Improving nonconformity responsibility decisions: a semi-automated model based on CRISP-DM

Batel Ziv, Yisrael Parmet

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


Nonconformity (NC) management is a fundamental process in production, yet the literature notion of it does not always align with what is practiced in reality. In particular, the literature often excludes the NC responsibility decision, which is a difficult, costly and time-consuming task assignment, but also an integral part of the NC management process. We propose a semi-automated model we call SANC, which improves the accuracy of NC responsibility decisions and significantly cuts their costs. We base our methodology on CRISP-DM and extend it to fit the semi-automated NC responsibility decision. Unlike the original CRISP-DM, SANC utilizes existing organizational resources, and thus extends the capabilities of CRISP-DM in terms of both achieving greater overall performance and broadening its appeal to more traditional production processes. We demonstrate this solution by implementing it in a large-scale assembly plant in the printing industry, that may result in savings of over $186 K according to our assessments.

Original languageEnglish
Pages (from-to)657-667
Number of pages11
JournalInternational Journal of System Assurance Engineering and Management
Issue number2
StatePublished - 1 Apr 2022


  • Machine-learning
  • Nonconformity (NC)
  • Process automation
  • Production management
  • Semi-automation

ASJC Scopus subject areas

  • Safety, Risk, Reliability and Quality
  • Strategy and Management


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