Testing for Data Quality Assessment: ACase Study from the Industry 4.0 Perspective

Driven by the significant improvement of technologies and applications into smart manufacturing, this paper describes a way of analyzing and evaluating the quality of real-world industrial data. More precisely, it focuses on developing a method for determining the quality of production data and performing analysis of quality in terms of KPIs, such as OEE index and its sub-indicators, i.e. availability, quality rate and efficiency. The main purpose of the work is to propose a method that allows determine the quality of the data used to calculate production efficiency scores. In addition to the requirements imposed upon properly selected measures, we discuss possibilities of verifying the validity and reliability of these sub-indicators in relation to major production losses. The method for data quality assessment, developed in terms of the provided real data gathered from the factory shop-floor monitoring and management systems, was tested for its correctness. Our research has shown that an analysis of the quality of production data can reveal strengths and weaknesses in the production process. Finally, based on our single-unit intrinsic case study results, we discuss results learned on data quality assessment from an industry perspective and provide recommendations in this area.