Warehouse Management System (WMS)

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

An AI-native, open-source warehouse management system covering receiving, putaway, picking, packing, shipping, and cycle counts — built to make modern WMS capability accessible beyond the enterprise tier.

The Warehouse Management System project is a candidate open-source platform for distribution centres, 3PLs, manufacturers, and e-commerce operators. It targets the gap between cost-prohibitive enterprise WMS suites (Manhattan, Blue Yonder, SAP EWM) and SMB inventory tools (Fishbowl) by providing AI-driven optimisation on an open foundation.

Why a New WMS?

Enterprise WMS first-year costs commonly run $500K–$3M+ (Manhattan, Blue Yonder, SAP EWM), with custom-quote-only pricing that creates a barrier to evaluation.
Mid-market SaaS WMS pricing typically runs $33K–$300K per year, still out of reach for many operators (CPC Group, 2026).
SMB tools such as Fishbowl ($329/month) lack advanced wave picking, labour engineering, slotting, and automation integration.
Implementation timelines for SAP EWM commonly run 12–24 months, and Blue Yonder/Manhattan deployments require significant professional services investment.
The only notable open-source alternative, OpenWMS.org (Apache 2.0), has narrow functional depth, no native EDI or GS1 label printing, and limited commercial support — leaving the open-source landscape effectively unserved.

Key Features

Inbound and Inventory

Inbound receiving with ASN (EDI 856) support
Directed put-away with bin-level location tracking
Lot and serial number tracking with FIFO / FEFO / LIFO rotation rules
Cycle counting and inventory reconciliation
Multi-location and multi-site inventory management

Outbound and Fulfilment

Pick, pack, and ship workflows with barcode and RF scanning
Wave and waveless order picking with task interleaving
Returns processing with disposition workflows
GS1 SSCC / GTIN label generation
EDI 940 / 945 shipping order and shipping advice support

AI-Driven Optimisation

AI-driven directed put-away that reasons about size, weight, temperature zone, demand pattern, and co-pick affinity
AI-powered slotting optimisation with continuous velocity-based recommendations
AI-optimised wave planning and task interleaving
Predictive cycle-count prioritisation ranking locations by transaction frequency, discrepancy history, and value
Predictive labour scheduling forecasting inbound and outbound volumes by hour

Operations and Insight

Natural-language warehouse performance query interface for DC managers
Labour tracking and productivity reporting
Role-based access controls and audit trails
Real-time operational dashboards and KPIs

Extended Capabilities (Backlog)

Yard management and dock appointment scheduling
Embedded Warehouse Execution System (WES) for automation coordination (AMR, AS/RS, conveyor, sortation)
3PL multi-client billing and onboarding
Native omnichannel OMS integration (ship-from-store, BOPIS)
FDA FSMA 204 and DSCSA compliance traceability

AI-Native Advantage

Incumbent WMS suites largely treat slotting, labour planning, and put-away as periodic or rule-based exercises. This project treats them as continuous AI-driven workloads: SKUs are re-slotted as velocity changes, staffing plans are forecast hour-by-hour from inbound and outbound signals, and put-away locations are recommended from multi-factor reasoning rather than static zone rules. A natural-language assistant lets DC managers ask operational questions ("today's pick rate by zone", "locations with the most putaway errors") without building custom reports.

Tech Stack and Deployment

The project is intended for cloud-native deployment using a microservices architecture, drawing on the Twelve-Factor patterns demonstrated by OpenWMS.org. Expected integration surface includes REST APIs with OAuth 2.0, EDI connectivity for ASN (856) and shipping transactions (940 / 945), GS1 standards (SSCC, GTIN, EPCIS) for identification and labelling, and pluggable connectors for ERP systems (SAP, Oracle, Microsoft) and automation hardware (RF, voice, AMR, AS/RS, conveyors). Mobile handheld support is a first-class requirement for floor operations.

Market Context

The global WMS market is estimated at USD 3.88–4.77 billion in 2025/2026, growing at a 17–22% CAGR to reach USD 10–16 billion by 2031–2033, with North America holding ~35% share and Asia-Pacific the fastest-growing region (Grand View Research, Mordor Intelligence, MarketsandMarkets). Cloud WMS is growing at a 22.6% CAGR as on-premise licences migrate. Primary buyers are VPs of Supply Chain or Distribution at retailers, 3PLs, and manufacturers; DC managers; IT directors managing WMS / ERP integration; and e-commerce operations leads scaling fulfilment.

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.