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

Abstract

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.

Internet Financial Risk Model Evaluation and Control Decision Based on Big Data

“…Based on Big Data. This paper uses the data mining CRISP-DM model to extract financial risk characteristics and combines the knowledge of system framework and information system to develop and design a new unit financial risk management mechanism, that is, response system programming [20]. This process treats the entire risk management process as a control system consisting of five main components: input, control system, execution system, project design, and data output.…”