Manufacturing is an interesting process in various engineering fields as it deals with production, technology, optimization, safety, and many other aspects. Manufacturing engineering systems are aimed at the full integration of the supply chain, quick response to external changes and unexpected disturbances from internal and external manufacturing environments, effective communication, personification of human factors into the manufacturing system, detect and charmingly recover from system failures, and tackle their implications on the manufacturing place.
The main challenges of manufacturing engineering are efficient and effective product design, acquisition of confirming material from approved suppliers, reduction of waste and lead time, identification of critical processes and productive integration of processes, human and environmental factors, adaptation of internal and external changes and ability of the manufacturing system to tackle unexpected events. These challenges are very important in continuously developing technologies to support the existing manufacturing systems based on competitiveness, product quality, system reliability, and the integration of human power with technologies.
Reviews in Concurrent Challenges in Manufacturing Engineering (RCCME) is a Research Topic publishing original and advanced primary research findings across all Manufacturing Engineering. The Research Topic publishes theoretical and applied engineering research papers. It covers problems in various industries using different methodologies such as applied quantitative methods, data science, design of experiments, process optimizations, optimization methods, artificial intelligence, and other engineering methods.
This Research Topic aims to gather quality scientific contributions from outstanding authors on the concurrent challenges manufacturing engineering now faces, to serve researchers and practitioners by fostering collaboration, sharing innovative solutions, and advancing the state-of-the-art methodologies essential for addressing contemporary industrial demands and enhancing global competitiveness.
The Research Topic aims to publish outstanding Original Research articles on the concurrent manufacturing challenges. These include applied Continuous Improvement, Lean Manufacturing Systems, Agile Manufacturing Systems, Adaptive Lean Manufacturing Systems, Forecasting, Cost Engineering, Smart and AI Manufacturing Systems, Optimization, and similar research areas.
Mainly, the topic focuses on papers that solve various industrial problems, innovations in industrial engineering, applied process optimisation, predictions with uncertain environments, process gap assessment and corrective actions, supply chain challenges, scheduling and delivery challenges, machine efficiencies, and alternative methods, predictive analysis, optimization, specification analysis, measurement system analysis, process capacity analysis, manufacturability analysis, applied conformance engineering, lean engineering practices, real-time data tracking systems, Artificial Intelligence, performance engineering, reliability analysis, and papers with experimental demonstrations.
The main interest of this Research Topic is publishing articles that are original findings in multidisciplinary engineering fields.
Standard areas to be covered by this Research Topic include but are not limited to:
• Industrial engineering: Plant layout optimization, manufacturing capability, sourcing, ergonomics, Lean Engineering, Root Cause Analysis and Corrective Actions, Continuous Improvement, and computer-integrated manufacturing systems
• Manufacturing: Artificial intelligence in manufacturing, Tooling, Overall Equipment Effectiveness (OEE), Precision engineering, and Risk
• Nanotechnologies: nanomaterial analysis, production of Nanosystems
• Design and Specification Analysis: System design, Specification Optimization, Cost of Quality, DFMEA
• Quantitative Modelling: Predictive Analysis, Design of Experiments, Numerical Optimizations, Signal Processing, MCMC, multi-scale models.
Keywords:
Problem solving tools, Six Sigma Applications, Data Analysis, Modelling, Artificial Intelligence, Design Optimization and Lean Engineering
Important Note:
All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
Manufacturing is an interesting process in various engineering fields as it deals with production, technology, optimization, safety, and many other aspects. Manufacturing engineering systems are aimed at the full integration of the supply chain, quick response to external changes and unexpected disturbances from internal and external manufacturing environments, effective communication, personification of human factors into the manufacturing system, detect and charmingly recover from system failures, and tackle their implications on the manufacturing place.
The main challenges of manufacturing engineering are efficient and effective product design, acquisition of confirming material from approved suppliers, reduction of waste and lead time, identification of critical processes and productive integration of processes, human and environmental factors, adaptation of internal and external changes and ability of the manufacturing system to tackle unexpected events. These challenges are very important in continuously developing technologies to support the existing manufacturing systems based on competitiveness, product quality, system reliability, and the integration of human power with technologies.
Reviews in Concurrent Challenges in Manufacturing Engineering (RCCME) is a Research Topic publishing original and advanced primary research findings across all Manufacturing Engineering. The Research Topic publishes theoretical and applied engineering research papers. It covers problems in various industries using different methodologies such as applied quantitative methods, data science, design of experiments, process optimizations, optimization methods, artificial intelligence, and other engineering methods.
This Research Topic aims to gather quality scientific contributions from outstanding authors on the concurrent challenges manufacturing engineering now faces, to serve researchers and practitioners by fostering collaboration, sharing innovative solutions, and advancing the state-of-the-art methodologies essential for addressing contemporary industrial demands and enhancing global competitiveness.
The Research Topic aims to publish outstanding Original Research articles on the concurrent manufacturing challenges. These include applied Continuous Improvement, Lean Manufacturing Systems, Agile Manufacturing Systems, Adaptive Lean Manufacturing Systems, Forecasting, Cost Engineering, Smart and AI Manufacturing Systems, Optimization, and similar research areas.
Mainly, the topic focuses on papers that solve various industrial problems, innovations in industrial engineering, applied process optimisation, predictions with uncertain environments, process gap assessment and corrective actions, supply chain challenges, scheduling and delivery challenges, machine efficiencies, and alternative methods, predictive analysis, optimization, specification analysis, measurement system analysis, process capacity analysis, manufacturability analysis, applied conformance engineering, lean engineering practices, real-time data tracking systems, Artificial Intelligence, performance engineering, reliability analysis, and papers with experimental demonstrations.
The main interest of this Research Topic is publishing articles that are original findings in multidisciplinary engineering fields.
Standard areas to be covered by this Research Topic include but are not limited to:
• Industrial engineering: Plant layout optimization, manufacturing capability, sourcing, ergonomics, Lean Engineering, Root Cause Analysis and Corrective Actions, Continuous Improvement, and computer-integrated manufacturing systems
• Manufacturing: Artificial intelligence in manufacturing, Tooling, Overall Equipment Effectiveness (OEE), Precision engineering, and Risk
• Nanotechnologies: nanomaterial analysis, production of Nanosystems
• Design and Specification Analysis: System design, Specification Optimization, Cost of Quality, DFMEA
• Quantitative Modelling: Predictive Analysis, Design of Experiments, Numerical Optimizations, Signal Processing, MCMC, multi-scale models.
Keywords:
Problem solving tools, Six Sigma Applications, Data Analysis, Modelling, Artificial Intelligence, Design Optimization and Lean Engineering
Important Note:
All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.