Closed-Loop Digital Twins Keep Holiday Shipping on Schedule

Closed-Loop Digital Twins Keep Holiday Shipping on Schedule
Closed-Loop Digital Twins Keep Holiday Shipping on Schedule

The conclusion of every year provides a familiar refrain in the intralogistics industry. As people worldwide purchase holiday gifts and goods, distribution and fulfillment centers keep the shuffle moving by picking, shipping, stocking, and recording around the clock. This is further aggregated as trends continue to skew away from brick-and-mortar stores toward online virtual queues, removing limits on the number of transactions occurring at any given time.
Thankfully, digitalization is continuously providing new methods for intelligent and efficient operation in warehouse settings. Modeling and simulation are known in the intralogistics industry to lower design and development costs, while reducing time spent troubleshooting equipment in the field during commissioning.
But there is perhaps a lesser-known but equally important benefit. Simulation can be used to continuously optimize operational efficiency, especially when using production data to calibrate a model, a methodology known as a closed-loop digital twin (CLDT). A digital twin is a virtual representation of a physical asset, and a CLDT expands on it by using historical data to improve accuracy over time.
CLDTs provide insights and recommendations throughout design, commissioning, and operations phases to improve system efficiency. And with the help of intelligent edge hardware and cloud analytics, CLDTs help users make informed decisions to adjust operations while a system is in service.

Intralogistics issues

Staff at intralogistics facilities are faced with the difficult task of maintaining optimal key performance indicators (KPIs) despite:

  • daily and unplanned changes in employee numbers.
  • large, and often unexpected, orders.
  • package handling bottlenecks.

The finite size of fulfillment centers requires the intelligent use of technology to make more efficient use of limited space (Figure 1). It also requires effectively planning the layout, strategically placing products, and improving material flow availability—while preserving maintainability and performance.

Figure 1: Tall storage and shuttle racks, with advanced lift and transport technology, make the most of available space in distribution and fulfillment centers.

Overcoming these hurdles to successful distribution and fulfillment center operation requires increasing application of digitalization and efficient automation concepts, with special focus required to cut costs and reduce risks when installing new material handling equipment. And to remain competitive and maintain the capacity to fill incoming orders, businesses must increase fulfillment speeds.
Facility models can help staff identify critical production points to achieve aggressive operational targets, but many of these models are rigid at best or inaccurate at worst. To maximize efficiency and productivity, staff need a model that can be tuned, but most facilities do not have the trained personnel or the time to manually perform these adjustments. Additionally, with so many control variables, it is difficult to know what to even simulate in a model. Models guided with artificial intelligence (AI) and machine learning (ML) can provide answers to these and other problems.
Though the challenges abound, there is a way to optimize efficiency and meet the many needs of modern fulfillment centers.

Digital twin during design phase

The digital twin methodology provides precise insight for optimizing parameters to improve KPIs. Expanding on this concept, a CLDT creates an accurate replica of assets’ current states to forecast accuracy, beyond that of a standard digital twin without feedback. CLDTs compare current state conditions with numerous adaptations to determine an optimal future state, aided by AI/ML.
Advanced simulation software helps warehouse managers digitalize the value chain at the time of conceptual design and during commissioning on the warehouse floor, and after deployment for review and analysis of methods to improve operational effectiveness.
A facility simulation tool provides a virtual view into distribution and fulfillment center operations. At design time, software engineers can create a multi-dimensional warehouse environment in conjunction with the facility designers, enabling a three-dimensional simulation of how the facility will perform.

Improving commissioning with virtual tools

During development and commissioning, a digital twin combined with programmable logic controller (PLC) and human machine interface simulation helps engineers identify bugs and inefficiencies in a machine, long before physical equipment and moving parts come into the picture. This setup makes it easy to find configuration problems early on, update code, load the changes to the simulation, and verify machine functionality.
Users can then simulate the distribution center layout, visualize material flow, monitor PLCs, configure intelligent industrial devices, and apply advanced statistical tools to analyze processes. These activities set up the facility for peak operation.

Optimizing operations

Throughout operations, a closed-loop system provides its greatest value when used with cloud collectors that ingest production data, providing continual finetuning to achieve optimal operational states. Simulation with a digital twin provides:

  • experimentation of multiple states by modeling hours of production and estimating results in a matter of seconds.
  • prediction of important KPIs, including throughput, utilization, idle time and others.

Closing the loop and supplying a simulation with historical data greatly improves simulation accuracy. The resulting CLDT enables finetuning of operations by providing reports with optimal parameters such as machine settings, labor allocations, and shipping/receiving capacities through a combination of simulations and AI/ML. Facility staff can set up automatic report generation at specific timeframes—for example, before or during shifts—or in preparation for a daily staff meeting.
Engineers can run these processes and monitor each in real-time, model production data, and optimize the facility configuration to determine a more efficient design (Figure 2). Monitoring includes the ability to visualize real-time PLC input/output updates in accordance with the program logic.

Figure 2: The Siemens Plant Simulation Tool can be used to create a digital twin for optimizing design of a new facility, or for experimenting with methods for improving operations in an existing warehouse.

