Advertisement

Identifying the Orientations of Sustainable Supply Chain Research Using Data Mining Techniques: Contributions and New Developments

  • Carlos Montenegro
  • Marco Segura
  • Edison Loza-Aguirre
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 865)

Abstract

To be effective, sustainable development must maintain an equilibrium between its social, environmental and economic efforts. Several studies have suggested that an unbalance exists about the attention given to those three dimensions; however, few contributions have demonstrated such unbalance. This research describes a synthesis of two manual and semiautomatic methods published in the technical literature and includes additional developments, conceived to speed up and increase the accuracy of the analysis of the sustainable orientation of a corpus. The results are compared with the previous studies on about ten years of literature from top-tier journals dealing with Sustainable Supply Chain issues. The results confirm unbalance on research in this field. They show that most of the studies have been focussed on environmental and economic aspects, leaving aside social issues.

Keywords

Sustainable Supply Chain LDA topic model Feature selection Content analysis Sustainable Development 

References

  1. 1.
    Carter, C., Easton, P.: Sustainable supply chain management: evolution and future directions. Int. J. Phys. Distr. Log. 41(1), 46–62 (2011)CrossRefGoogle Scholar
  2. 2.
    Pagell., M., Shevchenko, S.: Why research in sustainable supply chain management should have no future. J. Supply Chain. Manag. 50(1), 44–51 (2014)Google Scholar
  3. 3.
    Srivastava, S.: Green supply-chain management: a state-of-the-art literature review. Int. J. Manag. Rev. 9(1), 53–80 (2007)CrossRefGoogle Scholar
  4. 4.
    Carter, C., Rogers, D.: A framework of sustainable supply chain management: moving toward new theory. Int. J. Phys. Distr. Log. 38(5), 360–387 (2008)CrossRefGoogle Scholar
  5. 5.
    Carter, C., Jennings, M.: Social responsibility and supply chain relationships. Transp. Res. E-Log. 38(1), 37–52 (2002)CrossRefGoogle Scholar
  6. 6.
    Murphy, P., Poist, R.: Socially responsible logistics: an exploratory study. Transp. J. 41(4), 22–35 (2002)Google Scholar
  7. 7.
    Seuring, S.: Core issues in sustainable supply chain management—a Delphi study. Bus. Strateg. Environ. 17(8), 455–466 (2008)CrossRefGoogle Scholar
  8. 8.
    Elkington, J.: Cannibals with Forks: the Triple Bottom Line of 21st-Century Business. New Society Publishers, Gabriola Island, BC (1998)Google Scholar
  9. 9.
    Loza-Aguirre, E., Segura, M., Roa, H., Montenegro, C.: Unveiling unbalance on sustainable supply chain research: did we forget something? In: Rocha, Á., Guarda, T. (eds.) Proceedings of the International Conference on Information Technology and Systems (ICITS 2018). Advances in Intelligent Systems and Computing, vol. 721. Springer, Cham (2018)Google Scholar
  10. 10.
    Montenegro, C., Loza-Aguirre, E., Segura-Morales, M.: Using probabilistic topic models to study orientation of sustainable supply chain research. In: Rocha, Á., et al. (eds.) WorldCIST’18 2018, AISC, vol. 745. Springer, Cham (2018)Google Scholar
  11. 11.
    Muñoz, M., Rivera, J., Moneva, J.: Evaluating sustainability in organizations with a fuzzy logic approach. Ind. Manag. Data Syst. 108(6), 829–841 (2008)Google Scholar
  12. 12.
    Vimal, K., Vinodh, S.: Development of checklist for evaluating sustainability characteristics of manufacturing processes. Int. J. Proc. Manag. Bench. 3(2), 213–232 (2013)Google Scholar
  13. 13.
    Sloan, T.: Measuring the sustainability of global supply chains: current practices and future Directions. J. Glob. Bus Manag. 6(1), 1–16 (2010)Google Scholar
  14. 14.
