Seeing double: how digital twins will transform logistics and enable the shift from reaction to anticipation.

In recent years, the logistics industry has undergone a wholesale shift: Where once the only option to fulfilment challenges was via a reactive, spreadsheet-based approach, the rapid technological advances of recent years mean firms like FM Logistic now deploy solutions that deliver supply chain management that is predictive, proactive and robust.

At the heart of this transformation are three technologies that are frequently talked about but seldom fully understood: Digital twins, digital threads and simulations.

Thierry Dieudonne, Group Simulation and Data Crunching Manager at FM Logistic, says it is important to understand that this trio of terms – while often used interchangeably – have distinct, interconnected roles within a modern logistics ecosystem:

This approach contrasts with traditional simulation efforts that seek to address a specific issue, says Thierry Dieudonne, whether related to process improvement or flow optimisation – and that usually require investment.

“In contrast, the digital twin anticipates issues to help improve daily operations using currently available resources,” he says. “This anticipation element is where the value lies.”

Understanding this interconnection constitutes the first step in successfully implementing a digital twins approach – but, says Thierry Dieudonne, that is not the biggest obstacle to digital twin success. More significant is to ensure data quality and continuity of the digital thread. 

“Many operators fall into the ‘theoretical data trap’, and by that I mean that they believe standard theoretical calculations are enough,” he says. “That is not the case. A true digital twin only becomes powerful when fed with empirical data. Otherwise the outputs are likely to be flawed, which undermines the whole purpose of this approach.”

Another obstacle is that some believe that digital twins exist simply to justify major capital expenditure, such as warehouse expansions or the deployment of new robots. 

“Again, that’s not the case. The primary goal should be to find optimisation solutions using current resources, and without requiring any additional investment,” Thierry Dieudonne says, explaining that pre-project simulations can, for example, test the viability of a specific warehouse layout before a single brick is laid. Upstream of a project, digitisation or simulation/interactive presentations make it possible to visualise, simplify, and present concepts initially drafted on paper during the study phase.

“To give a real-world example: There was a simulation of a warehouse operation that was intended to evolve into a full digital twin after the investment was made,” he says. “But that never happened because the site’s commercial development changed, which meant the project was halted. In this case, the technology’s worth as a risk-mitigation tool was proven before any money was spent on that real-world build.”

An important point: implementing a digital twin requires both time and investment, and must be systematically integrated into continuous improvement processes to remain viable.

The value of digital twins in logistics goes further than avoiding costly capital outlays. When fuelled by clean, real-time data, digital twins can also solve major bottlenecks. By analysing usage histories and manufacturer databases, they can provide a predictive maintenance solution that stops machinery breakdowns before they happen, ensuring zero downtime. Digital twins also provide proactive volume management, guaranteed business continuity, and significantly reduced costs and lead times.

The benefits do not stop there. In Poland, for instance, FM Logistic used a digital twin for a client to resolve blockages on mechanised packing tables, which ensured that parcels reached their packing zones seamlessly. In another case, deploying a digital twin into an automated packing zone prevented bottlenecks by redefining aspects including sequencing, staff scheduling and warehouse management system (WMS) task assignments.

Simulations can help warehouses become more efficient in other ways too. Take standard picking processes: By simulating the preparation day, it can determine the best sequencing or slotting, saving time and effort for order pickers. And when it comes to co-packing, the system can suggest ahead of time which line is best for a particular work order, minimising parameter changes and optimising replenishments. 

“The benefits of digital twins in logistics are not limited to the warehouse floor,” he says, pointing out that they also allow logistics providers to react to sudden supply chain disruptions. 

“Simply by monitoring macroeconomic factors and social media trends – like a product going viral on TikTok – and by listening to the broader ecosystem, we can use a digital twin to turn global noise into actionable data,” Thierry Dieudonne says. “The digital twin runs immediate scenarios against that data to ensure that alternative logistical solutions are virtually tested before a crisis impacts the physical warehouse.”

Among the reasons for the rise of digital twins in logistics are greater market volatility and the impact of global crises. These shifts explain why digital twins have evolved from a luxury to a necessity.

“Simply put, it’s no longer viable to manage supply chains with static tools or purely theoretical calculations,” he says. “Operations need empirical data, they need AI and they need the technological capabilities to communicate across systems, including directly with robotics. In that way, they eliminate uncertainties before they affect operations.”

Not only do digital twins deliver service continuity; they can help with strategic planning to keep customers happy.

“By collaborating with clients to cross-reference these models with order forecasts, we can proactively test scenarios – whether that’s absorbing Black Friday shocks or redesigning an entire network,” says Thierry Dieudonne. “In these ways, digital twins help us to deliver maximum optimisation without the need for additional capital expenditure.”

Impressive though the technology is today, says Thierry Dieudonne, it will continue to evolve and improve. Within five years, he says, operators will be using these tools as far more than operational monitors – they will be strategic blueprints for the entire logistics network.

“One example here is network orchestration in which models that are currently isolated will merge into a connected Network Twin that orchestrates global hubs, instantly recalculating inventory when it detects a demand peak,” he says. “Another is what is called prescriptive autonomy which integrates AI so the system can self-optimise and make autonomous routing decisions without requiring human input.”

Other use cases will see operators increasingly deploy digital twins for green simulation to calculate the environmental impacts of fulfilment operations – for example, crafting solutions to cut the distances travelled by automated vehicles. They will also improve the ergonomics of distribution – ensuring heavy loads are distributed evenly during workload peaks to prevent injuries to employees.

Thierry Dieudonne envisions a day when digital twins will be a standard part of corporate tool chests, with executives using them before spending money or signing leases. For supply chains, the impact will be profound – these will not simply be managed; they will be mastered in the virtual world before a single physical product is even moved. 

“For all these reasons and more, digital twins in logistics will become ubiquitous, and far faster than most people imagine,” he says. “By combining digital threads with real-time digital twins and predictive simulations, firms will shift from their current reactive approach to firefighting problems to one that delivers zero-risk, fully automated strategic planning.”