1a. Industry and logistics

Scope and introduction

Industrial Virtual Worlds (VW), using a variety of immersive technologies, have significant potential to transform industry and logistics. These digital environments, accessible on multiple devices, allow for the creation and use of Digital Twins. By leveraging these VW, businesses can simulate and analyse complex manufacturing and supply chain processes, improving forecasting, efficiency, and reducing costs. These technologies also support predictive maintenance, optimized workflows, and data-driven decision-making. As they evolve, their cross-platform capabilities foster collaboration and innovation, making them essential for the future industrial landscape in various domains.

Industrial processes comprise several areas where VW can confer significant benefits. These include design and engineering, virtual testing, planning and manufacturing, operations (including training), and maintenance, repair, and overhaul (MRO). Logistics is an intrinsic component of all these stages and processes.

The industrial VW market is experiencing considerable growth, with recent reports projecting a compound annual growth rate of approximately 35%. Forecasts for the global market value in 2030 range from 100 billion USD to 395 billion USD by 2034.

Use cases

1a.1 Rapid prototyping through collaborative design, editing and simulations

This approach aims to enhance the cost and resource efficiency of prototype design cycles for designers and developers. By facilitating virtual experiences and testing of product and service versions and functions collaboratively with colleagues worldwide, it reduces the need for physical prototypes or expensive 3D printing.

Challenges and opportunities: Current practices often involve various development departments, frequently geographically dispersed, collaborating either virtually or on-site, but high transaction costs often lead to work division and limited collaboration. Prospective customers and end-users are typically engaged late in the development process, which can result in significant error costs and a risk of inadequate market acceptance.

Research and Innovation Objectives: Industry-grade head-mounted displays and multi-user platforms (2a). Back-end platforms, integration of context and usage data, and utilisation of physical geometric virtual models (2d, 2e). Distributed-ledger technologies and AI for enhanced collaborative design and prototyping (2d, 2f).

1a.2 Virtual integration of factory planning and simulation of series start-up

This scenario aims to minimise the time required for production line start-up and reduce downtime for existing lines by enabling factory planners and systems engineers to plan and simulate production line modifications in advance.

Challenges and opportunities: While some very large companies already utilise VW for planning new facilities, access to the necessary know-how and, crucially, a sufficient DT base of production equipment and building information modelling models are not readily available for small and medium-sized enterprises.

Research and Innovation Objectives: DTs for comprehensive factory and production equipment (2e). Interoperability with enterprise resource planning systems (2d). End-to-end integration and interoperability among simulation tools (2d, 2e).

1a.3 Model-Based Systems Engineering (MBSE)

This approach aims to efficiently coordinate development teams, end-users, and other key stakeholders, thereby accelerating feedback loops and ensuring that project outcomes align with expectations. Virtual world-based project models, leveraging extended reality (XR) technologies, offer an accessible, highly visual, and interactive unified language applicable across different engineering disciplines.

Challenges and opportunities: Engineering projects currently often revolve around various models that capture key aspects by highlighting important subsystems and elements while simplifying or omitting less relevant features. MBSE approaches exhibit considerable variation across different engineering disciplines.

Research and Innovation Objectives: Real-time 3D rendering engines (2c). XR head-mounted displays (2a). Collaborative multi-user VW for project coordination (2b).

1a.4 System control interfaces

This research topic focuses on enabling workers to remotely operate various machinery systems. XR head-mounted displays facilitate a shared, real-time spatial context between remote workers and on-site machinery, which is often crucial for effective remote control.

Challenges and opportunities: Current industrial practices frequently necessitate on-site presence and co-located interaction for system operation and control, which can lead to increased project costs and delays.

Research and Innovation Objectives: Advanced XR head-mounted displays (2a). Low-latency network connectivity to support real-time remote operation (2d).

1a.5 Manage and maintain intralogistics (AGVs, humanoids, forklifts)

This use case aims to minimise downtime and errors in intralogistics operations, ensuring that available resources are used efficiently. In photorealistic virtual environments, it is possible to train and simulate the behaviour of automated guided vehicles and humanoids.

Challenges and opportunities: Currently, intralogistics operations are handled by a multitude of proprietary tools, each managing specific aspects such as automated guided vehicle guidance or warehouse maintenance.

Research and Innovation Objectives: DTs for intralogistics assets (2e). Interoperability with enterprise resource planning systems (2d). 3D photorealistic training environments (2b). Advanced tracking and mapping technologies (2a, 2b).

1a.6 Training and onboarding in a virtual work environment

This research aims to enable employers to comprehensively train new employees and specialists at the earliest possible stage when new products and services are introduced, preparing them for new tasks.

Challenges and opportunities: Currently, when new products and services are introduced, employees often receive training late in the process, on the job, or are provided with product sheets, process descriptions, and design plans that are difficult to comprehend.

Research and Innovation Objectives: Advanced XR head-mounted displays (2a). Creation of DTs and human DT's (2e). Collaborative platforms with the capability to manipulate and interact with virtual environments (2b, 2c).

1a.7 Virtual dashboards

This research focuses on enabling shopfloor managers to access relevant data and collaboratively monitor and analyse the shopfloor in a virtual world, utilising services and applications including AI.

