Topics/Call for Papers

Topics of Interest for Submission include, but are Not Limited to:

I. Industry 4.0, 5.0 & Smart Manufacturing

  • Digital Transformation in Manufacturing: Strategies and challenges for digitalization, IT/OT convergence.
  • Industrial Internet of Things (IIoT): Sensor networks, data acquisition, connectivity, and cloud integration for manufacturing systems.
  • Cyber-Physical Systems (CPS): Design, implementation, and management of intelligent, connected manufacturing processes.
  • Big Data Analytics & Machine Learning in Manufacturing:
    • Predictive analytics for maintenance (predictive maintenance, prognostics and health management - PHM).
    • Prescriptive analytics for process optimization and decision-making.
    • Machine learning for quality control, defect detection, and process monitoring.
  • Digital Twins: Creating virtual models of physical assets, processes, and systems for simulation, optimization, and real-time monitoring.
  • Robotics & Automation:
    • Collaborative robots (cobots) and human-robot interaction in manufacturing.
    • Autonomous mobile robots (AMRs) for logistics and material handling.
    • Advanced automation for assembly, inspection, and hazardous environments.
  • Additive Manufacturing (3D/4D Printing):
    • Industrial applications of additive manufacturing (e.g., rapid prototyping, tooling, customized products).
    • New materials and processes for additive manufacturing.
    • Design for additive manufacturing.
  • Cybersecurity in Industrial Systems: Protecting OT networks, SCADA systems, and industrial control systems from cyber threats.
  • Edge Computing in Manufacturing: Real-time data processing and analytics at the factory floor for low-latency decisions.
  • Cloud Manufacturing: Leveraging cloud infrastructure for manufacturing resources, services, and collaboration.
  • Human-Centric Manufacturing (Industry 5.0): Emphasizing human well-being, sustainability, and resilience alongside efficiency.

II. Operations Research & Optimization

  • Mathematical Programming: Linear, nonlinear, integer, mixed-integer programming for optimization problems.
  • Stochastic Modeling & Simulation:
    • Discrete-event simulation for system performance analysis.
    • Monte Carlo simulation for risk assessment.
    • Queuing theory and network analysis.
  • Heuristics & Metaheuristics: Genetic algorithms, simulated annealing, ant colony optimization, particle swarm optimization for complex problems.
  • Decision Analysis & Support Systems: Tools and methodologies for making optimal decisions under uncertainty.
  • Multi-objective Optimization: Balancing conflicting objectives in manufacturing and supply chain design.
  • Scheduling & Sequencing: Production scheduling, job shop scheduling, project scheduling, and resource allocation.
  • Facility Layout & Location: Optimal design and placement of facilities, warehouses, and production lines.

III. Supply Chain Management & Logistics

  • Supply Chain Design & Optimization: Network design, facility location, capacity planning.
  • Supply Chain Resilience & Risk Management: Mitigating disruptions, disaster preparedness, and building robust supply chains.
  • Sustainable Supply Chains: Green logistics, carbon footprint reduction, circular economy principles in supply chains.
  • Global Supply Chain Management: Managing international logistics, trade policies, and cross-cultural collaborations.
  • Inventory Management: Demand forecasting, optimization models, JIT (Just-in-Time), VMI (Vendor-Managed Inventory).
  • Transportation & Logistics: Route optimization, fleet management, last-mile delivery, intermodal transport.
  • Blockchain for Supply Chain Transparency: Enhancing traceability, security, and trust in supply chain operations.
  • E-logistics & Omni-channel Supply Chains: Managing complex distribution networks for e-commerce.
  • Humanitarian Logistics: Supply chain management in disaster relief and humanitarian aid.

IV. Quality, Reliability & Maintenance Engineering

  • Total Quality Management (TQM): Principles, methodologies, and implementation of TQM systems.
  • Lean Manufacturing & Six Sigma: Methodologies for waste reduction, process improvement, and variability reduction.
  • Statistical Process Control (SPC): Monitoring and controlling processes using statistical methods.
  • Reliability Engineering: Design for reliability, reliability testing, and life data analysis.
  • Maintenance Strategies: Predictive maintenance, preventive maintenance, condition-based monitoring, RCM (Reliability-Centered Maintenance).
  • Quality Management Systems (QMS): ISO standards, auditing, and continuous improvement frameworks.
  • Failure Analysis & Root Cause Analysis: Identifying and addressing causes of defects and system failures.

V. Human Factors & Ergonomics

  • Workplace Design & Safety: Ergonomic design of workstations, tools, and environments to enhance safety and efficiency.
  • Human-Machine Interaction (HMI): Designing intuitive and effective interfaces for complex industrial systems.
  • Occupational Health & Safety (OHS): Risk assessment, accident prevention, and promoting well-being in industrial settings.
  • Cognitive Ergonomics: Understanding human cognitive processes in industrial tasks and decision-making.
  • Human Error Analysis & Prevention: Identifying and mitigating sources of human error in manufacturing processes.
  • Training & Development for Industry 4.0/5.0 Workforce: Reskilling and upskilling for advanced manufacturing environments.

VI. Engineering Management & Systems Thinking

  • Project Management in Engineering: Methodologies (Agile, Waterfall), risk management, resource allocation for industrial projects.
  • Engineering Economics & Cost Analysis: Investment appraisal, cost estimation, value engineering.
  • Technology Management & Innovation: Adoption of new technologies, R&D management, intellectual property.
  • Systems Thinking & Systems Engineering: Holistic approach to design, manage, and optimize complex industrial systems.
  • Performance Measurement & Management: Key performance indicators (KPIs), dashboards, balanced scorecards.
  • Decision Science & Analytics for Management: Leveraging data for strategic and operational decisions.
  • Organizational Design & Change Management: Structuring organizations for efficiency and adapting to new technologies.

VII. Sustainability & Green Industrial Engineering

  • Eco-design & Life Cycle Assessment (LCA): Designing products and processes with minimal environmental impact throughout their lifecycle.
  • Energy Efficiency in Manufacturing: Optimizing energy consumption, waste heat recovery, smart energy management.
  • Waste Valorization & Resource Recovery: Converting industrial waste into valuable products or energy.
  • Industrial Symbiosis: Collaborative networks for sharing resources and by-products among industries.
  • Sustainable Supply Chain Management: Integrating environmental and social considerations into the entire supply chain.
  • Circular Economy in Industrial Systems: Design for disassembly, repair, reuse, and recycling.

VIII. Specific Industry Applications & Case Studies

  • Healthcare Systems Engineering: Optimizing patient flow, hospital operations, supply chain in healthcare.
  • Service Systems Engineering: Applying IE principles to service industries (e.g., retail, finance, hospitality).
  • Aerospace & Automotive Manufacturing: Specific challenges and innovations in these sectors.
  • Food & Process Industries: Optimization of continuous processes, food safety, and cold chain logistics.
  • Defense & Security Applications of IE/MSE.