In this research article, authors intend to address the digitalization priorities of quality practices for Small and Medium Enterprises (SMEs) in perspective of adoption of Industry 4.0 technologies. Also, the digitalization is the need of the hour for continuous improvement to avoid disparity between paper-based quality practices and the digitalized engineering value chain. Quality, being an integral part of customer-orientation, needs to be at the forefront of transformation under digitalization. As a result, advanced quality and process control, high efficiency, full traceability, minimum error, and taking measures to prevent the repetition of errors with artificial intelligence support, integration between the software used, and high reporting functionality are targeted.ĭigitalization is an opportunity for manufacturing SMEs to transform, not only for the flexibility and agility, but also for improved responsiveness in meeting customer requirements. The related study is aimed to develop such software to monitor and report the measurement processes of the parts produced on the machining benches, which will be commercialized in the future. The decision made during the quality control process will be developed with the support of artificial intelligence and machine learning.
The novel system will be useful to support decision-makers in the automotive supply industry. Thanks to the Decision Support System (DSS) designed within the scope of this work, it will be possible to take the decision to stop production due to the detected errors much faster and the development of error-free human functions. Delays in error detection in the quality control process cause wasted materials and human resources, which are very valuable for the automotive supply industry.
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