Research Papers

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

[+] Author and Article Information
Ramin Sabbagh

Engineering Informatics Lab,
Texas State University,
San Marcos, TX 78666
e-mail: r_s343@txstate.edu

Farhad Ameri

Engineering Informatics Lab,
Texas State University,
San Marcos, TX 78666
e-mail: ameri@txstate.edu

Reid Yoder

Engineering Informatics Lab,
Texas State University,
San Marcos, TX 78666
e-mail: rjy15@txstate.edu

Contributed by the Computers and Information Division of ASME for publication in the JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING. Manuscript received October 9, 2017; final manuscript received March 5, 2018; published online June 12, 2018. Assoc. Editor: Jitesh H. Panchal.

J. Comput. Inf. Sci. Eng 18(3), 031009 (Jun 12, 2018) (14 pages) Paper No: JCISE-17-1217; doi: 10.1115/1.4039553 History: Received October 09, 2017; Revised March 05, 2018

Manufacturing capability (MC) analysis is a necessary step in the early stages of supply chain formation. In the contract manufacturing industry, companies often advertise their capabilities and services in an unstructured format on the company website. The unstructured capability data usually portray a 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 suppliers. This work proposes a novel framework for capability-based supplier classification that relies on the unstructured capability narratives available on the suppliers' websites. Four document classification algorithms, namely, support vector machine (SVM ), Naïve Bayes, random forest, and K-nearest neighbor (KNN) are used as the text classification techniques. 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 formal and informal vocabulary used in the contract machining industry for advertising manufacturing capabilities. A web-based tool is developed for the generation of the concept vector model associated with each capability narrative and extraction of features from the input documents. 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. It was concluded that thesaurus-guided method improves the precision of the classification process.

Copyright © 2018 by ASME
Your Session has timed out. Please sign back in to continue.


