Immersive collaborative business process and extended reality-driven industrial metaverse technologies for economic value co-creation in 3D digital twin factories

Abstract

Research background: Internet of Things devices and sensors, artificial intelligence-based digital asset trading and digital twin-based extended reality technologies, and autonomous robotic and enterprise resource planning systems can be leveraged in 3D semantic scene completion and metaverse-based commercial transactions across Internet of Things-based business environments. Distributed ledger and enterprise business technologies, shop-floor digital twin synthetic data, and 3D simulation and visualization systems configure integrated multi-physics workflows in hyper-realistic immersive industrial environments for artificial intelligence-based business value. Digital twin-based Internet of Robotic Things, robotic swarm and multi-modal machine learning algorithms (with regard to enterprise total factor productivity), and virtual and augmented reality simulation technologies are pivotal in spatial planning processes. Industrial product data and manufacturing value chain management support digital twin-based virtual factory modeling in collaborative immersive 3D visualization environments. Purpose of the article: We show that interconnected business process management and metaverse economic organizational structures, immersive economic and entrepreneurial knowledge image-based modeling (for big data-driven product development processes), and remote autonomous equipment control and monitoring integrate digital twin-enabled 6G Tactile Industrial Internet of Things, deep reinforcement learning and image processing algorithms, and event-driven signal processing for collaborative economic value co-creation. Deep learning-based visual recognition and industrial extended reality technologies, 3D production management modeling, and Internet of Things industrial and mobile sensing networks are pivotal in production operation management, as deep learning-based multi-source data fusion assists autonomous industrial manufacturing processes across interactive 3D immersive business and synthetic manufacturing environments. Collaborative robotic cyber-physical production and generative Artificial Intelligence of Things-based systems (in terms of managerial business value), artificial intelligence-based perceptual and cognitive technologies, and spatial mapping and machine intelligence algorithms enhance manufacturing process visualization, as industrial big data sharing and interoperability are functional in 3D semantic scene completion for sustainable business and economic growth across big data-driven immersive virtual industrial manufacturing environments. Methods: We inspected Tracxn (the Industrial Metaverse section) for the first 100 companies in terms of Tracxn score for X-corn status (i.e., Minicorn, Soonicorn, or none), total equity funding (USD), and company stage (i.e., Seed, Funding Raised, Unfunded, Public, Acquired, Acqui-Hired, and Series A, B, C, D), and identified three main topics for analysis that would lead to tangible business outcomes. We examined the performance management of shop floor virtualization: connected digital twins increase production and logistics process optimization in production environments across the industrial metaverse, facilitating photorealistic production system 3D modelling and simulation. We appraised integrated diagnostic functionalities of real-time simulation implementation for error elimination and machine parameter adjustment in immersive planned production lines by synthetic image data sets and collaborative workflows. We determined digital twin-based data synthesis operational procedures and interconnected use cases across industrial scalable infrastructures for value chain efficiency. Findings & value added: We identified the specific integrated operational simulation functions and production tasks, key performance indicators of shop floor autonomous and value creation systems, and industrial process parameters for predictive quality and fault detection, resulting in production loss red