Skip to main content

Advertisement

SpringerLink
Log in

Assessing product quality from the production process logs

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

Abstract

A real challenge for manufacturing industry is to be able to control not only the manufacturing process but also the production quality. Products that are suspected to be faulty are deviated from their nominal path in the production line and inspected more closely. The fact that some products deviate from the nominal path and others fail at some check operations can be used as an indicator of poor product quality. Based on this idea, this paper proposes a method to compute a product quality index or more exactly a penalty index taking into account both product path and production batches. The method relies on categorizing the products according to how they follow the production path and process mining techniques. The originality of the proposed index is to be built from advanced data analysis techniques enhanced by expert know-how. The quality index highlights risk of customer return, which is highly relevant information for the after sales service. The significance of the method is illustrated on a printed circuit board production line using surface mount technology at Vitesco Technologies. Data is collected from the real manufacturing execution system. The results obtained over more than 10,000 single electronic boards show that 91.7% of the products are in good compliance with respect to the requirements. For the other products, the method identifies the root causes of poor quality that may call for maintenance or reconfiguration actions.

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
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

Notes

  1. This notation is inspired by the Kleene star that, applied to a set of symbols, provides the set of all strings over these symbols, including the empty string [26].

References

  1. van der Aalst W (July 2012) Process mining: Overview and opportunities. ACM Trans. Manage. Inf. Syst., 3(2). https://doi.org/10.1145/2229156.2229157https://doi.org/10.1145/2229156.2229157

  2. Kang P, Lee H-, Cho S, Kim D, Park J, Park C-K, Doh S (2009) A virtual metrology system for semiconductor manufacturing. Expert Syst Appl 36(10):12554–12561

    Article  Google Scholar 

  3. Aalst W, Reijers HA, Weijters A, Dongen BF, Medeiros AK, Song M, Verbeek HMWE (2007) Business process mining: an industrial application. inf. syst. 32(5), 713-732. Inf Syst 32:713–732. https://doi.org/10.1016/j.is.2006.05.003

    Article  Google Scholar 

  4. Song M, van der Aalst WMP (2008) Towards comprehensive support for organizational mining. Decis. Support Syst. 46(1):300–317. https://doi.org/10.1016/j.dss.2008.07.002

    Article  Google Scholar 

  5. Pereira GB, Santos EAP, Maceno MMC (2020) Process mining project methodology in healthcare: a case study in a tertiary hospital. Network Modeling Analysis in Health Informatics and Bioinformatics 9 (1):1–14

    Article  Google Scholar 

  6. Van Der Aalst W (2016) Data science in action. In: Process mining, Springer, pp 3–23

  7. Weijters A, Aalst W, Medeiros A (2006) Process mining with the heuristics miner-algorithm. Cirp Annals-manufacturing Technology, 166

  8. Leemans SJJ, Fahland D, van der Aalst WMP (2013) Discovering block-structured process models from event logs containing infrequent behaviour. In: International conference on business process management, pp 66–78. Springer

  9. Leemans SJJ, Fahland D, van der Aalst WMP (2014) Discovering block-structured process models from incomplete event logs. In: International Conference on Applications and Theory of Petri Nets and Concurrency, pp 91–110. organization=,

  10. Leemans SJJ, Fahland D, van der Aalst WMP (2015) Scalable process discovery with guarantees. In: Enterprise, Business-Process and Information Systems Modeling, pp 85–101. Springer

  11. de Medeiros AKA, Weijters AJMM, van der Aalst WMP (2007) Genetic process mining: an experimental evaluation. Data Min Knowl Disc 14(2):245–304

    Article  MathSciNet  Google Scholar 

  12. Carmona J, van Dongen B, Solti A, Weidlich M (2018) Conformance checking. Springer, New York

    Book  Google Scholar 

  13. Rozinat A, Van der Aalst WMP (2005) Conformance testing: Measuring the fit and appropriateness of event logs and process models. In: International Conference on Business Process Management, pp 163–176. Springer

  14. Adriansyah A, Buijs JCAM (2012) Mining process performance from event logs. In: International Conference on Business Process Management, pp 217–218. Springer

  15. Adriansyah A, Van Dongen BF, Zannone N (2013) Controlling break-the-glass through alignment. In: 2013 International Conference on Social Computing, pp 606–611.

