Lowe’s has developed a real-time digital twin network for more than 1,700 retail stores across the United States. By fusing spatial data, product metadata, inventory movement, and store-level demand signals, Lowe’s enables employees to interact with a virtual representation of the store through augmented reality (AR). This digital twin helps optimize merchandising, streamline store operations, and empower employees with data-driven decision-making capabilities. The initiative showcases how retail spaces can be transformed into intelligent environments that continuously adapt to customer behavior and operational needs.

Project Description

Lowe’s Innovation Labs has built detailed digital twins of individual Lowe’s stores. These twins integrate shelf layouts, product locations, customer foot traffic, and stocking histories into a continuously updated virtual environment.
The aim is to give frontline employees “superpowers” by enabling them to visualize, simulate, and improve store operations using an AR interface.

Through wearable AR headsets, team members can compare real shelf conditions with optimized layouts, inspect items located above eye level, and analyze how adjustments could impact operational flow.
These store twins support day-to-day retail tasks—including planogram execution, shelf resets, replenishment, and end-cap optimization—by making invisible operational insights instantly accessible.

Technology & Digital Twin Approach

Lowe’s store digital twins are powered by several advanced technologies:

• NVIDIA Omniverse for 3D simulation and collaborative scene editing across store models

• AI-enhanced product recognition, identifying weight, volume, dimensions, and placement of items from screws to refrigerators

• Real-time data integration, updating store twins multiple times per day

• Augmented Reality Headsets, enabling in-store workers to overlay planograms, product information, and stocking instructions

• Simulation and experimentation tools for lightweight virtual testing of alternative layouts, merchandising approaches, and customer-flow designs

Heat-map visualizations reveal traffic patterns and product relationships.
Physics-based simulations estimate throughput and forecast behavioral responses to layout changes.
This allows Lowe’s to test hundreds of potential configurations virtually before implementing a small number physically.

Operational Benefits & Outcomes

• More efficient shelf resets through AR-assisted planogram execution

• Accelerated employee training, enabling associates to learn layouts and processes in the virtual environment

• Improved store layouts using foot-traffic heat maps and product affinity data

• Reduced operational risk and cost, as experiments occur virtually instead of in-store

• Higher merchandising effectiveness by simulating product placement and visibility

• Strategic forecasting through physics-based simulations of customer behavior and workflow

• Scalable innovation, with a platform that supports robotics, IoT sensors, and future generative-AI integrations

Key Facts

• ~1,700 Lowe’s stores represented as digital twins

• Twins refreshed multiple times per day

• NVIDIA Omniverse used for physics-based simulation

• AR tools support planogram compliance and shelf optimization

• Designed and developed by Lowe’s Innovation Labs

Conclusion

Lowe’s demonstrates how digital twins can fundamentally reshape operations in large-scale retail environments. By integrating real-time data, AR visualization, and spatial intelligence, the company transforms traditional brick-and-mortar stores into adaptive, responsive systems.
This comprehensive digital twin not only enhances operational efficiency but also enables Lowe’s to innovate rapidly—testing hundreds of ideas virtually before committing resources in the physical world.
As IoT, robotics, and generative AI are integrated into the platform, Lowe’s is well positioned to lead the next wave of digital transformation in retail.

<p><a href="https://www.lowesinnovationlabs.com/projects/store-digital-twin" target="_blank">https://www.lowesinnovationlabs.com/projects/store-digital-twin</a></p><p><a href="https://resources.nvidia.com/en-us-omniverse-enterprise/digital-twins-of-stores?xs=441747" target="_blank">https://resources.nvidia.com/en-us-omniverse-enterprise/digital-twins-of-stores?xs=441747</a></p><p><br></p>

BMW has expanded its digital-twin capabilities far beyond individual production processes. Through the iFactory global initiative, the company has developed a network of full-scale virtual factories, covering all 31 production sites worldwide. These real-time twins replicate complete shop-floor environments, enable collaborative planning across continents, and reduce factory-planning cycles by nearly a third. This enterprise-level system marks a shift from isolated digital models to a fully integrated, predictive manufacturing ecosystem.

