Value of data in multi-level supply chain decisions: a case study in the Dutch floriculture sector

Nguyen Quoc Viet, Behzad Behdani, Jacqueline Bloemhof-Ruwaard and Kai Hoberg

International Journal of Production Research, 2021, vol. 59, issue 5, 1368-1385

Abstract: While many supply chain decisions could take advantage of big data, firms struggle with investments into supply chain analytics since they are not able to assess the application areas and benefits of these initiatives. In this paper, we provide a multi-level perspective to assess the value of supply chain data. We develop a framework that highlights the connections between data characteristics and supply chain decisions with different time horizons (i.e. short- or long-term) as well as different supply chain levels (i.e. individual-firm level or supply-chain level). As data gets more complex in one or more of the 4 V dimensions (i.e. volume, variety, velocity, veracity), firms must assess how to best take advantage of the opportunities offered. We use the Dutch floriculture sector as a case study for our framework in which we highlight four data analytics applications to improve logistics processes. In the applications, we demonstrate how the data is used to support the decisions at different time horizons and supply-chain levels. We find that each of the big data’s Vs is required differently according to the decisions’ characteristics. Based on the findings, applications in other industries and promising directions for future research are discussed.

Date: 2021
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/00207543.2020.1821116 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:tprsxx:v:59:y:2021:i:5:p:1368-1385

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/TPRS20

DOI: 10.1080/00207543.2020.1821116

Access Statistics for this article

International Journal of Production Research is currently edited by Professor A. Dolgui

More articles in International Journal of Production Research from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().