Challenges and opportunities: Currently, shopfloor managers access different enterprise resource planning or evaluation tools, typically displayed in tables, spreadsheets, or two-dimensional environments.

Research and Innovation Objectives: DTs and robust interoperability with enterprise resource planning systems (2d, 2e). End-to-end integration and interoperability of simulation tools (2d, 2e). Enabling real-time data access and real-time streaming capabilities (2b, 2d, 2e).

1a.8 Public outreach for energy grid extensions and generation units planning

This research aims to allow citizens to visualise planned power grid lines, new wind or photovoltaic parks, and power plants before construction, enabling them to understand the environmental impact on their living surroundings.

Challenges and opportunities: Current methods rely on static photoshopped images from specific viewpoints or animations in virtual environments, which offer a more abstract and less tangible impression of the actual impacts of new assets.

Research and Innovation Objectives: AI for enhanced visualisation capabilities (2f). Mobile applications that integrate georeferenced planning data and virtual models of assets to be built.

1a.9 Life cycle continuity in structural and civil engineering projects

This research aims to provide infrastructure project stakeholders, such as architects, engineers, and construction managers, with a more comprehensive understanding of their project space and its main components throughout its entire lifecycle.

Challenges and opportunities: Current camera tracking often involves manual configuration and custom solutions. XR stage setups typically rely on proprietary integrations of displays, sensors, and game engines.

Research and Innovation Objectives: AR/VR/MR head-mounted displays and large XR displays (2a). AI and large language models for real-time guidance and assistance (2f).

1a.10 Virtual showrooms and product customisation in real-time

This research aims to enable sales representatives to clearly demonstrate innovative products and services and their benefits to potential customers.

Challenges and opportunities: Current methods involve elaborate presentations, trade fair appearances, films, and prototypes that often only abstractly illustrate the tangible benefits in a working or living environment.

Research and Innovation Objectives: 3D DTs with predictive and prescriptive analytics capabilities (2e). Physical places for presentation and interoperable virtual platforms.

1a.11 AI generated industrial Digital Twins and visualisations

This research aims to enable software engineers and 3D artists to generate 3D assets and DT of industrial machines and goods with the assistance of AI and scanning/mapping technology.

Challenges and opportunities: Currently, industrial 3D assets are typically created using CAD data and discipline-oriented software that can be operated only by few experts.

Research and Innovation Objectives: Integration of computer-aided design, AI, large language models, and 3D technology/game engines (2b, 2c, 2e, 2f). Development of advanced DTs (2e). Advancement of scanning and mapping technologies (2d, 2e).

1a.12 Collaborative remote maintenance and operations support

This research focuses on allowing technicians and remote engineering teams to visualise machines and relevant equipment in high-fidelity 3D to support activities such as remote inspection and collaboration.

Challenges and opportunities: Normally, maintenance and operations support necessitate on-site presence, which is not always feasible, resulting in downtime and project delays.

Research and Innovation Objectives: XR head-mounted displays and low-latency network Connectivity (2a, 2d). Consideration of human factors and User Experience (UX) in system design (2b, 3a, 3d). AI for enhanced support (2f).

1a.13 Exchange of complex industrial data

This research aims to enable manufacturing systems engineers to securely share DTs and Internet of Things data with suppliers and partners. This collaborative approach facilitates real-time optimisation of production, predictive maintenance, and supply chain operations.

Challenges and opportunities: Currently, data exchange predominantly occurs through centralised platforms or manual file transfers (e.g., email, FTP). Proprietary platforms often exhibit poor interoperability.

Research and Innovation Objectives: Interoperable DT standards, such as Asset Administration Shell and DT Definition Language (2d, 2e). Decentralised data exchange mechanisms, including IPFS, blockchain, and Solid Pods (2d). AI and machine learning for semantic data translation (2d, 2f). XR interfaces for immersive collaboration (2b). Integration of edge AI and federated learning for privacy-preserving computation (2d, 3b).

1a.14 Training AI systems and autonomous agents

This research focuses on enabling engineers and developers to train and test AI and machine learning models and autonomous systems, including robots and humanoids, within high-fidelity, multi-user virtual environments.

Challenges and opportunities: Current training methods for AI and autonomous systems heavily rely on physical prototypes, limited-scale simulations, or domain-specific datasets.

Research and Innovation Objectives: High-fidelity 3D simulation engines (2c). DTs with real-time physics modelling (2e). Synthetic data generation pipelines and integration with real-world sensor and control data (2f). UX design for immersive collaboration (2b). Sim-to-real transfer and domain adaptation (2d, 2e). Development of governance frameworks for virtual experimentation (3b).

Recommendations

To advance the integration and impact of VW within the industry and logistics sectors, a series of strategic recommendations are proposed:

1. Develop and promote interoperable standards for DTs and VW platforms to facilitate secure, decentralised data exchange.

2. Invest in advanced hardware and software solutions specifically tailored for industrial applications, including robust XR head-mounted displays and low-latency network connectivity.

3. Enhance accessibility and usability of VW solutions for small and medium-sized enterprises through low-code or no-code content authoring tools.

4. Prioritise the integration of AI and LLMs across the industrial VW ecosystem for AI-assisted generation of DTs and intelligent assistants.

5. Place strong emphasis on human factors and UX design to ensure that VW interfaces are intuitive, inclusive, and minimise cognitive load.