Forbes, H. , and Schaefer, D. , 2017, “Social Product Development: The Democratization of Design, Manufacture and Innovation,” Procedia CIRP, 60, pp. 404–409. [CrossRef]
Wu, D. , Liu, X. , Hebert, S. , Gentzsch, W. , and Terpenny, J. , 2017, “Democratizing Digital Design and Manufacturing Using High Performance Cloud Computing: Performance Evaluation and Benchmarking,” J. Manuf. Syst., 43(Pt. 2), pp. 316–326. [CrossRef]
Feng, S. C. , Bernstein, W. , Hedberg , T., Jr. , and Feeney, A. B. , 2017, “Toward Knowledge Management for Smart Manufacturing,” ASME J. Comput. Inf. Sci. Eng., 17(3), p. 031016. [CrossRef]
Manevitz, L. M. , and Yousef, M. , 2001, “One-Class SVMS for Document Classification,” J. Mach. Learn. Res., 2, pp. 139–154. http://dl.acm.org/citation.cfm?id=944790.944808
Saeys, Y. , Inza, I. , and Larrañaga, P. , 2007, “A Review of Feature Selection Techniques in Bioinformatics,” Bioinformatics, 23(19), pp. 2507–2517. [CrossRef] [PubMed]
Isaac, A., and Summers, E., 2009, “SKOS—Simple Knowledge Organization System,” Primer, World Wide Web Consortium (W3C).
Sabbagh, R. , and Ameri, F. , 2017, “A Thesaurus-Guided Text Analytics Technique for Capability-Based Classification of Manufacturing Suppliers,” ASME Paper No. IDETC2017-58110.
Ur-Rahman, N. , and A Harding, J. , 2012, “Textual Data Mining for Industrial Knowledge Management and Text Classification: A Business Oriented Approach,” Expert Syst. Appl., 39(5), pp. 4729–4739. [CrossRef]
Huang, L. , and Murphey, Y. , 2006, “Text Mining With Application to Engineering Diagnostics,” Advances in Applied Artificial Intelligence, Springer, Berlin, pp. 1309–1317. [CrossRef]
Edwards, B. , Zatorsky, M. , and Nayak, R. , 2008, “Clustering and Classification of Maintenance Logs Using Text Data Mining,” Seventh Australasian Data Mining Conference (AusDM 2008), Adelaide, Australia, Nov. 27–28, pp. 193–199.
Romanowski, C. J. , and Nagi, R. , 2002, “A Data Mining and Graph Theoretic Approach to Building Generic Bills of Materials,” IIE Annual Conference, Orlando, FL, May 19–22. https://pdfs.semanticscholar.org/c92f/479edaafcc2aea33643932cb315f236c729a.pdf
Romanowski, C. J. , and Nagi, R. , 2004, “A Data Mining Approach to Forming Generic Bills of Materials in Support of Variant Design Activities,” ASME J. Comput. Inf. Sci. Eng., 4(4), pp. 316–328. [CrossRef]
Jiao, J. R. , Zhang, L. L. , Pokharel, S. , and He, Z. , 2007, “Identifying Generic Routings for Product Families Based on Text Mining and Tree Matching,” Decis. Support Syst., 43(3), pp. 866–883. [CrossRef]
Jiao, J. , Zhang, L. , Zhang, Y. , and Pokharel, S. , 2008, “Association Rule Mining for Product and Process Variety Mapping,” Int. J. Comput. Integr. Manuf., 21(1), pp. 111–124. https://www.tandfonline.com/doi/abs/10.1080/09511920601182209
Teece, D. J. , Pisano, G. , and Shuen, A. , 1997, “Dynamic Capabilities and Strategic Management,” Strategic Manage. J., 18(7), pp. 509–533. [CrossRef]
Cleveland, G. , Schroeder, R. G. , and Anderson, J. C. , 1989, “A Theory of Production Competence*,” Decis. Sci., 20(4), pp. 655–668. [CrossRef]
Clark, K. B. , Hayes, R. , and Wheelwright, S. C. , 1988, Dynamic Manufacturing: Creating the Learning Organization, Free Press, New York.
Skinner, W. , 1969, “Manufacturing-Missing Link in Corporate Strategy,” Harvard Business Review, Boston, MA, pp. 136–145.
Miltenburg, J. , 2005, Manufacturing Strategy: How to Formulate and Implement a Winning Plan, CRC Press, Boca Raton, FL.
Slack, M. L. N. , 2011, Operations Strategy, 3rd ed., Pearson, London.
Ting Yu, L. , Yingying, X. , Chen, Y. , Xiaoliang, L. , Bo Hu, L. , Liqin, G. , and Chi, X. , 2016, Manufacturing Capability Service Modeling, Management and Evaluation for Matching Supply and Demand in Cloud Manufacturing, Springer, Singapore, pp. 35–48.
Zhang, L. , Luo, Y.-L. , Tao, F. , Ren, L. , and Guo, H. , 2010, “Key Technologies for the Construction of Manufacturing Cloud,” Comp. Integr. Manuf. Syst., 16(11), pp. 2510–2520.
Berry, M. W. , and Castellanos, M. , 2008, Survey of Text Mining II, Vol. 6, Springer, New York. [CrossRef]
Srivastava, A. , and Sahami, M. , 2009, Text Mining: Classification, Clustering, and Applications, 1st ed., Chapman & Hall/CRC Press, Boca Raton, FL. [CrossRef]
Yazdizadeh, P. , and Ameri, F. , 2017, “Concept-Based Text Mining Technique for Semantic Classification of Manufacturing Suppliers,” Smart Sustainable Manuf. Syst., 1(1), pp. 28–51.
Nick, T. , 2017, “How Semantic Technologies Enable Domain Experts to Steer Cognitive Applications,” Semantic Web Company, Vienna, Austria, Technical Report No. CEMA42878117. https://www.poolparty.biz/wp-content/uploads/2017/08/IDC_Paper_How_Semantic_Technologies_Steer_Cognitive_Applications.pdf


Grahic Jump Location
Fig. 1

The SKOS concept diagram for Swiss machining process

Grahic Jump Location
Fig. 2

Document frequency of some of the concepts (categorized based on schema) in an intermediate stage of thesaurus development (before deleting less frequent concepts)

Grahic Jump Location
Fig. 3

The total number of concepts under each concept scheme in the manufacturing the MC thesaurus

Grahic Jump Location
Fig. 4

The total number of concepts under each top concept of the MC thesaurus

Grahic Jump Location
Fig. 5

Proposed manufacturer classification framework

Grahic Jump Location
Fig. 6

Concept weighting schema

Grahic Jump Location
Fig. 7

Concept model builder function

Grahic Jump Location
Fig. 8

The user interface for extracting capability text

Grahic Jump Location
Fig. 9

Sample capability narrative tagged by MC thesaurus concepts

Grahic Jump Location
Fig. 10

Extracted concepts with their frequencies from the sample capability narrative

Grahic Jump Location
Fig. 11

The precision of text classification techniques. By applying concept weighting, the overall precision improves for all four techniques.

Grahic Jump Location
Fig. 12

Comparison BoW and BoC methods for ten trial runs for heavy machining (HM) and complex machining




Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In