Evaluating Supply Chain Resource Limits From News Articles and Earnings Call Transcripts: An Application of Integrated Factor Analysis and Analytical Network Process

dc.contributor.author Chu, Chih-Yuan
dc.contributor.author Okudan Kremer, Gül
dc.contributor.author Gunay, Elif
dc.contributor.author Al-Araidah, Omar
dc.contributor.author Kremer, Gül
dc.contributor.department Industrial and Manufacturing Systems Engineering
dc.date 2020-11-12T17:34:14.000
dc.date.accessioned 2021-02-26T00:54:27Z
dc.date.available 2021-02-26T00:54:27Z
dc.date.copyright Wed Jan 01 00:00:00 UTC 2020
dc.date.issued 2020-11-03
dc.description.abstract <p>Due to the impact of globalization, companies have extended their borders across nations to launch products more competitively. However, globalization affects various uncertainties and risks that may limit the performance of supply chains. Research indicates that models that incorporate uncertainties and risks will help to improve the resilience of global supply chains. In the era of technology, we experience the abundance of textual data from various web-media resources related to companies, which can be deployed to understand the impact of risks on the chain. Accordingly, this study aims to utilize textual data collected from news articles and earnings call transcripts to assess the vulnerability of the suppliers and the chain. Among many, we considered supply chain resource limits as a subcomponent of vulnerability and collected textual data associated with its sub-factors. Then, we proposed an integrated factor analysis and Analytical Network Process (ANP) method to model the company’s supply chain resource limits index. Specifically, factor analysis was used to determine the latent constructs of the variables that are grouped under resource limits and their correlations. This latent construct and correlations were then applied as the interdependencies among variables in the ANP to discover the final importance weights of the variables in terms of supply chain resource limits. The results of the study showed that the shortages of capacity, components, and energy supply are the most critical sub-factors. The company’s supply chain resource limits index (SCRLI) can be further calculated to assist decision-makers of an enterprise in supply chain configuration design, and improve the supply chain resilience.</p>
dc.description.comments <p>This proceeding is published as Chu, Chih-Yuan, Elif E. Gunay, Omar Al-Araidah, and Gül E. Kremer. "Evaluating Supply Chain Resource Limits From News Articles and Earnings Call Transcripts: An Application of Integrated Factor Analysis and Analytical Network Process." <em>25th Design for Manufacturing and the Life Cycle Conference (DFMLC)</em> 6 (2020): V006T06A013. DOI: <a href="https://doi.org/10.1115/DETC2020-22699" target="_blank">10.1115/DETC2020-22699</a>. Posted with permission.</p>
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/imse_conf/205/
dc.identifier.articleid 1206
dc.identifier.contextkey 20169384
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath imse_conf/205
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/96489
dc.language.iso en
dc.source.bitstream archive/lib.dr.iastate.edu/imse_conf/205/0-2020_OkudanKremerGul_ASME_PermGrant.pdf|||Fri Jan 14 22:25:36 UTC 2022
dc.source.bitstream archive/lib.dr.iastate.edu/imse_conf/205/2020_OkudanKremerGul_EvaluatingSupply.pdf|||Fri Jan 14 22:25:37 UTC 2022
dc.source.uri 10.1115/DETC2020-22699
dc.subject.disciplines Operational Research
dc.subject.keywords supply chain management
dc.subject.keywords text analytics
dc.subject.keywords factor analysis
dc.subject.keywords analytical network process (ANP)
dc.title Evaluating Supply Chain Resource Limits From News Articles and Earnings Call Transcripts: An Application of Integrated Factor Analysis and Analytical Network Process
dc.type article
dc.type.genre conference
dspace.entity.type Publication
relation.isAuthorOfPublication 023d6574-26eb-4afb-a91f-c79d5c3c9554
relation.isOrgUnitOfPublication 51d8b1a0-5b93-4ee8-990a-a0e04d3501b1
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