IDEAS home Printed from https://ideas.repec.org/p/eti/rdpsjp/24005.html
   My bibliography  Save this paper

Using Supply Chain Network Information and High-frequency Mobility Data to Forecast Firm Dynamics (Japanese)

Author

Listed:
  • KATO Rui
  • MIYAKAWA Daisuke
  • YANAOKA Masaki
  • YUKIMOTO Shinji

Abstract

The use of GPS location data is increasingly common in recent years. In this paper, we use individual-level GPS location data to measure the size of factory-level populations and to forecast the leasing demand of the transaction partners of the companies for which the factory-level population is measured. First, we use GPS location data to measure changes in the population at the main factories of companies in the manufacturing industry. Second, using such measured data and their lease contract data, we construct a machine learning-based prediction model of leasing demand within the company’s suppliers. Except for the periods when corporate activities were greatly disturbed by the COVID-19 pandemic, the use of the GPS location data improves the prediction power of the leasing demand.

Suggested Citation

  • KATO Rui & MIYAKAWA Daisuke & YANAOKA Masaki & YUKIMOTO Shinji, 2024. "Using Supply Chain Network Information and High-frequency Mobility Data to Forecast Firm Dynamics (Japanese)," Discussion Papers (Japanese) 24005, Research Institute of Economy, Trade and Industry (RIETI).
  • Handle: RePEc:eti:rdpsjp:24005
    as

    Download full text from publisher

    File URL: https://www.rieti.go.jp/jp/publications/dp/24j005.pdf
    Download Restriction: no
    ---><---

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:eti:rdpsjp:24005. 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: TANIMOTO, Toko (email available below). General contact details of provider: https://edirc.repec.org/data/rietijp.html .

    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.