Application of the Taguchi method to improve a medical device cutting process

  • Jorge Limon-Romero
  • Diego Tlapa
  • Yolanda Baez-Lopez
  • Aide Maldonado-Macias
  • Leonardo Rivera-Cadavid
Open Access


Companies currently are immersed in a highly competitive world; therefore, they must adopt continuous process and product improvement techniques to remain in business and retain loyal customers. This paper presents the analysis of a medical device manufacturing company to determine what factors influence the length variation of plastic tubes in a cutting process. These tubes are part of intensive care sets; therefore, their length and diameter are critical features. Recently, products were rejected for not meeting the tube length specification, causing customer complaints regarding product quality. The Taguchi method was applied to find significant factors that determine the best configuration in the cutting process to approximate this quality characteristic to its desired target value. Important factors and their corresponding best levels were identified. After the proposed process adjustment was implemented, the process capability index (Cpk) increased from 0.90 to 1.58, which indicated a considerable reduction in customer complaints and corresponding costs.


Taguchi method Cutting process Medical device Capability improvement 


  1. 1.
    Escanciano C, Iglesias F (2012) Quality management and integrated total quality in Spanish mining: results of an empirical study. DYNA 171:167–174Google Scholar
  2. 2.
    Sitnikov C (2012) Six sigma as a strategic tool for companies. Young Econ J Rev Tinerilor Econ 19:94–102Google Scholar
  3. 3.
    Vijaya SM (2013) Synergies of lean six sigma. IUP J Oper Manag 1:21–31Google Scholar
  4. 4.
    Gil-Gómez H, Oltra-Badenes R, Adarme-Jaimes W (2014) Service quality management based on the application of the ITIL standard. DYNA 186:51–56.
  5. 5.
    Antony J, Capon N (1998) Teaching experimental design techniques to industrial engineers. Int J Eng Educ 5:335–343. doi: 10.1108/00438020110391873 Google Scholar
  6. 6.
    Mathews PG (2005) Design of experiments with Minitab; ASQ Quality press. Milwaukee, USAGoogle Scholar
  7. 7.
    Antony J, Antony FJ (2001) Teaching the Taguchi method to industrial engineers. Work Study 4:141–149. doi: 10.1108/00438020110391873 CrossRefGoogle Scholar
  8. 8.
    Peker M, Sen B, Kumru PY (2012) An efficient solving of the traveling salesman problem: the ant colony system having parameters optimized by the Taguchi method. Turkish J Electric Eng Comput Sci 1:2015–2036. doi: 10.3906/elk-1109-44 Google Scholar
  9. 9.
    Antony J, Perry D, Wang C, Kumar M (2006) An application of Taguchi method experimental design for new product design and development process. Assem Autom 1:18–24. doi: 10.1108/01445150610645611 CrossRefGoogle Scholar
  10. 10.
    Agastra E, Pelosi G, Selleri S, Taddei R (2013) Taguchi’s method for multi-objective optimization problems. Int J RF Microwave Comput Aided Eng 3:357–366. doi: 10.1002/mmce.20680 CrossRefGoogle Scholar
  11. 11.
    Roy RK (2001) Design of experiments using the Taguchi approach: 16 steps to product and process improvement. John Wiley & Sons Inc, New York, USAGoogle Scholar
  12. 12.
    Taguchi G, Jugulum R (2002) The Mahalanobis-Taguchi strategy: a pattern technology system. John Wiley & Sons, New York, USACrossRefGoogle Scholar
  13. 13.
    Gu F, Hall P, Miles NJ, Ding Q, Wu T (2014) Improvement of mechanical properties of recycled plastic blends via optimizing processing parameters using the Taguchi method and principal component analysis. Mater Des 62:189–198. doi: 10.1016/j.matdes.2014.05.013 CrossRefGoogle Scholar
  14. 14.
    Inei-Shizukawa G, Velasco-Bedrán HA, Gutiérrez-López GF, Hernández-Sánchez H (2009) Statistical approach to optimization of ethanol fermentation by saccharomyces cerevisiae in the presence of Valfor® 100 Zeolite Naa. Revista de Ingeniería Química 3:265–270Google Scholar
  15. 15.
