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

Author

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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)

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.

Suggested Citation

  • Kristina Zgodavova & Peter Bober & Vidosav Majstorovic & Katarina Monkova & Gilberto Santos & Darina Juhaszova, 2020. "Innovative Methods for Small Mixed Batches Production System Improvement: The Case of a Bakery Machine Manufacturer," Sustainability, MDPI, vol. 12(15), pages 1-20, August.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:15:p:6266-:d:394210
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    References listed on IDEAS

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    1. Zoran Jurkovic & Goran Cukor & Miran Brezocnik & Tomislav Brajkovic, 2018. "A comparison of machine learning methods for cutting parameters prediction in high speed turning process," Journal of Intelligent Manufacturing, Springer, vol. 29(8), pages 1683-1693, December.
    2. Raine Isaksson, 2005. "Economic sustainability and the cost of poor quality," Corporate Social Responsibility and Environmental Management, John Wiley & Sons, vol. 12(4), pages 197-209, December.
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    Cited by:

    1. Gilberto Santos & Jose Carlos Sá & Maria João Félix & Luís Barreto & Filipe Carvalho & Manuel Doiro & Kristína Zgodavová & Miladin Stefanović, 2021. "New Needed Quality Management Skills for Quality Managers 4.0," Sustainability, MDPI, vol. 13(11), pages 1-22, May.
    2. Sérgio Carqueijó & Delfina Ramos & Joaquim Gonçalves & Sandro Carvalho & Federica Murmura & Laura Bravi & Manuel Doiro & Gilberto Santos & Kristína Zgodavová, 2022. "The Importance of Fab Labs in the Development of New Products toward Mass Customization," Sustainability, MDPI, vol. 14(14), pages 1-19, July.
    3. Brylowski, Martin & Schröder, Meike & Lodemann, Sebastian & Kersten, Wolfgang, 2021. "Machine learning in supply chain management: A scoping review," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Kersten, Wolfgang & Ringle, Christian M. & Blecker, Thorsten (ed.), Adapting to the Future: How Digitalization Shapes Sustainable Logistics and Resilient Supply Chain Management. Proceedings of the Hamburg Internationa, volume 31, pages 377-406, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.
    4. Francisco José Gomes Silva & Konstantinos Kirytopoulos & Luis Pinto Ferreira & José Carlos Sá & Gilberto Santos & Maria Carolina Cancela Nogueira, 2022. "The three pillars of sustainability and agile project management: How do they influence each other," Corporate Social Responsibility and Environmental Management, John Wiley & Sons, vol. 29(5), pages 1495-1512, September.
    5. Thaís Vieira Nunhes & Maximilian Espuny & Thalita Lauá Reis Campos & Gilberto Santos & Merce Bernardo & Otávio José Oliveira, 2022. "Guidelines to build the bridge between sustainability and integrated management systems: A way to increase stakeholder engagement toward sustainable development," Corporate Social Responsibility and Environmental Management, John Wiley & Sons, vol. 29(5), pages 1617-1635, September.
    6. Zhonghua Sun & Manuel Doiro & José Carlos Sá & Gilberto Santos, 2023. "Shaping the Conscious Behaviors of Product Designers in the Early Stages of Projects: Promoting Correct Material Selection and Green Self-Identity through a New Conceptual Model," Sustainability, MDPI, vol. 15(19), pages 1-18, October.

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