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2019 | OriginalPaper | Chapter

Enhancing Supply Chain Risk Management by Applying Machine Learning to Identify Risks

Author : Ahmad Pajam Hassan

Published in: Business Information Systems

Publisher: Springer International Publishing

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Abstract

Supply chain risks negatively affect the success of an OEM in automotive industry. Finding relevant information for supply chain risk management (SCRM) is a critical task. This investigation utilizes machine learning to find risk within textual documents. It contributes to the supply chain management (SCM) by designing (i) a conceptual model for supply risk identification in textual data. This addresses the requirement to see the direct connection between data analytics and SCM. (ii) An experiment in which a prototype is evaluated contributes the requirement to have more empirical insight in the interdisciplinary field of data analytics in SCRM.

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Frequent words that carry irrelevant information (i.e. pronouns, prepositions, conjunctions, etc.) [22]. In https://github.com/stopwords-iso/stopwords-de/blob/master/raw/stop-words-german.txt (accessed 09.08.2018).

Stemming is the process of removing suffixes from a term to generate word stems [27].

http://ana.cachopo.org/datasets-for-single-label-text-categorization.

http://www.cad.zju.edu.cn/home/dengcai/Data/TextData.html.

http://sites.labic.icmc.usp.br/text.

http://qwone.com/~jason/20Newsgroups/.

https://opennlp.apache.org/docs/1.8.0/manual/opennlp.html#tools.tokenizer.

https://github.com/stopwords-iso/stopwords-de/blob/master/stopwords-de.txt.

https://github.com/snowballstem.

http://snowballstem.org/algorithms/german/stemmer.html.

The distribution of the supply risk is approximately retained in training and test set.

Tang, C.S.: Perspectives in supply chain risk management. Int. J. Prod. Econ. 103, 451–488 (2006)CrossRef

Tiwari, S., Wee, H.M., Daryanto, Y.: Big data analytics in supply chain management between 2010 and 2016: insights to industries. Comput. Ind. Eng. 115, 319–330 (2018)CrossRef

Wang, G., Gunasekaran, A., Ngai, E.W.T., Papadopoulos, T.: Big data analytics in logistics and supply chain management: certain investigations for research and applications. Int. J. Prod. Econ. 176, 98–110 (2016)CrossRef

Gunasekaran, A., et al.: Big data and predictive analytics for supply chain and organizational performance. J. Bus. Res. 70, 308–317 (2017)CrossRef

Waller, M.A., Fawcett, S.E.: Data science, predictive analytics, and big data: a revolution that will transform supply chain design and management. J. Bus. Logistics 34(2), 77–84 (2013)CrossRef

Ho, W., Zheng, T., Yildiz, H., Talluri, S.: Supply chain risk management: a literature review. Int. J. Prod. Econ. 53(16), 5031–5069 (2015)CrossRef

Thun, J., Hoenig, D.: An empirical analysis of supply chain risk management in the German automotive industry. Int. J. Prod. Econ. 131, 242–249 (2011)CrossRef

Ellis, S.C., Henry, R.M., Shockley, J.: Buyer perceptions of supply disruption risk: a behavioral view and empirical assessment. J. Oper. Manag. 28, 34–46 (2010)CrossRef

ISO 31000:2018: Risk management – guidelines

10.

Walsworth, J.: Fire, explosions rip through instrument panel plant in Michigan. In: automotivenews.com by Crain Communications Inc., 2 May 2018. http://www.autonews.com/article/20180502/OEM10/180509951/fire-magnesium-supplier-general-motors-michigan. Accessed 30 July 2018

11.

Offermann, P., Blom, S., Schönherr, M., Bub, U.: Artifact types in information systems design science – a literature review. In: Winter, R., Zhao, J.L., Aier, S. (eds.) DESRIST 2010. LNCS, vol. 6105, pp. 77–92. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13335-0_6CrossRef

12.

Johannesson, P., Perjons, E.: A method framework for design science research. In: An Introduction to Design Science, pp. 75–89. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10632-8_4

13.

Aggarwal, C.C., Zhai, C.: A survey of text classification algorithms. In: Aggarwal, C., Zhai, C. (eds.) Mining Text Data, pp. 163–222. Springer, Boston (2012). https://doi.org/10.1007/978-1-4614-3223-4_6CrossRef

14.

Liu, A.B.: Web data mining: exploring hyperlinks, contents, and usage data. Springer (2007)

15.

Abbasi, A., Chen, H.: CyberGate: a system and design framework for text analysis of computer-mediated communication. MIS Q. 32(4), 811–837 (2008)CrossRef

16.

Altınel, B., Ganiz, M. C., Diri, B.: A corpus-based semantic kernel for text classification by using meaning values of terms. In: Engineering Applications of Artificial Intelligence, vol. 43, pp. 54–66 (2015)CrossRef

17.

Mironczuk, M.M., Protasiewicz, J.: A recent overview of the state-of-the-art elements of text classification. Expert Syst. Appl. 106, 36–54 (2018)CrossRef

18.

