IDEAS home Printed from https://ideas.repec.org/a/taf/tprsxx/v61y2023i9p2828-2839.html
   My bibliography  Save this article

Managing demand volatility of pharmaceutical products in times of disruption through news sentiment analysis

Author

Listed:
  • Angie Nguyen
  • Robert Pellerin
  • Samir Lamouri
  • Béranger Lekens

Abstract

Unplanned events such as epidemic outbreaks, natural disasters, or major scandals are usually accompanied by supply chain disruption and highly volatile demand. Besides, authors have recently outlined the need for new applications of artificial intelligence to provide decision support in times of crisis. In particular, natural language processing allows for deriving an understanding from unstructured data in human languages, such as online news content, which can provide valuable information during disruptive events. This article contributes to this research strand as it aims to leverage textual data from news through sentiment analysis and predict demand volatility of pharmaceutical products in times of crisis. As a result, (1) a deep-learning-based sentiment analysis model was developed to extract and structure information from medicines-related news; (2) a framework allowing for combining extracted information from unstructured data with structured data of medicines demand was defined; and (3) an approach combining efficient artificial intelligence techniques with existing forecasting models was proposed to enhance demand forecasting in times of disruption. Additionally, the framework was applied to two examples of disruptive events in France: a pharmaceutical scandal and the COVID-19 pandemic. Findings outlined that using sentiment analysis allowed for enhancing demand forecasting accuracy.

Suggested Citation

  • Angie Nguyen & Robert Pellerin & Samir Lamouri & Béranger Lekens, 2023. "Managing demand volatility of pharmaceutical products in times of disruption through news sentiment analysis," International Journal of Production Research, Taylor & Francis Journals, vol. 61(9), pages 2828-2839, May.
    • Handle: RePEc:taf:tprsxx:v:61:y:2023:i:9:p:2828-2839
    DOI: 10.1080/00207543.2022.2070044
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/00207543.2022.2070044
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/00207543.2022.2070044?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.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Xu, Qianwen Ariel & Jayne, Chrisina & Chang, Victor, 2024. "An emoji feature-incorporated multi-view deep learning for explainable sentiment classification of social media reviews," Technological Forecasting and Social Change, Elsevier, vol. 202(C).
    2. Soltanzadeh, Shima & Rafiee, Majid & Weber, Gerhard-Wilhelm, 2024. "Disruption, panic buying, and pricing: A comprehensive game-theoretic exploration," Journal of Retailing and Consumer Services, Elsevier, vol. 78(C).

    More about this item

    Statistics

    Access and download statistics

    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:taf:tprsxx:v:61:y:2023:i:9:p:2828-2839. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/TPRS20 .

    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.