Transportation Management System (TMS)

Part of the worlds-biggest-software-project initiative.

An AI-native, open-source TMS for route optimisation, carrier management, and freight audit across multi-modal logistics.

The Transportation Management System (TMS) project is a candidate open-source platform for shippers, 3PLs, and freight brokers who need to plan, execute, and audit transportation across road, ocean, air, and rail. It targets the gap between heavyweight enterprise suites (Oracle OTM, SAP TM, Manhattan Active) and narrow visibility overlays (project44), bringing AI-driven optimisation, freight audit, and shipment visibility into a single, deployable system.

Why Transportation Management System (TMS)?

Incumbent enterprise platforms (Oracle OTM, SAP TM, e2open) carry high implementation cost, long deployment timelines (12-24 months for SAP TM), and deep ecosystem dependencies that lock buyers in.
Manhattan Active Transportation leads on optimisation but sits at an enterprise price point that is inaccessible to mid-market shippers and 3PLs.
Visibility platforms such as project44 deliver excellent ETA prediction but are not full TMS solutions - they lack rate shopping, tendering, load building, and freight payment.
Mid-market platforms (MercuryGate, 3Gtms) trade away international coverage, optimisation sophistication, and AI features for accessibility.
Freight audit, carrier bid evaluation, and exception management remain heavily manual or rule-based across the market, leaving clear room for an AI-native open-source alternative.

Key Features

Load Planning and Optimisation

Automatic shipment consolidation and carrier selection
Multi-modal route building across road, LTL, ocean, air, and rail
Continuous re-optimisation as new orders arrive and conditions change
Backhaul and multi-stop route planning
Carbon-aware load building that weights emissions alongside cost and time

Carrier Management and Tendering

Rate card management and contract administration
Carrier tendering and bid/ask workflows with automated awarding
Carrier performance analytics and automated scorecarding
EDI X12 (204/214/990) and REST API connectivity for tender and shipment status
Shipper-carrier collaboration portal with real-time visibility

Shipment Visibility and Exception Management

Real-time multi-modal shipment tracking with status and ETA
AI-powered ETA prediction from traffic, weather, and historical carrier data
Exception management with AI-recommended resolutions
Carrier performance dashboards and KPI reporting
Mobile-first interfaces for drivers and operations staff

Freight Audit and Settlement

Automated invoice-to-shipment matching with discrepancy flagging
ML-based detection of rating errors, duplicate invoices, and service failures
Payment reconciliation and settlement workflows
Carrier dispute learning loop that improves audit accuracy over time

Compliance and Trade

Hours of Service (HOS) compliance via ELD integration for US motor carriers
NMFC commodity classification for North American LTL rating
International trade compliance hooks (ISF, AMS, Incoterms 2020)
GS1 EPCIS event-based visibility data for interoperable track-and-trace

AI-Native Advantage

AI capabilities are designed in from the start rather than bolted on. Continuous load re-optimisation responds to live traffic, weather, ELD data, and dock availability instead of running as static batch plans. ETA prediction and exception triage learn from historical lane and carrier behaviour. A natural-language visibility assistant lets operations staff query shipment status, exceptions, and carrier performance in plain language, and freight audit improves itself by learning from dispute outcomes. Carrier bid evaluation, spot-rate forecasting, and lane-level demand forecasting move carrier selection and capacity procurement from manual judgement to ML-supported decisions.

Tech Stack & Deployment

The platform is intended to support cloud-native and self-hosted deployment, mirroring the cloud-native architectures of leading incumbents while remaining accessible to operators that require on-premise control. Carrier integration is built around EDI X12 (204/214/990) plus modern REST APIs, with GS1 EPCIS for cross-party visibility events. ELD integration is required for US HOS compliance. Integration surfaces include WMS, ERP, order management, and third-party freight payment platforms.

Market Context

The global TMS market is estimated at USD 21.75 billion in 2026 and projected to grow to USD 82.43 billion by 2034 at a 17.72% CAGR, with North America accounting for roughly 43% of regional share (Grand View Research; Precedence Research; Mordor Intelligence). Incumbent pricing ranges from around USD 450/month for entry-level Oracle OTM up to seven-figure annual enterprise deployments; most mid-market and enterprise vendors quote custom pricing. Primary buyers are logistics directors at shippers, 3PLs and freight brokers, operations managers responsible for shipment visibility, finance teams running freight audit, and international trade compliance officers.

Local Development

Local development runs against Postgres and Redis in Docker, with the API/worker either in Docker or run directly for faster iteration, and the frontend always run directly for hot reload.

Fresh start (empty database):

docker compose up -d postgres redis
docker compose run --rm api alembic upgrade head
docker compose run --rm api python -m scripts.seed
docker compose up -d api worker
cd frontend && npm run dev   # http://localhost:3000

Subsequent runs (database already migrated and seeded):

docker compose up -d postgres redis api worker
cd frontend && npm run dev

Sign-in for local testing: visit http://localhost:8080/auth/login?next=http://localhost:3000/ to obtain a session cookie without a real HAP OIDC round-trip. This is gated by TMS_HAP_DEV_TOKEN (set to dev-token in docker-compose.yml) and must never be set in a production environment.

Reset the database: docker compose down -v removes the Postgres volume, then re-run the fresh-start sequence above.

See .env.example for the full list of environment variables.

Project Status

This project is in the research and specification phase.
Contributions, feedback, and domain expertise are welcome.

Contributing

We welcome contributions from developers, domain experts, and potential users. See CONTRIBUTING.md for guidelines.

Important: All contributions must be your own original work or clearly attributed open-source material with a compatible licence. Copyright infringement and licence violations will not be tolerated and will result in immediate removal of the offending contribution. If you are unsure whether a piece of code, text, or other material is safe to contribute, open an issue and ask before submitting.

Licence

Licence to be determined. See discussion for context.

TMS_DEMO