Skip to main content

Advertisement

SpringerLink
Log in

An integrated SMED-fuzzy FMEA model for reducing setup time

  • Published:
Journal of Intelligent Manufacturing Aims and scope Submit manuscript

Abstract

Today, the companies apply lean or customized production methods, which enable the production of different kinds of products in small quantities, to meet different customer demands. But, the increase in the product variety leads to an increase in the number of setups and thus production time. The companies aim to reduce the setup time by improving activities and by eliminating the problems causing extending setup time. Single minute exchange of die (SMED) method is the most common setup method that makes it possible to perform equipment setup operations in fewer than 10 min, i.e. several minutes expressed by a single digit. It is possible to further reduce setup times by integrating quality tools and methods into the SMED method. In this study, it is developed a novel SMED model that integrating the traditional SMED and fuzzy failure modes and effects analysis (fuzzy-FMEA) methods. Fuzzy FMEA method is used to prevent problems causing further extending setup time on setup activities. A new operation worksheet, “Setup Observation and Analysis Form” that leads the analyst in during the investigation of the machine and its set-up process, is also designed. The new approach is applied to set up a plastic injection mold for a pen manufacturing company. The setup time is reduced from 71.32 to 36.97 min, achieved a 48% improvement.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  • Assaf, R., & Haddad, T. (2014). Performance improvement using the single minute exchange of die (SMED) methodology in an aluminium profiles extrusion production system. In The 1st international conference on industrial, systems and manufacturing engineering (ISME’14).

  • Cakmakci, M. (2009). Process improvement: performance analysis of the setup time reduction-SMED in the automobile industry. International Journal of Advanced Manufacturing Technology, 41(1), 168–179.

    Article  Google Scholar 

  • Chanamool, N., & Naenna, T. (2016). Fuzzy FMEA application to improve decision-making process in an emergency department. Applied Soft Computing, 43, 441–453.

    Article  Google Scholar 

  • Chang, K.-H., Chang, Y.-C., & Lai, P.-T. (2014). Applying the concept of exponential approach to enhance the assessment capability of FMEA. Journal of Intelligent Manufacturing, 25, 1413–1427.

    Article  Google Scholar 

  • Chen, S., Fan, S., Xiong, J., & Zhang, W. (2017). The design of JMP/SAP based six sigma management system and its application in SMED. Procedia Engineering, 174, 416–424.

    Article  Google Scholar 

  • Coimbra, E. A. (2009). Total flow management: Achieving excellence with Kaizen and lean supply chains. Zug: Kaizen Institute.

    Google Scholar 

  • Ford Motor Company, Chrysler LLC, Ford Motor Company, General Motors Corporation. (2008). Potential failure mode and effects analysis (FMEA) Reference Manual (3rd ed., pp. 57–59).

  • Geramian, A., Shahin, A., Minaei, B., & Antony, J. (2020). Enhanced FMEA: An integrative approach of fuzzy logic-based FMEA and collective process capability analysis. Journal of the Operational Research Society, 71(5), 800–812.

    Article  Google Scholar 

  • Karasu, M. K., Cakmakci, M., Cakiroglu, M., Ayva, E., & Demirel-Ortabas, N. (2014). Improvement of changeover times via Taguchi empowered SMED/case study on injection molding production. Measurement, 47, 741–748.

    Article  Google Scholar 

  • Karasu, M. K., & Salum, L. (2018). FIS-SMED: A fuzzy inference system application for plastic injection mold changeover. International Journal of Advanced Manufacturing Technology, 94, 545–559.

    Article  Google Scholar 

  • Liu, H. C., Chen, Y. Z., You, J. X., & Li, H. (2016). Risk evaluation in failure mode and effects analysis using fuzzy digraph and matrix approach. Journal of Intelligent Manufacturing, 27(4), 805–816.

    Article  Google Scholar 

  • Lozano, J., Saenz-Diez, J. C., Martinez, E., Jimenez, E., & Blanco, J. (2019). Centerline-SMED integration for machine changeovers improvement in food industry. Production Planning & Control, 30(9), 764–778.

    Article  Google Scholar 

  • Ma, H., Chu, X., Xue, D., & Chen, D. (2019). Identification of to-be-improved components for redesign of complex products and systems based on fuzzy QFD and FMEA. Journal of Intelligent Manufacturing, 30, 623–639.

