Advances in Production Management Systems. Production Management Systems for Volatile, Uncertain, Complex, and Ambiguous Environments
Description
The six-volume set IFIP AICT 728-729 constitutes the refereed proceedings of the 43rd IFIP WG 5.7 International Conference on Advances in Production Management Systems, APMS 2024, held in Chemnitz, Germany, during September 8-12, 2024.
The 201 full papers presented together were carefully reviewed and selected from 224 submissions. The APMS 2024 conference proceedings are organized into six volumes, covering a large spectrum of research addressing the overall topic of the conference "Production Management Systems for Volatile, Uncertain, Complex, and Ambiguous Environments".
Part I: advancing eco-efficient and circular industrial practices; barriers and challenges for transition towards circular and sustainable production processes and servitized business models; implementing the EU green deal: challenges and solutions for a sustainable supply chain; risk analysis and sustainability in an uncertain system in a digital era.
Part II: smart and sustainable supply chain management in the society 5.0 era; human-centred manufacturing and logistics systems design and management for the operator 5.0; inclusive work systems design: applying technology to accommodate individual workers' needs; evolving workforce skills and competencies for industry 5.0; experiential learning in engineering education.
Part III: lean thinking models for operational excellence and sustainability in the industry 4.0 era; human in command - operator 4.0/5.0 in the age of AI and robotic systems; hybrid intelligence - decision-making for AI-enabled industry 5.0; mechanism design for smart and sustainable supply chains.
Part IV: digital transformation approaches in production and management; new horizons for intelligent manufacturing systems with IoT, AI, and digital twins.
Part V: smart manufacturing assets as drivers for the twin transition towards green and digital business; engineering and managing AI for advances in asset lifecycle and maintenance management; transforming engineer-to-Order projects, supply chains, and systems in turbulent times; methods and tools to achieve the digital and sustainable servitization of manufacturing companies; open knowledge networks for smart manufacturing; applications of artificial intelligence in manufacturing; intralogistics.
Part VI: modelling supply chain and production systems; resilience management in supply chains; digital twin concepts in production and services; optimization; additive manufacturing; advances in production management systems.
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Content
.- Digital Transformation Approaches in Production and Management.
.- Leveraging Sentiment Analysis and Reporting for Re-Designing Business Processes using Large Language Models: A SentiProMo Case Study in Airline Check-In Processes.
.- Development of a Framework for Data-Supported Personas.
.- Fuzzy Maturity Model for Transformative Procurement Readiness: Procurement 4.0 perspective.
.- Guiding Process Mining Projects with the IPMM Framework: A Case Study with a German Manufacturer.
.- Assessing Additive Manufacturing and Digital Inventory Ecosystem in the Oil & Gas Context.
.- Unlocking the potential of blockchain in road freight transportation operations.
.- Digital Transformation Towards Human-Centricity: A Systematic Literature Review.
.- Literature Review on the Current State-of-the-Art in Research and Technological Advancements in the Field of Machine Learning applied to Predictive Maintenance.
.- Coordinating Digital Transformation: Exploring IT Organizational Adaptations in International Manufacturing Networks.
.- Building Digital Capabilities in Manufacturing SMEs.
.- Dashboard Development for the Quality Department of a Company from the Automotive Industry.
.- Digitalizing Smallholder Farmer Agri-Food Supply Chains: A Case Study from a Developing Economy.
.- Let them eat data - a concept that allows us to share data from farm to fork (and back to the farm.
.- Simplifying data analysis: A visualization framework and practical application for complex BEV data.
.- Integrating AI with lean manufacturing in the context of industry 4.0/5.0: Current Trends and Applications.
.- New Horizons for Intelligent Manufacturing Systems with IoT, AI, and Digital Twins.
.- Dynamic pricing for fashion supply chain with blockchain supported value authentication.
.- An Explorative Study of AI Applications in Composite Material Extrusion Additive Manufacturing.
.- Explainable Artificial Intelligence in Manufacturing Operations: a bibliometric analysis.
.- Pre-Post Analysis on Multi-Skill Development using Flow Line Data at Expressway Service Area Facilities.
.- Human - Data Analytics Interaction through Voice Assistance in Electric Vehicle's Battery Testing.
.- Towards a Maturity Model for Intelligent Digital Twins in Manufacturing.
.- Information Modeling for Data-driven Digital Twin Simulation: Insights from Case Studies of Port Logistics and Urban Traffic Systems.
.- Lenses on Data: Toward an Application Perspective on Data in Manufacturing.
.- A Methodology for Identification of Reconfigurability Enablers and Application in a Manufacturing System.
.-Computer Vision-based Digital Twin and Digital Services for Dynamic Production and Logistics Environment.
.- Digital Twin-driven Reinforcement Learning for Dynamic Path Planning of AGV Systems.
.- Instance Segmentation and Digital Twin Use Case for WIP Tracking in Heavy Industry.
.- An Authoring Tool for Mixed Reality Interfaces for Digital Twins in Manufacturing.
.- Advanced Time Block Analysis for Manual Assembly Tasks in Manufacturing through Machine Learning Approaches.
.- Digital Twins (DT) applied to the customization of 3D printed Scara robots using intelligent manufacturing.
.- Centering on humans - Intersectionality in vision systems for human order picking.
.- Graph-to-sequence Approach for Job shop Scheduling Problem.
.- Product-centric Simulation for Proactive Evaluation of Production Plan in Shipyard.
.- A Mobile Air-Purification Device and Digital Twin for Managing Hazardous Gases at Industrial Sites.
