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Contextual Improvement Planning by Fuzzy-Rough Machine Learning: A Novel Bipolar Approach for Business Analytics

Abstract

Nearly all companies need to retrieve valuable information from business data to increase its efficiency or value, and the rising interests of research in this domain could be named as business analytics. Because most of the problems (obstacles) faced by business have to consider a group of complex and interrelated factors, conventional statistics models (e.g., regression) have constraints in resolving these interrelated and complex problems. Therefore, this study proposes a novel multiple attribute decision-making model to resolve—from ranking/selection to improvement planning—the problems of business analytics in finance, based on the similarity with positive contexts (rules) and the dissimilarity with negative ones. The proposed model not only enhances the previous method (i.e., dominance-based rough set approach, DRSA) on ranking within the same decision class, but also provides a contextual approach to guide businesses for systematic improvements. Infused with the modified VIKOR method, the proposed model could support a company to transform analytics into priority contexts, which may guide improvement planning. To show the proposed model, a group of semiconductor companies in Taiwan is analyzed as an empirical case, and three companies are taken as examples to illustrate the ranking and improvement planning processes. The obtained findings thus contribute to bridge the applications of data-driven business analytics to the field of decision science in practice.

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References

  1. Davenport, T.H., Harris, J.G.: Competing on Analytics: The New Science of Winning. Harvard Business Press, Cambridge (2013)

    Google Scholar 

  2. Kohavi, R., Rothleder, N.J., Simoudis, E.: Emerging trends in business analytics. Commun. ACM 45, 45–48 (2002)

    Article  Google Scholar 

  3. Sharma, R., Reynolds, P., Scheepers, R., Seddon, P.B.: Business analytics and competitive advantage: a review and a research agenda. In: Respicio, A., Adam, F., Phillips, G., Teixeira, C., Telhada, J. (eds.) Bridging the Socio-Technical Gap in Decision Support systems, pp. 187–198. IOS press, Washington (2010)

    Google Scholar 

  4. S. LaValle, M. Hopkins, E. Lesser, R. Shockley, Analytics: The new path to value. IBM Institute for Business Value. October (2010)

  5. LaValle, S., Lesser, E., Shockley, R., Hopkins, M.S., Kruschwitz, N.: Big data, analytics and the path from insights to value. MIT Sloan Manag. Rev. 52, 21–31 (2011)

    Google Scholar 

  6. Lam, M.: Neural network techniques for financial performance prediction: integrating fundamental and technical analysis. Decis. Support Syst. 37, 567–581 (2004)

    Article  Google Scholar 

  7. K.Y. Shen, The modeling of earnings prediction by time-delay neural network. In C.S. Zhang (ed.): Materials Science and Information Technology, Advanced Materials Research. 433, 907–911 (2012)

  8. B. Ganter, G. Stumme, R. Wille (eds.), Formal Concept Analysis: foundations and Applications. Springer, New York (2005)

  9. Lai, R.K., Fan, C.Y., Huang, W.H., Chang, P.C.: Evolving and clustering fuzzy decision tree for financial time series data forecasting. Expert Syst. Appl. 36, 3761–3773 (2009)

    Article  Google Scholar 

  10. Fang, S.K., Shyng, J.Y., Lee, W.S., Tzeng, G.H.: Exploring the preference of customers between financial companies and agents based on TCA. Knowl.-Based Syst. 27, 137–151 (2012)

    Article  Google Scholar 

  11. Priss, U.: Formal concept analysis in information science. ARIST 40, 521–543 (2006)

    Google Scholar 

  12. Opricovic, S., Tzeng, G.H.: Extended VIKOR method in comparison with outranking methods. Eur. J. Oper. Res. 178, 514–529 (2007)

    Article  MATH  Google Scholar 

  13. Tversky, A., Kahneman, D.: Advances in prospect theory: cumulative representation of uncertainty. J. Risk Uncertain. 5, 297–323 (1992)

    Article  MATH  Google Scholar 

  14. Dubois, D., Prade, H.: Gradualness, uncertainty and bipolarity: making sense of fuzzy sets. Fuzzy Sets Syst. 192, 3–24 (2012)

    MathSciNet  Article  MATH  Google Scholar 

  15. Hu, S.K., Lu, M.T., Tzeng, G.H.: Exploring smart phone improvements based on a hybrid MCDM model. Expert Syst. Appl. 41, 4401–4413 (2014)

    Article  Google Scholar 

  16. Lu, M.T., Lin, S.W., Tzeng, G.H.: Improving RFID adoption in Taiwan’s healthcare industry based on a DEMATEL technique with a hybrid MCDM model. Decis. Support Syst. 56, 259–269 (2013)

