Skip to main content
Log in

Computer-aided design of experiments: an enabler of agile manufacturing

  • ORIGINAL ARTICLE
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

The ever-rising competition forces the modern organisations to provide variety of products and services that would suit individual customers within a very short period. The organisations which are able to meet this competitive strategy are said to have progressed towards agile manufacturing (AM). Modern researchers are striving to evolve enablers that would help the traditional manufacturing organisations to march towards AM goals. In this direction, this paper points out that the synergic power of two approaches, namely design of experiments (DOE) and computer-aided design (CAD), are inadequately exploited by both researchers and practitioners to achieve AM. In this context, the terminology computer-aided DOE (CADOE) is proposed. An implementation study is presented which revealed that CADOE would facilitate time compression and enhance accuracy, which are the major enablers of achieving AM.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Agrawal RK, Hurriyet H (2004) The advent of manufacturing technology and its implications for the development of the value chain. Int J Phys Distrib Log Manage 34:319–336 doi:10.1108/09600030410533619

    Article  Google Scholar 

  2. Duguay CR, Landry S, Pasin F (1997) From mass production to flexible/agile production. Int J Oper Prod Manage 17:1183–1195 doi:10.1108/01443579710182936

    Article  Google Scholar 

  3. Worley JM, Doolen TL (2006) The role of communication and management support in a lean manufacturing implementation. Manage Decis 44:228–245 doi:10.1108/00251740610650210

    Article  Google Scholar 

  4. Sullivan WG, McDonald TN, Van Aken EM (2002) Equipment replacement decisions and lean manufacturing. Robot Comput-Int Manuf 18:255–265 doi:10.1016/S0736-5845(02)00016-9

    Article  Google Scholar 

  5. Sarkis J (2001) Benchmarking for agility. Benchmarking Int J 8:88–107

    Article  Google Scholar 

  6. Meredith S, Francis D (2000) Journey towards agility: the agile wheel explored. TQM Mag 12:137–143 doi:10.1108/09544780010318398

    Article  Google Scholar 

  7. Brown S, Bessant J (2003) The manufacturing strategy-capabilities links in mass customisation and agile manufacturing—an exploratory study. Int J Oper Prod Manage 23:707–730 doi:10.1108/01443570310481522

    Article  Google Scholar 

  8. Vazquez-Bustelo D, Avella L (2006) Agile manufacturing: industrial case studies in Spain. Technovation 26:1147–1161 doi:10.1016/j.technovation.2005.11.006

    Article  Google Scholar 

  9. Yusuf YY, Sarhadi M, Gunasekaran A (1999) Agile manufacturing: the drivers, concepts and attributes. Int J Prod Econ 62:33–43 doi:10.1016/S0925-5273(98)00219-9

    Article  Google Scholar 

  10. van Assen MF (2000) Agile-based competence management: the relation between agile manufacturing and time-based competence management. Int J Agile Manage Syst 2:142–155 doi:10.1108/14654650010337168

    Article  Google Scholar 

  11. Simchi-Levi D, Kaminsky P, Simchi-Levi E (2005) Designing and managing the supply chain-concepts, strategies and case studies. Tata McGraw-Hill, New Delhi

    Google Scholar 

  12. Cho H, Jung M, Kim M (1996) Enabling techniques of agile manufacturing and its related activities in Korea. Comput Ind Eng 30:323–334 doi:10.1016/0360-8352(96)00001-0

    Article  Google Scholar 

  13. Antony J (2001) Improving the manufacturing process quality using design of experiments: a case study. Int J Oper Prod Manage 21:812–822 doi:10.1108/01443570110390499

    Article  Google Scholar 

  14. Li CC, Sheu TS, Wang YR (1997) Some thoughts on the evolution of quality engineering. Ind Manage Data Syst 97:153–157 doi:10.1108/02635579710173239

    Article  Google Scholar 

  15. Foster ST Jr, Gallup L (2002) On functional differences and quality understanding. Benchmarking Int J 9:86–102

    Article  Google Scholar 

  16. Rogers JS, Farrington PA, Schroer BJ, Hubbard RG (1994) Automated process planning system for turned parts. Int Manuf Syst 5:41–47 doi:10.1108/09576069410069530

    Article  Google Scholar 

  17. Lin YJ, Uhler A (2002) Shortening the design for assemble process time for torque converter development. Assembly Autom 22:248–259 doi:10.1108/01445150210436464

    Article  Google Scholar 

  18. Jin-Hai L, Anderson AR, Harrison RT (2003) The evolution of agile manufacturing. Bus Process Manage J 9:170–189 doi:10.1108/14637150310468380

    Article  Google Scholar 

  19. Dowlatshahi S, Cao Q (2006) The relationships among virtual enterprise, information technology, and business performance in agile manufacturing: an industry perspective. Eur J Oper Res 174:835–860 doi:10.1016/j.ejor.2005.02.074

    Article  MATH  Google Scholar 

  20. Vokurka RJ, Fliedner G (1998) The journey toward agility. Ind Manage Data Syst 98:165–171 doi:10.1108/02635579810219336

    Article  Google Scholar 

  21. Fabricius F (1994) A seven step procedure for design for manufacture. World Class Des Manuf 1:23–30 doi:10.1108/09642369210054243

    Article  Google Scholar 

  22. Juran JM, Gryna FM (1995) Quality planning and analysis. Tata McGraw Hill, India

    Google Scholar 

  23. Borissova A, Fairweather M, Goltz GE (2006) Combinatorial process and plant design for agile manufacture. Res Eng Des 17:1–12 doi:10.1007/s00163-006-0013-7

    Article  Google Scholar 

  24. Lee GH (1998) Design of components and manufacturing systems for agile manufacturing. Int J Prod Res 36:1023–1044 doi:10.1080/002075498193507

    Article  MATH  Google Scholar 

  25. Devadasan SR, Goshteeswaran S, Gokulachandran J (2005) Design for quality in agile manufacturing environment through modified orthogonal array-based experimentation. J Manuf Tech Manage 16:576–597 doi:10.1108/17410380510609456

    Article  Google Scholar 

  26. Shoemaker J (2006) Moldflow design guide—a resource for plastics engineers. Hanser, USA

    Google Scholar 

  27. Flynn BB, Schroeder RG, Flynn EJ, Sakakibara S, Bates KA (1997) World-class manufacturing project: overview and selected results. Int J Oper Prod Manage 17:671–685 doi:10.1108/01443579710175592

    Article  Google Scholar 

  28. Kumar A, Motwani J (1995) A methodology for assessing time-based competitive advantage of manufacturing firms. Int J Oper Prod Manage 15:36–53 doi:10.1108/01443579510080409

    Article  Google Scholar 

  29. Lin C-T, Chiu H, Tseng Y-H (2006) Agility evaluation using fuzzy logic. Int J Prod Econ 101:353–368 doi:10.1016/j.ijpe.2005.01.011

    Article  Google Scholar 

  30. Dowlatshahi S (2004) An application of design of experiments for optimization of plastic injection molding processes. J Manuf Tech Manage 15:445–454 doi:10.1108/17410380410547852

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to S. Vinodh.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Vinodh, S., Sundararaj, G., Devadasan, S.R. et al. Computer-aided design of experiments: an enabler of agile manufacturing. Int J Adv Manuf Technol 44, 940–954 (2009). https://doi.org/10.1007/s00170-008-1903-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-008-1903-9

Keywords

Navigation