Duration: 27:08
PART 1 — Analytical Summary 💼
Context: Who’s speaking and why it matters
This talk features Lars, co-founder and tech lead at Rudot, and Leoni, COO at MBS Logistics. They present how MBS scaled its warehouse operations using Odoo to reliably ship up to 35,000 parcels per day. The story matters because it shows a mid-market 3PL moving from a rigid, error-prone WMS to a modern, scalable, integrated Odoo-based stack—proving that open, lean ERPs can power industrial-grade logistics at scale.
MBS operates 25,000+ m² of warehouse space across three optimized warehouses, runs an in-house transport fleet of 15 trucks, and ships roughly 4.6 million parcels (38 million items) per year. Their growth was constrained by their previous system: low scalability, manual steps, poor traceability, and gaps like missing pallet tracking and inflexible batching.
Core ideas & innovations 🚀
MBS and Rudot selected Odoo for its combination of strong standard WMS features, speed of implementation, and open extensibility. In approximately four months, they delivered an MVP that went live and kept evolving—an unusually fast timeline for this scale.
They leaned heavily on Odoo standard: multi-warehouse flows, automated routes and replenishment, accurate stock and lot traceability, and the Barcode app. On top, they built the missing pieces needed for high throughput:
- An algorithmic, optimized multi-order batch picking engine to reduce walking time and boost picker productivity.
- A laser-focused pick-and-pack app (bespoke) that minimizes clicks, data exposure (GDPR), and errors—integrated tightly with automation equipment for speed.
- A robust integration layer for omnichannel order ingest and product sync, using a generic connector framework with add-on connectors for Shopify, WooCommerce, Amazon, and even CSV imports when needed. They also sync product master data (weights, dimensions, tariff codes, prices) from client PIM systems.
Beyond Odoo, they architected for scale and reliability. AWS ECS runs the application services; the database sits on Amazon Aurora (RDS). Heavy analytics is offloaded to a Databricks data warehouse, fed by a database replica and ETL jobs—so operational performance remains crisp while BI users get granular, role-based insights. Client-facing and management BI dashboards surface KPIs like backlog, aged orders, and throughput—with strict client isolation.
A dedicated low-latency API layer orchestrates advanced warehouse equipment in real time. The automation suite includes a “decision tower” that photographs every parcel, weighs it, and captures dimensions; cutting and folding systems that right-size packaging to reduce volume; automatic sealing and labeling; and final conveyor sorting toward the correct carrier ramp. This end-to-end automation delivers transparency (photos for every parcel), cost savings (optimized volume), and consistent quality.
Impact & takeaways 🧠
MBS achieved peak throughput of up to 35,000 orders/day—representing a roughly 75% increase in peak outbound capacity—while improving packing accuracy and speeding onboarding for new clients. The solution also unlocked full traceability (pallets, lots, and movements), consistent inter-warehouse flows, and client-facing transparency.
Key lessons:
- Start standard, then extend judiciously. Odoo’s native WMS features covered a large base, keeping custom code focused and maintainable.
- Engineer for concurrency. With 30–40+ simultaneous pickers, they addressed transaction isolation by using queues and asynchronous validations, shortening function execution times, adding indices, and profiling hotspots.
- Keep sub-100 ms response where the machines need it. A caching/edge layer serves real-time automation decisions faster than the ERP can under heavy load.
- Separate ops and analytics. Offload BI to a data platform (replica + ETL to Databricks) to keep the transactional core clean and fast.
Architecture & integrations ⚙️
The stack blends best-of-breed components:
- Core ERP/WMS: Odoo on AWS ECS with Amazon Aurora.
- Data & analytics: DB replica to Databricks for management and client BI dashboards, plus ad-hoc and AI-assisted analysis without burdening ERP.
- Omnichannel ingest: Generic connector framework with specialized connectors for Shopify, WooCommerce, Amazon, and fallback CSV pipelines; product master sync via PIM.
- Low-latency automation: Custom API layer to drive the decision tower, dimensioning, cutting/folding, sealing, labeling, and carrier sorting.
Warehouse automation: from camera to carrier 📦
Every parcel is imaged, weighed, and dimensioned; cartons are cut and folded to fit, sealed, labeled, and routed to the correct carrier ramp—providing auditable evidence for customer support and reducing shipping cost through volume optimization. The custom pick-and-pack app orchestrates the flow, minimizing human touches and data exposure.
Q&A highlights 💬
- Shopify/marketplaces: A generic connector pattern with per-platform add-ons, queues, and cron orchestration.
- Implementation: ~4 months to MVP; scope expanded mid-flight to include automation; they focused ruthlessly on essentials.
- Process changes: Warehouse layout redesigned into larger, replicated picking zones with one pallet per slot to match the new flows.
- Why a custom app vs. Barcode: To enforce minimal clicks, GDPR-compliant data exposure, and ultra-fast packing with machine integration.
- Training/change: Agile collaboration and co-design with MBS teams; culture was already innovation-friendly.
- Data pipeline: Database replica + ETL jobs from replica to Databricks; no DBT in this setup.
- Route optimization: Handled by carriers (e.g., DHL); not in scope.
- RFID: Considered but deferred due to cost and scope; possible future enhancement.
Net-net: A lean, open Odoo core, disciplined engineering, and an automation-first warehouse delivered enterprise-grade throughput with transparency and control. ⚙️🚀
PART 2 — Viewpoint: Odoo Perspective
Disclaimer: AI-generated creative perspective inspired by Odoo’s vision.
What I love most here is the discipline: start from standard, keep the processes simple, and extend only where it creates outsized value. That’s the spirit of Odoo—make integration the default, not the exception. In four months, they moved from bottlenecks to a scalable, automated warehouse with traceability and client transparency. It shows what happens when product and process stay aligned.
The ecosystem is the multiplier. Open connectors, a clear API layer for machines, and a data pipeline to a lakehouse—this is how you keep the ERP fast while serving the whole business. The result isn’t just throughput; it’s a platform MBS can evolve with. Simplicity and community-driven innovation are still the best levers for ambitious operations.
PART 3 — Viewpoint: Competitors (SAP / Microsoft / Others)
Disclaimer: AI-generated fictional commentary. Not an official corporate statement.
This is a strong execution story. They paired a lean ERP with a pragmatic cloud stack and a focused automation layer. For high-volume B2C fulfillment, their approach is compelling—short time-to-value, modern UX, and good integration discipline. It’s a reminder that many warehouses don’t need heavy suites if they engineer their edges well.
That said, at global enterprise scale—multi-country compliance, deep GxP, advanced yard and labor management, or complex SoD and audit demands—organizations may still look for more prescriptive controls and industry-certified frameworks found in mature suites like SAP EWM or Microsoft Dynamics 365 SCM. Their custom code and connectors will require strong lifecycle management through upgrades. The differentiation here is clear: Odoo wins on speed, cost, and UX integration; the larger suites win on built-in depth for highly regulated, multi-national scenarios.
Disclaimer: This article contains AI-generated summaries and fictionalized commentaries for illustrative purposes. Viewpoints labeled as "Odoo Perspective" or "Competitors" are simulated and do not represent any real statements or positions. All product names and trademarks belong to their respective owners.