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

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 af...

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Bibliographic Details
Published in:Sustainability 2020, Vol.12 (15), p.6266
Main Authors: Zgodavova, Kristina, Bober, Peter, Majstorovic, Vidosav, Monkova, Katarina, Santos, Gilberto, Juhaszova, Darina
Format: Article
Language:English
Subjects:
Artificial intelligence
Bakeries
Case studies
Data analysis
Design of experiments
Inspection
Lean manufacturing
Learning algorithms
Learning theory
Machine learning
Manufacturers
Methods
Neural networks
Operators (mathematics)
Prediction models
Process control
Process controls
Productivity
Quality
Scrap
Six Sigma
Sustainability
Turnkey contracts
Vision systems
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Summary: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.
ISSN:2071-1050
2071-1050
DOI:10.3390/su12156266