Management Systems in Production Engineering

Aims and Scope

The journal is dedicated to advancing research, innovation, and practical solutions in the dynamic field of production engineering and management. It serves as a platform for knowledge exchange between academia and industry, supporting collaboration and the dissemination of cutting-edge developments in the discipline, including the rapidly growing application of artificial intelligence across production systems.

The journal welcomes original scientific papers, including theoretical analyses, experimental research, and case studies, authored by scholars, researchers, and industry practitioners. It promotes the integration of theoretical frameworks with practical applications, responding to the evolving challenges and opportunities in modern manufacturing and engineering management, and to the transformation brought about by AI-driven and data-driven technologies.

Scope and Topics

The journal addresses a wide range of interdisciplinary themes, including but not limited to:

• Knowledge management, project management, and innovation processes in production systems
• Manufacturing engineering, smart manufacturing, automation, robotics, and maintenance strategies
• Artificial intelligence and machine learning in production engineering, including predictive maintenance, computer vision for quality inspection, process optimisation, production scheduling, generative and large language models in engineering and managerial practice
• Quality management, sustainable manufacturing, occupational safety, and risk management
• Social, economic, organizational, and ethical aspects of production systems, including the responsible and trustworthy deployment of AI, workforce competences, and human–AI collaboration
• Digital transformation, Industry 4.0 and Industry 5.0, digital twins, lean manufacturing, and supply chain resilience
• Data analytics, decision support systems, and AI governance in industrial contexts

Articles presenting a holistic or systems-based approach to current production engineering problems are particularly encouraged, including studies that assess the organizational, economic, and environmental consequences of introducing AI into production systems rather than its technical performance alone. Due to its interdisciplinary nature, the journal is also relevant to researchers in operations management, industrial engineering, logistics, data-driven decision-making, and related fields.

With a focus on emerging trends, such as green manufacturing, circular economy, artificial intelligence and data analytics in production, and human-centric design, Management Systems in Production Engineering aims to contribute to the ongoing development of a sustainable, innovative, and competitive manufacturing sector.