 11(4), pages 279-291, December.
    5. Tran, Thi Huong & Kummer, Sebastian, 2015. "Service Supply Chain Risk Management: Distinctions from Manufacturing," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Kersten, Wolfgang & Blecker, Thorsten & Ringle, Christian M. (ed.), Innovations and Strategies for Logistics and Supply Chains: Technologies, Business Models and Risk Management. Proceedings of the Hamburg Internationa, volume 20, pages 503-532, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.
    6. Weihua Liu & Enze Bai & Liwei Liu & Wanying Wei, 2017. "A Framework of Sustainable Service Supply Chain Management: A Literature Review and Research Agenda," Sustainability, MDPI, vol. 9(3), pages 1-25, March.
    7. Fahimeh Aliakbari Nouri & Mohsen Shafiei Nikabadi & Laya Olfat, 2019. "The Role of Supply Chain Features in the Effectiveness of Sustainability Practices in the Service Supply Chain: Application of Fuzzy Rule-Based System," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 18(03), pages 867-899, May.
    8. Di Wang & Weihua Liu & Yanjie Liang & Shuang Wei, 2023. "Decision optimization in service supply chain: the impact of demand and supply-driven data value and altruistic behavior," Annals of Operations Research, Springer, vol. 324(1), pages 971-992, May.
    9. Okoroigwe, Edmund & Madhlopa, Amos, 2016. "An integrated combined cycle system driven by a solar tower: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 337-350.
    10. Tian, Xuecheng & Yan, Ran & Liu, Yannick & Wang, Shuaian, 2023. "A smart predict-then-optimize method for targeted and cost-effective maritime transportation," Transportation Research Part B: Methodological, Elsevier, vol. 172(C), pages 32-52.
    11. Deprez, Laurens & Antonio, Katrien & Boute, Robert, 2021. "Pricing service maintenance contracts using predictive analytics," European Journal of Operational Research, Elsevier, vol. 290(2), pages 530-545.
    12. Fernando Rojas & Peter Wanke & Víctor Leiva & Mauricio Huerta & Carlos Martin-Barreiro, 2022. "Modeling Inventory Cost Savings and Supply Chain Success Factors: A Hybrid Robust Compromise Multi-Criteria Approach," Mathematics, MDPI, vol. 10(16), pages 1-18, August.
    13. Keliang Wang & Leonardo Lozano & Carlos Cardonha & David Bergman, 2023. "Optimizing over an Ensemble of Trained Neural Networks," INFORMS Journal on Computing, INFORMS, vol. 35(3), pages 652-674, May.
    14. Viet Anh Nguyen & Fan Zhang & Shanshan Wang & Jose Blanchet & Erick Delage & Yinyu Ye, 2021. "Robustifying Conditional Portfolio Decisions via Optimal Transport," Papers 2103.16451, arXiv.org, revised Apr 2024.
    15. Meng Qi & Ying Cao & Zuo-Jun (Max) Shen, 2022. "Distributionally Robust Conditional Quantile Prediction with Fixed Design," Management Science, INFORMS, vol. 68(3), pages 1639-1658, March.
    16. Alma Sheko & Alma Braimllari (Spaho), 2018. "Information Technology Inhibitors and Information Quality in Supply Chain Management: A PLS-SEM Analysis," Academic Journal of Interdisciplinary Studies, Richtmann Publishing Ltd, vol. 7, November.
    17. James H. Merrick & John E. T. Bistline & Geoffrey J. Blanford, 2021. "On representation of energy storage in electricity planning models," Papers 2105.03707, arXiv.org, revised May 2021.
    18. Ivanov, Dmitry & Dolgui, Alexandre & Sokolov, Boris, 2022. "Cloud supply chain: Integrating Industry 4.0 and digital platforms in the “Supply Chain-as-a-Service”," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 160(C).
    19. Adriana Tiron-Tudor & Delia Deliu, 2021. "Big Data’s Disruptive Effect on Job Profiles: Management Accountants’ Case Study," JRFM, MDPI, vol. 14(8), pages 1-26, August.
    20. Santanu Mandal & Souvik Roy & G. Amar Raju, 2016. "Tourism supply chain agility: an empirical examination using resource-based view," International Journal of Business Forecasting and Marketing Intelligence, Inderscience Enterprises Ltd, vol. 2(2), pages 151-173.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:infsem:v:20:y:2022:i:1:d:10.1007_s10257-022-00551-w. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.