Journal of Manufacturing Systems

Aims & Scope

The scope of the Journal of Manufacturing Systems includes, but is not limited to, the following areas: Factory and production network design, process planning, assembly planning, scheduling; Smart sensor networks, real-time monitoring, distributed system control; Human-machine interaction, human-robot collaborative assembly, operator ergonomics; Multi-physics modelling, simulation and optimisation, virtual and augmented reality in manufacturing; Diagnosis and prognosis, predictive maintenance, lifecycle analysis, product-service systems; Design and operation for sustainability, energy efficiency in production and logistics; Global and regional production networks, material handling, logistics; Mass customisation and personalisation, complexity management; Cyber-physical production systems, big data analytics and machine learning, industrial Internet; Systems issues related to additive and subtractive manufacturing, micro-electromechanical systems.

View Aims & Scope

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Metrics & Ranking

Impact Factor

Year	Value
2025	14.2

SJR (SCImago Journal Rank)

Year	Value
2024	3.633

Quartile

Year	Value
2024	Q1

h-index

Year	Value
2024	120

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Abstracting & Indexing

Journal is indexed in leading academic databases, ensuring global visibility and accessibility of our peer-reviewed research.

• Scopus
• Google Scholar
• Web of Science

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Subjects & Keywords

Journal’s research areas, covering key disciplines and specialized sub-topics in Computer Science and Engineering, designed to support cutting-edge academic discovery.

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Most Cited Articles

The Most Cited Articles section features the journal's most impactful research, based on citation counts. These articles have been referenced frequently by other researchers, indicating their significant contribution to their respective fields.

• Industry 4.0 and Industry 5.0—Inception, conception and perception

  Citation: 1647

  Authors: Xun, Yuqian, Birgit, Lihui

• Review of digital twin about concepts, technologies, and industrial applications

  Citation: 1360

  Authors: Mengnan, Shuiliang, Huiyue, Cunzhi

• Deep learning for smart manufacturing: Methods and applications

  Citation: 1307

  Authors: Jinjiang, Yulin, Laibin, Robert X., Dazhong

• Data-driven smart manufacturing

  Citation: 1293

  Authors: Fei, Qinglin, Ang, Andrew

• Enabling technologies and tools for digital twin

  Citation: 1076

  Authors: Qinglin, Fei, Tianliang, Nabil, Ang, Yongli, Lihui, A.Y.C.

• Industry 5.0: Prospect and retrospect

  Citation: 857

  Authors: Jiewu, Weinan, Baicun, Pai, Cunbo, Qiang, Thorsten, Dimitris, Lihui

• Digital twin modeling

  Citation: 850

  Authors: Fei, Bin, Qinglin, Jiangfeng, Ping

• A critical review of smart manufacturing & Industry 4.0 maturity models: Implications for small and medium-sized enterprises (SMEs)

  Citation: 845

  Authors: Sameer, Muztoba Ahmad, David, Thorsten

• Current status and advancement of cyber-physical systems in manufacturing

  Citation: 744

  Authors: Lihui, Martin, Mauro

• Design of reconfigurable manufacturing systems

  Citation: 620

  Authors: Yoram, Moshe

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