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Why Digital Twins Are Becoming Essential in Automated Distribution Centers

Discover how digital twins help automated distribution centers boost decision confidence, optimize flow, and reduce execution risk before making costly changes.
As distribution centers scale automation, decision confidence—not machinery—has become the true differentiator. Digital Twins are emerging as a critical tool for understanding system behavior before operational or capital decisions are made.
 
Automation has transformed case-pick and pick-pack distribution. High-speed conveyors, automated storage, robotics, and advanced WMS platforms have dramatically increased throughput and consistency. But as automation density increases, so does system complexity.
 
In highly automated environments, the challenge is no longer whether operations can scale—it’s whether leaders can predict how the system will behave before making the next change. That is where Digital Twins are increasingly playing a strategic role.

Automation Scales Execution — Digital Twins Scale Decision Confidence

Automation excels at executing defined workflows. Digital Twins complement automation by helping leaders understand what to optimize next—and what not to.
 
As SKU counts rise, picking profiles diversify, and service windows tighten, decisions around flow, labor, and capital carry higher execution risk. Digital Twins provide a way to evaluate those decisions virtually, before they impact live operations.

What Is a Digital Twin (Operational Perspective)

From an operational standpoint, a Digital Twin is a living, data-driven virtual representation of a distribution center. It continuously updates using real operational inputs, such as:
This virtual model allows teams to explore how flow behaves across the system without disrupting production.
 
Digital Twins enable organizations to:
Importantly, Digital Twins are not a replacement for automation. They are a way to optimize, protect, and extend existing investments.

Why Digital Twins Fit Complex Case-Pick & Pick-Pack Operations

Organizations operating large, automated case-pick and pick-pack DCs often share similar characteristics:
At this level of maturity, the primary operational risk shifts from capacity constraints to misaligned flow.
 
Digital Twins help leaders answer system-level questions such as:
These are not questions traditional KPIs alone can answer.

A Practical Way to Think About Digital Twin Adoption

Rather than approaching Digital Twins as a large, monolithic initiative, leading organizations adopt them in stages.
 
🟢 Short Term: Explore
🟡 Mid Term: Scale
🔵 Long Term: Differentiate
The objective isn’t perfection—it’s better decisions with lower execution risk.
Discover how digital twins help automated distribution centers boost decision confidence, optimize flow, and reduce execution risk before making costly changes - infographic

Who Is Already Applying This Approach

Digital Twins are increasingly used by organizations operating complex, automated distribution networks, including:
Across industries, a clear pattern is emerging: the more automated the operation, the more valuable the Digital Twin becomes.

Digital Twins as Part of the AI-Enabled Operating Model

What makes Digital Twins especially powerful is their ability to integrate with analytics and AI. When combined, they enable:
In this role, Digital Twins help leaders see the system—not just the metrics.

Final Thoughts

Automation delivers speed and consistency. Digital Twins deliver understanding.
 
Together, they allow leaders to reduce risk before execution, improve capital decisions, and operate complex distribution systems with greater confidence.
 
As distribution centers continue to scale automation, Digital Twins are becoming less of a differentiator—and more of a foundational capability for intelligent operations.
 
Carlos Salazar is an Executive Supply Chain Leader with more than 20 years of experience across international business, engineering, and operations.

Carlos Salazar

Senior Contributor

Carlos Salazar is an Executive Supply Chain Leader with 20 years of experience spanning international business, engineering, and operations.  Certified in PMP, Lean Six Sigma, and advanced artificial intelligence (AI) for supply chain leadership, Carlos combines disciplined execution with innovation to build resilient, end-to-end supply chain solutions.

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