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Cited by 21Scheduling and Optimization AlgorithmsManagement and Optimization TechniquesDigital Transformation in Industry

Comparing Modern Manufacturing Tools and Their Effect on Zero-Defect Manufacturing Strategies

Open access
Trebuna, Peter, Pekarcikova, Miriam, Dic, Michal·2022·Applied Sciences
DOI 10.3390/app122211487
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Abstract

The aim of most manufacturing and production factories can be defined as achieving smart and sustainable long-term production systems, which means moving towards strategies defined by Zero-Defect Manufacturing strategies with many areas of improvement, such as: lowering overall cost and energy consumption, amount of scrapped output and wasting raw material and improving overall lead times, production status overview, and planning abilities. These facts were the motivations behind the writing of this paper. The authors considered if the classical architecture of Zero-Defect Manufacturing can be improved by additional tools conventionally used in modern manufacturing. The authors have selected Advanced Planning and Scheduling software tools. To prove that different scheduling methods can have a serious impact on overall production results, we prepared a simple case base comparing different scheduling rules. The theoretical basis for writing this manuscript was prepared by studying classical ZDM methodology and defining the industry gap. The methodology is based on the Zero-Defect Manufacturing architecture, which is essential for high-level implementation in industrial practice. Adding new tools, such as Scheduling and the Industrial Internet of Things, to the classic ZDM architecture improves overall methodology. The impact of different scheduling strategies, which is also described in this study, depends on industry and working conditions. The scheduling rules were compared by several key performance indicators, such as lead time and the number of late/unfinished orders. The study realized, with the practical accent shown this challenge, further research in connection with digitalization.

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Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Almost all mistakes can be prevented in some way.

    “Almost all mistakes can be prevented in some way [5].”

    From Poka Yoke in Smart Production Systems with Pick-to-Light Implementation to Increase Efficiency (2023) · cites this paper as [5]

    Section 2.3 says zero-defect manufacturing uses prediction to identify problems before they happen, allowing time to prevent them. That supports the possibility of preventing manufacturing defects, but not the broader claim that almost all mistakes can be prevented. (LLM, conf=0.83)

    Experimental — accuracy not yet measured

  • Pick to Light increases the effectiveness and process efficiency of picking goods from shelving plans, and its light indicators minimize confusion and increase speed and accuracy.

    “In this way, the possibility of confusion or the incorrect selection of goods is minimized and the speed and accuracy of the process is increased [4,5].”

    From Poka Yoke in Smart Production Systems with Pick-to-Light Implementation to Increase Efficiency (2023) · cites this paper as [5]

    The scheduling discussion in Section 2.5.1 concerns production lead times and setup times, not goods picking. Searches for Pick-to-Light, light indicators, and incorrect selection found no passage in our copy addressing whether the system reduces confusion or improves picking speed and accuracy. (LLM, conf=0.76)

    Experimental — accuracy not yet measured

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Research works printed but not linked: 4

  1. Sousa, J.; Mendonça, J.P.; Kiritsis, D. Zero-defect manufacturing the approach for higher manufacturing sustainability in the era of Industry 4.0: A position paper. Group, École Polytechnique Fédérale de Lausanne-EPFL, Lausanne, Switzerland MEtRICs Research Centre, University of Minho, Guimarães, Portugal. Int. J. Prod. Res. 2021, 60, 73-91.
  2. Stefanik, A.; Grznar, P.; Micieta, B. Tools for Continual Process Improvement-Simulation and Benchmarking, Intelligent Manu- facturing and Automation. In Proceedings of the 14th International Symposium of the Danube-Adria-Association for Automa- tion and Manufacturing, Zadar, Croatia, 23-26 October 2003; pp. 443-444.
  3. Tao, F.; Yhang, M.; NEE, A.Y.C. Digital Twin Driven Smart Manufacturing; Academic Press: Chennai, India, 2019; p. 282.
  4. Vermesan, O.; Friess, P.M. Digitising the Industry-Internet of Things Connecting the Physical, Digital and Virtual Worlds; CRC Press: Boca Raton, FL, USA, 2022.

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Authors
Trebuna, P., Pekarcikova, M., & Dic, M.
Affiliations
  • Department of Industrial and Digital Engineering, Faculty of Mechanical Engineering, Technical University of Košice, Park Komenského 9, 042 00 Košice, Slovakia
  • Technical University of Košice (SK)
ORCID
Venue
Applied Sciences
Publisher
MDPI AG
Published
2022-11-12
Volume
12
Issue
22
Pages
11487
ISSN
2076-3417 (ISSN-L)
DOI
10.3390/app122211487
OpenAlex
W4309007155
Cited by
21
References
29
Citations by year
2026: 5 · 2025: 4 · 2024: 3 · 2023: 9
FWCI
1.84
Type
journal article
Language
en
Open access
Licence
Locations
  • Applied Sciences (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • Agentúra na Podporu Výskumu a Vývoja (APVV-19, APVV-17-0258, 1/0508/22, APVV-19-0418, 19-0418, 17-0258)
  • Kultúrna a Edukacná Grantová Agentúra MŠVVaŠ SR (19-0418, 1/0508/22, 17-0258, APVV-17-0258, -4/2020)
  • Vedecká Grantová Agentúra MŠVVaŠ SR a SAV (1/0438/20, 19-0418, 1/0508/22, 17-0258)
Topics
  • Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Scheduling and Optimization Algorithms
  • Social Sciences › Business, Management and Accounting › Strategy and Management › Management and Optimization Techniques
  • Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Digital Transformation in Industry
Keywords
zero-defect manufacturing, advanced planning and scheduling, scheduling rules, Industrial Internet of Things, production lead time, digitalization of manufacturing, sustainable production, key performance indicators

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