Supply chain AI that plans ahead, not just reacts.
We build AI systems for 3PLs, retailers, and manufacturers that optimise routes, forecast demand, prevent maintenance failures, and reduce overstock - integrated with the systems your operations team already uses.
Discuss your logistics AI project88%
demand forecast accuracy at 14-day horizon
$4.2M
overstock reduction for a 3PL operator
48–72h
advance notice on predicted maintenance failures
What we build
Route Optimisation
Multi-stop route planning with time windows, vehicle constraints, and live traffic. Reduces fuel cost and delivery time simultaneously. Handles 1K–100K+ stops per day.
Demand Forecasting
SKU-level demand forecasting at 88% accuracy for 14-day horizons. Integrates promotional calendars, seasonality, and external signals into a unified time-series pipeline.
Predictive Maintenance
Sensor data from vehicles and equipment processed through anomaly detection models. Maintenance alerts surfaced 48–72 hours before predicted failure.
Inventory Intelligence
Reorder point optimisation, safety stock calculation, and overstock risk scoring - connected to your WMS or ERP for closed-loop inventory management.
The supply chain data challenge
Logistics AI is harder than it looks. Here is why - and what we do before we write a single model.
Real-time data streams
Telematics, GPS, order events, and sensor feeds arriving at sub-second intervals from fleets, warehouses, and suppliers - requiring streaming ingestion architecture, not batch.
External signals at scale
Demand forecasting accuracy requires weather, holiday, event, and economic signals integrated in real time. Sourcing, normalising, and updating these signals is a data engineering problem, not just a modelling problem.
Multiple systems of record
Most logistics operations have a WMS, TMS, ERP, and carrier API - each with different schemas, latencies, and reliability. AI built on top of fragmented data is fragile. We fix the data layer first.
Route optimisation
Inputs
Delivery stops with address & time window
Vehicle type, capacity, and start location
Driver shift constraints
Live traffic and road conditions
Priority orders and SLA flags
Outputs
Optimised route per vehicle
ETA per stop with confidence range
Load plan and vehicle assignment
Re-routing triggers on live events
Integration with TMS / driver app
Algorithm selection
VRP (Vehicle Routing Problem)
Exact solver for small fleets (< 50 vehicles). Optimal solution, high compute cost.
Metaheuristic (OR-Tools, LKH)
Near-optimal solution for large fleets in seconds. Google OR-Tools as default. LKH for extreme scale.
ML-augmented routing
Historical route performance fed back into routing parameters. Learns local patterns keyword solvers miss (traffic, access restrictions).
Demand forecasting
Internal signals
Historical sales by SKU
Price & promotion history
Customer order patterns
Returns and cancellations
External signals
Weather forecast
Holiday & event calendar
Economic indicators
Competitor price signals
Accuracy target: 88% MAPE at SKU level, 14-day horizon. Model: Ensemble of LightGBM, Prophet, and LSTM, with automatic model selection per SKU based on data characteristics. Outputs integrated with ERP reorder triggers and WMS safety stock parameters.
Predictive maintenance
Unplanned downtime costs 3–5× more than planned maintenance. Here is how we catch failures before they happen.
Sensor ingestion
Vibration, temperature, pressure, and RPM sensors ingested via MQTT or OPC-UA. 1,000–100,000 readings/second per asset.
Anomaly detection
Isolation Forest and LSTM autoencoder models run on streaming data. Deviation from learned normal behaviour triggers anomaly score.
Maintenance scheduling
Predicted failure window mapped to maintenance slot. Priority score calculated from failure probability × downtime cost × part availability.
"Demand forecasting at 88% accuracy - $4.2M overstock reduction for a 3PL operator."
Ensemble forecasting model across 28,000 SKUs for a US third-party logistics provider. External signals: weather, holiday calendar, economic index. Integrated with Manhattan WMS for automated reorder triggers.
Impact comparison
What changes when logistics runs on AI
Return on investment
Estimated impact for a mid-size 3PL
Overstock & carrying cost
Before
$6.8M annual overstock write-off
After
$2.6M - 62% reduction via SKU-level demand AI
~$4.2M saved
Fleet fuel & route cost
Before
Avg. 28% empty miles; manual routing
After
12% empty miles; AI route optimisation across 600 vehicles
~$1.1M saved
Unplanned maintenance
Before
14 unplanned breakdowns per quarter, avg. $38K cost each
After
3 unplanned breakdowns per quarter post-predictive maintenance
~$418K saved
Labour - demand planning
Before
8 FTE demand planners working weekly cycles
After
3 FTE analysts overseeing AI model outputs and exceptions
~$540K saved
Figures based on an anonymised client engagement. Actual results vary by fleet size, SKU count, and existing process maturity.
Client perspective
“We went from a 3-day inventory review cycle to a daily automated signal. Overstock write-offs dropped by $4.2M in the first year - with the same planning team.”
VP of Operations
US Third-Party Logistics Provider, 28,000 SKUs
Integration & trust
Built to fit your operations stack
WMS integrations
Manhattan WMS, Blue Yonder, SAP EWM, and custom warehouse management systems via REST or EDI.
TMS integrations
Oracle TMS, MercuryGate, and carrier API connections for real-time shipment data and rerouting.
ERP connectivity
SAP S/4HANA, Oracle NetSuite, and Microsoft Dynamics 365 for closed-loop inventory and procurement.
Telematics platforms
Samsara, Geotab, and custom OBD integrations for vehicle sensor data ingestion and fleet telemetry.
Cloud infrastructure
AWS, Azure, and GCP deployments. Multi-region for latency-sensitive last-mile applications.
Data security
SOC 2 Type II aligned practices. All data encrypted in transit and at rest. PII isolation by customer.