International Journal of Production Management and Engineering
Scope & Guideline
Driving Collaboration in Manufacturing Engineering
Introduction
Aims and Scopes
- Production Optimization:
The journal emphasizes methodologies for optimizing production processes, including dynamic lot sizing, facility layout planning, and scheduling algorithms that enhance efficiency and reduce costs. - Sustainability in Manufacturing:
A core area is the exploration of sustainable practices in manufacturing, such as lean and green methodologies, that aim to minimize waste and improve environmental performance. - Integration of Technology and Production:
The journal investigates the intersection of modern technologies, such as Industry 4.0, machine learning, and cyber-physical systems, with traditional production management principles. - Supply Chain Management:
Research on supply chain dynamics, risk assessment, and logistics is a significant focus, addressing challenges and strategies for effective supply chain operations. - Human-Centric Approaches:
The journal promotes studies on human factors in production systems, emphasizing ergonomics and workforce collaboration to enhance productivity and safety. - Emerging Business Models:
Exploration of new business models, such as servitization and digital transformation, reflecting the evolving landscape of production and manufacturing.
Trending and Emerging
- Integration of Industry 4.0 and Lean Manufacturing:
There is a growing body of research exploring the integration of lean manufacturing principles with Industry 4.0 technologies, highlighting the need for efficient, data-driven production systems. - Sustainable Manufacturing Practices:
Increasing focus on sustainability, particularly through lean-green methodologies and eco-productivity tools, indicates a significant trend towards environmentally responsible production. - Human-Centric Production Systems:
Emerging studies on human-centric approaches in production, including ergonomics and workforce engagement, reflect a shift towards recognizing the importance of human factors in operational efficiency. - Advanced Analytics and Machine Learning:
The rise of machine learning and data analytics in manufacturing processes is a prominent theme, emphasizing the role of technology in enhancing decision-making and operational effectiveness. - Supply Chain Resilience:
Research on supply chain risk management and resilience, particularly in light of global disruptions like the COVID-19 pandemic, is increasingly relevant, signifying a need for adaptive strategies.
Declining or Waning
- Traditional Industrial Engineering:
As the field evolves, traditional industrial engineering methodologies are receiving less attention, with a noticeable shift towards integrating advanced technologies and data science. - Basic Quality Management Practices:
Research focusing solely on basic quality management practices, without integration with modern techniques such as Six Sigma or Lean, has become less prominent, indicating a trend towards more comprehensive approaches. - Static Supply Chain Models:
The publication of static models for supply chain management is declining, as the emphasis is shifting towards dynamic and adaptive models that can respond to real-time changes and uncertainties. - Single-Factor Analysis:
There is a waning interest in studies that analyze production issues based on single factors, as the complexity of modern production environments calls for multi-faceted approaches.
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