Samsung’s AI Factory Moves Supply Chain Automation to Data Layer

Machines have taken over the logistics and manufacturing world. This isn’t new or surprising. Warehouse automation began in the middle of the last century, when Automated Storage and Retrieval Systems (ASRS) were introduced in the 1950s.

Since then, robotic arms, autonomous forklifts, conveyor systems and fleets of mobile robots have been navigating warehouse aisles. The value proposition was straightforward enough: install automation hardware, reduce labor costs and improve throughput.

Artificial intelligence (AI) is pushing things even further. By adding intelligence capabilities to automated operations, the supply chain’s center of gravity is now being pushed from equipment deployment to data-layer orchestration. Instead of asking how many robots a warehouse can deploy, companies are increasingly asking how intelligent systems can coordinate machines, workers and logistics flows in real time.

Samsung Electronics, for example, on Sunday (March 1) announced a new strategy to transition all manufacturing operations into “AI-Driven Factories” by 2030.

“The next phase of manufacturing innovation lies in building autonomous environments where AI truly understands operational contexts in real time and independently executes optimal decisions,” YoungSoo Lee, executive vice president and head of global technology research at Samsung Electronics, said in a statement.

In this emerging phase, the strategic value of automation is less about physical capacity and more about software-defined logistics performance that help serve as a foundation for how effectively companies can adapt to demand volatility, supply disruptions and unpredictable trade conditions.

See also: Earnings Season Made It Clear: Digitize Supply Chains or Fall Behind

How Data-Layer Integrations Are Rewiring Warehouse Strategies

The evolution of supply chain automation is ultimately becoming about ecosystems rather than individual technologies. Robotics, AI, sensors, warehouse management software and transportation platforms are converging into integrated operational environments.

Central to Samsung’s own initiative is the deployment of digital twin simulations and specialized AI agents that monitor production quality, coordinate logistics and manage predictive maintenance. These systems analyze operational data across manufacturing facilities to optimize workflows and identify potential disruptions before they cascade through supply chains.

A similar transformation is unfolding inside warehouses. Automation leaders are increasingly partnering with technology providers that specialize in computer vision, data analytics and operational intelligence, including Amazon, who has turned its own smart factory capabilities into marketplace solutions.

Computer-vision AI, for example, allows facilities to monitor warehouse activity at scale. Cameras and sensors track the movement of goods, equipment and workers, generating continuous data streams about inventory flow, congestion points and equipment performance.

For logistics leaders, the challenge now is one grounded in architectural questions. The competitive advantage lies here not in deploying the most robots but in integrating systems that can coordinate across facilities, transportation networks and supplier ecosystems.

See also: Cascading Middle East Risks Make Geopolitics an Operations Problem

Building Resilience Into the Supply Chain

The shift toward data-layer integration is addressing several long-standing operational challenges in logistics. Traditional supply chains suffer from fragmented information across procurement, production and distribution. Data-layer integration helps connect these previously siloed processes, enabling operators to monitor supply chain health in real time.

“The current trade landscape that we see today is marked by widespread volatility, complete unpredictability,” Dean Bain, senior vice president, supply chain at Coupa, told PYMNTS in an earlier interview. “We’re seeing businesses grappling with rising costs, with margin erosion, with trying to figure out how they deal with this uncertainty and provide greater agility to their business.

“[What is crucial is the] ability for them to identify what alternate sourcing options there are and to use data to ​m​​ake data-driven decisions that ultimately protect the profitability and the market position of that company,” Bain said.

Predictive maintenance is also emerging as a key benefit of integrated automation. Sensors embedded in warehouse infrastructure and robotics platforms generate performance data that AI systems analyze to forecast equipment failures before they occur.

If a warehouse experiences congestion, automated systems can reroute tasks and adjust workflows in real time. And if a supplier delay occurs, AI systems can recalculate inventory allocations across distribution centers.

Still, tariffs are reshaping what product leaders choose to work on, according to findings in “Tariffs Turn Up the Heat as Product Leaders Confront Peak Uncertainty,” a December PYMNTS Intelligence report from The 2025 Certainty Project. A majority (60%) of product leaders say tariff-driven uncertainty has constrained their firms’ ability to fund AI and automation.

At the same time, 98% of surveyed product leaders expect Gen AI to improve internal workflows within three years, according to a new PYMNTS Intelligence report, “From Experiment to Imperative: US Product Leaders Bet on Gen AI.”

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