Digital twins have established themselves as a forward-looking tool in intralogistics, production and engineering. They promise numerous advantages and raise optimization potential in order processing. But what is behind the term, where are digital twins used and what are the benefits of real-time localization in warehouse operations?
Major changes in the market are affecting manufacturing companies and the logistics sector: customers expect ever faster delivery on a small ecological footprint in increasingly complex global supply chains. Solutions are revealed in technological progress: IoT, AI and autonomous forklift trucks promise optimized workflows in the global marketplace. Their integration in the warehouse is accompanied by numerous challenges, from reliable operation and data security to the question of return on investment (ROI). Digital twins are on everyone’s mind in this context and serve as a platform for integrating process-optimizing technologies. They enable the simulation and optimization of virtual processes before they are implemented in the warehouse. But what exactly is behind these digital twins?
Definition: What is a „digital twin“?
The digital twin is the virtual replication of a physical object, system or process. This digital representation makes it possible to collect, analyze and simulate data in order to better understand, optimize and predict the real world counterpart. The digital twin is provided with data by sensors (such as position and distance sensors, temperature sensors, optical sensors, gas and chemical sensors or force and pressure sensors) and other data sources that reflect the state, working conditions or location of the physical object. This makes it a powerful method for virtually mapping physical worlds to gain profound insights, improve decision-making and optimize processes.
Examples: “digital twins” in production, engineering and logistics
Digital twins are made up of a physical object that is depicted in the virtual model. It simulates the physical object and replicates it. The prerequisite for this is data (big data). The more data and the more up-to-date it is, the more meaningful the predictions of the digital twin are. Real-time data is essential in the automated factory, for example in the warehouse when using autonomous forklift trucks or robots. Networks and communication protocols that enable data exchange between the physical object and the virtual model are another key component. There are various types of digital twins that differ in terms of complexity, depth of detail, data sources and interaction options depending on the specific requirements in the area of application:
Best Practice: ELA Container introduces digital twin for transparent warehouse processes
ELA Container is a globally active company for mobile space and building solutions made from containers. The time-consuming manual warehouse management in the 50,000 container rental park was to be replaced by a solution that offers complete transparency of the warehouse stock with automated bookings using real-time localization (3D position data). A completely search-free warehouse was chosen with the Warehouse Execution System from IdentPro. It switched from WLAN to LTE for future-proof signal transmission. The forklift trucks were equipped with LiDAR sensors to record the surroundings and all movements to the nearest centimeter and second. Software modules for creating the digital twin in real time, visualization and a 3D forklift guidance system were set up. As a result, the loading time was reduced from 2 hours to 10 minutes and an ROI of less than 15 months was achieved. Evaluations of the real-time warehouse data successively lead to further warehouse and route optimizations.
Benefits: Transparency, optimized processes, informed decision-making
Digital twins enable real-time monitoring of all movements in the warehouse. The virtual simulation of the warehouse allows companies to determine optimal routes for the placement of goods, picking and packing processes and the use of automation technologies. The analysis of the material flow within the warehouse or between different locations enables the identification of optimal transport routes, the coordination of industrial trucks for maximum capacity utilization or more efficient picking and sorting processes.
Digital twins also do their service in maintenance and system management: real-time data monitoring allows the status of warehouse automation systems to be predicted and maintenance requirements to be identified at an early stage. This significantly reduces unplanned downtime. Safety and quality management scenarios can be simulated, as can the optimal use of resources to achieve ecological sustainability goals.
Digital twins create visibility across the entire supply chain and therefore also beyond the warehouse: delays can be identified at an early stage and counteracted. Digital twins also support collaboration with suppliers and logistics service providers. Simulations also help to make informed decisions before investments are made in expanding warehouse capacities or introducing new technologies.
This is what IdentPro’s real-time digital twin brings to your warehouse:
Future prospects: Digital twins in a global networked world
Digital twins are the basis for fully interlinked and autonomous warehouses with extensive use of IoT and AI. They are already an essential part of modern manufacturing companies and intralogistics. Where large players already have a knowledge advantage over smaller market players with fewer resources, the actual optimization potential is far from exhausted. The industry is moving towards warehouse management that is controlled by networked digital twins and autonomous as well as manned forklift trucks. This is because both competitive pressure and rising customer expectations are increasing the need for optimized, transparent logistics processes. AI, machine learning and real-time data analysis are making digital twins increasingly efficient. Cross-linked and synchronized digital twins across company boundaries promise increasingly efficient supply chains. Falling implementation costs due to technological progress and targeted support programs will further accelerate the development of a standard and the increasing implementation of virtual twins in the coming years.
Next Step: Seven steps to a digital twin
analysis of requirements and target setting
proof of concept and pilot project
data infrastructure and integration
technology selection and partners
implementation and training
monitoring and optimization
scaling and further development
Wiki: Digital twin
Augmented Reality: A technology that embeds digital information in the real world. It can be used to display data and visualizations of the digital twin in the physical environment.
Big Data: The large amount of structured and unstructured data that is collected. Digital twins often require the processing and analysis of big data to enable precise models and predictions.
Cloud Computing: The provision of computing resources via the internet. Cloud services are often used to store and process data and to access digital twins.
Cyber-physical system (CPS): A combination of computerized elements and physical processes that are closely connected and influence each other. Digital twins are often part of CPS.
Digital Thread: A consistent digital representation of information over the entire life cycle of a product or system, which enables the connection between design, production, operation and maintenance.
Digital Twin: The virtual representation of a physical object, process or system that makes it possible to monitor, analyze and simulate its behavior and performance in real time.
Edge Computing: Processing data close to the source where it is generated (e.g. in a warehouse or factory) to reduce latency and improve real-time processing.
Internet of Things (IoT): A network of physical devices, vehicles, buildings and other objects equipped with sensors, software, electronics and connectivity to collect and exchange data.
Machine Learning and Artificial intelligence (AI): Technologies that enable the digital twin to learn from data, recognize patterns and make decisions to improve or automate processes.
Simulation and modeling: The process of creating models that replicate the behavior of a real object or system. These models are often used for predictions, analyses and tests.