By calibrating the closed-loop digital twin of the intralogistics industry, warehouse managers are running operations at the highest level, releasing orders for emergencies.

The end of each year provides a familiar adverb for the intralogistics industry. As people around the world buy holiday gifts and merchandise, distribution and fulfillment centers keep chaos through round-the-clock selection, transportation, storage, and recording. As the trend continues to shift from physical stores to online virtual queues, the restriction on the number of transactions that can occur at any given time is removed, which is further aggregated. Fortunately, digitization continues to provide new methods for intelligent and efficient operations in warehouse environments. It is well known in the intralogistics industry that modeling and simulation can reduce design and development costs, while reducing the time it takes to troubleshoot equipment on-site during commissioning. But perhaps there is another little-known but equally important benefit. Simulation can be used to continuously optimize operational efficiency, especially when using production data to calibrate the model. This method is called closed-loop digital twin (CLDT). A digital twin is a virtual representation of a physical asset, and CLDT expands it by using historical data to improve accuracy over time. CLDT provides insights and suggestions throughout the design, commissioning, and operation stages to improve system efficiency. With the help of intelligent edge hardware and cloud analysis, CLDT can help users make wise decisions to adjust operations during system service.  

Employees of internal logistics facilities face the daunting task of maintaining the best key performance indicators (KPIs), despite:

The limited size of the operations center requires intelligent use of technology to more efficiently use the limited space (Figure 1). It also requires effective layout planning, strategic placement of products, and increased material flow availability—while maintaining maintainability and performance. Figure 1: Tall storage and shuttle shelves, using advanced lifting and transportation technology, make full use of the available space in the distribution and fulfillment center. To overcome these obstacles to successfully carry out distribution and fulfillment center operations, more applications of digital and efficient automation concepts are required, and special attention must be paid to cutting costs and reducing risks when installing new material handling equipment. And in order to remain competitive and maintain the ability to meet new orders, companies must increase the speed of fulfillment. Facility models can help employees identify key production points to achieve positive operational goals, but many of these models are rigid at best or inaccurate at worst. To maximize efficiency and productivity, employees need a model that can be adjusted, but most facilities do not have trained personnel or time to perform these adjustments manually. In addition, with so many control variables, it is even difficult to know what to simulate in the model. Models guided by artificial intelligence (AI) and machine learning (ML) can provide answers to these and other questions. Although challenges abound, there is a way to optimize efficiency and meet the many needs of modern fulfillment centers.  

The digital twin approach provides precise insights for optimizing parameters to improve KPIs. Extending this concept, CLDT creates an accurate copy of the current state of the asset to predict accuracy beyond the standard digital twin without feedback. With the help of AI/ML, CLDT compares current state conditions with multiple adaptations to determine the best future state. Advanced simulation software can help warehouse managers to digitize the value chain during conceptual design, during warehouse site commissioning, and after deployment to review and analyze methods to improve operational efficiency. Facility simulation tools provide a virtual view of distribution and fulfillment center operations. When designing, software engineers and facility designers can create a multi-dimensional warehouse environment to simulate the operation of the facility in three dimensions.  

In the development and commissioning process, digital twins, combined with programmable logic controllers (PLC) and human-machine interface simulation, can help engineers identify errors and inefficiencies in machines long before physical equipment and moving parts appear. This setup makes it easy to detect configuration problems early, update the code, load changes into the simulation, and verify machine functionality. Then, users can simulate the distribution center layout, visualize material flow, monitor PLC, configure intelligent industrial equipment, and apply advanced statistical tools to analyze the process. These activities establish facilities for peak operations.  

Throughout the entire operation process, the closed-loop system can provide maximum value when used together with the cloud collector that ingests production data, and provides continuous fine-tuning to achieve the best operating state. Using a digital twin for simulation provides:

Closing the loop and providing a simulation with historical data greatly improves the accuracy of the simulation. The resulting CLDT uses a combination of simulation and AI/ML to provide reports with the best parameters (such as machine settings, labor distribution, and transportation/receiving capabilities) to achieve fine-tuning of operations. Facility employees can set up automatic report generation within a specific time frame—for example, before or during shifts—or prepare for daily employee meetings. Engineers can run these processes and monitor each process in real time, simulate production data, and optimize facility configurations to determine more efficient designs (Figure 2). Monitoring includes the ability to visualize real-time PLC input/output updates based on program logic. Figure 2: Siemens factory simulation tools can be used to create digital twin models to optimize the design of new facilities or to test ways to improve existing warehouse operations. When human-based analysis is needed to enhance the decision-making process, easy-to-understand model visualization can provide insight into facility operations. Visual simulation helps employees identify production bottlenecks and areas where excessive resources are allocated. It can simulate multiple scenarios to answer situational questions-such as "What if there are fewer employees at the picking station?" or "If too many robots are sent to one area (for example, picking) instead of another area (for example, Loading) What should I do?". CLDT software enables users to adjust control variables and visualize their effects on operations, thereby quickly solving these and other problems. Through simulation iteration, the robot can learn the best path and movement, and the trackless automatic guided vehicle (AGV) can determine the best route. The software runs thousands of possible motion schemes, while considering all the suggested equipment and its location on the warehouse floor, and provides the most efficient robot motion and AGV route. The model will continue to be optimized over time because it will ingest historical data. Visual elements help users better understand the numbers and point out where changes need to be made. These software tools evaluate the optimal utilization of machines and labor—for example, ensuring that the warehouse has enough trucks in the loading area to handle shipments, but not too many. When a large number of parameters and theoretical combinations are involved, automated software helps eliminate redundant or impractical experiments by intelligently identifying feasible experiments. This can reduce thousands of combinations to tens or less, and finally determine the best parameter set (Figure 3). Figure 3: Siemens factory simulation software and its HEEDS design exploration and optimization engine to determine suitable variants and optimal designs.

