An I 4.0 Reviews on Digital Value Stream Twin
Keywords:
Industry 4.0, Digital Twin, Value stream mapping, Lean ManufacturingAbstract
Nowadays agile and practical methods for lean manufacturing and production process reengineering are in urgent need for Small Enterprises (SMEs). However, due to the limitations in obtaining accurate performance evaluations of the production line, traditional value stream mapping (VSM) methods are insufficient or unpractical in undertaking production process reengineering tasks. The application of the lean methodology and its well-known method, Value Stream Mapping (VSM), has not received much attention in the industry efficiency assessment context. On the other hand, Industry 4.0 refers to the ongoing fourth industrial revolution promoting connectivity and information sharing with some key enabling technologies, including the and digital twin. Digitalization is leading to an increasing availability of production data. The use of data has the potential to support the VSM with targeted data preparation. In this regard, the concept of Digital Twin (DT) offers the capability of providing the required database to systematically collect and condense this data. This paper provides a framework for the Digital Value Stream Twin (DVST). In addition, requirements for the implementation of a DVST in practice will be elaborated.
References
A. Thelen, Z. Xiaoge, O. Fink, L. Yand, S. Ghosh, B. Youn, M. Todd, A comprehensive review of digital twin — part 1: modeling and twinning enabling technologies (2022)
A. Thelen, Z. Xiaoge, O. Fink, L. Yand, S. Ghosh, B. Youn, M. Todd, A comprehensive review of digital twin—part 2: roles of uncertainty quantification and optimization, a battery digital twin, and perspectives (2022)
Aidan Fuller; Zhong Fan; Charles Day; Chris Barlow; Digital Twin: Enabling Technologies, Challenges And Open Research (2020)
Y. Jiang, S. Yin, J. Dong, and O. Kaynak, A review on soft sensors for monitoring, control, and optimization of industrial processes IEEE Sensors Journal (2021)
P. Kadlec, R. Grbi ́c, and B. Gabrys, Review of adaptation mechanisms for data-driven soft sensors (2011)
Adil Rasheed; Omer San; Trond Kvamsdal; Digital Twin: Values, Challenges and, Enablers From A Modeling Perspective (2020)
J. Vachálek, L. Bartalsky, M. Morhác, M. Lokšík, O. Rovný, D.Šišmišová, The Digital Twin of an Industrial Production Line Within the Industry 4.0 Concept (2017)
Y. Jiang, S. Yin, J. Dong, and O. Kaynak, A review on soft sensors for monitoring, control, and optimization of industrial processes, IEEE Sensors Journal (2021)
M.J. McKenzie, J.E. Bossuyt, P.M. Boutron, I. Hoffmann, T.C.Mulrow, C.D. Shamseer, L. Tetzlaff, J.M. Akl, E.A. Brennan, The PRISMA 2020 statement: An updated guideline for reporting systematic reviews (2021)
B. Kitchenham, O. P. Brereton, D. Budgen, M. Turner, J. Bailey, and S. Linkman, Systematic literature reviews in software engineering—A systematic literature review (2009)
W. Kritzinger, M. Karner, G; Traar, J. Henjes, W. Shin, Digital Twin in manufacturing: A categorical literature review and classification (2018)
Q. Qinglin; F. Tao; Digital Twin and Big Data toward Smart Manufacturing and Industry 4.0: 360-Degree Comparison (2018)
C. Zhang; G. Zhou; J. He; Z. Li; W. Cheng; A Data- and Knowledge-driven Framework for Digital Twin Manufacturing Cell (2019)
J. Bao; D. Guo; J. Li; J. Zhang; The Modeling and Operations for The Digital Twin in The Context of Manufacturing (2020)
S. Moghadaszadeh Bazaz; M. Lohtander; J. Varis; 5-Dimensional Definition for A Manufacturing Digital Twin (2019)
A. Redelinghuys, A. Basson, K. Kruger; A six-layer architecture for the digital twin: a manufacturing case study implementation (2020)
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