Linguistic summarization to support supply network decisions

Sena Aydoğan (), Gül E. Okudan Kremer () and Diyar Akay ()
Additional contact information
Sena Aydoğan: Gazi University
Gül E. Okudan Kremer: Iowa State University
Diyar Akay: Gazi University

Journal of Intelligent Manufacturing, 2021, vol. 32, issue 6, No 5, 1573-1586

Abstract: Abstract A supply chain network architecture is a key element of designing and modeling a supply chain to better understand the cost and time associated with the distribution of products with available resources and market locations. Due to the large size of combinations for product design and supplier choices; descriptive, predictive and prescriptive analytics are needed to design, control and then improve a supply chain network. Current study is the first instance in the supply network management field using linguistic summarization (LS), a descriptive analytics tool generating natural language-based summaries of raw data with the help of fuzzy sets. This study has developed a LS method for revealing information from a realistic complex network of a bike supply chain, and it produces network description phrases by using fuzzy set theory to model linguistic/textual terms. The truth degree of generated summaries is calculated by fuzzy cardinality-based methods instead of scalar cardinality-based methods to overcome inherent disadvantages. The results of the study are interpreted in two ways: word clouds are used for single objective cases, and list of sentences that exceed a threshold value are used for bi-objective cases. LS-based findings, explanations and strategic decisions are directed at decision support to increase supply network performance, efficiency and sustainability.

Keywords: Supply network design; Linguistic summarization; Fuzzy set theory; Decision support (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10845-020-01677-9 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

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:spr:joinma:v:32:y:2021:i:6:d:10.1007_s10845-020-01677-9

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10845

DOI: 10.1007/s10845-020-01677-9

Access Statistics for this article

Journal of Intelligent Manufacturing is currently edited by Andrew Kusiak

More articles in Journal of Intelligent Manufacturing from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().