  16. Adriansyah A, Munoz-Gama J, Carmona J, van Dongen BF, van der Aalst WMP (2015) Measuring precision of modeled behavior. Information systems and e-Business Management 13(1):37–67

    Article  Google Scholar 

  17. Pika A, Van Der Aalst WMP, Fidge CJ, Ter Hofstede AHM, Wynn MT (2013) Profiling event logs to configure risk indicators for process delays. In: International Conference on Advanced Information Systems Engineering, pp 465–481. Springer

  18. Kalenkova A, Burattin A, de Leoni M, van der Aalst W, Sperduti A (2019) Discovering high-level bpmn process models from event data. Bus Process Manag J

  19. Mans RS, Van der Aalst WMP, Vanwersch RJB (2015) Process mining in healthcare: evaluating and exploiting operational healthcare processes. Springer, New York

    Book  Google Scholar 

  20. Yang H, Park M, Cho M, Song M, Kim S (2014) A system architecture for manufacturing process analysis based on big data and process mining techniques. In: 2014 IEEE International Conference on Big Data (Big Data), pp 1024– 1029

  21. Schuh G, Gützlaff A, Cremer S, Schmitz S, Ayati A (2020) A data model to apply process mining in end-to-end order processing processes of manufacturing companies. In: 2020 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), pp 151–155

  22. Hong Tu TB, Song M (2016) Analysis and prediction cost of manufacturing process based on process mining. In: 2016 International Conference on Industrial Engineering, Management Science and Application (ICIMSA), pp 1–5

  23. Ruschel E, Santos EAP, Loures EFR (2020) Establishment of maintenance inspection intervals: an application of process mining techniques in manufacturing. J Intell Manuf 31(1):53– 72

    Article  Google Scholar 

  24. Son S, Yahya BN, Song M, Choi S, Hyeon J, Lee B, Jang Y, Sung N (2014) Process mining for manufacturing process analysis: a case study. In: Proceeding of 2nd Asia Pacific Conference on Business Process Management, Brisbane, Australia

  25. Shaw MJ (2012) Information-based manufacturing: Technology, strategy and industrial applications. Springer US, New York. https://books.google.fr/books?id=F6wJCAAAQBAJ

    Google Scholar 

  26. Kleene SC (2002) Mathematical logic. Courier Corporation

  27. Berti A, van Zelst SJ, van der Aalst W (2019) Process mining for python (pm4py): bridging the gap between process-and data science. arXiv preprint arXiv:1905.06169

  28. Myles AJ, Feudale RN, Liu Y, Woody NA, Brown SD (2004) An introduction to decision tree modeling. Journal of Chemometrics: A Journal of the Chemometrics Society 18(6):275–285

    Article  Google Scholar 

Download references

Funding

This project is supported by ANITI through the French “Investing for the Future – PIA3” program under the Grant agreement ANR-19-PI3A-0004.

Author information

Authors and Affiliations

  1. LAAS-CNRS, Université de Toulouse, CNRS, INSA, Toulouse, France

    Le Toan Duong, Louise Travé-Massuyès & Audine Subias

  2. ANITI, Université Fédérale Toulouse Midi Pyrénées, Toulouse, France

    Le Toan Duong, Louise Travé-Massuyès, Audine Subias & Nathalie Barbosa Roa

  3. Vitesco Technologies France SAS, Toulouse, France

    Le Toan Duong & Nathalie Barbosa Roa

Authors
  1. Le Toan Duong
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Louise Travé-Massuyès
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Audine Subias
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Nathalie Barbosa Roa
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Audine Subias.

Ethics declarations

Conflict of interest

The authors declare no competing interests.

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

Duong, L.T., Travé-Massuyès, L., Subias, A. et al. Assessing product quality from the production process logs. Int J Adv Manuf Technol 117, 1615–1631 (2021). https://doi.org/10.1007/s00170-021-07764-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-021-07764-2

Keywords

Search

Navigation