Predictive Warehouse Optimisation
PULPO WMS — Predictive Warehouse Optimisation Engine
This project is shared with the written permission of PULPO WMS. Underlying client data, production metrics, and commercially sensitive results remain confidential under NDA. The dashboard shown runs on anonymised synthetic data generated for demonstration purposes.
About the Company
PULPO WMS
3PL & eCommerce Warehouse Management System
PULPO WMS is a cloud-native SaaS warehouse management system built for e-commerce merchants, 3PLs, and fulfilment operations. Operating across 20+ countries, PULPO provides an API-first platform covering picking, packing, inventory counting, returns, and incoming goods management.
This project was commissioned as a university collaboration to build a predictive intelligence layer on top of their existing platform.
20+
countries
Trusted by fulfilment experts
99.9%
uptime
Low-latency performance
2m 8s
response time
Support keeps warehouses moving
Modern architecture
Built on MACH principles
Built for
To lead warehouse operations with a digital warehouse twin
My Role
- Project lead — coordinated across PULPO, the university, and a 4-person team.
- Designed and built the product proximity model.
- Integrated all 4 models into a single end-to-end pipeline.
- Co-created EDA and helped with zone allocation model design.
The Opportunity
A predictive intelligence layer on top of the operational core
PULPO WMS already handles the operational layer — picking, packing, inventory. The project extended this with a predictive intelligence layer: using the rich transaction data PULPO captures to make smarter decisions about where products should live and when to reorder them.
Most warehouses organise products by category rather than demand velocity. By applying ML to PULPO's data, we could recommend zone placements that reduce picker travel time and automate replenishment before stockouts occur.
The Solution
A 4-model pipeline
Each model feeds the next — forecasts drive placement, placement drives replenishment — forming a single predictive loop on top of PULPO's operational data.
Model 1
Demand Forecast
LightGBMPer-SKU, per-merchant demand forecast across 3 monthly horizons using LightGBM with a Tweedie objective — well suited to sparse, zero-inflated warehouse demand.
OutputFeeds the zone allocation and replenishment modelsModel 2
Product Proximity
K-MeansClusters products by co-purchase behaviour using a sparse co-occurrence matrix enriched with category signals (70% co-occurrence, 30% category).
OutputEnables the LP to co-locate frequently bought products in the same zoneModel 3
Zone Allocation
Linear ProgrammingPuLP/CBC solver maximises picks × zone efficiency subject to zone capacity constraints. Affinity weighting keeps co-purchase clusters together.
OutputRecommended zone reassignment per productModel 4
Auto-Replenishment
Safety Stock + EOQCalculates reorder points per product per zone using safety stock (Z=1.65, 95% service level), supplier lead time, and forecast demand.
OutputTriggers classified by urgency — CRITICAL through LOW — with suggested order quantities
Live Dashboard
The pipeline, end-to-end
The dashboard below demonstrates the full pipeline end-to-end on anonymised synthetic data — production data is confidential.
Embedded AI Agent
Ask the pipeline anything
The dashboard includes an embedded AI agent powered by the DeepSeek API that answers natural language questions about any model output, warehouse metric, or configuration decision. Responses are grounded in the pipeline's actual outputs — bridging the gap between data science outputs and operational decision-making.
Key Takeaways
What the pipeline proved
Tweedie objective outperformed standard regression on sparse warehouse demand data.
Co-purchase clustering is more effective than category-based grouping for zone co-location.
LP zone allocation delivers meaningful throughput improvements even with conservative capacity constraints.