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Prescriptive analytics: Literature review and research challenges

Author

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  • Lepenioti, Katerina
  • Bousdekis, Alexandros
  • Apostolou, Dimitris
  • Mentzas, Gregoris

Abstract

Business analytics aims to enable organizations to make quicker, better, and more intelligent decisions with the aim to create business value. To date, the major focus in the academic and industrial realms is on descriptive and predictive analytics. Nevertheless, prescriptive analytics, which seeks to find the best course of action for the future, has been increasingly gathering the research interest. Prescriptive analytics is often considered as the next step towards increasing data analytics maturity and leading to optimized decision making ahead of time for business performance improvement. This paper investigates the existing literature pertaining to prescriptive analytics and prominent methods for its implementation, provides clarity on the research field of prescriptive analytics, synthesizes the literature review in order to identify the existing research challenges, and outlines directions for future research.

Suggested Citation

  • Lepenioti, Katerina & Bousdekis, Alexandros & Apostolou, Dimitris & Mentzas, Gregoris, 2020. "Prescriptive analytics: Literature review and research challenges," International Journal of Information Management, Elsevier, vol. 50(C), pages 57-70.
    • Handle: RePEc:eee:ininma:v:50:y:2020:i:c:p:57-70
    DOI: 10.1016/j.ijinfomgt.2019.04.003
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    Citations

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    Cited by:

    1. Gasparini, Gaia & Brunelli, Matteo & Chiriac, Marius Dan, 2022. "Multi-period portfolio decision analysis: A case study in the infrastructure management sector," Operations Research Perspectives, Elsevier, vol. 9(C).
    2. Yap, Wei Yim & Hsieh, Cheng-Hsien & Lee, Paul Tae-Woo, 2023. "Shipping connectivity data analytics: Implications for maritime policy," Transport Policy, Elsevier, vol. 132(C), pages 112-127.
    3. Sel, Burakhan & Minner, Stefan, 2022. "A hedging policy for seaborne forward freight markets based on probabilistic forecasts," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 166(C).
    4. Rajat Kumar Behera & Pradip Kumar Bala & Nripendra P. Rana & Hatice Kizgin, 2022. "A Techno-Business Platform to Improve Customer Experience Following the Brand Crisis Recovery: A B2B Perspective," Information Systems Frontiers, Springer, vol. 24(6), pages 2027-2051, December.
    5. Wang, Shuaian & Yan, Ran, 2023. "Fundamental challenge and solution methods in prescriptive analytics for freight transportation," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 169(C).
    6. Hayajneh, Jamal Abdelrahman .M. & Elayan, Malek Bakheet Haroun & Abdellatif, Mamdouh Abdallah Mohamed & Abubakar, A. Mohammed, 2022. "Impact of business analytics and π-shaped skills on innovative performance: Findings from PLS-SEM and fsQCA," Technology in Society, Elsevier, vol. 68(C).
    7. Latinovic, Zoran & Chatterjee, Sharmila C., 2022. "Achieving the promise of AI and ML in delivering economic and relational customer value in B2B," Journal of Business Research, Elsevier, vol. 144(C), pages 966-974.
    8. Day, Min-Yuh & Ni, Yensen, 2023. "Do clean energy indices outperform using contrarian strategies based on contrarian trading rules?," Energy, Elsevier, vol. 272(C).
    9. Friederike Paetz & Winfried J. Steiner & Harald Hruschka, 2022. "“Advanced data analysis techniques with marketing applications”," Journal of Business Economics, Springer, vol. 92(4), pages 557-561, May.
    10. Oesterreich, Thuy Duong & Anton, Eduard & Teuteberg, Frank & Dwivedi, Yogesh K, 2022. "The role of the social and technical factors in creating business value from big data analytics: A meta-analysis," Journal of Business Research, Elsevier, vol. 153(C), pages 128-149.
    11. Filom, Siyavash & Amiri, Amir M. & Razavi, Saiedeh, 2022. "Applications of machine learning methods in port operations – A systematic literature review," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 161(C).
    12. Christina C. Bartenschlager & Milena Grieger & Johanna Erber & Tobias Neidel & Stefan Borgmann & Jörg J. Vehreschild & Markus Steinbrecher & Siegbert Rieg & Melanie Stecher & Christine Dhillon & Maria, 2023. "Covid-19 triage in the emergency department 2.0: how analytics and AI transform a human-made algorithm for the prediction of clinical pathways," Health Care Management Science, Springer, vol. 26(3), pages 412-429, September.

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