When human-based analysis is required to augment a decision-making process, easily-understood model visualization provides insights into facility operations. Visual simulation helps staff identify production bottlenecks and areas where excess resources are allocated. It enables simulation of multiple scenarios to answer situational questions—such as ‘What happens if there are fewer associates at a picking station?’ or ‘What if too many robots are being sent to one area (e.g., picking) vs. another (e.g., loading)?’. CLDT software provides users with the capability to adjust control variables and visualize their effects on operations, quickly resolving these and other issues.
Through iterations of simulations, robots can learn optimal paths and movements, and trackless automated guided vehicles (AGVs) can determine best routing. The software runs thousands of possible movement schemes, taking into consideration all proposed equipment and their location on the warehouse floor, and provides the most efficient robot movements and AGV routes. The model continues to optimize over time as it ingests historical data.
Visual elements help users better understand the numbers and point out where changes need to be made. These software tools evaluate the best utilization of machines and labor—for instance, ensuring a warehouse has enough trucks available at the loading bay to handle outgoing shipments, but not too many.
In situations involving a large number of parameters and theoretical combinations, automated software helps eliminate redundant or impractical experiments by intelligently identifying those that are feasible. This can reduce thousands of combinations to tens or fewer, ultimately identifying the best set of parameters (Figure 3).

Figure 3: Siemens Plant Simulation software, with its HEEDS design exploration and optimization engine, determines suitable variants and the best design.

Edge devices calibrate the closed-loop digital twin

To calibrate the CLDT, the first step is connecting the digital twin with the automation equipment to feed data to the model. Edge devices are prime interfaces for data collection because they can preprocess machine data before sending it to the cloud, where it is used for synchronization with the CLDT’s historical data-based optimization algorithm (Figure 4).

Figure 4: Edge devices are the link between facility floor automation and data analytics.

The use of industrial edge controllers reduces the number of devices connecting to machines on the plant floor, and it provides the means for an on-premises simulation solution, or advanced analysis in a cloud-based simulation.
With hybrid cloud-connected solutions, edge devices provide remote access to machine data, enable data pre-processing at the field level, and unlock further data analytics tools in the cloud.
With on-premise solutions, they keep data local, process it with minimal latency using onboard edge apps, and quickly provide results.

Simplifying IT-OT integration

In addition to the role they play in the CLDT, edge devices can detect and notify staff of operational issues, such as with conveyor drive trains. By flagging problematic conditions prior to equipment failure, they create opportunities to address issues during off-peak periods. This helps plant personnel identify spare parts, mitigating impacts of lengthy lead times, and reducing downtime when product demand is highest.
By performing simulation in the cloud on an open IoT cloud platform, users can map data from the plant floor to the digital twin model. This data can then be leveraged to create insights for optimizing conditions and control variables on the production lines to maximize throughput and other KPIs.
Some cloud platforms include a dedicated app for preparing and aggregating time series data into a simulation application (Figure 5).

Figure 5: Siemens Industrial Edge and Cloud native apps empower users to calibrate their digital twin with historical production data to improve the accuracy of its discrete event simulation, helping optimize operational parameters on the facility floor.

The model accuracy improves over time, as more data is collected and aligned with control variable inputs and predictions. For example, a loading bay may be busier than the model predicted because station times increased, and the model adjusts accordingly based on the data. This ensures the next production predictions are more accurate.

Visualizing virtual devices

Once the model is live, users can view the entire facility floor—complete with conveyors, automated lifts and scanners, stack lights, AGVs, and material flows—operating in sync.
Additionally, many other equipment types can be staged in the simulation workspace:

  • high-density storage and retrieval systems for food, hard items, and consumer-packaged goods.
  • functional safety devices.
  • advanced optical identification devices for product movement tracing.
  • scalable and flexible shuttle systems.
  • control components for advanced shuttle vehicles, onboard and non-onboard.
  • real-time, fail-safe communication with industrial wireless local area networks for reliable, high-speed, and cyber-secure connectivity among devices in the warehouse.
  • industrial RFID systems for product index tracking.
  • edge computing devices.
  • compact and mobile controllers embedded in other equipment.


A manufacturer of stacker cranes created a machine monitoring dashboard in the Siemens MindSphere cloud to display data from industrial edge devices. The dashboard displays utilization percentage, energy consumption, operation hours, drive and motor data, alarms, and other statistics in a user-selectable time range—helping users increase OEE.
In another example, a global shipping company implemented an ultramodern sorting and conveyance system at one of its international airport locations (Figure 6).

Figure 6: An ultramodern sorting and conveyance system implemented by a global shipping company at one of its international airport locations.

Using Siemens components, including S7-1500 PLCs and SINAMICS drives, the shipping company doubled its sorter transport speed and peak capacity, achieving eight feet per second and 9,000 parcels of various size per hour, respectively.

CLDTs yield reliability and efficiency

As society becomes increasingly connected, warehouse technology is advancing, but customer and corporate expectations are increasing as well. Left to outdated tools and methods, distribution and fulfillment centers cannot keep up. By embracing tools like a closed loop digital twin, managers can stay ahead of the curve.
Digital twins are already widely accepted to identify potential issues early in design and development, reducing errors and speeding up physical commissioning, but their value multiplies during operation. By connecting digital twins to operational data and calibrating the models, facility managers can unlock automatically generated production insights and optimization recommendations.
A calibrated CLDT reduces the time required to manually monitor production data, and eliminates human guesswork involved in planning procedural changes and resource reallocation to increase efficiency. This translates to streamlined operation, higher productivity, reduced downtime, and increased on-time delivery. Especially during a season characterized by shipping delays and lost orders, fulfillment centers adopting progressive digitalization can ensure reliability in demanding markets.
All figures courtesy of Siemens.

About The Author

Colm Gavin is the Portfolio Development Manager for Siemens Digital Industries Software, and he is responsible for the promotion of digitalization topics for machine and line builders in the United States. With over 21 years at Siemens, he is leveraging his experience in discrete manufacturing to help companies take advantage of new innovations coming with Industry 4.0. Prior to his current role, Colm was responsible for marketing Siemens’ Totally Integrated Automation Portal software in the U.S., and he worked on the software’s development with Siemens Germany.

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