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A Thesaurus-Guided Text Analytics Technique for Capability-Based Classification of Manufacturing Suppliers

[+] Author Affiliations
Ramin Sabbagh, Farhad Ameri

Texas State University, San Marcos, TX

Paper No. DETC2017-67652, pp. V001T02A075; 12 pages
doi:10.1115/DETC2017-67652
From:
  • ASME 2017 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
  • Volume 1: 37th Computers and Information in Engineering Conference
  • Cleveland, Ohio, USA, August 6–9, 2017
  • Conference Sponsors: Design Engineering Division, Computers and Information in Engineering Division
  • ISBN: 978-0-7918-5811-0
  • Copyright © 2017 by ASME

abstract

Capability analysis is a necessary step in the early stages of supply chain formation. Most existing approaches to manufacturing capability evaluation and analysis use structured and formal capability models as input. However, manufacturing suppliers often publish their capability data in an unstructured format. The unstructured capability data usually portrays a more realistic view of the services a supplier can offer. If parsed and analyzed properly, unstructured capability data can be used effectively for initial screening and characterization of manufacturing suppliers specially when dealing with a large pool of prospective suppliers. This work proposes a novel framework for capability-based supplier classification that relies on the unstructured capability narratives available on the suppliers’ websites. Naïve Bayes is used as the text classification technique. One of the innovative aspects of this work is incorporating a thesaurus-guided method for feature selection and tokenization of capability data. The thesaurus contains the informal vocabulary used in the contract machining industry for advertising manufacturing capabilities. An Entity Extractor Tool (EET) is developed for the generation of the concept vector model associated with each capability narrative. The proposed supplier classification framework is validated experimentally through forming two capability classes, namely, heavy component machining and difficult and complex machining, based on real capability data.

Copyright © 2017 by ASME

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