Ferrovalle is deploying INFORM’s Syncrotess Optimization Plus to automate yard, equipment and train operations at its intermodal terminal in Mexico City. The Smart Yard project will add an AI-powered optimization layer to Ferrovalle’s existing terminal operating system, coordinating container movements, cranes, terminal tractors and train loading while incorporating customs clearance into operational decisions.
Ferrovalle, one of Mexico’s major rail freight hubs, has selected INFORM’s Syncrotess Optimization Plus to power a new Smart Yard initiative at its intermodal operation in Mexico City.
The project is aimed at moving more of the terminal’s planning and dispatch processes from manual, experience-based decisions toward continuously updated optimization. Rather than replacing Ferrovalle’s existing Terminal Operating System (TOS), INFORM’s software will operate alongside it as an additional decision and optimization layer.
Ferrovalle’s intermodal division handled around 550,000 TEUs in 2025, according to INFORM, placing the operation among Latin America’s largest inland intermodal facilities.
The implementation will cover container storage, equipment deployment, train loading and train discharge. Syncrotess will consume operational information from Ferrovalle’s existing systems and generate coordinated recommendations as conditions change.
That architecture reflects a broader change in logistics software. AI is increasingly being applied not simply to analyze historical supply-chain data but to make operational decisions closer to the point where trucks, containers, cranes and trains are actually being dispatched.
McKinsey’s 2026 State of Digital Logistics Survey found that nearly 90% of surveyed shippers had adopted at least one transportation AI use case, while 96% had deployed at least one AI or digital use case in warehousing. The research also found that organizations gaining value from these technologies are increasingly moving analytics into operating environments where decisions are made.
For Ferrovalle, the initial optimized fleet consists of eight rubber-tired gantry cranes, four reach stackers and 14 terminal tractors. The company aims to increase equipment throughput, improve the proportion of billable moves and meet service-level targets for truck handling, train loading and train discharge.
The software combines several specialized optimization components: Yard Optimizer, Crane Optimizer, Vehicle Optimizer and Train Load Optimizer.
Instead of optimizing each activity independently, the system is designed to coordinate decisions across them. A change in train availability, for example, can affect where containers should be positioned, which crane should handle them and which terminal tractor should perform the associated movement.
That coordination is particularly complicated at Ferrovalle because the project spans two different yard environments: customs-cleared and customs-controlled areas.
The system must account for different processes involving maritime and cross-border containers while preventing cargo from being assigned to train loading before the required customs and documentation approvals have been completed.
The integration with Ferrovalle’s TOS is therefore central to the project. The systems will exchange train-consist information, load plans, container status, clearance information and operational updates. INFORM says the TOS will confirm customs and documentation clearance before the optimization platform assigns a container for train loading.
That creates an example of AI optimization operating within established business rules rather than replacing them. The optimization engine can determine an efficient operational plan, but regulatory and operational constraints remain part of the decision process.
The platform will also connect with Ferrovalle’s Equipment Control System. The integration is intended to automate weight verification during routine crane lifts, potentially removing separate weighing stops from the handling workflow.
Train planning is another target for automation. INFORM says Ferrovalle’s planners currently perform much of the train-loading planning manually. The Train Load Optimizer will generate proposed load plans based on available containers, train configurations, operational restrictions and customs-clearance status.
Human planners will retain the ability to review and modify those plans.
That human-in-the-loop architecture is notable because operational AI in logistics does not necessarily require autonomous decision-making. Optimization software can generate recommendations while dispatchers remain responsible for reviewing exceptions and applying operational judgment.
INFORM describes Syncrotess as a platform for optimizing planning and execution across complex logistics processes, with AI-based real-time optimization intended to reduce manual planning and synchronize operational decisions.
The Ferrovalle deployment also illustrates why AI logistics projects increasingly depend on integration rather than standalone models. A useful optimization system needs access to the operational state of equipment, containers, trains and regulatory processes before it can make meaningful recommendations.
McKinsey’s latest logistics research argues that AI and digital capabilities are becoming standard features rather than differentiators by themselves. The more significant question is how effectively companies connect those technologies to operating decisions and measurable outcomes.
IDC similarly expects AI and machine learning to become increasingly important in dynamic shipment planning and network optimization. Its 2025 supply-chain research projected that by 2028, 60% of large supply-chain organizations would use AI/ML for dynamic shipment planning and network optimization.
For Ferrovalle, the immediate focus is more localized: improving how one of Mexico’s major inland intermodal operations coordinates its yard and rail activities.
The contract was signed in early September 2026, with implementation expected to take approximately nine months. Go-live is planned for June 2027.
If successful, the project will give Ferrovalle a model for integrating AI optimization with an existing TOS rather than undertaking a wholesale replacement of its digital infrastructure. That approach could become increasingly relevant for logistics operators that already have operational systems but need a more dynamic layer for planning and execution.
Market Landscape
AI in logistics is moving from analytics and forecasting toward real-time operational optimization. Transportation, warehousing and terminal operators are increasingly using AI to coordinate resources, respond to disruptions and make planning decisions closer to execution.
McKinsey’s 2026 research found that 88% of surveyed shippers said transportation AI and digital tools had met or exceeded expectations, while 84% said the same for warehouse applications.
The competitive landscape includes traditional TOS vendors, supply-chain software providers, industrial automation companies and AI optimization specialists. INFORM’s approach centers on adding optimization intelligence to existing operational systems rather than requiring customers to replace their core platforms.
Top Insights
- Ferrovalle is adding INFORM’s Syncrotess Optimization Plus as an AI optimization layer alongside its existing terminal operating system.
- The project covers eight RTG cranes, four reach stackers and 14 terminal tractors across two different customs environments.
- AI optimization will coordinate yard storage, equipment deployment, train loading and discharge rather than treating each operation separately.
- Customs clearance data will become part of the automated decision process before containers are assigned for train loading.
- McKinsey reports that nearly 90% of surveyed shippers now use at least one transportation AI application.
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