The concept of digital twins has emerged as a powerful foundational tool to drive improvements in warehouse productivity and efficiency. To define what exactly it is, a digital twin is a virtual replica of a physical asset, process, or system. In the warehouse context, a digital twin can be created to represent the physical layout, inventory, equipment, and workflows of a warehouse. But do you really need to look specifically at digital twin solutions, or might some of these already be available in the automation you already have? Let’s start by looking at the true benefits of digital twin concepts and what they can specifically mean to bringing productivity and efficiency gains to you operation.

How the digital twin concept drives benefit

By using advanced analytics and machine learning algorithms, digital twins can provide real-time insights and recommendations to optimize operations, reduce costs, and increase productivity. Specifically, through modeling and simulation of a digital twin you’re able to ‘virtually try before you buy’ — modelling different scenarios quickly and easily without interrupting operations, or committing significant time and capital.

Simulation is the most widely used benefit of a digital twin. Simulation allows you to model hypothetical scenarios and physical changes without having to physically change the asset. Physical change (i.e., changing the structure of the warehouse, modifying processes, etc.) is often prohibitively expensive and a risk to business continuity.

A second benefit is visibility – with a real time model that leverages sensors / IoT for telemetry of various activities in the warehouse, you gain insight to where issues are, are able optimize weak areas, and have better situational awareness at your fingertips that otherwise you’d have to rely on walking around/people, excel reports after the fact, etc.

What are some of the specific areas where digital twin concepts can be of real value in productivity and efficiency?

Layout optimization

One of the key benefits of digital twins is that they enable warehouse managers to simulate different layouts and configurations to find the most optimal one before any physical changes are made. For instance, a digital twin can help managers determine the best location for each product based on its velocity, size, etc. This can reduce the time and effort required for picking and packing, ultimately leading to improved productivity.

Digital twins can also be used to optimize the placement of equipment and machinery, such as conveyors and pallet jacks. By simulating different layouts and testing the efficiency of each one, warehouse managers can identify the most effective configuration to maximize throughput and minimize bottlenecks.

Inventory management

Another area where digital twins can be beneficial is inventory management. By creating a digital twin of a warehouse’s inventory, managers can gain insights into the location, quantity, and movement of products. This can help them identify opportunities for optimizing inventory levels, reducing stockouts, and improving order fulfillment times.

Moreover, digital twins can enable managers to simulate different scenarios and evaluate the impact of each one on inventory levels and order fulfillment times. For example, they can test the effect of increasing or decreasing safety stock levels or changing the replenishment frequency of certain products. Another scenario could be to simulate the variability of demand/seasonality and its effect on inventory. If the digital twin extends beyond the DC to the supply chain, you can have a better evaluation of the entire chain’s responsiveness to these types of events.

Equipment monitoring

Digital twins can also be used to monitor and analyze the performance of warehouse equipment and machinery. By integrating sensors and other IoT devices with the digital twin, managers can track the real-time status and condition of equipment, such as conveyor belts and forklifts.

This data can be used to identify potential issues before they become problems, optimize maintenance schedules, and improve the lifespan of equipment. By using predictive analytics and machine learning algorithms, digital twins can also provide recommendations on how to optimize equipment utilization and reduce downtime. Twinning also allows warehouse managers to schedule repairs and replacements at the most convenient times, reducing the impact on operations.

Process optimization

Digital twins can also help warehouse managers optimize processes and workflows. By creating a digital twin of a warehouse’s processes, managers can identify areas where bottlenecks occur, where processes can be improved, and where resources can be better allocated.

For instance, they can simulate different scenarios to evaluate the impact of changing the allocation of resources, such as labor, on the productivity of the warehouse. They can also identify the most efficient picking and packing routes and allocate resources accordingly.

Furthermore, digital twins can be used to test the impact of new technologies or process improvements before they are implemented in the physical warehouse. This can help managers evaluate the feasibility and impact of changes before committing resources to them.

Real-time analytics and monitoring

Digital twins can provide real-time analytics and monitoring of warehouse operations. By integrating with sensors and other IoT devices, digital twins can track the performance of equipment, monitor inventory levels, and identify potential issues before they become problems.

This real-time monitoring can enable managers to quickly respond to changes in the warehouse environment, such as unexpected increases in demand or disruptions in the supply chain. By using machine learning algorithms, digital twins can provide recommendations on how to optimize operations in response to these changes, ultimately leading to improved productivity and efficiency.

A communication and training tool

In addition to these practical uses, digital twins can also help improve communication and collaboration within warehouses. With a digital twin, workers can visualize and understand the layout of the warehouse, which can improve communication and coordination. This can help reduce errors and improve overall efficiency.

Solving the complex question of where to start

There is no doubt that implementing digital twin concepts can be a game changer for many warehouse and DC operators. But as with many new technology alternatives, getting started with a digital twin is the complex mystery for most. Does a DC need to buy a bunch of IoT or PLC sensors to wire up throughout the warehouse, their truck fleet, or on individual products or associates? Do they purchase a 3D warehouse simulation and modeling tool? Do those products integrate easily with the WMS or with each other to form a Digital Thread? And if you are looking at any of these tools, do analytics (such as forecasting, order completion time prediction, ‘what-if’ analysis & simulation, etc.) come with any of them.

As a suggestion, perhaps the WMS can be an initial digital twin for the DC. It is in fact a digital (SW) representation of the inventory of the warehouse – where it’s stored on the racks, how it’s stored (quantities by case/each/carton), who buys it, how frequently, costs, etc. Most modern WMS’ provide forecasting and analytics. Modular software applications, including Lucas Systems warehouse optimization suite, integrate with the WMS to provide updates on product movement, such as what’s been picked, by who, and for what order. Can you use it to simulate and visualize various scenarios that get beyond basic spreadsheets, or management interfaces or overviews?

The real ‘killer application’ of a digital twin or any other such resource is in its ability to model, bring enhanced visibility or visualization capabilities or empower simulation. Whether a digital twin, modular app, software, WMS, of any combination thereof, the most dramatic value and benefit these tools can provide is being able to simulate changes to the physical asset or physical processes and see the results, using machine learning to determine optimal solutions, running analytics on the data to predict the future, and having a birds-eye view of warehouse operations in real time for greater situational awareness.

Mark McCleary is Chief Technology Officer at Lucas Systems, leading all technology research and product development. During Mark’s career, his proven management and execution skills have translated to key results, including the development of several notable products exceeding $100M in revenue, the growth of new business lines, and the development of several key technology differentiators.

Prior to joining Lucas, Mark was a senior engineering executive in the self-driving industry with Uber ATG and Aurora Innovation. In these roles, he led the design and development of autonomous passenger cars and trucks, with responsibility for safety-critical system architecture, autonomous behavior design, hardware and sensor system design, embedded software development, verification and validation, and vehicle integration.