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Freight pricing in 2026 no longer behaves like a stable commercial model. Over the past few years, global supply chains have operated through near-continuous disruption; fuel volatility, tariff uncertainty, carrier reshuffling, regional conflict, and increasingly fragmented sourcing strategies. Pricing structures that were once relatively stable are now constantly shifting across carriers, trade lanes, and transport modes. What used to be managed through governed tariff schedules has evolved into a continuously moving pricing environment where base freight, bunker adjustments, congestion surcharges, peak season charges, subject-to conditions, and short-validity spot rates all move independently of one another. And every one of those variables needs to resolve correctly at the point of quotation, execution, costing, and billing.

Asia–Europe spot rates fluctuated by more than 200% during peak disruption cycles.
Emergency surcharges, shortened validity windows, and continuous routing adjustments are no longer exceptional events. They have become part of day-to-day freight operations.

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At scale, this creates a dependency many organisations underestimate: your system is only as reliable as the way your pricing is structured inside it. The issue is no longer simply the volume of rate maintenance. The issue is that pricing itself is no longer consistently system-readable. Collectively, these issues create a single outcome: pricing becomes unpredictable at runtime.

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This Is Not a Rate Entry Problem

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Most organisations respond to this by trying to automate input — upload tools, RPA scripts, bulk ingestion pipelines. But speed does not fix structure. If the system cannot interpret pricing logic consistently, automation simply accelerates inconsistency at scale. The same errors, the same gaps, the same unpredictability — just faster, and harder to trace.

This is why many rate automation initiatives fail when they begin at the tooling layer. Because the problem does not sit in how rates are entered. It sits in how pricing is structured. And when that structure is not aligned to how the business actually prices across carriers, conditions, and exceptions — the system cannot behave predictably, no matter how advanced the automation layer is.

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Reframing Rate Automation

Within Cherry Global, and through its subsidiary TNPSFL, rate automation is approached differently. The focus is not on accelerating rate entry. The focus is on making pricing system-readable, system-governed, and system-trusted. That requires stepping back from rate sheets and looking at how pricing behaves inside the system itself.

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What We Change Structurally

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The objective is deterministic pricing behaviour — the system produces the same output, every time, for the same scenario. The following areas are typically restructured:

• Rate structure normalisation

Carrier tariffs are re-engineered into standardised templates so base freight, surcharges, and conditional charges follow a consistent system logic across carriers.

• Charge code rationalisation

Duplicate and conflicting mappings are eliminated so costing, billing, and reporting align without manual interpretation.

• Embedded surcharge logic

Surcharges and subject-to charges are moved into system-driven logic instead of being applied operationally.

• Validity control

Overlapping and conflicting validity periods are removed so rate resolution behaves predictably.

• Conditional pricing configuration

Exceptions are converted into structured rules, allowing the system to resolve pricing without human intervention.

How This Played Out in Real Environments

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‍Client 1

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In one global logistics network, rate maintenance had effectively become a full-time operational function. Over 10,000 rate lines were being processed monthly, with multiple resources dedicated purely to maintaining carrier tariffs across air, ocean, and road. Despite this level of effort, the system was not behaving reliably.

• Auto-rating failed across key lanes.

• Costing required manual validation during execution.

• Pricing outputs could not be trusted without intervention.

The pricing environment was re-engineered.

• Carrier tariffs were standardised into system-compatible templates.  

• Charge codes were rationalised across costing and billing layers.  

• Surcharges and subject-to conditions were embedded into structured system logic  

• Conflicting validity periods were eliminated

Once pricing behaviour stabilised, automation was introduced through controlled rate ingestion workflows. The outcome was immediate and measurable. Over 487 hours per month of operational effort were eliminated, and system-generated pricing became reliable — removing the need for validation across sales, operations, and finance.

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Client 2

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The challenge looked different on the surface, but stemmed from the same root cause. The organisation operated across a large network of global carriers, with highly dynamic pricing driven by spot rates, surcharges, and conditional charges. Automation had already been attempted, but could not be sustained. The system could not consistently support both cost and sell rate automation.

• Rate ingestion was fragmented.

• Surcharge logic behaved inconsistently.

• RMS capabilities existed, but could not be used reliably.

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Instead of layering more automation, the pricing architecture was corrected.

• A rate consolidation layer aggregated tariffs from multiple sources into structured templates

• Conditional pricing and surcharge logic were standardised

• Integration and RPA layers enabled direct ingestion into system rate tables

• Direct carrier connectivity stabilised upstream rate inputs

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With structure in place, automation began to work as intended.

• High-volume rate ingestion became consistent.

• Hundreds of rate lines could be processed in minutes.

• Accuracy stabilised between 98–99.5%, supported by periodic rate audits.

Most importantly, the system was able to support end-to-end rate automation across both cost and sell pricing — something that was not possible before. What both environments make clear is this: Rate maintenance was never the real constraint. It was the system’s inability to interpret pricing consistently. Once that was corrected, automation stopped being an effort. And when that structure is aligned, the system stops needing intervention — and starts driving the business the way it was intended to.

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