Operational Excellence in Pharmaceutical Quality Control Labs: Driver of an Effective Quality System

  • Stephan KöhlerEmail author
  • Thomas Friedli
  • Prabir Basu
Research Article



Scholars often associate Operational Excellence (OPEX) enablers with high operational performance. Several studies have concluded that advancing OPEX enablers helps improve organizational performance. This paper aims to better understand the relation between OPEX enablers and a robust quality control lab. Integral to pharmaceutical quality systems, robust quality control labs ensure effective drug provision to patients. We examine how implementing OPEX enablers in quality control labs contributes to strengthening quality effectiveness a pharmaceutical manufacturing organization.


The analysis was based on a database of the University of St.Gallen comprising 53 pharmaceutical quality control labs. An independent t-test and multiple linear regressions were used to study the relation between OPEX enablers and quality control lab effectiveness.


The results show that highly effective quality control labs exhibit significantly higher OPEX enabler implementation. Thirty-five percent of the variance in lab performance can be explained by enabler implementation.


High OPEX enabler implementation contributes to robust quality control labs achieving high-level quality and service. Organizations thus attain effective and efficient drug release.


Empirical study Enablers Quality control lab Operational excellence Pharmaceutical quality system 



We particularly wish to thank Nuala Calnan, PhD, for her feedback throughout the research.

Funding Information

This research was conducted as part of the US Food and Drug Administration (FDA) research grant 1U01FD005675-01 awarded to the University of St.Gallen.