    Article  Google Scholar 

  • Maalouf, M. M., & Zaduminska, Z. (2019). A case study of VSM and SMED in the food processing industry. Management & Production Engineering Review, 10(2), 60–68.

    Google Scholar 

  • Mamdani, E. H., & Assilian, S. (1975). An experiment in linguistic synthesis with a fuzzy logic controller. International Journal of Man-Machine Studies, 7(1), 1–13.

    Article  Google Scholar 

  • McIntosh, R., Owen, G., Culley, S., & Mileham, T. (2007). Changeover improvement: Reinterpreting Shingo’s “SMED” methodology. IEEE Transactions on Engineering Management, 54(1), 98–111.

    Article  Google Scholar 

  • Moxham, C., & Greatbanks, R. (2001). Prerequisites for the implementation of the SMED methodology a study in a textile processing environment. International Journal of Quality & Reliability Management, 18(4), 404–414.

    Article  Google Scholar 

  • Pannesi, R. T. (1995). Lead time competitiveness in make-to-order manufacturing firm. International Journal of Production Research, 3(6), 150–163.

    Google Scholar 

  • Park, J., Park, C., & Ahn, S. (2018). Assessment of structural risks using the fuzzy weighted Euclidean FMEA and block diagram analysis. International Journal of Advanced Manufacturing Technology, 99, 2071–2080.

    Article  Google Scholar 

  • Ribeiro, D., Braga, F., Sousa, R., & Carmo, Silva S. (2011). An application of the SMED methodology in an electric power controls company. Romanian Review Precision Mechanics, Optics and Mechatronics., 40, 115–122.

    Google Scholar 

  • Safari, H., Faraji, Z., & Majidian, S. (2016). Identifying and evaluating enterprise architecture risks using FMEA and fuzzy VIKOR. Journal of Intelligent Manufacturing, 27, 475–486.

    Article  Google Scholar 

  • Shingo, S. (1985). A revolution in manufacturing: The SMED system. Productivity Press, USA translated by Dillon, A.P.

  • Singh, B. J., & Khanduja, D. (2012). Risk Management in complex changeover through CFMEA: An empirical investigation. International Journal ofIndustrial and Systems Engineering, 10(4), 470–494.

    Article  Google Scholar 

  • Stadnicka, D. (2015). Setup analysis: Combining SMED with other tools. Management and Production Engineering Review., 6, 36–50.

    Article  Google Scholar 

  • Stamatis, D. H. (1995). Failure mode and effect analysis: FMEA from theory to execution. Milwaukee: ASQ Quality Press.

    Google Scholar 

  • Trovinger, S. C., & Bohn, R. E. (2005). Setup time reduction for electronics assembly: Combining simple (SMED) and IT-based methods. Production and Operations Management, 14(2), 205–217.

    Article  Google Scholar 

  • Ulutas, B. (2011). An application of SMED methodology. International Journal of Industrial and Manufacturing Engineering, 5(7), 1194–1197.

    Google Scholar 

  • Van Broekhoven, E., & De Baets, B. (2006). Fast and accurate centre of gravity defuzzification of fuzzy system outputs defined on trapezoidal fuzzy partitions. Fuzzy Sets and Systems, 157(7), 904–918.

    Article  Google Scholar 

  • Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338–353.

    Article  Google Scholar 

  • Zaman, M. B., Kobayashi, E., Wakabayashi, N., Khanfir, S., Pitana, T., & Maimun, A. (2014). Fuzzy FMEA model for risk evaluation of ship collisions in the Malacca Strait: Based on AIS data. Journal of Simulation, 8(1), 91–104.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Industrial Engineering, Türkisch-Deutsche Universität, Istanbul, Turkey

    Kübra Yazıcı

  2. Department of Industrial Engineering, Sakarya University, Sakarya, Turkey

    Seda Hatice Gökler & Semra Boran

Authors
  1. Kübra Yazıcı
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Seda Hatice Gökler
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Semra Boran
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kübra Yazıcı.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yazıcı, K., Gökler, S.H. & Boran, S. An integrated SMED-fuzzy FMEA model for reducing setup time. J Intell Manuf 32, 1547–1561 (2021). https://doi.org/10.1007/s10845-020-01675-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10845-020-01675-x

Keywords

Search

Navigation