Project Description

BMW’s journey toward a fully virtualized production landscape began over a decade ago with early digital models of specific manufacturing processes. Today, the transformation has evolved into a global system twin, built from high-resolution 3D scans and continuously enriched with operational data streams.

Each production site — from Regensburg to Shenyang — is represented as a navigable, interactive virtual replica. Employees can “walk through” any factory from any device, review layouts, simulate production scenarios, and collaborate across time zones. This networked factory twin forms the digital backbone of BMW’s global iFactory strategy, unifying Lean, Green and Digital practices under a single operational framework.

Technology & Digital Twin Approach

The system twin integrates multiple data layers:

• High-precision 3D scans forming the geometric foundation of each plant

• Real-time production and scheduling data mapping day-to-day behaviour

• Simulation models for layout changes, line balancing, and material flow

• Cross-factory interoperability, supporting global design and planning

The virtual factories are connected via NVIDIA Omniverse, enabling synchronized collaboration. Changes to a virtual line segment or layout can be propagated to other relevant sites instantly, creating a consistent and up-to-date digital environment across the entire production network.

BMW’s internal “Factory Viewer” application provides access to over 15,000 employees, facilitating rapid decision-making and decentralized innovation.

Operational Benefits & Outcomes

• 30% reduction in production planning time through integrated simulation and early validation

• De-risked plant changes by testing new layouts, processes, and flows virtually

• Real-time remote collaboration for experts across continents

• Predictive insights into bottlenecks, energy consumption, and production stability

• Standardized best practices rolled out instantly across all sites via the shared twin environment

• Improved sustainability, driven by optimized layouts, reduced material movement, and minimized rework

This shift toward a unified virtual factory network enables BMW to run production as an adaptive, data-driven system rather than a set of individual plants.

Key Facts

• Global scope: 31 fully virtualized production sites

• Users: ~15,000 employees across manufacturing, planning and engineering

• Platform: NVIDIA Omniverse + BMW Factory Viewer

• Strategic alignment: Lean, Green, Digital pillars of the iFactory initiative

• Core function: Integrated system twin for real-time, predictive planning

Conclusion

BMW’s global system-twin architecture marks a new stage in manufacturing innovation. Instead of focusing solely on virtualizing individual processes, the company has created a holistic, enterprise-wide digital-twin ecosystem that supports predictive operations, accelerates factory transformation and enhances sustainability.

By operating a seamless digital replica of its entire production network, BMW sets a benchmark for future automotive manufacturing — one where factories are planned, optimized and improved collaboratively in the virtual world long before changes reach the physical floor.

<p><a href="https://blogs.nvidia.com/blog/bmw-group-nvidia-omniverse/" target="_blank">https://blogs.nvidia.com/blog/bmw-group-nvidia-omniverse/</a></p><p><a href="https://www.bmwgroup.com/en/news/general/2022/bmw-ifactory-digital.html" target="_blank">https://www.bmwgroup.com/en/news/general/2022/bmw-ifactory-digital.html</a></p><p><a href="https://www.cio.com/article/3975188/how-bmw-is-digitizing-automotive-production.html" target="_blank">https://www.cio.com/article/3975188/how-bmw-is-digitizing-automotive-production.html</a></p><p>https://www.iotworldtoday.com/smart-cities/bmw-outlines-new-sustainable-focused-production-strategy</p>

A case study shows how a digital twin can uncover insights into Battery Energy Storage System (BESS) performance. Digital twins are rapidly becoming a cornerstone technology in the energy sector — especially for large-scale Battery Energy Storage Systems (BESS). In a 21.6 MWh installation in Zuidbroek, the Netherlands, a digital twin demonstrated how deeply data-driven models can illuminate the true condition and performance of a battery system.

Making the Invisible Visible

While the built-in Battery Management System (BMS) provided only limited visibility, the digital twin uncovered a previously hidden deviation of around 4% in the system’s State of Health. A small number — but one with major implications for lifetime prediction, operational strategy, and revenue optimization.

Just like intelligent engines in aviation, this digital twin allowed operators to get ahead of emerging issues, detecting them long before they could become critical.