  1. 1.
    McKinsey & Company, “Evolving beyond global regulators: an operational lens.” 2013.Google Scholar
  2. 2.
    FDA, “Drug shortages.” 2017.Google Scholar
  3. 3.
    ISPE and PEW, “Drug Shortages,” 2017.Google Scholar
  4. 4.
    Friedli T, Köhler S, Buess P. Cultural excellence as the foundation for effectiveness of the pharmaceutical quality system. Pharma Focus Asia. 2017;8(28):40–3.Google Scholar
  5. 5.
    FDA, “Pharmaceutical quality control labs.” 1993.Google Scholar
  6. 6.
    May M. Leaning the quality control laboratory: Pharmaceutical Manufacturing; 2014.Google Scholar
  7. 7.
    T. Friedli, S. Köhler, P. Buess, P. Basu, and N. Calnan, “FDA quality metrics research final report,” 2017.Google Scholar
  8. 8.
    FDA, “Submission of quality metrics data—guidance for industry draft.” 2016.Google Scholar
  9. 9.
    FDA, “Request for quality metrics—guidance for industry.” 2015.Google Scholar
  10. 10.
    Yu LX, Kopcha M. The future of pharmaceutical quality and the path to get there. Int J Pharm. 2017;528(1–2):354–9.CrossRefPubMedGoogle Scholar
  11. 11.
    Porsche Consulting, “Operational excellence in the pharmaceutical industry survey 2013.” 2013.Google Scholar
  12. 12.
    Pettersen J. Defining lean production: some conceptual and practical issues. TQM J. 2009;21(2):127–42.CrossRefGoogle Scholar
  13. 13.
    Shingo S. Non-stock production: the Shingo system for continuous improvement. Cambridge: Productivity Press; 1988.Google Scholar
  14. 14.
    Imai M. Kaizen: the key to Japan’s competitive success. New York: McGraw-Hill; 1986.Google Scholar
  15. 15.
    Feld WM. Lean manufacturing: tools, techniques, and how to use them. Boca Raton: CRC Press; 2000.CrossRefGoogle Scholar
  16. 16.
    Womack JP, Jones DT, Roos D. The machine that changed the world. New York: Macmillan Publishing Company; 1990.Google Scholar
  17. 17.
    Hines P, Holweg M, Rich N. Learning to evolve: a review of contemporary lean thinking. Int J Oper Prod Manag. 2004;24(10):994–1011.CrossRefGoogle Scholar
  18. 18.
    Hayes RH, Wheelwright SC. Competing through manufacturing. Harv Bus Rev. 1985;63(1):99–109.Google Scholar
  19. 19.
    J. Barbarite and R. Maslaton, “Managing efficiency in a quality organization,” Pharmaceutical Processing, 2008.Google Scholar
  20. 20.
    Cua KO, McKone-Sweet K, Schroeder RG. Improving performance through an integrated manufacturing program. Qual Manag J. 2006;13(3):45–60.CrossRefGoogle Scholar
  21. 21.
    Jochimsen B, Napier NK. Organizational culture, performance, and competitive advantage: what next? In: Wilkinson TJ, Kannan VR, editors. Strategic Management in the 21st Century. 2nd ed. Santa Barbara: Praeger; 2013. p. 233–54.Google Scholar
  22. 22.
    Furlan A, Vinelli A, Dal Point G. Complementarity and lean manufacturing bundles: an empirical analysis. Int J Oper Prod Manag. 2011;31(8):835–50.CrossRefGoogle Scholar
  23. 23.
    Shah R, Ward PT. Lean manufacturing: context, practice bundles, and performance. J Oper Manag. 2003;21(2):129–49.CrossRefGoogle Scholar
  24. 24.
    Friedli T, Bellm D. OPEX: a definition. In: Friedli T, Basu P, Bellm D, Werani J, editors. Leading pharmaceutical operational excellence. Berlin: Springer; 2013. p. 7–26.CrossRefGoogle Scholar
  25. 25.
    White RE, Pearson JN, Wilson JR. JIT manufacturing: a survey of implementations in small and large U.S. manufacturers. Manag Sci. 1999;45(1):1–15.CrossRefGoogle Scholar
  26. 26.
    Cua KO, McKone KE, Schroeder RG. Relationships between implementation of TQM, JIT, and TPM and manufacturing performance. J Oper Manag. 2001;19(6):675–94.CrossRefGoogle Scholar
  27. 27.
    Ahmad S, Schroeder RG, Sinha KK. The role of infrastructure practices in the effectiveness of JIT practices: implications for plant competitiveness. J Eng Technol Manag. 2003;20(3):161–91.CrossRefGoogle Scholar
  28. 28.
    Challis D, Samson D, Lawson B. Impact of technological, organizational and human resource investments on employee and manufacturing performance: Australian and New Zealand evidence. Int J Prod Res. 2005;43(1):81–107.CrossRefGoogle Scholar
  29. 29.
    Matsui Y. An empirical analysis of just-in-time production in Japanese manufacturing companies. Int J Prod Econ. 2007;108(1–2):153–64.CrossRefGoogle Scholar
  30. 30.
    Shah R, Ward PT. Defining and developing measures of lean production. J Oper Manag. 2007;25(4):785–805.CrossRefGoogle Scholar
  31. 31.
    Gebauer H, Kickuth M, Friedli T. Lean management practices in the pharmaceutical industry. Int J Serv Oper Manag. 2009;5(4):463–85.Google Scholar
  32. 32.
    Chen Z, Tan KH. The impact of organization ownership structure on JIT implementation and production operations performance. Int J Oper Prod Manag Ind Manag. 2013;33(9):1202–29.CrossRefGoogle Scholar
  33. 33.
    Friedli T, Bellm D, Werani J, Basu P. Leading pharmaceutical operational excellence. Berlin: Springer; 2013.CrossRefGoogle Scholar
  34. 34.
    Pegels CC. The Toyota production system—lessons for American management. Int J Oper Prod Manag. 1984;4(1):3–11.CrossRefGoogle Scholar
  35. 35.
    Lee SM, Ebrahimpour M. Just-in-time production system: some requirements for implementation. Int J Oper Prod Manag. 1984;4(4):3–15.CrossRefGoogle Scholar
  36. 36.
    Voss CA, Robinson SJ. Application of just-in-time manufacturing techniques in the United Kingdom. Int J Oper Prod Manag. 1987;7(4):46–52.CrossRefGoogle Scholar
  37. 37.
    Richey D. The Shingo prize for excellence in manufacturing. J Qual Particip. 1996;19(4):28–31.Google Scholar
  38. 38.
    Imai M. Gemba kaizen: a commonsense approach to a continuous improvement strategy. 2nd ed. New York: McGraw-Hill; 2012.Google Scholar
  39. 39.
    Gupta S, Jain SK. A literature review of lean manufacturing. Int J Manag Sci Eng Manag. 2013;8(4):241–9.Google Scholar
  40. 40.
    Flynn BB, Schroeder RG, Sakakibara S. A framework for quality management research and an associated measurement instrument. J Oper Manag. 1994;11(4):339–66.CrossRefGoogle Scholar
  41. 41.
    Ferdows K, De Meyer A. Lasting improvements in manufacturing performance: in search of a new theory. J Oper Manag. Apr. 1990;9(2):168–84.CrossRefGoogle Scholar
  42. 42.
    Ghosh M. Lean manufacturing performance in Indian manufacturing plants. J Manuf Technol Manag. 2012;24(1):113–22.CrossRefGoogle Scholar
  43. 43.
    Belekoukias I, Garza-Reyes JA, Kumar V. The impact of lean methods and tools on the operational performance of manufacturing organisations. Int J Prod Res. 2014;52(18):5346–66.CrossRefGoogle Scholar
  44. 44.
    Weinberg SL, Abramowitz SK. Statistics using SPSS: an integrative approach. 2nd ed. Cambridge: University Press; 2008.Google Scholar
  45. 45.
    Bagozzi RP, Yi Y. On the evaluation of structural equation models. J Acad Mark Sci. 1988;16(1):74–94.CrossRefGoogle Scholar
  46. 46.
    Kim JS, Arnold P. Operationalizing manufacturing strategy. Int J Oper Prod Manag. Dec. 1996;16(12):45–73.CrossRefGoogle Scholar
  47. 47.
    Schroeder RG, Shah R, Peng DX. The cumulative capability ‘sand cone’ model revisited: a new perspective for manufacturing strategy. Int J Prod Res. 2011;49(16):4879–901.CrossRefGoogle Scholar
  48. 48.
    Guide VDR, Ketokivi M. Notes from the editors: redefining some methodological criteria for the journal. J Oper Manag. 2015;37:v–viii.CrossRefGoogle Scholar
  49. 49.
    Flynn BB, Schroeder RG, Flynn EJ. World class manufacturing: an investigation of Hayes and Wheelwright’s foundation. J Oper Manag. 1999;17(3):249–69.CrossRefGoogle Scholar
  50. 50.
    Huizingh E. Applied statistics with SPSS. Los Angeles: Sage Publications; 2008.Google Scholar
  51. 51.
    F. Brosius, “Lineare regression,” in SPSS 21, Heidelberg: MITP, 2013, pp. 541–592.Google Scholar
  52. 52.
    Flynn BB, Schroeder RM, Sakakibara S. The impact of quality management practices on performance and competitive advantage. Decis Sci. 1995;25(4):659–91.CrossRefGoogle Scholar
  53. 53.
    Gößler A, Grübner A. An empirical model of the relationship between manufacturing capabilities. Int J Oper Prod Manag. 2006;26(5):458–85.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Instiute of Technology ManagementUniversity of St. GallenSt. GallenSwitzerland
  2. 2.Mount ProspectUSA

Personalised recommendations