Skip to main content
SpringerLink
Log in

Cycle time prediction and improvement of chipset assembly and test production line based on variability

  • Production Management
  • Published:
Production Engineering Aims and scope Submit manuscript

Abstract

Variability plays a very important role in the system performance in a chipset assembly and test production line with stochastic environment. So far only few variations have been considered in chipset assembly and test production line performance prediction and improvement. In this paper, a novel approach about cycle time prediction and improvement is proposed by combining with internal benchmarking and variability. Firstly, the composing of cycle time segment through queueing model based on Factory Physics is established, which is under the condition variability quantification and machine sudden large failure. Secondly, to obtain the optimal direction of the cycle time, an internal benchmarking is applied to analyze the influence of variability on cycle time among the workstations. Finally, a case study is performed to evaluate the effectiveness of the proposed method. Results show that the proposed method is an effective method to predict and improve the cycle time of the chipset assembly and test production line with variability.

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.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Che ZH (2017) A multi-objective optimization algorithm for solving the supplier selection problem with assembly sequence planning and assembly line balancing. Comput Ind Eng 105:247–259. https://doi.org/10.1016/j.cie.2016.12.036

    Article  Google Scholar 

  2. Li LH, Mo R (2015) Production task queue optimization based on multi-attribute evaluation for complex product assembly workshop. Plos One 10(9):1–24. https://doi.org/10.1371/journal.pone.0134343

    Google Scholar 

  3. Colledani M, Tolio T (2006) Impact of quality control on production system performance. Cirp Ann Manuf Technol 55 (1):453–456. https://doi.org/10.1016/s0007-8506(07)60457-0

    Article  Google Scholar 

  4. Lin YC, Tsai CH, Li RK, Chen CP, Chen HC (2008) The study of the cycle time improvement by work-in-process statistical process control method for IC Foundry manufacturing. Asian J Quality 9(3):71–91. https://doi.org/10.1108/15982688200800028

    Article  Google Scholar 

  5. Kose SY, Kilincci O (2015) Hybrid approach for buffer allocation in open serial production lines. Comput Oper Res 60:67–78. https://doi.org/10.1016/j.cor.2015.01.009

    Article  MATH  Google Scholar 

  6. Shihabudin Ismail M, Hussain MI, Zain ZM, Muhd N (2015) A management guide for determination of optimal buffer allocation in unpaced production line using 6 steps OBA. Appl Mech Mater 793:637–641. https://doi.org/10.4028/www.scientific.net/AMM.793.637

    Article  Google Scholar 

  7. Nahas N (2014) Buffer allocation and preventive maintenance optimization in unreliable production lines. J Intell Manuf 28(1):85–93. https://doi.org/10.1007/s10845-014-0963-y

    Article  Google Scholar 

  8. Krampe J (2013) Cycle-time determination and process control of sequencing batch membrane bioreactors. Water Sci Technol 67 (9):2083–2090. https://doi.org/10.2166/wst.2013.096

    Article  Google Scholar 

  9. Akcalt E, Nemoto K, Uzsoy R (2001) Cycle-time improvements for photolithography process in semiconductor manufacturing. IEEE Trans Semicond Manuf 14(1):48–56

    Article  Google Scholar 

  10. Swamidass PM, Majerus C (1991) Statistical control of manufacturing cycle time and project time: lessons from statistical process control. Int J Prod Res 29(3):551–563. https://doi.org/10.1080/00207549108930088

    Article  Google Scholar 

  11. Yugma C, Blue J, Dauzère-Pérès S, Obeid A (2014) Integration of scheduling and advanced process control in semiconductor manufacturing: review and outlook. J Sched 18(2):195–205. https://doi.org/10.1007/s10951-014-0381-1

    Article  MathSciNet  MATH  Google Scholar 

  12. Cheikhrouhou N, Hachen C, Glardon R (2009) A Markovian model for the hybrid manufacturing planning and control method ‘double speed single production line’. Comput Ind Eng 57(3):1022–1032. https://doi.org/10.1016/j.cie.2009.04.013