A System That Learns and Acts Predictively

The digital twin integrates real-time operating data with physics-based models on cell, module, and system level. Electrical, thermal, and chemical parameters converge into a living, learning representation of the asset.

By simulating thousands of operational scenarios, the twin enables decision-making that is precisely aligned with stress factors, market signals, and environmental conditions.

This transforms the BESS into a system that is not only operated — but understood, optimized, and continuously improved.

Greater Efficiency, Lower Risk, Higher Returns

The digital-twin-enabled approach delivers critical advantages:

Predictive maintenance: early detection of degradation and anomalies

Performance optimization: improved cycling, charging strategies, and market-response behavior

Reliability & safety: transparent insights from individual cells up to the full installation

Economic value: increased revenue potential while extending system lifetime

Each of these contributes to making BESS assets more stable, predictable, and profitable — all essential characteristics in today’s volatile energy landscape.

A Glimpse into the Future of Energy Storage

This case study clearly demonstrates: digital twins are more than monitoring tools. They are strategic enablers that turn complex energy storage infrastructure into intelligent, resilient assets.

With every insight produced by the digital twin, the battery becomes not just a system that stores energy — but an asset that can be optimized, safeguarded, and strategically steered.

<p>https://25373524.fs1.hubspotusercontent-eu1.net/hubfs/25373524/Marketing/White%20Papers%20-%20In%20use/20250430_3E_Mastering_storage_performance_report.pdf</p>

The CARDIMED project (H2020, GA: 101112731) is making important advancement in water management by developing a Hybrid Digital Twin of the real-world industrial water network infrastructure. Digital Twins hold great potential towards continuous monitoring, analysis, and optimization of operations, ultimately improving efficiency and sustainability.

A key application that the CARDIMED project explores relates to industrial water management, through recovery of rainwater, stormwater and wastewater and reutilization in other processes, cooling, cleaning, storage, and redistribution in other plants. Key objectives of this application include ensuring water quality compliance to safeguard that the water meets established safety standards, and asset management to enhance water reuse across different industrial processes.

Beyond monitoring, the Digital Twin also plays a critical role in optimizing operational strategies by evaluating different approaches to improve network performance. It further supports the implementation of Nature-Based Solutions by tracking key performance indicators over time, ensuring that these environmentally friendly initiatives are effective. Additionally, it features a failure alert system, detecting and managing potential breakdowns to maintain reliable operations.

The Hybrid Digital Twin is built on an advanced digital framework that ensures seamless real-time synchronization between the physical and digital entities. A network of sensors continuously collects data from the physical water network, including key assets such as pumps, heat exchangers, and cooling towers. This real-time data is transmitted and integrated into the digital model, allowing for dynamic updates of the hydraulic system and asset performance. The collected data undergoes advanced analytics, providing actionable insights that predict future system behavior. These insights, derived from physical modeling, predictive models and automated decision-making tools, enable the real-time optimization and proactive management of the industrial water network.

In conclusion, the CARDIMED Digital Twin will revolutionize water management by providing a smarter, more efficient, and more sustainable approach to industrial water use.

<p>CARDIMED Horizon Europe project, "Climate Adaptation and Resilience Demonstrated In the MEDiterranean region", Grant agreement ID: 101112731</p>

As part of the MOSES project, under the EU's Horizon 2020 framework, CORE has pioneered the transition from traditional manual docking procedures to an autonomous swarm of tugboats. These advancements were made possible by creating a sophisticated simulation environment and training the agents through deep reinforcement learning techniques to optimize docking strategies. This digital twin technology, coupled with an AutoPilot control system, exemplifies a significant leap forward in maritime operations, reducing docking time and enhancing port service availability and environmental sustainability.

The docking procedure of large vessels and containerships is a very important and complex operation in the maritime sector and typically involves a series of different subprocesses for its execution. Existing docking procedures rely on manually operated conventional tugboats, where the tugboat captain has to detect the vessel and its approach point, approach delicately with an allowed velocity, and berth the ship in a collaborative way while interacting with fellow tugboat captains. During all these actions, the captain must deal with a dynamic environment and many uncertainties, which increases the docking time and, hence, affects the port services availability and port emissions.