    Rico L, Noriega S, Garcia JL, Martinez EA, Ñeco R, Estrada FJ (2010) Effect of the side cutting–edge angle on the surface roughness for aluminum 1350 in the turning operation by Taguchi method. J Appl Res Technol 3:395–405Google Scholar
  16. 16.
    Taguchi G, Chowdhury S, Wu Y (2005) Taguchi’s quality engineering handbook. John Wiley & Sons, New Jersey, USAzbMATHGoogle Scholar
  17. 17.
    Akbarzadeh A, Kouravand S, Imani B (2013) Robust design of a bimetallic micro thermal sensor using Taguchi method. J Optim Theory Appl 1:188–198. doi: 10.1007/s10957-012-0171-x MathSciNetCrossRefzbMATHGoogle Scholar
  18. 18.
    Ramkumar R, Ragupathy A (2013) Optimization of cooling tower performance analysis using Taguchi method. Therm Sci 2:457–470. doi: 10.2298/TSCI110528024R CrossRefGoogle Scholar
  19. 19.
    Senthilkumar N, Ganapathy T, Tamizharasan T (2014) Optimization of machining and geometrical parameters in turning process using Taguchi method. Aust J Mech Eng 2:233–246Google Scholar
  20. 20.
    Deniz F (2013) Optimization of biosorption conditions for color removal by Taguchi DOE methodology. Environmental Progress and Sustainable Energy 32(4):1129–1133. doi: 10.1002/ep.11740 CrossRefGoogle Scholar
  21. 21.
    KoonChun L, SooKing L, Pehchiong T (2015) Optimization of electrostatic separation process for maximizing biowaste recovery using Taguchi method and ANOVA. Polish Journal of Environmental Studies 3:1125–1131. doi: 10.15244/pjoes/30927Google Scholar
  22. 22.
    Chil-Chyuan K, Hsin-You L (2015) Dimensional accuracy optimization of the micro-plastic injection molding process using the Taguchi design method. Mater Sci Medziagotyra 2:244–248. doi: 10.5755/ Google Scholar
  23. 23.
    Sun Y, Zuo D, Zhu Y, Li J (2013) Using Taguchi method to optimize polishing parameters in ice fixed abrasive polishing. Mater Manuf Process 28:923–927. doi: 10.1080/10426914.2013.792419 CrossRefGoogle Scholar
  24. 24.
    Montgomery DC (2009) Design and analysis of experiments, 7th edn. John Wiley & Sons, New Jersey, USAGoogle Scholar
  25. 25.
    Co HC (2008) Confirmation testing of the Taguchi methods by artificial neural-networks simulation. Int J Prod Res 17:4671–4685. doi: 10.1080/00207540701213502 CrossRefzbMATHGoogle Scholar
  26. 26.
    Chun-Liang L, Yi-Shun C, Yi-Hua L, Yeh-Hsiang H, Shu-Syuan H (2013) Optimization of a fuzzy-logic-control-based five-stage battery charger using a fuzzy-based Taguchi method. Energies 7:3528–3547. doi: 10.3390/en6073528 Google Scholar
  27. 27.
    Recioui A (2013) Application of hybrid Taguchi-genetic algorithm to the multiobjective design optimization of Yagi-Uda Atennas. Wirel Pers Commun 2:1403–1420. doi: 10.1007/s11277-012-0882-1 CrossRefGoogle Scholar
  28. 28.
    Recioui A, Bentarzi H (2013) Capacity optimization of MIMO wireless communications systems using a hybrid genetic-Taguchi algorithm. Wirel Pers Commun 2:1003–1019. doi: 10.1007/s11277-012-0857-2 CrossRefGoogle Scholar
  29. 29.
    Chao-Lieh Y, Kun-Tzu Y (2013) Multi-objective optimization of glass fiber cutting process by applying the fuzzy-based Taguchi method. Int J Reliab Qual Saf Eng 2:1–17. doi: 10.1142/S0218539313500083 Google Scholar
  30. 30.
    Canessa E, Bielenberg G, Allende H (2014) Robust design in multiobjective systems using Taguchi’s parameter design approach and a Pareto genetic algorithm. Revista Facultad de Ingeniería Universidad de Antioquia 72:73–86Google Scholar

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© The Author(s) 2016

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  • Jorge Limon-Romero
    • 1
  • Diego Tlapa
    • 1
  • Yolanda Baez-Lopez
    • 1
  • Aide Maldonado-Macias
    • 2
  • Leonardo Rivera-Cadavid
    • 3
  1. 1.Facultad de Ingeniería, Arquitectura y DiseñoUniversidad Autónoma de Baja CaliforniaEnsenadaMexico
  2. 2.Department of Industrial and Manufacturing EngineeringUniversidad Autónoma de Ciudad JuarezJuarezMexico
  3. 3.School of Industrial EngineeringUniversidad del Valle, Ciudad Universitaria MeléndezCaliColombia

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