Abstract
One of the common problems of organizations with turn-key projects is the high scrap rate. There exist such traditional methods as Lean Six Sigma (LSS) and DMAIC tools that analyze causes and suggest solutions. New emerging intelligent technologies should influence these methods and tools as they affect many areas of our life. The purpose of this paper is to present the innovative Small Mixed Batches (SMB). The standard set of LSS tools is extended by intelligent technologies such as artificial neural networks (ANN) and machine learning. The proposed method uses the data-driven quality strategy to improve the turning process at the bakery machine manufacturer. The case study shows the step-by-step DMAIC procedure of critical to quality (CTQ) characteristics improvement. Findings from the data analysis lead to a change of measurement instrument, training of operators, and lathe machine set-up correction. However, the scrap rate did not decrease significantly. Therefore the advanced mathematical model based on ANN was built. This model predicts the CTQ characteristics from the inspection certificate of the input material. The prediction model is a part of a newly designed process control scheme using machine learning algorithms to reduce the variability even for input material with different properties from new suppliers. Further research will be focused on the validation of the proposed control scheme, and acquired experiences will be used to support business sustainability.
Highlights
The rapidly changing economic and market environment and increasing pressure for a sustainable lifestyle bring changes in the behavior and the habits of people
The results of the newly developed Lean Six Sigma (LSS)-Small Mixed Batches (SMB) method applied on the turning process are described
The following steps were taken as part of the improvement: the operator was trained for Lean Six Sigma, the measuring instrument was replaced, and the lathe machine set-up changed according to the material supplier
Summary
The rapidly changing economic and market environment and increasing pressure for a sustainable lifestyle bring changes in the behavior and the habits of people. Engineering can produce at the precision level of microns, and measurement technologies have shifted to high-sensitive optical sensors and imaging technologies This changing environment places new demands on innovation and change, but most industrial organizations, according to the survey [2,3], are not yet in a state where they would introduce intelligent technologies. These technologies, in the context of Quality 4.0, allow the processing and presentation of a large amount of data from past measurements that can be used to look back for the trends and forward-looking decision making [4,5], where new business can appear based on new technologies [6], aiming at value creation [7] with increasingly demanding customers. Quality has been studied only on final parts or products [12]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
