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The bottleneck is not a lack of will. It is a lack of operational confidence. To move seriously from road to rail, shippers need more than tracks and terminals. They need a level of data transparency that the industry has consistently struggled to provide.
Rail intermodality is not held back by a lack of conviction. It is held back by a lack of operational confidence.
The paradox of data: available, but not accessible
A common misconception is that the rail industry lacks data. In reality, a significant volume of operational information already exists. Platforms such as RailData, operated by the International Union of Railways (UIC), function as centralised hubs for technical and tracking data across European freight railway undertakings.
The problem is one of asymmetry. This data is designed for railway operators, not for industrial shippers. There is a structural gap between the institutional railway environment and the operational world of a petrochemical company. For a shipper, getting a reliable real-time status update on a wagon should be a standard digital exchange. In practice, it often remains a manual process, dependent on phone calls, email threads, and PDF documents that travel between actors without ever being consolidated.
The barrier is the trust factor regarding data.
The Deloitte-facilitated analysis produced for EPCA's ITN workshop in February 2025 confirmed this directly. Transparency of shipment status was identified as one of the structural advantages of road over rail, and data integration efforts across the chain (between logistics service providers, railway undertakings, terminal operators, and their IT systems) are expected to close this gap only partially by 2030. The analysis also concluded that most potential gains from modal shift will not materialise automatically: they are contingent on shippers and their partners acting on digitalisation, data standards, and cross-chain collaboration.
In practice, a petrochemical shipper operating by rail is not working with a single stream of operational information. Multiple independent actors are involved simultaneously: the production site, the rail carrier, the terminal operator, the wagon lessor, the downstream customer. Each generates their own version of events. A wagon recorded as available by the lessor may be listed as in transit by the carrier. A loading confirmed by the plant may not yet appear in the terminal's system. A border delay may become critical long before the supply chain planner is notified.
None of this is the result of bad faith. It is simply the consequence of a multi-actor transport chain operating across national borders, different railway undertakings, and heterogeneous IT environments. Road freight largely avoids this problem not because it is better managed, but because the chain is shorter and the feedback loops are faster. In rail, the gap between what is happening and what can be seen is wider, and the cost of operating inside that gap is higher than most organisations fully account for.
What this means for shippers looking to increase modal shift
The implications are practical and affect decisions at every level of the supply chain.
At the planning level, a shipper who cannot trust wagon availability data will systematically over-dimension their fleet as a buffer against uncertainty. This hidden cost of inefficiency is precisely what prevents the business case for rail from scaling. At the execution level, a supply chain planner who does not know where a wagon is until it is two days overdue cannot proactively reroute, cannot notify the customer in time, and cannot apply contractual penalties with any confidence. At the reporting level, a head of transport who cannot reconcile the carrier's delivery record with the plant's reception data will struggle to build the service level evidence that justifies a further commitment to the rail mode.
Shippers are being asked to commit volume to a mode they cannot see clearly enough to manage with confidence. When faced with that choice, experienced supply chain managers default to the mode they can see. Which remains, overwhelmingly, road.
The gap is closable, but it requires deliberate effort
The trust factor regarding data is not a fixed structural constraint. Unlike infrastructure investment or regulatory harmonisation, which depend on actors and timelines far outside any individual shipper's control, this is a gap that organisations can begin to close from within their own supply chain relationships. The question is not only whether shippers hold the right data, but whether they can access it readily, in real time, and in a centralised system. Three levers are available, and they are mutually reinforcing.
- Data governance, starting internally. Most petrochemical companies already hold more operational rail data than they actively use: carrier invoices, terminal receipts, wagon tracking feeds, production planning records. The first step is often deciding which existing information is reliable enough to support decisions: naming an owner, defining the reference events, establishing how discrepancies are reconciled. The tools and systems that support this process are must-have if you want to scale. What cannot wait is the decision itself, because without it, no system produces reliable outputs.
- Interoperability and regulatory alignment. The European Data Act, applicable since September 2025, strengthens shippers' legal right to access data generated in the course of their operations. This is a meaningful step forward. However, legal access alone does not solve the technical friction. Most petrochemical companies operate complex supply chains where rail is one component among many. They need data that integrates directly into their own planning, execution, and reporting systems, not data that requires logging into a separate carrier portal. Without a common standard for rail data exchange, every integration remains a bespoke and costly IT project. The Data Act is a necessary condition. Standardisation is the sufficient one.
- Unlocking fragmented information. A significant share of operational rail data does not sit in structured systems at all. It travels in PDF documents attached to emails, in spreadsheets maintained by individual planners, in unformatted messages exchanged between logistics teams. Consolidating this information has historically been slow and expensive, requiring manual extraction or rigid tools that failed at the slightest format change. Artificial intelligence (AI) and, more specifically, large language models (LLMs) are changing this equation. The ability to read, extract, and structure unformatted documents at scale and at a marginal cost means that a large part of the operational data that rail chains already generate can now be made usable. For shippers, this opens a practical path to building data foundations from what already exists, without waiting for upstream actors to standardise their outputs.
These three levers share a common logic: the organisations making the fastest progress on modal shift are not those waiting for the industry to align itself. They are those that have started, within their own operations, to make their rail data reliable enough to act on. That shift, from data as a byproduct of logistics to data as a foundation for transport decisions, is where the most durable progress is being made.
A window that will not stay open indefinitely
European freight policy is moving in a direction that will increase pressure to demonstrate progress on modal shift. The carbon accounting requirements already reshaping procurement decisions in chemicals and petrochemicals will extend further into transport within the next regulatory cycle. Driver availability on road, already a structural constraint in several European markets, is unlikely to improve. The economics of intermodal rail, particularly on the corridors that matter most to petrochemical shippers (Rhine-Danube, North Sea-Mediterranean, Baltic-Adriatic), are becoming more competitive as frequencies improve and terminal investment accelerates.
The companies making genuine progress on modal shift are not necessarily those that have invested the most in new infrastructure. They are those that have decided, deliberately, to treat data governance and data infrastructure as core components of their transport strategy. That decision is available to any organisation that chooses to make it, and the returns compound over time as the chain becomes more legible, more predictable, and more reliable.
Rail in petrochemical supply chains does not lack a business case. It lacks the operational foundation to execute that case with confidence.
The missing link in European petrochemical intermodality is not a train. It is the confidence to run the supply chain from one.