    Blei, D.: Probabilistic topic models. Commun. ACM 55(4), 77–84 (2012)CrossRefGoogle Scholar
  15. 15.
    Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. J. Mach. Learn. Res. 3, 1157–1182 (2003)zbMATHGoogle Scholar
  16. 16.
    Chandrashekar, G., Sahin, F.: A survey on feature selection methods. Comput. Electr. Eng. 40, 16–28 (2014)CrossRefGoogle Scholar
  17. 17.
    Saeys, Y., Inza, I., Larrañaga, P.: A review of feature selection techniques in bioinformatics. Bioinform. Rev. 23(19), 2507–2517 (2007)Google Scholar
  18. 18.
    Dy, J., Brodley, C.: Feature selection for unsupervised learning. J. Mach. Learn. Res. 5, 845–889 (2004)MathSciNetzbMATHGoogle Scholar
  19. 19.
    Law, M., Figueiredo, M., Jain, A.: Simultaneous feature selection and clustering using mixture models. IEEE Trans. Pattern Anal. Mach. Intell. 26(9), 1154–1166 (2004)CrossRefGoogle Scholar
  20. 20.
    Kohav, R., John, G.: Wrappers for feature subset selection. Artif. Intell. 97, 273–324 (1997)CrossRefGoogle Scholar
  21. 21.
    Bolón-Canedo, V., Sánchez-Maroño, N., Alonso-Betanzos, A.: Feature Selection for High-Dimensional Data. Springer International Publishing, Cham (2015)CrossRefGoogle Scholar
  22. 22.
    MathWorks: Neural Network Toolbox™. User’s Guide. R2014a, The MathWorks, Inc. (2014)Google Scholar
  23. 23.
    Barreto, G., Mota, J., Souza, L., Frota, R., Aguayo, L. Yamamoto, J., Macedo, P.: Competitive neural networks for fault detection and diagnosis in 3G cellular systems. In: de Souza, J.N., et al. (eds.) ICT 2004, Berlin (2004)Google Scholar
  24. 24.
    Kaur, R., Sachdeva, M., Kumar, G.: Study and comparison of feature selection approaches for intrusion detection. Int. J. Comput. Appl. (2016)Google Scholar
  25. 25.
    Arguello, B.: A Survey of Feature Selection Methods: Algorithms and Software, Austin (2015)Google Scholar
  26. 26.
    Friedman, N., Geiger, D., Goldszmidt, M.: Bayesian network classifiers. Mach. Learn. 29, 131–163 (1997)CrossRefGoogle Scholar
  27. 27.
    Cheng, J., Greiner, R.: Comparing Bayesian network classifiers. In: Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence, Stockholm (1999)Google Scholar
  28. 28.
    Parameter estimation for text analysis. http://www.arbylon.net/publications/text-est.pdf
  29. 29.
  30. 30.
    Griffiths, T., Steyvers, M., Tanenbaum, J.: Topics in semantic representation. Psychol. Rev. 114(2), 211–244 (2007)CrossRefGoogle Scholar
  31. 31.
    Deveaud, R., Sanjuan, E., Bellot, P.: Accurate and effective latent concept for ad hoc information retrieval. Rev. Sci. Tech. Inf. 17, 61–84 (2014)Google Scholar
  32. 32.
    Arun, R., Suresh, V., Veni, C., Murthy, M.: On finding the natural number of topics with latent Dirichlet allocation: some observations. In: Zaki, M., Xu, J. (eds.) Advances in Knowledge Discovery and Data Mining, pp. 391–402. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  33. 33.
    Cao, J., Xia, T., Li, J., Zhang, Y., Tang, S.: A density-based method for adaptive LDA model selection. Neurocomputing 72(7–9), 1775–1781 (2009)CrossRefGoogle Scholar
  34. 34.
    Liu, L., Tang, L., Dong, W., Yao, S., Zhou, W.: An overview of topic modeling and its current applications in bioinformatics. SpringerPlus, vol. 5, nº 1608, pp. 1–22 (2016)Google Scholar
  35. 35.
    The University of Waikato, WEKA Manual for Version 3-7-8, Hamilton, New Zealand (2013)Google Scholar
  36. 36.
    Witten, I., Eibe, F., Hall, M.: Data Mining: Practical Machine Learning Tools and Techniques. The Morgan Kaufmann Series in Data Management Systems (2011)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Carlos Montenegro
    • 1
  • Marco Segura
    • 1
  • Edison Loza-Aguirre
    • 1
    • 2
  1. 1.Escuela Politécnica NacionalQuitoEcuador
  2. 2.CERAG FRE 3748 CNRS/UGAGrenoble Cedex 9France

Personalised recommendations