    Article  Google Scholar 

  17. Peng, K.H., Tzeng, G.H.: A hybrid dynamic MADM model for problem-improvement in economics and business. Technol. Econ. Dev. Econ. 19, 638–660 (2013)

    Article  Google Scholar 

  18. Shen, K.Y., Yan, M.R., Tzeng, G.H.: Combining VIKOR-DANP model for glamor stock selection and stock performance improvement. Knowl.-Based Syst. 58, 86–97 (2014)

    Article  Google Scholar 

  19. Davenport, T.H.: Competing on analytics. Harv. Bus. Rev. 84, 98–107 (2006)

    Google Scholar 

  20. Bronzo, M., Resende, P.T.V., Oliveira, M.P.V., Oliveira, M.P.V., McCormack, K.P., Sousa, P.R., Ferreira, R.L.: Improving performance aligning business analytics with process orientation. Int. J. Inf. Manag. 33, 300–307 (2013)

    Article  Google Scholar 

  21. Delen, D., Demirkan, H.: Data, information and analytics as services. Decis. Support Syst. 55, 359–363 (2013)

    Article  Google Scholar 

  22. Doumpos, M., Zopounidis, C.: Preference disaggregation and statistical learning for multicriteria decision support: a review. Eur. J. Oper. Res. 209, 203–214 (2011)

    MathSciNet  Article  MATH  Google Scholar 

  23. Pawlak, Z.: Rough sets. Int. J. Comput. Inform. Sci. 11, 341–356 (1982)

    MathSciNet  Article  MATH  Google Scholar 

  24. Greco, S., Matarazzo, B., Slowinski, R.: Multicriteria classification by dominance-based rough set approach. In: Handbook of Data Mining and Knowledge Discovery. Oxford University Press, New York (2002)

  25. Greco, S., Matarazzo, B., Slowinski, R.: Rough sets theory for multicriteria decision analysis. Eur. J. Oper. Res. 129, 1–47 (2001)

    MathSciNet  Article  MATH  Google Scholar 

  26. Greco, S., Matarazzo, B., Slowinski, R., Stefanowski, J.: Variable consistency model of dominance-based rough sets approach. In: Ziarko, W., Tao, Y. (eds.) Rough Sets and Current Trends in Computing, pp. 170–181. Springer, Berlin, Heidelberg (2001)

    Chapter  Google Scholar 

  27. Liou, J.J.H., Yen, L., Tzeng, G.H.: Using decision rules to achieve mass customization of airline services. Eur. J. Oper. Res. 205, 680–686 (2010)

    Article  MATH  Google Scholar 

  28. Ko, Y.C., Fujita, H., Tzeng, G.H.: Using DRSA and fuzzy measure to enlighten policy making for enhancing national competitiveness by WCY 2011. In: Jiang, H., Ding, W., Ali, M., Wu, X. (eds.) Advanced Research in Applied Artificial Intelligence, pp. 709–719. Springer, Berlin, Heidelberg (2012)

    Chapter  Google Scholar 

  29. Shen, K.Y., Tzeng, G.H.: DRSA-based neuro-fuzzy inference systems for the financial performance prediction of commercial banks. Int. J. Fuzzy Syst. 16, 173–183 (2014)

    Google Scholar 

  30. Shen, K.Y., Tzeng, G.H.: A decision rule-based soft computing model for supporting financial performance improvement of the banking industry. Soft. Comput. 19, 859–874 (2015)

    Article  Google Scholar 

  31. Fang, S.K., Shyng, J.Y., Lee, W.S., Tzeng, G.H.: Exploring the preference of customers between financial companies and agents based on TCA. Knowl.-Based Syst. 27, 137–151 (2012)

    Article  Google Scholar 

  32. Błaszczyński, J., Greco, S., Słowiński, R.: Multi-criteria classification—a new scheme for application of dominance-based decision rules. Eur. J. Oper. Res. 181, 1030–1044 (2007)

    Article  MATH  Google Scholar 

  33. Tzeng, G.H., Huang, J.J.: Multiple Attribute Decision Making: methods and Applications. CRC Press, Boca Raton (2011)

    MATH  Google Scholar 

  34. Opricovic, S., Tzeng, G.H.: Compromise solution by MCDM methods: a comparative analysis of VIKOR and TOPSIS. Eur. J. Oper. Res. 156, 445–455 (2004)