    Article  Google Scholar 

  13. Kotevski Z, Jovanoski B, Minovski R (2015) Simulation model for improved production planning and control through quality, cycle time and batch size management. J Eng Manag Compet 5(1):40–45. https://doi.org/10.5937/jemc1501040K

    Google Scholar 

  14. Abdelsalam HME, Bao HP (2006) A simulation-based optimization framework for product development cycle time reduction. IEEE Trans Eng Manage 53(1):69–85. https://doi.org/10.1109/tem.2005.861805

    Article  Google Scholar 

  15. Marsudi M, Shafeek H (2013) Production Line Performance by Using Queuing Model. In: Paper presented at the 7th IFAC Conference on Manufacturing Modelling, Management, and Control, Saint Petersburg, Russia, June 19–21, 2013

  16. Shahkar GH, Tareghian HR (2001) Designing a production line through optimisation of M/G/c/ using simulation. Math Eng Indus 8(2):123–136

    Article  MathSciNet  MATH  Google Scholar 

  17. Gilenson M, Hassoun M, Yedidsion L (2014) Setting quality control requirements to balance between cycle time and yield in a semiconductor production line. In: Paper presented at the Winter Simulation Conference, 7–10 Dec 2014

  18. Chien C, Hu C (2006) Segmented WIP Control for Cycle Time Reduction. In: Paper presented at the IEEE International Symposium on Semiconductor Manufacturing, 25–27 Sept 2006

  19. Chien C, Hsiao C, Meng C, Hong K (2005) Cycle time prediction and control based on production line status and manufacturing data mining. In: Paper presented at the IEEE International Symposium on Semiconductor Manufacturing, 13–15 Sept 2005

  20. Lee YH, Kim T (2002) Manufacturing cycle time reduction using balance control in the semiconductor fabrication line. Prod Plan Control 13(6):529–540. https://doi.org/10.1080/0953728021000014954

    Article  Google Scholar 

  21. Kalisch S, Ringel R, Weigang J (2008) Managing WIP and cycle time with the help of loop control. In: Paper presented at the Simulation Conference, 7–10 Dec. 2008

  22. Ming CC, Ling T, Subramaniam M, Guan O (2006) Integrated production control system in managing tool uptime, cycle-time and capacity. In: Paper presented at the Semiconductor Manufacturing, 25–27 Sept 2006

  23. Kouikoglou V, Phillis Y (1994) Discrete event modeling and optimization of unreliable production lines with random rates. IEEE Trans Robot Autom 10(2):153–159

    Article  Google Scholar 

  24. Mehmet P, Mustafa Y, Bilal T (2016) Variability modelling and balancing of stochastic assembly lines. Int J Prod Res 54(19):5761–5782. https://doi.org/10.1080/00207543.2016.1177236

    Article  Google Scholar 

  25. Tiacci L (2012) Event and object oriented simulation to fast evaluate operational objectives of mixed model assembly lines problems. Simul Model Pract Theory 24(2):35–48

    Article  Google Scholar 

  26. Hopp WJ, Spearman ML (2000) Factory physics. McGraw-Hill/Irwin, New York

    Google Scholar 

  27. Jacobs J, Etman L, Campen EV, Rooda J (2003) Characterization of operational time variability using effective process times. IEEE Trans Semicond Manuf 16(3):511–520

    Article  Google Scholar 

Download references

Acknowledgements

The authors are thankful to the anonymous reviewers for their constructive and helpful comments that have led to this much improved manuscript. This work was supported by the National Natural Science Foundation of China under Grant No. 71671026, and also supported by the Science & Technology Department of Sichuan Province under Grant No. 18ZDYF2575.

Author information

Authors and Affiliations

  1. School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, No. 2006, Xiyuan Avenue, West hi-tech zone, Chengdu, 611731, People’s Republic of China

    Changjun Li, Bo Li & Qiang Hu

Authors
  1. Changjun Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bo Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Qiang Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Bo Li.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, C., Li, B. & Hu, Q. Cycle time prediction and improvement of chipset assembly and test production line based on variability. Prod. Eng. Res. Devel. 12, 319–330 (2018). https://doi.org/10.1007/s11740-018-0801-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11740-018-0801-8

Keywords

Search

Navigation