Challenges in Developing Digital Twins for Labor-intensive Manufacturing Systems: A Step towards Human-centricity

Many existing manufacturing systems still rely heavily on human workers as the backbone of their production processes. Such systems are commonly termed labor-intensive. Developing Digital Twins for labor-intensive manufacturing lines is a complex and challenging task as human involvement adds another level of uncertainty. While Digital Twins o ff er numerous benefits, such as improved e ffi ciency, reduced downtime

• Publication year

  2024

• Venue

  ANT/EDI40

• Publication date

  Unknown publication date

• Fields of study

  Computer Science, Engineering

• Identifiers
  DOI 10.1016/j.procs.2024.06.073
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• Validation of Data-Driven Reliability Models in Manufacturing - Work in Progress

  2024cited by this paper
• Using Worker Position Data for Human-Driven Decision Support in Labour-Intensive Manufacturing

  2023cited by this paper
• A framework for data-driven digital twins for smart manufacturing

  2022cited by this paper
• Requirements for Data-Driven Reliability Modeling and Simulation of Smart Manufacturing Systems

  2021influential reference
• Manufacturing worker perceptions of using wearable inertial sensors for multiple work shifts.

  2021cited by this paper
• Who Will Fill China&Apos;S Shoes? The Global Evolution of Labor-Intensive Manufacturing

  2020cited by this paper
• Industry 5.0—A Human-Centric Solution

  2019cited by this paper
• Digital Human and Robot Simulation in Automotive Assembly using Siemens Process Simulate: A Feasibility Study

  2019cited by this paper
• Digital Twins and Cyber–Physical Systems toward Smart Manufacturing and Industry 4.0: Correlation and Comparison

  2019cited by this paper
• Taking Into Account Workers’ Fatigue in Production Tasks: A Combined Simulation Framework

  2018cited by this paper
• Digital Twin Requirements in the Context of Industry 4.0

  2018cited by this paper
• Placing the operator at the centre of Industry 4.0 design: Modelling and assessing human activities within cyber-physical systems

  2018cited by this paper
• Toward a Digital Twin for real-time geometry assurance in individualized production

  2017cited by this paper
• A Survey of Current Challenges in Manufacturing Industry and Preparation for Industry 4.0

  2016cited by this paper
• Cooperative Manipulation

  2016cited by this paper
• Human-machine-interaction in the industry 4.0 era

  2014cited by this paper
• Discovering Petri Nets from Event Logs

  2013cited by this paper
• Modelling and Simulation of Human-Centred Assembly Systems - A Real Case Study

  2004cited by this paper
• The case for reconfigurable hardware in wearable computing

  2003cited by this paper
• Work/rest: Part II The scientific basis (knowledge base) for the guide 1 The recommendations provi

  1998cited by this paper
• Stochastic Petri Nets

  1996cited by this paper

• Industry 4.0 in Labor Intensive Industries, Opportunities and Challenges

  2025cites this paper
• System Variations and Their Impact on the Validation of Digital Twin Models in Dynamic Manufacturing Environments

  2025cites this paper
• Applications of Industry 4.0 Technologies In Labor-Intensive Manufacturing

  2025cites this paper
• Toward a Unified Security Framework for Digital Twin Architectures

  2024cites this paper
• Discovering Simulation Models from Labor-Intensive Manufacturing Systems

  2024influential citation
• Calibration of Spatial Position Errors in Virtual and Real Spaces: Improving the Results of AR-Guided Wiring

  2024cites this paper