PROJECT EXAMPLES

Project 1: Enhancing Last-Mile Delivery Efficiency through Urban Digital Twins

Background: Last-mile delivery faces increasing challenges with urban congestion and environmental concerns due to rising e-commerce demand. Traditional logistics solutions, such as route optimization, often fall short in dynamic urban settings where real-time traffic, weather, and pedestrian flows impact efficiency. Urban digital twins, which are virtual models of urban environments updated with real-time data, offer a solution by providing logistics providers with up-to-date insights for adaptive route planning and demand forecasting. This approach enables more efficient, sustainable delivery operations, helping logistics companies and cities meet delivery demands while reducing environmental impacts. Student Activities: The IRES students will leverage generative AI techniques and digital twins to improve last-mile delivery systems in urban environments. The students will create digital twins that predict traffic patterns and analyze urban infrastructure. The generative AI component will utilize real-time data from various sources, including GPS, traffic cameras, and historical delivery trends, to continuously adapt and improve delivery routes.

Project 2: Digital Twin for Supply Chain Disruption

Background: Supply chain disruptions affect global trade, which highlights the need for robust predictive tools. The use of digital twins for supply chain disruption is becoming critical in the face of growing uncertainty and the need for more resilient, adaptive supply chains. Recent studies demonstrated that companies using digital twins can reduce the time needed to respond to supply chain disruptions, which minimizes financial losses and maintains service levels during crises . Further, the integration of digital twins with other emerging technologies such as blockchain can enhance transparency and security, further strengthening supply chain resilience. Student Activities: Students will develop a digital twin of a critical component of the supply chain to model potential disruptions such as supplier failures or transport delays. The focus will be on using past disruption data to predict future challenges and to devise quick-response strategies. The effectiveness of different mitigation strategies under simulated conditions will be analyzed.

Project 3: Digital Twin for Warehouse Inventory Optimization

Background: Overstocking or understocking lead to increased supply chain operational costs. Efficient warehouse management is a critical component of supply chain operations, with inventory optimization playing a central role in reducing costs, improving service levels, and enhancing overall operational efficiency. However, modern warehouses face significant challenges due to increasing product variety, shorter delivery times, and unpredictable demand patterns. Traditional inventory management techniques often fall short of addressing these complexities, especially in dynamic and uncertain environments. The use of digital twins for warehouse inventory optimization can reduce inventory holding costs while improving order fulfillment rates and reducing stockouts. Student Activities: Implement a digital twin model for real-time monitoring and management of warehouse inventory. The model will predict stock levels based on varying demand patterns analyzed through historical data. Students will test the model by applying it to real-time data from a Hamburg warehouse during their stay.

Project 4: Digital Twin for Small-Scale Maritime Logistics Model

Background: Small-scale maritime operations (e.g., short-sea shipping, inland waterways, and coastal transport) are often overlooked in broader logistic strategies. These operations are often used for regional distribution, inter-island trade, or short-distance transport of goods to ports for larger international shipping. Digital twins can enhance the management of small-scale maritime assets by utilizing real-time data from sensors embedded in ships to provide insights into the condition of critical components such as engines, hulls, and cargo holds. The digital twin can analyze this data to predict potential equipment failures and schedule preventive maintenance, reducing downtime and ensuring that vessels are in optimal condition for transport. Student Activities: IRES students will develop a digital twin to simulate operations of small vessels, focusing on optimizing schedules and cargo handling. The twin will use historical weather data and port activity logs to plan and adjust maritime operations to improve punctuality and reduce turnaround times.

Project 5: Fleet Management for Autonomous Ground Vehicles Utilizing Digital Twins

Background: Autonomous vehicles (AVs) in logistics can improve efficiency but require sophisticated coordination systems. Fleet management for AVs involves coordinating the movement, maintenance, and operational scheduling of multiple vehicles across various environments. Traditional fleet management systems often struggle to cope with the dynamic and unpredictable nature of autonomous vehicle operations, particularly in complex environments such as ports, warehouses, or urban areas. By simulating the movement of vehicles in a virtual environment, the digital twin can analyze traffic conditions and dynamically adjust routes to minimize delays and maximize productivity. Student Activities: IRES students will develop a digital twin for a small fleet of autonomous vehicles. The model will simulate vehicle operations, including route optimization, battery management, and load balancing. Students will apply real-time data from a test fleet in Hamburg to validate the model.

Project 6: Digital Twin for Efficient Intermodal Connections

Background: Intermodal transportation, which involves the movement of goods using multiple modes of transportation, plays a crucial role in global logistics. Efficient intermodal connections are essential for reducing transit times, minimizing costs, and ensuring the seamless flow of goods across different transportation networks. Managing intermodal connections requires coordinating various transportation modes and precise synchronization of schedules between modes .  Using a digital twin in intermodal logistics can provide the ability to predict and mitigate potential disruptions. Real-time data from ports, rail yards, and road networks can be utilized to monitor traffic, weather conditions, and equipment availability. This allows for real-time adjustments to delivery schedules to ensure goods are transferred between modes with minimal delays. Further, the digital twin can simulate alternate routing options and evaluate the impact of different transportation strategies, such as rerouting goods to less congested terminals or adjusting departure times to avoid traffic bottlenecks. Student activities: IRES students will construct a digital twin of an intermodal hub (e.g., where truck cargo is transferred to rail). The digital twin will analyze and optimize the timing and processes involved in the transfer of goods, with a focus on minimizing idle times and synchronizing inbound and outbound logistics flows.