To calibrate the CLDT, the first step is to connect the digital twin to an automated device to provide data to the model. Edge devices are the main interface for data collection because they can preprocess machine data before sending it to the cloud, where it is used to synchronize with CLDT's historical data-based optimization algorithm (Figure 4). Figure 4: Edge devices are the link between facility floor automation and data analysis. The use of industrial edge controllers reduces the number of devices connected to machines on the factory floor and provides a method for advanced analysis in local simulation solutions or cloud-based simulations. Through the hybrid cloud connection solution, edge devices provide remote access to machine data, enable data preprocessing at the field level, and unlock more data analysis tools in the cloud. With an on-premise solution, they save the data locally, use onboard edge applications to process the data with minimal delay, and provide results quickly.  

In addition to its role in CLDT, edge devices can also detect and notify employees of operational problems, such as conveyor belt drive systems. By flagging problematic situations before equipment failures, they create opportunities to solve problems during off-peak periods. This helps factory personnel identify spare parts, reduce the impact of long delivery times, and reduce downtime when product demand is highest. By performing simulation in the cloud on the open IoT cloud platform, users can map data from the factory floor to the digital twin model. This data can then be used to create insights to optimize conditions and control variables on the production line to maximize throughput and other KPIs. Some cloud platforms include a dedicated application for preparing time series data and aggregating it into a simulation application (Figure 5). Figure 5: Siemens industrial edge and cloud native applications enable users to calibrate their digital twins using historical production data to improve the accuracy of their discrete event simulations and help optimize operating parameters on facility floors. Over time, the accuracy of the model will improve because more and more data is collected and aligned with the control variable input and prediction. For example, due to the increase in station time, the loading area may be busier than the model predicts, and the model will adjust accordingly based on the data. This ensures that the next production forecast is more accurate.  

After the model is online, users can view the entire facility floor-including synchronously operating conveyor belts, automatic elevators and scanners, stacking lights, AGVs, and material flows. In addition, many other equipment types can be performed in the simulation workspace:

A stacker crane manufacturer created a machine monitoring dashboard in the Siemens MindSphere cloud to display data from industrial edge devices. The dashboard displays utilization, energy consumption, running time, drive and motor data, alarms, and other statistical data within a user-selectable time range-helping users improve OEE. In another example, a global shipping company implemented an ultra-modern sorting and transportation system at one of its international airport locations (Figure 6). Figure 6: An ultra-modern sorting and transportation system implemented by a global shipping company at one of its international airports. Using Siemens components, including S7-1500 PLC and SINAMICS drives, the shipping company doubled its sorter transport speed and peak capacity to 8 feet per second and 9,000 packages of different sizes per hour.  

With the ever-increasing social ties and the continuous advancement of warehouse technology, the expectations of customers and enterprises are also increasing. Left to outdated tools and methods, distribution and fulfillment centers cannot keep up. By using tools such as closed-loop digital twins, managers can stay ahead. Digital twins have been widely accepted and can identify potential problems early in design and development, reduce errors and speed up physical debugging, but their value will multiply during operation. By connecting the digital twin to operational data and calibrating the model, facility managers can unlock automatically generated production insights and optimization recommendations. The calibrated CLDT reduces the time required to manually monitor production data and eliminates the manual guesswork involved in planning program changes and reallocating resources to improve efficiency. This means simplified operations, higher productivity, reduced downtime and increased on-time delivery. Especially in the seasons characterized by shipping delays and lost orders, the use of progressive digital fulfillment centers can ensure reliability in demanding markets. All data are provided by Siemens.

Colm Gavin is the product portfolio development manager of Siemens Digital Industries Software. He is responsible for promoting digital themes for machine and production line manufacturers in the United States. He has worked for Siemens for more than 21 years, using his experience in discrete manufacturing to help the company take advantage of the new innovations brought about by Industry 4.0. Prior to his current position, Colm was responsible for the sales of Siemens' Totally Integrated Automation Portal software in the United States and developed the software in cooperation with Siemens in Germany.

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