Flexible Services and Manufacturing Journal

, Volume 23, Issue 3, pp 263–289 | Cite as

Manufacturing intelligence for class prediction and rule generation to support human capital decisions for high-tech industries

Article

Abstract

Human capital is one of the critical resources for high-tech industries such as semiconductor manufacturing to maintain their competitive advantages, yet it is seldom addressed in literature. Owing to the changing nature of knowledge workers in high-tech industries, jobs cannot be easily delineated. Thus, conventional personnel selection approaches based on static job characteristics no longer suffice. Focusing on the needs in real settings, this study aims to develop a manufacturing intelligence framework that integrates the rough set theory, support vector machine, and decision tree to extract useful patterns and intelligence from huge human resource data and production data to enhance the decision quality of human resource management that include identifying high-potential talents who fit the company culture and allocating the job with functional nature that matches the characteristics of the talent. To assess the validity of this approach, empirical studies were conducted on the basis of real data collected from semiconductor companies for comparison. The results have shown the practical viability of this approach.

Keywords

Data mining Manufacturing intelligence Support vector machine Rough set theory Decision tree Human capital 

Notes

Acknowledgments

This research was partially sponsored by the National Science Council, Taiwan (NSC 97-2221-E-007-111-MY3) and Faculty Empower Project of National Tsing Hua University (98N2953E1).

