A Knowledge Workers Acquisition Problem under Expanding and Volatile Demand: An Application of the Korean Information Security Service Industry

  • Park, Hyun-Min (Department of Business Administration, Pai Chai University) ;
  • Lim, Dae-Eun (Mechatronics and Manufacturing Technology Center, Samsung Electronics) ;
  • Kim, Tae-Sung (Department of Management Information Systems, Chungbuk National University) ;
  • Kim, Kil-Hwan (Internet Research Lab. Electronics and Telecommunications Research Institute) ;
  • Kim, Soo-Hyun (Department of Business Administration, Pai Chai University)
  • 투고 : 2010.12.01
  • 심사 : 2011.03.10
  • 발행 : 2011.05.31

초록

The aim of this paper is to consider the process of supplying trained workers with knowledge and skills for upcoming business opportunities and the process of training apprentices to be prepared to meet future demands in an IT service firm. As the demand for new workers fluctuates, a firm should employ a buffer workforce such as apprentices or interns. However, as a result of rapid business development, the capacity of the buffer may be exceeded, thus requiring the company to recruit skilled workers from outside the firm. Therefore, it is important for a firm to map out a strategy for manpower planning so as to fulfill the demands of new business and minimize the operation costs related to training apprentices and recruiting experienced workers. First, this paper analyzes the supply and demand of workers for the IT service in a knowledge-intensive field. It then presents optimal human resource planning strategies via the familiar method of stochastic process. Also, we illustrate that our model is applied to the human resource planning of an information security service firm in South Korea.

키워드

참고문헌

  1. Anderson, E. G., "Managing the impact of high market growth and learning on knowledge worker productivity and service quality," European Journal of Operational Research 134, 3 (2001a), 508-524. https://doi.org/10.1016/S0377-2217(00)00273-3
  2. Anderson, E. G., "The stationary staff‐planning problem with business cycle and learning effects," Management Science 47, 6 (2001b), 817-832. https://doi.org/10.1287/mnsc.47.6.817.9815
  3. Bartholomew, D. J., Stochastic Models for Social Processes, (2nd ed.), New York: John Wiley, 1973.
  4. Bartholomew, D. J. and A. F. Forbes, Statistical Techniques for Manpower Planning, New York: John Wiley, 1979.
  5. Campbell, G. M., "Cross‐utilization of workers whose capabilities differ," Management Science 45, 5 (1999), 722-732. https://doi.org/10.1287/mnsc.45.5.722
  6. Cappelli, P., "Talent management for the twenty‐first century," Harvard Business Review (March, 2008a), 74-81.
  7. Cappelli, P., Talent on Demand: Managing Talent in an Age of Uncertainty, Boston: Harvard Business Press, 2008b.
  8. De Feyter, T., "Modeling heterogeneity in manpower planning: Dividing the personnel system into more homogeneous subgroups," Applied Stochastic Models in Business and Industry 22, 4 (2006), 321-334. https://doi.org/10.1002/asmb.619
  9. Grinold, R. C., "Manpower planning with uncertain requirements," Operations Research 24, 3 (1976), 387-399. https://doi.org/10.1287/opre.24.3.387
  10. Grinold, R. C. and K. T. Marshall, Manpower Planning Models, New York: North‐Holland, 1977.
  11. Grinold, R. C. and R. E. Stanford, Optimal control of a graded manpower system. Management Science 20, 8 (1974), 1201-1216. https://doi.org/10.1287/mnsc.20.8.1201
  12. Jun, H. J., T. S. Kim, J. H. Yoo, and S. H. Gee, "Development of skills framework for information security workforce," Journal of the Korea Institute of Information Security and Cryptology 19, 3 (2009), 143-152 (in Korean).
  13. Knowledge Information Security Industry Association, Knowledge Information Security Industry Markets and Trends in Korea: Year 2009, http://www.kisia.or.kr/new/korean/sub/data04_01.php, 2010 (in Korean).
  14. Martel, A. and W. Price, "Stochastic planning applied to human resource planning," Journal of the Operational Research Society 32, 3 (1981), 185-196.
  15. Price, W., "Solving goal‐programming manpower models using advanced network codes," Journal of the Operational Research Society 29, 12 (1978), 1231-1239. https://doi.org/10.1057/jors.1978.267
  16. Price, W., A. Martel, and K. Lewis, "A review of mathematical models in human resource planning," Omega 8, 6 (1980), 639-645. https://doi.org/10.1016/0305-0483(80)90005-5
  17. Purkiss, C., "Corporate manpower planning: a review of models," European Journal of Operational Research 8, 4 (1981), 315-323. https://doi.org/10.1016/0377-2217(81)90001-1
  18. Sennott, L. I., M. P. Van Oyen, and S. M. R. Iravani, "Optimal dynamic assignment of a flexible worker on an open production line with specialists," European Journal of Operational Research 170, 2 (2006), 541-566. https://doi.org/10.1016/j.ejor.2004.06.030
  19. Wolff, R. W., Stochastic Modeling and the Theory of Queues, New Jersey: Prentice Hall, 1989.
  20. Zanakis, S. H. and M. W. Maret, "A Markovian goal programming approach to aggregate manpower planning," Journal of the Operational Research Society 32, 1 (1981), 55-63. https://doi.org/10.1057/jors.1981.8