Digital Twins Are Transforming Supply Chains

Supply chains today seem to operate in a constant state of flux. Demand shifts quickly. Transportation networks face ongoing volatility. Service expectations continue to rise while cost pressures remain intense.

In response, many organizations are investing in digital twins.

A digital twin is a virtual, real-time model of a supply chain. It integrates data from suppliers, transportation networks, warehouses, inventory, and demand signals into a single dynamic environment. This allows leaders to simulate decisions, test scenarios, and evaluate outcomes before making changes in the physical world.

With a well-built digital twin, organizations can anticipate disruption, evaluate network changes before committing capital, adjust inventory strategies dynamically, and stress test operations under a wide range of conditions. According to a 2025 study, companies implementing digital twins saw cost reductions along with improvements in responsiveness, sustainability, and customer satisfaction – all key to scalability and growth.

Yet despite the promise, many companies struggle to move beyond pilots and isolated use cases.

 

Building a Digital Twin System That Works

A digital twin is different from “just” a technology layer. It represents how the business actually operates. That means it must reflect real constraints, real variability, and real decision-making processes in order to enable growth.

Analysts from McKinsey found that digital twins can help enable “self-healing” supply chains, which can optimize and respond to pressures across inventory, transport decisions, energy efficiency, and more. The good news is, most organizations already have pieces of the puzzle, like visibility tools, planning systems, and analytics platforms.

What they often lack is a leader who can connect these elements into a cohesive, continuously updated model that the business trusts and uses.

At its core, the work starts with a deep understanding of the physical supply chain. Leaders must be able to map how goods move through the network, where constraints exist, how variability shows up, and how decisions in one area affect outcomes elsewhere. Without that grounding, models quickly drift away from reality.

Equally important is a practical understanding of data. Supply chain data is often fragmented across systems such as ERP, TMS, and WMS platforms. It is inconsistent, sometimes delayed, and rarely structured in a way that is immediately usable. Successful leaders know how to work within these constraints, improving data quality over time while still moving forward.

The next requirement is the ability to translate modeling into decisions. A digital twin creates value only when it changes how the business operates. That might mean adjusting safety stock levels, redesigning transportation flows, or shifting sourcing strategies. The strongest leaders can clearly connect simulation outputs to specific actions and measurable results.

Finally, there is the challenge of adoption. A digital twin must be used by planning, operations, transportation, and finance teams to have real impact. That requires alignment, communication, and trust. It also requires embedding the model into existing workflows rather than positioning it as a separate analytical exercise.

 

A Practical Lens for Evaluating Talent

Because digital twin initiatives sit at the intersection of multiple disciplines, evaluating talent requires a broader perspective than traditional hiring approaches.

A useful way to assess candidates is to focus on four core dimensions.

1. System Thinking: Strong candidates understand how different parts of the supply chain interact. They can explain how a change in sourcing affects transportation, how inventory decisions influence service, and how constraints propagate across the network.
2. Ownership: Distinguish between individuals who have observed digital initiatives and those who have built and implemented them. Relevant experience should include taking responsibility for outcomes.
3. Technical expertise matters, but the ability to communicate insights in a way that drives business decisions is what creates value. Leaders must be able to bridge the gap between data science and operations.
4. Execution. Digital twin initiatives often begin with strong momentum but lose traction during implementation. Candidates should be able to demonstrate how they drove adoption, aligned stakeholders, and sustained change over time.

Looking at talent through this lens helps organizations move beyond surface-level qualifications and focus on the capabilities that determine success. When it all comes together, growth and value creation are real. Take one example from DHL’s digital twin implementation:

• 20% increase in overall operational efficiency
• 15% improvement in space utilization
• 20% decrease in carbon emissions from warehouse operations
• 25% reduction in energy consumption
• Ongoing improvement in capacity planning, responsiveness, and waste reduction

 

Building and scaling a digital twin requires operational experience, analytical capability, technical fluency, and leadership presence. Most professionals develop depth in one or two of these areas. Fewer develop all of them in a way that translates into enterprise impact.

In addition, many of the most effective leaders in this space are already in roles where they are actively building and improving these capabilities. Partnering with a specialized, experienced partner, like GESG, can help open the doors to this passive talent pool.

 

Moving From Capability to Advantage

Digital twins represent an important shift in how supply chains are managed. They enable organizations to move from reacting to events toward anticipating and shaping outcomes.

However, the technology alone does not create that shift. The advantage comes from having the right people in place to design, build, and operationalize the capability in a way that the business can rely on.

Organizations that approach digital twins as both a technology investment and a talent strategy are far more likely to realize their full value. As supply chains become more complex, the ability to model, simulate, and adapt in real time will become a core capability rather than a competitive edge — and having the right leadership in place will set growth-oriented, value-creating organizations apart from the rest.