Chen, J., Huang, H., Tian, S., Qu, Y.: Feature selection for text classification with naive Bayes. Expert Syst. Appl. 36(3), 5432–5435 (2009)CrossRef

19.

Forman, G.: An extensive empirical study of feature selection metrics for text classification. J. Mach. Learn. Res. 3, 1289–1305 (2003)MATH

20.

John, G.H., Kohavi, R., Pfleger, K.: Irrelevant features and the subset selection problem. In: Proceedings of the 11th International Conference on machine learning, pp. 121–129. Morgan Kaufmann, San Francisco (1994)CrossRef

21.

Zhu, J., Xie, Q., Zheng, K.: An improved early detection method of type-2 diabetes mellitus using multiple classifier system. Inf. Sci. 292, 1–14 (2015)CrossRef

22.

Aas, K., Eikvil, L.: Text categorisation: a survey. Technical report. Norwegian Computing Center (1999)

23.

Guzella, T.S., Caminhas, W.M.: A review of machine learning approaches to spam filtering. Expert Syst. Appl. 36(7), 10206–10222 (2009)CrossRef

24.

Adeva, J.G., Atxa, J.P., Carrillo, M.U., Zengotitabengoa, E.A.: Automatic text classification to support systematic reviews in medicine. Expert Syst. Appl. 41(4, Part 1), 1498–1508 (2014)CrossRef

25.

Altınel, B., Ganiz, M.C., Diri, B.: A novel higher-order semantic kernel. In: Proceedings of the IEEE 10th International Conference on Electronics Computer and Computation (ICECCO), pp. 216–219 (2013)

26.

Ali, R., Lee, S., Chung, T.C.: Accurate multi-criteria decision making methodology for recommending machine learning algorithm. Expert Syst. Appl. 71, 257–278 (2017)CrossRef

27.

Porter, M.F.: An algorithm for suffix stripping. Program 14(3), 130–137 (1980)CrossRef

28.

Pinheiro, R.H.W., Cavalcanti, G.D.C., Tsang, I.R.: Combining dissimilarity spaces for text categorization. Inf. Sci. 406–407, 87–101 (2017)CrossRef

29.

Sabbah, T., et al.: Modified frequency-based term weighting schemes for text classification. Appl. Soft Comput. 58, 193–206 (2017)CrossRef

30.

Cardoso-Cachopo, A.: Improving methods for single-label text categorization. PdD Thesis, Instituto Superior Tecnico, Universidade Tecnica de Lisboa (2007)

31.

Slaton, G., McGill, M.J.: An Introduction to Modern Information Retrieval. McGraw-Hill, New York (1983)

32.

Slaton, G., Buckley, C.: Term weighting approaches in automatic text retrieval. Inf. Process. Manage. 24(5), 513–523 (1988)CrossRef

33.

Buckley, C., Slaton, G., Allan, J., Singhal, A.: Automatic query expansion using SMART: TREC 3. In: Proceedings of the 3rd Text Retrieval Conference, NIST (1994)

34.

Dumais, S.T.: Improving the retrieval of information from external sources. Behav. Res. Methods Instrum. Comput. 23(2), 229–236 (1991)CrossRef

35.

Yang, Y., Pedersen, J.O.: A comparative study on feature selection in text categorization. ICML 97, 412–420 (1997)

36.

Joachims, T.: Text categorization with support vector machines: learning with many relevant features. In: Nédellec, C., Rouveirol, C. (eds.) ECML 1998. LNCS, vol. 1398, pp. 137–142. Springer, Heidelberg (1998). https://doi.org/10.1007/BFb0026683CrossRef

37.

Chang, C.-C., Lin, C.-J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2(3), 2:27:1–27:27 (2011)CrossRef

38.

Greve, W., Wentura, D.: Wissenschaftliche Beobachtung. Eine Einführung. Psychologie Verlags Union, München (1997)

39.

Landis, J.R., Koch, G.G.: The measurement of observer agreement for categorical data. Biometrics 33(1), 159–174 (1977)CrossRef

40.

Hsu, C.-W., Chang, C.-C., Lin, C.-J.: A practical guide to support vector classification. Technical report, Department of Computer Science, National Taiwan University (2003)

41.

Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. J. Mach. Learn. Res. 13, 281–305 (2012)MathSciNetMATH

42.

Candanedo, I.S., Nieves, E.H., González, S.R., Martín, M.T.S., Briones, A.G.: Machine learning predictive model for industry 4.0. In: Uden, L., Hadzima, B., Ting, I.-H. (eds.) KMO 2018. CCIS, vol. 877, pp. 501–510. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-95204-8_42CrossRef

Title: Enhancing Supply Chain Risk Management by Applying Machine Learning to Identify Risks
Author: Ahmad Pajam Hassan
Publisher: Springer International Publishing
Book: Business Information Systems
Print ISBN: 978-3-030-20481-5

Electronic ISBN: 978-3-030-20482-2

Copyright Year: 2019
DOI: https://doi.org/10.1007/978-3-030-20482-2_16

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