Skip to main content
SpringerLink
Log in

Variables adjustable multiple dependent state sampling plans with a loss-based capability index

  • ORIGINAL ARTICLE
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

Acceptance sampling plans are a cross-functional quality control instrument for benchmarking compliant standards of incoming as well as outgoing lots. The multiple dependent state (MDS) plan has been proposed and proven to advantage the conventional single sampling plan (SSP). However, the MDS plan presents a contradictory situation: Its performance depreciates as the number of previously considered lots increases. This outcome implies that the greater the number of cumulative lot-sentencing results one intends to include is, the greater the sample size is required for inspection, where a loosened criterion for quality acceptance levels, unfavorably, is introduced as well. Therefore, we proposed a modified MDS plan that depends on the loss-based capability index to accommodate an adjustable mechanism, namely, the adjustable multiple dependent state (AMDS) sampling plan. The AMDS plan allows encompassing not only a greater number of preceding lots in the lot disposition decision but also reducing the sample size required for the inspection and raising the lot-sentencing quality standards to stimulate the supplier attaining with persistently reliable submissions for a long-term supplier-consumer relationship. In comparison to the plans’ performance, our proposed AMDS plans demonstrate superior cost-effectiveness, discriminatory powers, and average run lengths than those of the SSP and MDS sampling plans. Furthermore, we tabulated the plan parameters under numerous conditions and delivered a real industry case to demonstrate its applicability.

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
Fig. 10

Similar content being viewed by others

References

  1. Arizono I, Miyazaki T, Takemoto Y (2013) Variable sampling inspection plans with screening indexed by Taguchi’s quality loss for optimising average total inspection. Int J Prod Res 52:405–418

    Article  Google Scholar 

  2. Aslam M, Yen CH, Chang CH, Jun CH (2014) Multiple dependent state variable sampling plans with process loss consideration. Int J Adv Manuf Technol 71:1337–1343

    Article  Google Scholar 

  3. Aslam M, Balamurali S, Jun CH (2019a) A new multiple dependent state sampling plan based on the process capability index. Commun Statistics: Simulation and Computation. https://doi.org/10.1080/03610918.2019.1588307

  4. Aslam M, Azam M, Albassam M (2019b) Sampling plan using process loss index using multiple dependent state sampling under neutrosophic statistics. IEEE Access. https://doi.org/10.1109/ACCESS.2019.2906408

  5. Boyles RA (1991) The Taguchi capability index. J Qual Technol 23:17–26

    Article  Google Scholar 

  6. Cao M, Zhang Q (2010) Supply chain collaborative advantage: a firm’s perspective. Int J Prod Econ 128(1):358–367

    Article  Google Scholar 

  7. Chan LK, Cheng SW, Spiring FA (1988) A new measure of process capability Cpm. J Qual Technol 20(3):162–175

    Article  Google Scholar 

  8. Fernández AJ (2016) Explicit quasi-optimal inspection schemes from nonconformity count data with controlled producer and consumer risks. Appl Math Model 40(23):10616–10630

    Article  MathSciNet  Google Scholar 

  9. Hsiang TC, Taguchi G (1985) A tutorial on quality control and assurance – the Taguchi methods. Joint Meetings of the American Statistical Association, Las Vegas

    Google Scholar 

  10. Johnson, S.G., 2018. The NLopt nonlinear-optimization package, http://ab-initio.mit.edu/nlopt. Accessed 18 Oct 2019

  11. Kane VE (1986) Process capability indices. J Qual Technol 18(1):41–52

    Article  Google Scholar 

  12. Montgomery DC (2009) Statistical quality control, a modern introduction, 6th edn. New York, John Wiley & Sons

    MATH  Google Scholar 

  13. Pearn WL, Shu MH (2003) Lower confidence bounds with sample size information of Cpm applied to production yield assurance. Int J Prod Res 41(15):3581–3599

    Article  Google Scholar 

  14. Pearn WL, Wu CW (2007) An effective decision making method for product acceptance. Omega: The International Journal of Management Science 35(1):12–21

    Article  Google Scholar 

  15. Powell MJD (1998) Direct search algorithms for optimization calculations. Acta Numerica 7:287–336

    Article  MathSciNet  Google Scholar 

  16. R Core Team (2020) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna URL http://www.R-project.org/. Accessed 15 Feb 2020

  17. Ruczinski I, 1996. The relation between Cpm and the degree of Includence. Doctoral Dissertation, University of Würzberg, Würzberg

  18. Samsung Electro-Mechanics, Passive Component/Chip Resistor. Retrieved December 1, 2019, from http://www.samsungsem.co.kr/global/product/passive-component/chip-resistor/general-resistor/index.jsp

  19. Schilling EG (2005) Average run length and the oc curve of sampling plans. Qual Eng 17(3):399–404

    Article  Google Scholar 

  20. Schilling EG and Neubauer DV, 2017. Acceptance sampling in quality control. 3rd, CRC Press

  21. Vännman K, Kotz S (1995) A superstructure of capability indices: distributional properties and implications. Scand J Stat 22(4):477–491

    MathSciNet  MATH  Google Scholar 

  22. Wang ZH, Wu CW (2019) Improved inspection scheme with a loss-based capability index. Int J Adv Manuf Technol 104:1321–1331

    Article  Google Scholar 

  23. Wu CW, Wang ZH (2017) Developing a variables multiple dependent state sampling plan with simultaneous consideration of process yield and quality loss. Int J Prod Res 55(8):2351–2364

    Article  Google Scholar 

  24. Wu CW, Lee AHI, Chen YW (2015a) A novel lot sentencing method by variables inspection considering multiple dependent state. Qual Reliab Eng Int 32:985–994

    Article  Google Scholar 

  25. Wu CW, Wang ZH, Shu MH (2015b) A lots-dependent variables sampling plan considering supplier’s process loss and buyer’s stipulated specifications requirement. Int J Prod Res 53(60):6308–6319

    Article  Google Scholar 

  26. Wu CW, Shu MH, Chang YN (2018) Variable-sampling plans based on lifetime-performance index under exponential distribution with censoring and its extensions. Appl Math Model 55:81–93

    Article  MathSciNet  Google Scholar 

Download references

Funding

This work was partially supported by the Ministry of Science and Technology of Taiwan under grant no. MOST 107-2221-E-992-064-MY3.

Author information

Authors and Affiliations

  1. Department of Industrial Engineering and Management, Cheng Shiu University, Kaohsiung, Taiwan

    Bi-Min Hsu

  2. Department of Industrial Engineering and Management, National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan

    Ming-Hung Shu & To-Cheng Wang

  3. Department of Healthcare Administration and Medical Informatics, Kaohsiung Medical University, Kaohsiung, Taiwan

    Ming-Hung Shu

  4. Department of Aviation Management, Republic of China Air Force Academy, Kaohsiung, Taiwan

    To-Cheng Wang

Authors
  1. Bi-Min Hsu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ming-Hung Shu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. To-Cheng Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to To-Cheng Wang.

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

Hsu, BM., Shu, MH. & Wang, TC. Variables adjustable multiple dependent state sampling plans with a loss-based capability index. Int J Adv Manuf Technol 107, 2163–2175 (2020). https://doi.org/10.1007/s00170-020-05137-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-020-05137-9

Keywords

Search

Navigation