IE interfaces (산업공학)

Korean Institute of Industrial Engineers (대한산업공학회)
권호 표지
DOI QR코드

DOI QR Code

An Analytic Network Process(ANP) Approach to Forecasting of Technology Development Success : The Case of MRAM Technology

네트워크분석과정(ANP)을 이용한 기술개발 성공 예측 : MRAM 기술을 중심으로

  • Jeon, Jeong-Hwan (Department of Industrial and Systems Engineering, GyeongSang National University) ;
  • Cho, Hyun-Myung (Korea Railroad Research Institute) ;
  • Lee, Hak-Yeon (The Graduate School of Public Policy and Information Technology, Seoul National University of Science and Technology)
  • 전정환 (경상대학교 산업시스템공학부) ;
  • 조현명 (한국철도기술연구원) ;
  • 이학연 (서울과학기술대학교 IT정책전문대학원)
  • Received : 2011.04.22
  • Accepted : 2012.02.01
  • Published : 2012.09.01
Download PDF
⟨ Previous Next ⟩

Abstract

Forecasting probability or likelihood of technology development success has been a crucial factor for critical decisions in technology management such as R&D project selection and go or no-go decision of new product development (NPD) projects. This paper proposes an analytic network process (ANP) approach to forecasting of technology development success. Reviewing literature on factors affecting technology development success has constructed the ANP model composed of four criteria clusters : R&D characteristics, R&D competency, technological characteristics, and technological environment. An alternative cluster comprised of two elements, success and failure is also included in the model. The working of the proposed approach is provided with the help of a case study example of MRAM (magnetic random access memory) technology.

Keywords

References

  1. Astebro, T. (2004), Key success factors for technological entrepreneurs' R&D projects, IEEE Transactions on Engineering Management, 51(3), 314-321. https://doi.org/10.1109/TEM.2004.830863
  2. Balachandra, R. and Friar, J. (1997), Factors for success in R&D projects and new product innovation : a contextual framework, IEEE Transactions on Engineering Management, 44, 276-287. https://doi.org/10.1109/17.618169
  3. Barclay, I. and Dann, Z. (2000), New-product- development performance evaluation : A product-complexity-based methodology, IEEE Proceedings, Science, Measurement and Technology, 147(2), 41-55. https://doi.org/10.1049/ip-smt:20000077
  4. Belton, V. and Goodwin, P. (1996), Remarks on the application of the analytic hierarchy process to judgmental forecasting, International journal of forecasting, 12(1), 155-161. https://doi.org/10.1016/0169-2070(95)00643-5
  5. Carbonell, P., Escudero, A. R., and Aleman, J. M. (2004), Technology newness and impact of go/no-go criteria on new product success, Marketing Letters, 15(2-3), 81-97. https://doi.org/10.1023/B:MARK.0000047386.33282.51
  6. Coldrick, S., Longhurst, P., Ivey, P., and Hannis, J. (2005), An R&D options selection model for investment decisions, Technovation, 25(3), 185-193. https://doi.org/10.1016/S0166-4972(03)00099-3
  7. Cooper, R. G. (1981), An empirically derived new product project selection model, IEEE Transactions on Engineering Management, 28, 54-61.
  8. Cooper, R. G. and Kleinschmidt, E. J. (1987), New products : What separates winners from losers?, Journal of Product Innovation Management, 4(3), 169-184. https://doi.org/10.1016/0737-6782(87)90002-6
  9. Cooper, R. G. (1999), From experience : The invisible success factors in product innovation, Journal of Product Innovation and Management, 16(2), 115-133. https://doi.org/10.1016/S0737-6782(98)00061-7
  10. Cooper, R. G., Edgett, S. J., and Kleinschmidt, E. J. (2001), Portfolio management for new product development : Results of an industry practices study, R&D management, 31(4), 361-380. https://doi.org/10.1111/1467-9310.00225
  11. Galbraith, C. S., Erhlich, S. B., and DeNoble, A. F. (2006), Predicting technology success : identifying key predictors and assessing expert evaluation for advanced technologies, Journal of Technology Transfer, 31, 673-684. https://doi.org/10.1007/s10961-006-0022-8
  12. Grynkewich, G., Akerman, J., Brown, P., Butcher, B., Dave, R. W., DeHerrera, M., Durlam, M., Engel, B. N., Janesky, J., Pietambaram, S., Rizzo, N. D., Slaughter, J. M., Smith, K. Sun, J. J., and Tehrani, S. (2004), MRS Bulletin, 29, 818. https://doi.org/10.1557/mrs2004.234
  13. Griffin A. (1997), Modeling and measuring product development cycle time across industries, Journal of Engineering and Technology Management, 14(1), 1-24. https://doi.org/10.1016/S0923-4748(97)00004-0
  14. Heslop, L. A., McGregor, E., and Griffith, M. (2001), Development of a technology readiness assessment measure : the cloverleaf model of technology transfer, Journal of Technology Transfer, 26, 369-384. https://doi.org/10.1023/A:1011139021356
  15. Iamratanakul, S., Patanakul, P., and Milosevic, D. (2008), Innovation and factors affecting the success of NPD projects : Literature explorations and descriptions, International journal of management science and engineering management, 3(3), 176-189.
  16. Kim, K. and Lee. S. Y. (2004), Future emerging new memory technologies, Integrated Ferroelectrics, 64, 3-14. https://doi.org/10.1080/10584580490893277
  17. Liberatore, M. J. and Titus, G. J. (1983), The practice of management science n R&D project management, Management Science, 29(8), 962-974. https://doi.org/10.1287/mnsc.29.8.962
  18. Lee, H., Lee, S., and Park, Y. (2009), Selection of technology acquisition mode using the analytic network process, Mathematical and Computer Modelling, 49(5/6), 1274-1282. https://doi.org/10.1016/j.mcm.2008.08.010
  19. Maidique, M. A. and Zirger, B. J. (1985), The new product learning cycle, Research Policy, 14, 299-313. https://doi.org/10.1016/0048-7333(85)90001-0
  20. Meade, L. M. and Presely, A. (2002), R&D project selection using the analytic network process, IEEE Transction on Engineering Management, 49(1), 59-66. https://doi.org/10.1109/17.985748
  21. Mohanty, R. P., Agarwal, R., Choudhury, A. K., and Tiwari, M. K. (2005), A fuzzy ANP-based approach to R&D project selection : A case study, International Journal of Production Research, 43(24), 5199-5216. https://doi.org/10.1080/00207540500219031
  22. Montoya-Weiss, M. and Calantone, R. (1994), Determinants of New Product Performance : A Review and Meta-Analysis, Journal of Product Innovation Management, 11(5), 397-417. https://doi.org/10.1016/0737-6782(94)90029-9
  23. Niemira M. P. and Saaty T. L. (2004), An analytic network process model for financial-crisis forecasting, International Journal of Forecasting, 20, 573-587. https://doi.org/10.1016/j.ijforecast.2003.09.013
  24. Rabino, S. and Wright, A. (1993), Accelerated product introductions and emerging managerial accounting perspectives : Implications for marketing managers in the technology sector, Journal of Product Innovation Management, 10(2), 126-135. https://doi.org/10.1016/0737-6782(93)90004-A
  25. Saaty, T. L. (1996), Decision Making with Dependence and Feedback : The Analytic Network Process. Pittsburgh, PA : RWS Publications.
  26. Sun, H. and Wing, W. C. (2005), Critical success factors for new product development in the Hong Kong toy industry, Technovation, 25(3), 293-303. https://doi.org/10.1016/S0166-4972(03)00097-X