References

  1. Appleyard MM, Brown C (2001) Employment practices and semiconductor manufacturing performance. Ind Relat 40(3):436–471CrossRefGoogle Scholar
  2. Baesens B, Mues C, Martens D, Vanthienen J (2009) 50 years of data mining and OR: upcoming trends and challenges. J Oper Res Soc 60:S16–S23MATHCrossRefGoogle Scholar
  3. Berry MJ, Linoff G (1997) Data mining techniques: for marketing, sales, and customer support. Wiley, New YorkGoogle Scholar
  4. Breiman L, Friedman JH, Olshen RA, Stone PJ (1984) Classification and regression trees. Wadsworth International Group, CaliforniaMATHGoogle Scholar
  5. Cao LJ, Tay FEH (2003) Support vector machine with adaptive parameters in financial time series forecasting. IEEE Trans Neural Netw 14(6):1506–1518CrossRefGoogle Scholar
  6. Charest M, Delisle S, Cervantes O, Shen YF (2008) Bridging the gap between data mining and decision support: a case-based reasoning and ontology approach. Intell Data Anal 12(2):211–236Google Scholar
  7. Chen MS, Han J, Yu PS (1996) Data mining: an overview from a database perspective. IEEE Trans Knowl Data Eng 8(6):866–883CrossRefGoogle Scholar
  8. Chien CF, Chen LF (2007) Using rough set theory to recruit and retain high-potential talents for semiconductor manufacturing. IEEE Trans Semicond Manuf 20(4):528–541CrossRefGoogle Scholar
  9. Chien CF, Wang I, Chen LF (2005) Using data mining to improve the quality of human resource management of operators in semiconductor manufactures. J Qual 12(1):9–28Google Scholar
  10. Chien CF, Wang W, Cheng J (2007) Data mining for yield enhancement in semiconductor manufacturing and an empirical study. Expert Syst Appl 33(1):192–198CrossRefGoogle Scholar
  11. Chien CF, Chen Y, Peng J (2010) Manufacturing intelligence for semiconductor demand forecast based on technology diffusion and product life cycle. Int J Prod Econ 128(2):496–509CrossRefGoogle Scholar
  12. Cho V, Ngai E (2003) Data mining for selection of insurance sales agents. Expert Syst 20(3):123–132CrossRefGoogle Scholar
  13. Choo YH, Abu Bakar A, Hamdan AR (2008) The fitness-rough: a new attribute reduction method based on statistical and rough set theory. Intell Data Anal 12(1):73–87Google Scholar
  14. Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(1):273–297MATHGoogle Scholar
  15. Cristianini N, Shawe-Taylor J (2000) An introduction to support vector machines. Cambridge University Press, CambridgeGoogle Scholar
  16. de Souza JT, Matwin S, Japkowicz N (2006) Parallelizing feature selection. Algorithmica 45(3):433–456MathSciNetMATHCrossRefGoogle Scholar
  17. Fayyad U, Piatesky-Shapiro G, Smyth P (1996) The KDD process for extracting useful knowledge from volumes of data. Commun ACM 39:27–34CrossRefGoogle Scholar
  18. He J, Hu H, Harrison R, Tai PC, Pan Y (2006) Rule generation for protein secondary structure prediction with support vector machines and decision tree. IEEE Trans Nanobiosci 5(1):46–53CrossRefGoogle Scholar
  19. Hooper RS, Galvin TP, Kilmer RA, Liebowitz J (1998) Use of an expert system in a personnel selection process. Expert Syst Appl 14(4):425–432CrossRefGoogle Scholar
  20. Hough LM, Oswald FL (2000) Personnel selection: looking toward the future: remembering the past. Annu Rev Psychol 51:631–664CrossRefGoogle Scholar
  21. Hsu S, Chien CF (2007) Hybrid data mining approach for pattern extraction from wafer bin map to improve yield in semiconductor manufacturing. Int J Prod Econ 107(1):88–103CrossRefGoogle Scholar
  22. Huang LC, Kuo RJ, Huang HJ (2002) A neural network modeling for human resource talent selection. Int J Hum Res Dev Manag 1(2/3/4):206–219Google Scholar
  23. Kass GV (1980) An exploratory technique for investigating large quantities of categorical data. Appl Stat 29(2):119–127CrossRefGoogle Scholar
  24. Kovach KA, Cathcart CE (1999) Human resource information systems (HRIS): providing business with rapid data access, information exchange and strategic advantage. Public Pers Manag 28(2):275–282Google Scholar
  25. Krishnan R, Sivakumar G, Bhattacharya P (1999) Extracting decision trees from trained neural networks. Pattern Recognit 32(12):1999–2009CrossRefGoogle Scholar
  26. Kuo C, Chien CF, Chen C (2010) Manufacturing intelligence to exploit the value of production and tool data to reduce cycle time. IEEE transactions on automation science and engineering (Digital Object Identifier  10.1109/TASE.2010.2040999)
  27. Li P, Wang Z (2004) Mining classification rules using rough sets and neural networks. Eur J Oper Res 157(5–6):439–448MATHCrossRefGoogle Scholar
  28. Lievens F, Van Dam K, Anderson N (2002) Recent trends and challenges in personnel selection. Pers Rev 31:580–601CrossRefGoogle Scholar
  29. Mak B, Munakata T (2002) Rule extraction from expert heuristics: a comparative study of rough sets with neural networks and ID3. Eur J Oper Res 136:212–229MATHCrossRefGoogle Scholar
  30. Mohanty RP, Deshmukh SG (1997) Evolution of a decision support system for human planning in a petroleum company. Int J Prod Econ 51(3):251–261CrossRefGoogle Scholar
  31. Ntuen CA, Chestnut JA (1995) An expert-system for selecting manufacturing workers for training. Expert Syst Appl 9(3):309–332CrossRefGoogle Scholar
  32. Pawlak Z (1982) Rough sets. Int J Inf Comput Sci 11:341–356MathSciNetMATHCrossRefGoogle Scholar
  33. Pawlak Z (1997) Rough sets approach to knowledge-based decision support. Eur J Oper Res 99:48–57MATHCrossRefGoogle Scholar
  34. Pyle D (1999) Data preparation for data mining. Morgan Kaufrnann, San Francisco, CAGoogle Scholar
  35. Quinlan JR (1986) Induction of decision tree. Mach Learn 1(1):81–106Google Scholar
  36. Quinlan JR (1993) C4.5: programs for machine learning. Morgan Kaufmann, San Mateo, CAGoogle Scholar
  37. Robertson IT, Smith M (2001) Personnel selection. J Occup Org Psychol 74(4):441–472CrossRefGoogle Scholar
  38. Shih PC, Liu CJ (2006) Face detection using discriminating feature analysis and support vector machine. Pattern Recognit 39(2):260–276CrossRefGoogle Scholar
  39. Su CT, Yih YW, Chen LS (2006) Knowledge acquisition through information granulation for imbalanced data. Expert Syst Appl 31(3):531–541CrossRefGoogle Scholar
  40. Swiniarski RW, Skowron A (2003) Rough set methods in feature selection and recognition. Pattern Recognit Lett 24(6):833–849MATHCrossRefGoogle Scholar
  41. Tavana M, Kennedy DT, Joglekar P (1996) A group decision support framework for consensus ranking of technical manager candidates. Omega-Int J Manag Sci 24(5):523–538CrossRefGoogle Scholar
  42. Wang S, Dash M, Chia LT (2006) Efficient data reduction in multimedia data. Appl Intell 25:359–374CrossRefGoogle Scholar
  43. Warsaw University (2005) Rough set exploration system, version 2.2, Logic Group, Institute of Mathematics, Warsaw University, Poland, [on line]. Available: http://logic.mimuw.edu.pl/~rses/
  44. Zhan YM, Zeng XY, Sun JC (2005) Rough set-based feature selection method. Prog Nat Sci 15(3):280–284CrossRefGoogle Scholar
  45. Zhong N, Dong J, Ohsuga S (2001) Using rough sets with heuristics for feature selection. J Intell Inf Syst 16:199–214MATHCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.Graduate Program of Business ManagementFu-Jen Catholic UniversityTaipeiTaiwan
  2. 2.Department of Industrial Engineering and Engineering ManagementNational Tsing Hua UniversityHsinchuTaiwan

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