    Article  MATH  Google Scholar 

  35. Yu, P.L.: A class of solutions for group decision problems. Manag. Sci. 19, 936–946 (1973)

    MathSciNet  Article  MATH  Google Scholar 

  36. Zeleny, M., Cochrane, J.L.: Multiple Criteria Decision Making, vol. 25. McGraw-Hill, New York (1982)

    MATH  Google Scholar 

  37. Simon, H.A.: Models of Bounded Rationality: empirically Grounded Economic Reason, vol. 3. MIT Press, London (1982)

    Google Scholar 

  38. Hsu, C.H., Wang, F.K., Tzeng, G.H.: The best vendor selection for conducting the recycled material based on a hybrid MCDM model combining DANP with VIKOR. Resour. Conserv. Recycl. 66, 95–111 (2012)

    Article  Google Scholar 

  39. Liou, J.J.H., Tsai, C.Y., Lin, R.H., Tzeng, G.H.: A modified VIKOR multiple-criteria decision method for improving domestic airlines service quality. J. Air Transp. Manag. 17, 57–61 (2011)

    Article  Google Scholar 

  40. Ou Yang, Y.P., Shieh, H.M., Leu, J.D., Tzeng, G.H.: A VIKOR-based multiple criteria decision method for improving information security risk. Int. J. Inf. Technol. & Decis. Mak. 8, 267–287 (2009)

    Article  MATH  Google Scholar 

  41. Liou, J.J.H.: New concepts and trends of MCDM for tomorrow–in honor of Professor Gwo-Hshiung Tzeng on the occasion of his 70th birthday. Technol. Econ. Dev. Econ. 19, 367–375 (2013)

    Article  Google Scholar 

  42. Liou, J.J.H., Tzeng, G.H.: Comments on “Multiple criteria decision making (MCDM) methods in economics: an overview”. Technol. Econ. Dev. Econ. 18, 672–695 (2012)

    Article  Google Scholar 

  43. Taiwan Economics Journal (TEJ): http://www.tej.com.tw/. Accessed in 2014

  44. Błaszczynski, J., Greco, S., Matarazzo, B., Słowiński, R., Szela̧g, M.: JMAF-dominance-based rough set data analysis framework. In: Skowron, A., Suraj, Z. (eds.) Rough Sets and Intelligent Systems-Professor Zdzisław Pawlak in Memoriam, pp. 185–209. Springer, New York (2013)

    Chapter  Google Scholar 

  45. Stewart, T.J., French, S., Rios, J.: Integrating multicriteria decision analysis and scenario planning—review and extension. Omega 41, 679–688 (2013)

    Article  Google Scholar 

Download references

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Appendices

Appendix 1

#Positive Rules (associated with Good DC)

  1. 1.

    (AR_turnover ≥ H) & (Inventory ≥ H) & (ROA ≥ M) & (EPS ≥ M) => (DC ≥ Good)

  2. 2.

    (Speed ≥ M) & (AR_turnover ≥ H) & (EPS ≥ M) & (CashFlow ≥ H) & (CashFlow_adq ≥ M) => (DC ≥ Good)

  3. 3.

    (Debt ≥ M) & (InterestCoverage ≥ M) & (AR_turnover ≥ H) & (Inventory ≥ M) & (EPS ≥ H) & (CashFlow ≥ M) => (DC ≥ Good)

  4. 4.

    (NetPafterTax ≥ H) & (EPS ≥ M) & (CashFlow ≥ M) & (CashFlow_adq ≥ H) & (CashFlow_reinv ≥ M) => (DC ≥ Good)

  5. 5.

    (LongCapital ≥ M) & (AR_days ≥ H) & (ROA ≥ M) & (CashFlow ≥ H) => (DC ≥ Good)

  6. 6.

    (LongCapital ≥ M) & (Speed ≥ M) & (AR_turnover ≥ M) & (AR_days ≥ H) & (ROE ≥ M) & (CashFlow_adq ≥ M) => (DC ≥ Good)

  7. 7.

    (LongCapital ≥ H) & (ROE ≥ H) & (CashFlow ≥ H) => (DC ≥ Good)

  8. 8.

    (FixAssetTurnover ≥ M) & (EPS ≥ M) & (CashFlow ≥ H) & (CashFlow_adq ≥ H) => (DC ≥ Good)

  9. 9.

    (LongCapital ≥ H) & (Liquidity ≥ H) & (Speed ≥ M) & (EPS ≥ H) => (DC ≥ Good)

  10. 10.

    (LongCapital ≥ H) & (Liquidity ≥ H) & (AR_days ≥ M) & (NetPbeforeTax ≥ H) => (DC ≥ Good)

  11. 11.

    (LongCapital ≥ M) & (InterestCoverage ≥ M) & (ROA ≥ H) & (NetPafterTax ≥ M) & (CashFlow ≥ M) => (DC ≥ Good)

  12. 12.

