Innovative Methods for Small Mixed Batches Production System Improvement: The Case of a Bakery Machine Manufacturer

Kristina Zgodavova, Peter Bober, Vidosav Majstorovic, Katarina Monkova, Gilberto Santos and Darina Juhaszova
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Kristina Zgodavova: Faculty of Materials, Metallurgy and Recycling, Technical University of Košice, 04200 Košice, Slovakia
Peter Bober: Faculty of Electrical Engineering and Informatics, Technical University of Košice, 04200 Košice, Slovakia
Vidosav Majstorovic: Faculty of Mechanical Engineering, University of Belgrade, 11000 Belgrade, Serbia
Katarina Monkova: Faculty of Manufacturing Technologies, Technical University of Košice, 08001 Prešov, Slovakia
Gilberto Santos: School of Design, Polytechnic Institute Cavado Ave, Campus do IPCA, 4750-810 Barcelos, Portugal
Darina Juhaszova: Faculty of Materials, Metallurgy and Recycling, Technical University of Košice, 04200 Košice, Slovakia

Sustainability, 2020, vol. 12, issue 15, 1-20

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.

Keywords: artificial neural network; lean six sigma; machine learning; process capability; small mixed batches; turning process (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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