Linguistic summarization to support supply network decisions


AYDOĞAN S., Okudan Kremer G. E. , AKAY D.

JOURNAL OF INTELLIGENT MANUFACTURING, vol.32, no.6, pp.1573-1586, 2021 (Peer-Reviewed Journal) identifier identifier

  • Publication Type: Article / Article
  • Volume: 32 Issue: 6
  • Publication Date: 2021
  • Doi Number: 10.1007/s10845-020-01677-9
  • Journal Name: JOURNAL OF INTELLIGENT MANUFACTURING
  • Journal Indexes: Science Citation Index Expanded, Scopus, IBZ Online, ABI/INFORM, Applied Science & Technology Source, Business Source Elite, Business Source Premier, Compendex, Computer & Applied Sciences, INSPEC
  • Page Numbers: pp.1573-1586
  • Keywords: Supply network design, Linguistic summarization, Fuzzy set theory, Decision support, BIG DATA ANALYTICS, CHAIN NETWORK, FUZZY, REPRESENTATION, PERSPECTIVE, MANAGEMENT, DISCOVERY, DESIGN, LEVEL

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.