DOI QR코드

DOI QR Code

Quantifying the Technology Level of Production System for Technology Transfer

  • Received : 2011.01.09
  • Accepted : 2011.04.12
  • Published : 2011.06.01

Abstract

This paper develops a technology level quantification (TLQ) model by utilizing a learning curve. Original learning curve shows the relationship between cumulative number of units and the required time for the unit. On the other hand, in our developed model, the technology level, such as speed of production and quality of the produced items, is expressed as a function of not cumulative number of units but time, for increasing generality. Furthermore, for expressing each learning that consists of conceptual learning and operational learning, S-curve is utilized in our developed model. By fitting the S-curve and/or decomposing into some activities, our TQL model can be applied to approximate organizational and complicated process. Some variations in time and levels, parameters of our developed model are shown. By using the parameters, the procedure to identify our developed model is proposed. Also, the influential factors for the parameters of our developed model are discussed with classifying the factors into technoware, infoware, humanware, and orgaware. The expected technology level is utilized for expecting the capacity of production system, and the expected capacity can be utilized in predicting various changes in the organization and deciding managerial decision about TT. A case study in manufacturing industry shows the effectiveness of the developed model.

Keywords

References

  1. Anderson Jr., E. G. (2001), Managing the impact of high market growth and learning on knowledge worker productivity and service quality, European J. Operational Research, 134, 508-524. https://doi.org/10.1016/S0377-2217(00)00273-3
  2. Andrade, M. C., Filho, R. C. P., Espozel, A. M., Maia, L. O. A., and Qassim, R. Y. (1999), Activity-based costing for production learning, Int. J. Production Economics, 62, 175-180. https://doi.org/10.1016/S0925-5273(97)00136-9
  3. Armbruster, D., Gel, E. S., and Murakami, J. (2007), Bucket brigades with worker learning, European J. Operational Research, 176, 264-274. https://doi.org/10.1016/j.ejor.2005.06.052
  4. Badiru, A. B. (1992), Computational survey of univariate and multivariate learning curve models, IEEE Trans. Engineering Management, 39, 176-188.
  5. Carr, G. W. (1946), Peacetime cost estimating requires new learning curves, Aviation, 45, 76-77.
  6. Corbett, C. J., Blackburn, J. D., and Van Wassenhove, L. N. (1999), Partnerships to improve supply chain, Sloan Manage. Review, 40, 71-82.
  7. Jaber, M. Y. and Bonney, M. (2003), Lot sizing with learning and forgetting in set-ups and in product quality, Int. J. Production Economics, 83, 95-111. https://doi.org/10.1016/S0925-5273(02)00322-5
  8. Jaber, M. Y., Bonney, M., Guiffrida, A. L. (2010), Coordinating a three-level supply chain with learning-based continuous improvement, Int. J. Production Economics, 127, 27-38. https://doi.org/10.1016/j.ijpe.2010.04.010
  9. Jaber, M. Y., Bonney, M., and Moualek, I. (2009), Lot sizing with learning, forgetting and entropy cost, Int. J. Production Economics, 118, 19-25. https://doi.org/10.1016/j.ijpe.2008.08.006
  10. Jaber, M. Y. and Khan, M. (2010), Managing yield by lot splitting in a serial production line with learning, rework and scrap, Int. J. Production Economics, 124, 32-39. https://doi.org/10.1016/j.ijpe.2009.09.004
  11. Jaber, M. Y. and Kher, H. V. (2002), The dual-phase learning-forgetting model, Int. J. Production Economics, 76, 229-242. https://doi.org/10.1016/S0925-5273(01)00169-4
  12. Jaber, M. Y. and Sikström, S. (2004), A numerical comparison of three potential learning and forgetting models, Int. J. Production Economics, 92, 281-294. https://doi.org/10.1016/j.ijpe.2003.10.019
  13. Kahen, G. (1995), Assessment of information technology for developing countries: Appropriateness, local constraints, IT characteristics and impacts, Int. J. Computer Applications in Technology, 5, 325-332.
  14. Kim, D. H. (1993), The link between individual and organizational learning, Sloan Manage. Rev., 35, 37-50.
  15. Lapre, M. A., Mukherjee, A. S., and Van Wassenhove, L. N. (2000), Behind the learning curve: Linking learning activities to waste reduction, Manage. Science, 46, 597-611.
  16. Lapre, M. A. and Van Wassenhove, L. N. (2001), Creating and transferring knowledge for productivity improvement in factories, Manage, Science. 47, 1311-1325. https://doi.org/10.1287/mnsc.47.10.1311.10264
  17. Lapre, M. A. and Van Wassenhove, L. N. (2003), Managing learning curves in factories by creating and transferring knowledge, California Manage. Rev., 46, 53-71. https://doi.org/10.2307/41166231
  18. Li, G. and Rajagopalan, S. (1998), A learning curve model with knowledge depreciation, European J. Operational Research, 105, 143-154. https://doi.org/10.1016/S0377-2217(97)00033-7
  19. Lieven, L., Demeester, L. L., and Qi, M. (2005), Managing learning resources for consecutive product generations, Int. J. Production Economics, 95, 265-283. https://doi.org/10.1016/j.ijpe.2004.01.005
  20. Mukherjee, A. S., Lapre, M. A., and Van Wassenhove, L. N. (1998), Knowledge driven quality improvement, Manage. Science, 44, S35-S49. https://doi.org/10.1287/mnsc.44.11.S35
  21. Ngwenyama, O., Guergachi, A., and McLaren, T. (2007), Using the learning curve to maximize IT productivity: A decision analysis model for timing software upgrades, Int. J. Production Economics, 105, 524-535. https://doi.org/10.1016/j.ijpe.2006.02.013
  22. Plaza, M., Ngwenyama, O. K., and Rohlf, K. (2010), A comparative analysis of learning curves: Implications for new technology implementation management, European J. Operational Research, 200, 518-528. https://doi.org/10.1016/j.ejor.2009.01.010
  23. Plaza, M. and Rohlf, K. (2008), Learning and perfor-mance in ERP implementation projects: A learn-ing-curve model for analyzing and managing consult-ing costs, Int. J. Production Economics, 115, 72-85. https://doi.org/10.1016/j.ijpe.2008.05.005
  24. Tarakci, H., Tang, K., and Teyarachakul, S. (2009), Learning effects on maintenance outsourcing, European J. Operational Research, 192, 138-150. https://doi.org/10.1016/j.ejor.2007.09.016
  25. Terwiesch, C. and Bohn, R. E. (2001), Learning and process improvement during production ramp-up, Int. J. Production Economics, 70, 1-19. https://doi.org/10.1016/S0925-5273(00)00045-1
  26. Vigil, D. P. and Sarper, H. (1994), Estimating the effects of parameter variability on learning curve model predictions, Int. J. Production Economics, 34, 187-200. https://doi.org/10.1016/0925-5273(94)90035-3
  27. Wright, T. P. (1936), Factors affecting the cost of airplanes, J. Aeronautical Sciences, 3, 122-128. https://doi.org/10.2514/8.155

Cited by

  1. A Comparative Study of the Effect of University Competence on Technology Transfer and Commercialization and Start-ups vol.40, pp.5, 2014, https://doi.org/10.7232/JKIIE.2014.40.5.462