    (LongCapital ≥ M) & (InterestCoverage ≥ M) & (ROA ≥ H) & (CashFlow ≥ M) & (CashFlow_reinv ≥ H) => (DC ≥ Good)

  13. 13.

    (LongCapital ≥ M) & (InterestCoverage ≥ H) & (Inventory ≥ M) & (ROE ≥ M) & (EPS ≥ H) => (DC ≥ Good)

#Negative Rules (associated with Bad DC)

  1. 14.

    (EPS ≤ L) => (DC ≤ Bad)

  2. 15.

    (LongCapital ≤ L) & (Liquidity ≤ M) => (DC ≤ Bad)

  3. 16.

    (Speed ≤ L) & (ROA ≤ M) => (DC ≤ Bad)

  4. 17.

    (AR_turnover ≤ M) & (AR_days ≤ L) => (DC ≤ Bad)

  5. 18.

    (AR_days ≤ M) & (Inventory ≤ L) => (DC ≤ Bad)

  6. 19.

    (Debt ≤ M) & (CashFlow ≤ L) => (DC ≤ Bad)

  7. 20.

    (CashFlow ≤ L) & (CashFlow_adq ≤ L) => (DC ≤ Bad)

  8. 21.

    (NetPbeforeTax ≤ L) & (CashFlow_adq ≤ L) => (DC ≤ Bad)

  9. 22.

    (Debt ≤ L) & (CashFlow_adq ≤ L) => (DC ≤ Bad)

  10. 23.

    (Liquidity ≤ M) & (InterestCoverage ≤ L) => (DC ≤ Bad)

  11. 24.

    (Debt ≤ M) & (AR_turnover ≤ M) & (FixAssetTurnover ≤ M) & (CashFlow_reinv ≤ M) => (DC ≤ Bad)

Appendix 2

The calculations of fuzzy evaluations on the three alternatives were based on collecting the five domain experts’ perceptions regarding three scales: Unsatisfied (U), Neutral (N), and Satisfied (S), ranging from 0.00 to 1.00 by the triangular fuzzy membership function. The collected fuzzy numbers on the three scales from the five experts are in Table 9. The figures from a company, industrial average, max, min, and standard deviation on each attribute were provided with the requirements of attributes in the questionnaire. Take “A 1 ≥ M” in C 3 (company A) for example, the evaluation of the requirement (A 1 ≥ M) for A was based on the five experts’ opinions; their opinions on this requirement were “N,” “N,” “N,” “U,” and “U”.

Table 9 Fuzzy numbers for the three scales by the five experts

Based on Table 9 and (17), the fuzzy evaluations (by the five experts) of company A on this requirement (i.e., A 1M) was (0.40, 0.50, 0.60) ⊕ (0.35, 0.50, 0.65) ⊕ (0.40, 0.50, 0.60) ⊕ (0.00, 0.00, 0.25) ⊕ (0.00, 0.00, 0.30)/5 = (0.23, 0.30, 0.48). The fuzzy-averaged triangular membership function can be illustrated in Fig. 3, and the defuzzified performance score was (0.23 + (0.48−0.23) + (0.30−0.23))/3 = 0.34. (Table 9 and Fig. 3.)

Fig. 3
figure 3

Averaged fuzzy evaluation as triangular membership function

Appendix 3

Take the case of A 3 attribute (i.e., Liquidity) of company A for example, the industrial average \( \overline{A}_{3} = 333.42 \) and SD3 = 327.35, and the raw figure of company A on Liquidity = 212.98. Therefore, its Z-score-based transformation equals to −0.3679 ((212.98–333.42)/327.35 = 0.3679) by (19); thus, it was categorized as “M” because −0.5244 < −0.3679 < 0.5244. In this case, it would be regarded as fully satisfied for the premise “A 3 ≤ M” in Table 5; in other words, its performance evaluation on the requirement “A 3 ≤ M” would be 1.00 for calculating the synthesized bipolar decision model. Otherwise, if Z-score-based transformation was higher than 0.5244 (e.g., 0.8888), its performance evaluation on the requirement “A 3 ≤ M” would be 0.00.

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Shen, KY., Tzeng, GH. Contextual Improvement Planning by Fuzzy-Rough Machine Learning: A Novel Bipolar Approach for Business Analytics. Int. J. Fuzzy Syst. 18, 940–955 (2016). https://doi.org/10.1007/s40815-016-0215-8

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Keywords

  • Business analytics (BA)
  • Fuzzy-rough machine learning
  • Multiple attribute decision making (MADM)
  • Dominance-based rough set approach (DRSA)
  • Modified VIKOR method
  • Bipolar decision model