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Autor: Ralph Hensel-Unger

AI-Assisted Work Planning

AI-Assisted Work Planning

Extracting expert knowledge from historical data for streamlined efficiency and error mitigation
Jochen Deuse ORCID Icon, Mathias Keil, Nils Killich, Ralph Hensel-Unger
Global competitive pressure is forcing companies to use resources efficiently, especially in high-wage countries. This is further intensified by market and legislative pressure for sustainable products and processes. In the face of digital and ecological change, holistic approaches to optimizing manual work processes are essential. An AI-supported assistance system for work plan creation is intended to remedy this and thus enable more efficient process design.
Industry 4.0 Science | Volume 40 | 2024 | Edition 5 | Pages 74-80 | DOI 10.30844/I4SE.24.5.74
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  • I4S+
  • Industry 4.0
    • Automation
    • Digital Twin
    • Factory Planning
    • Industry 4.0
    • Internet of Things
    • Lean Production
    • Sustainability
    • Manufacturing Systems
    • Adaptability
  • Artificial Intelligence
  • Functions
    • Start-up Management
    • Maintenance
    • Logistics
    • Assembly
    • Product Development
    • Production Planning
    • Production Control
    • Process Management
    • Quality Management
    • Risk Management
    • Safety
  • Tools
    • Additive Manufacturing
    • Analytics
    • Augmented Reality
    • Blockchain
    • Modularization
    • Training
    • Robotics
    • Sensors
    • Simulation
    • Software
  • Management
    • Services
    • Dynamics
    • Energy Efficiency
    • Leadership
    • Business Models
    • Innovation
    • SME
    • Management
    • Product Piracy
    • Resource Efficiency
    • Strategy
    • Profitability
  • Journal
    • Current Issue
    • Editorial Calendar
    • Editorial Board
    • Order in Print
    • All E-Journals
    • Annual Table of Contents
    • List of Reviewers
  • Information
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