Skip to main content

Improve Phase: I Is for Improve

  • Chapter
Six Sigma for Students

Abstract

The Improve phase aims to identify ways to improve the outcomes of the process and system and minimize the variation throughout the system. In other words, the Improve phase aims for the identification and development of multiple alternatives for increasing performance and for selecting and implementing best alternative/s for improvement. The Six Sigma team focuses on specific changes that may have the desired impacts on the relevant processes by redesigning the process, eliminating NVA activities and wastes, and testing them using such methods as simulation, optimization, Design of Experiment (DOE), lean implementation, and Failure Modes and Effects Analysis (FMEA).

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

Access this chapter

eBook
USD 16.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 129.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    7 http://asq.org/learn-about-quality/process-analysis-tools/overview/fmea.html

References

  • Ahmed, A., Page, J., and J. Olsen. (2017). Process improvement based on an integrated approach of DMAIC and multi-method simulation. The 22nd international congress on modelling and simulation, Hobart, Tasmania, 3–8thDecember 2017.

    Google Scholar 

  • Altiparmak, F., Dengiz, B., & Bulgak, A. A. (2002). Optimization of buffer sizes in assembly systems using intelligent techniques. In Proceedings of the Winter Simulation Conference (Vol. 2, pp. 1157–1162). IEEE.

    Chapter  Google Scholar 

  • DaÄŸsuyu, C., Göçmen, E., Narlı, M., & Kokangül, A. (2016). Classical and fuzzy FMEA risk analysis in a sterilization unit. Computers & Industrial Engineering, 101, 286–294.

    Article  Google Scholar 

  • Fattahi, R., & Khalilzadeh, M. (2018). Risk evaluation using a novel hybrid method based on FMEA, extended MULTIMOORA, and AHP methods under fuzzy environment. Safety Science, 102, 290–300.

    Article  Google Scholar 

  • Hussein, N. A., Abdelmaguid, T. F., Tawfik, B. S., & Ahmed, N. G. (2017). Mitigating overcrowding in emergency departments using Six Sigma and simulation: A case study in Egypt. Operations Research for Health Care, 15, 1–12.

    Article  Google Scholar 

  • ISO 3534-3. Statistics — Vocabulary and symbols —Part 3: Design of experiments. ISO.

    Google Scholar 

  • Karnon, J., Stahl, J., Brennan, A., Caro, J. J., Mar, J., & Möller, J. (2012). Modeling using discrete event simulation: A report of the ISPOR-SMDM modeling good research practices task force–4. Medical Decision Making, 32(5), 701–711.

    Article  Google Scholar 

  • King, B. (1989). Hoshin planning: The developmental approach. Salem: GOAL/QPC.

    Google Scholar 

  • Liu, H. C. (2016). FMEA using uncertainty theories and MCDM methods. In FMEA using uncertainty theories and MCDM methods (pp. 13–27). Singapore: Springer.

    Google Scholar 

  • Montgomery, D. C. (2013). Introduction to statistical quality. New York: Wiley/NYC.

    Google Scholar 

  • Ricki G. I. (2008). Introduction to simulation. Proceedings of the 2008 Winter Simulation Conference.

    Google Scholar 

  • Shannon, R. E. (1975). Systems simulation – The art and science. Englewood Cliffs: Prentice-Hall.

    Google Scholar 

  • Shingo, S. (1985). Zero quality control: Source inspection and the Poka-Yoke System. Massachusetts: Productivity Press.

    Google Scholar 

  • Stewart, D. M., & Grout, J. R. (2001). The human side of mistake-proofing. Production and Operations Management, 10(4), 440–459.

    Article  Google Scholar 

  • Taneja, M., & Manchanda, A. (2013). Six sigma an approach to improve productivity in manufacturing industry. International Journal of Engineering Trends and Technology (IJETT), 5(6), 281–286.

    Google Scholar 

  • Ungureanu, D., Sisak, F., Kristaly, D. M., & Moraru, S. (2005). Simulation modeling. Input data collection and analysis. In The 14th international scientific and applied science conference electronics ET, Sozopol, Bulgaria: 43–50.

    Google Scholar 

Further Readings

  • Greasley, A. (2017). Simulation modelling for business. London, UK: Routledge.

    Google Scholar 

  • Monden, Y. (1998). Toyota production system, an integrated approach to just-in-time. Norcross: Engineering and Management Press.

    Google Scholar 

  • Montgomery, D. C. (2017). Design and analysis of experiments. New York: Wiley.

    Google Scholar 

  • Morgan, B. J. (2018). Elements of simulation. Milton: Routledge.

    Book  Google Scholar 

  • Myers, R. H., Montgomery, D. C., & Anderson-Cook, C. M. (2016). Response surface methodology: Process and product optimization using designed experiments. Hoboken: Wiley.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Fatma Pakdil .

Rights and permissions

Reprints and permissions

Copyright information

© 2020 The Author(s)

About this chapter

Cite this chapter

Pakdil, F. (2020). Improve Phase: I Is for Improve. In: Six Sigma for Students. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-40709-4_10

Download citation

Publish with us

Policies and ethics