As part of the MOSES project (H2020, 861678), a cross over from the current manual system to an autonomous tugboats swarm system has been accomplished, to assist in the manoeuvring of a containership and docking at berth. As a first step, a virtual environment had been created, utilising the Unity3D software and its associated modules, to simulate the real-life components, such as the port, water mass, tugboats and containership. To create a virtual environment as close as possible with the real one a series of hydrodynamic simulations were conducted to analyse the navigation and evaluate the hydrodynamic parameters, such as the friction resistances for each ship object separately. Additionally, Finite Element Model simulations (FEM) were employed to assess the interactions between the tugboats and the containership, by evaluating the force-reactions and stresses.

The simulation environment served as a training environment for the developed swarm intelligence machine learning algorithm by allowing agents to learn from their experiences. Specifically, through the ML-Agents toolkit the agents (tugboats) were extensively trained utilising deep reinforcement learning techniques, where the learning procedure is based on the interaction of the agents with the environment and the accumulation of feedback (rewards or penalties), while the agents collected observations through LiDAR and GPS sensors. The goal is to discover optimal strategies that maximize cumulative rewards over time. The digital twin was deployed at the edge, along with an AutoPilot system to control the steering and thrust of the tugboats based on the digital twin’s inference. The digital twin was successfully demonstrated at the Faaborg port in Denmark (at TRL6), when applied in a swarm of two tugboats achieving over 25% reduction of the manoeuvring and docking time and, hence, a corresponding reduction of port emissions and increase of port services availability.

<p class="MsoNormal" style="margin: 0cm; text-align: justify; font-size: 11pt; font-family: Aptos, sans-serif;"><span lang="EN-GB"><a href="https://moses-h2020.eu/" style="color: rgb(70, 120, 134); text-decoration: underline;">https://moses-h2020.eu/</a><o:p></o:p></span></p><p class="MsoNormal" style="margin: 0cm; text-align: justify; font-size: 11pt; font-family: Aptos, sans-serif;"><span lang="EN-GB">&nbsp;</span></p><p class="MsoNormal" style="margin: 0cm; text-align: justify; font-size: 11pt; font-family: Aptos, sans-serif;"><span lang="EN-GB"><a href="https://www.youtube.com/watch?v=28P-BRpVXRY&amp;ab_channel=MOSESProject" style="color: rgb(70, 120, 134); text-decoration: underline;">https://www.youtube.com/watch?v=28P-BRpVXRY&amp;ab_channel=MOSESProject</a><o:p></o:p></span></p><p class="MsoNormal" style="margin: 0cm; text-align: justify; font-size: 11pt; font-family: Aptos, sans-serif;"><span lang="EN-GB">&nbsp;</span></p><p class="MsoNormal" style="margin: 0cm; text-align: justify; font-size: 11pt; font-family: Aptos, sans-serif;"><span lang="EN-GB"><a href="https://www.researchgate.net/publication/371260713_MOSES_Autonomous_tugboat_swarm_operation_Operational_scenarios_requirements_and_architecture" style="color: rgb(70, 120, 134); text-decoration: underline;">https://www.researchgate.net/publication/371260713_MOSES_Autonomous_tugboat_swarm_operation_Operational_scenarios_requirements_and_architecture</a><span class="MsoHyperlink" style="color: rgb(70, 120, 134); text-decoration-line: underline;"><o:p></o:p></span></span></p><p class="MsoNormal" style="margin: 0cm; text-align: justify; font-size: 11pt; font-family: Aptos, sans-serif;"><span lang="EN-GB">&nbsp;</span></p><p class="MsoNormal" style="margin: 0cm; text-align: justify; font-size: 11pt; font-family: Aptos, sans-serif;"><span lang="EN-GB"><a href="https://www.core-innovation.com/" style="color: rgb(70, 120, 134); text-decoration: underline;">https://www.core-innovation.com/</a>&nbsp;<o:p></o:p></span></p>