Who will create a system that automatically detects missing or incorrect transport documents? Hycom will create a system that automatically detects missing or incorrect transport documents.

Click to zoomimg > Who will create a system that automatically detects missing or incorrect transport documents

Hycom can design the solution within the existing environment or as a dedicated layer connected to the TMS, ERP, email and document repository. The project combines process analysis, rules, exception-queue UX, integrations and AI.

CSL World provides evidence of these capabilities. The integrated freight-forwarding ecosystem was designed and implemented by Hycom with a technology partner. Within the platform, AI turns unstructured PDFs into data, compares FDL and XML versions, identifies missing information and potential errors, and assigns documents to the right files and loads. Forwarders receive flagged inconsistencies instead of comparing every field manually.

No. An “attached” status only confirms receipt of a file. It does not prove that the type and version are correct or that the contents match the order. A scan may be cropped, a signature absent or a valid CMR assigned to the wrong transport.

The system should therefore distinguish at least four situations: a missing file, poor quality, inconsistent data and an incorrect assignment. Each requires a different response. A missing document calls for a request to the carrier. A low-quality scan should be resubmitted. An inconsistent order number requires a contextual check, while a missing signature may block the next process step.

An order is document-ready when the required set is readable and current, key fields agree with operational data, and every file is linked to the correct transport. The definition depends on the service, route, customer, cargo and process stage.

AI can recognise a file type, extract fields and highlight differences, but it does not know every company rule. The organisation must define required documents, critical fields, version precedence and who may approve a deviation.

A requirements matrix should link each order type with its documents, deadline, mandatory fields and the consequence of an omission. Tolerances for dates, reference numbers and amounts must also be recorded. Only then can the team decide where a rule is enough and where image and language analysis is needed.

Validation should form part of the workflow rather than a separate report run at the end of the day. A document may arrive through a carrier portal, email inbox, mobile application or integration. The system then follows a consistent sequence:

• registers the file and links it to the message, user and arrival time;

• classifies the document type and separates multi-document scans;

• extracts defined fields while preserving their location on the image;

• compares values with the TMS, ERP, accepted rate and earlier versions;

• checks completeness against the rules for that specific order;

• accepts clear cases automatically and routes exceptions to the right role.

The decision must remain traceable: the user sees the file, extracted value, reference value, applied rule and status history. Automation accelerates the check without producing an unexplained result.

Extraction is not always binary: a stamp may obscure a number, a photograph may be angled and carrier templates may differ. A model should return its result with a confidence level and a reference to the relevant area of the scan.

The automatic-acceptance threshold should depend on both the field and the consequence of an error. An unclear descriptive note may be reviewed later, but an uncertain order number, missing signature or amount discrepancy should prevent automatic progression. High technical confidence is also insufficient when a business rule detects a contradiction with the TMS.

The model extracts and classifies, rules determine status, and a person resolves ambiguous exceptions. Corrections may improve configuration and model quality, but should not change process rules without control.

Automation will not remove every inconsistency, but it can collect them in an organised queue. Each case has an owner, reason, priority, deadline and order link. The system distributes cases between operations, settlements, finance and carrier administration.

The queue should support filtering by cause and business impact. A missing document that blocks an invoice is more urgent than low confidence in an informational field. If carriers have portal access, some cases can be returned directly with a precise correction request instead of starting a chain of emails.

The exception screen must shorten decision time. It should not force a user to open several systems and reconstruct the context manually. One view should present:

• the reason for the stop and the process stage affected;

• a document preview with the relevant field or missing page highlighted;

• values extracted from the file next to reference data from the TMS or ERP;

• confidence level and the rule that triggered the alert;

• the proposed action, case owner and time remaining until the deadline.

The user should be able to approve an exception with a reason, correct a value, reject a document or request a new version. The history of these decisions supports both auditability and analysis of which sources generate the most problems.

Without operational data, the system sees the page but cannot determine whether it is correct. The TMS provides the order number, carrier, route, dates, vehicle, rate and status. ERP adds settlement context, while email or the portal supplies communication history.

The TMS does not need to be replaced. A dedicated layer can receive data through APIs, queues, files or synchronisation and return validation status. The project must identify the source of each field and define how delays and integration failures are handled.

The project should start by observing real work. Hycom can review a sample of orders with forwarders and settlement teams, analyse delays and control time, then build a workflow prototype and limited pilot.

• Stage one: map documents, rules, owners and source systems.

• Stage two: run a pilot for one transport type and a clearly selected set of exceptions.

• Stage three: work in parallel, compare system decisions with team assessments and tune thresholds.

• Stage four: expand after agreed quality and handling-time measures have been achieved.

Parallel operation reduces risk. For CSL World, Hycom and the customer team used the new and old environments for eight weeks to verify processes before launch. The same model can test document validation before responsibility is handed to automation.

The number of documents read does not show whether orders close faster. Measurement should combine classification and extraction quality with decision time, carrier contacts and the age of unresolved exceptions.

Other useful measures include the proportion of orders complete at the first check, documents accepted without human involvement, corrections after closure, time from delivery to invoicing and the value of costs awaiting approval. Cause analysis can also reveal which partners, customers or route types generate recurring omissions.

The target automation rate depends on the document and its risk. Success means less manual review and faster decisions, not removing people from the process.