Information requirements analysis for process-oriented decision support via predictive quality models in production

Procedia CIRP, vol. 130, pp. 1428–1434

Abstract

In production environments, failure management and process optimization are essential elements for preventing interruptions and fulfilling customer expectations in terms of product quality. In this context, not only the establishment of standardized failure management and process optimization processes are of central importance, but also the use of modern analysis methods for the efficient identification and correction of product and process failures. Advances in the field of machine learning enable the prediction of quality characteristics during production, supporting in the identification of potential defects. However, merely predicting defects proves insufficient for supporting the decision-making process toward specific measures for process optimization. This paper examines the information demand for decision support through predictive quality models in production by means of a targeted information requirements analysis. For this purpose, established methods for failure analysis and process optimization, such as fault tree analysis, 8D-reports, and design of experiments, were evaluated. Thereupon, ten hypotheses with two additional research questions regarding the information demand were derived. Based on the derived hypotheses and questions, a cross-industry semi-structured expert survey (n=33) was conducted, followed by an evaluation of the results with regards to the information requirements for process-oriented decision support. The survey results (Cronbach’s alpha=0.77) confirm that predicting possible deviations and defects alone is insufficient for supporting decision-making process. The results indicate that additional information is required to support decision-making through predictive quality models with the objective of optimising the production process. The identification of important characteristics and interactions as well as the associated effect size is of paramount importance. Regarding the requirements for the provision of information via predictive quality models, the study reveals that the correctness of the determined causes, as well as the repeatability, real-time capability, and scalability of the analysis, along with independence from the underlying predictive quality models, are required.

Authors 4

1. Daniel Buschmann Aachen Laboratory for Machine Tools and Production Engineering (WZL)

   RWTH Aachen University

   Affiliation as printed

   Laboratory for Machine Tools and Production Engineering, WZL of RWTH Aachen University, Campus-Boulevard 30, 52074 Aachen, Germany

2. Tobias Schulze corresponding Aachen Laboratory for Machine Tools and Production Engineering (WZL)

   RWTH Aachen University

   Affiliation as printed

   Laboratory for Machine Tools and Production Engineering, WZL of RWTH Aachen University, Campus-Boulevard 30, 52074 Aachen, Germany

3. Sebastian Beckschulte Aachen Laboratory for Machine Tools and Production Engineering (WZL)

   RWTH Aachen University

   Affiliation as printed

   Laboratory for Machine Tools and Production Engineering, WZL of RWTH Aachen University, Campus-Boulevard 30, 52074 Aachen, Germany

4. Robert Heinrich Schmitt Aachen Fraunhofer Institute for Production Technology IPT Laboratory for Machine Tools and Production Engineering (WZL)

   Fraunhofer Institute for Production Technology IPT · RWTH Aachen University

   Affiliation as printed

   Fraunhofer Institute for Production Technology IPT, Steinbachstraße 17, 52074 Aachen, Germany

   Laboratory for Machine Tools and Production Engineering, WZL of RWTH Aachen University, Campus-Boulevard 30, 52074 Aachen, Germany