Linguistic summarization to support supply network decisions
- PDF / 1,532,711 Bytes
- 14 Pages / 595.276 x 790.866 pts Page_size
- 45 Downloads / 183 Views
Linguistic summarization to support supply network decisions Sena Aydogan ˘ 1
· Gül E. Okudan Kremer2 · Diyar Akay1
Received: 10 February 2020 / Accepted: 20 September 2020 © Springer Science+Business Media, LLC, part of Springer Nature 2020
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
Introduction Efforts to analyze logistics infrastructure to achieve longterm strategic objectives play an important role in designing supply chain networks. Traditionally, node-to-node evaluation of supply network interactions has been carried out. However, a substantial number of studies has shown that interactions within a supply network have become more complex (Choi et al. 2001; Hamta et al. 2018; Perera et al. 2017). A supply network includes various suppliers and customers and their complex interactions. In a supply network, each specific entity is defined by a node, and the connection between nodes may pertain to product, information and money flows,
B
Sena Aydo˘gan [email protected] Gül E. Okudan Kremer [email protected] Diyar Akay [email protected]
1
Department of Industrial Engineering, Gazi University, Ankara 06570, Turkey
2
Department of Industrial and Manufacturing Systems Engineering, Iowa State University, Ames, IA 50011, USA
etc. The acquisition of useful information from supply networks could guide successful decision-making. Data mining refers to a series of approaches, commonly classified as descriptive and predictive methods, that aim at exploring large databases for the purpose of useful information extraction (Han et al. 2012). Although the predictive methods use the
Data Loading...
