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Supply Chain Optimization Reducing Logistics Costs by $3.8M

Built AI system optimizing routes, inventory, and warehouse allocation across 15 distribution centers.

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Logistics10 WEEKSVERIFIED RESULTSRegional logistics operator

Supply Chain Optimization Reducing Logistics Costs by $3.8M

Built AI system optimizing routes, inventory, and warehouse allocation across 15 distribution centers.

Founder note: this is written to be practical. We focus on what we learned in delivery, what changed for the team, and what results held up after rollout.

INDUSTRYLogistics
CLIENT TYPERegional logistics operator
TIMELINE10 weeks
DELIVERY SCOPERoute optimization, inventory forecasting, supplier scoring
domain

The Real Problem

1. Inefficient route planning causing high fuel costs

2. Inventory misalignment between warehouses

3. $12M annual logistics spend seemed high but hard to optimize

4. No system-wide visibility into supply chain

5. Manual planning was taking weeks for seasonal changes

build

What We Actually Changed

Built AI supply chain optimizer:

- Real-time inventory optimization across 15 centers

- Route optimization considering traffic, fuel, delivery windows

- Demand forecasting to right-size inventory

- Supplier selection optimization

- Warehouse allocation optimization

- Integration with ERP, WMS, TMS systems

- Automated rebalancing recommendations

- Real-time tracking and alerts

TOOLS AND METHODS USED

PythonOptimization algorithmsMachine learningPostgreSQLGoogle Maps APIERP/WMS/TMS APIsReal-time trackingAWS
layers

Delivery Journey

PHASE 1

System Audit

Analyzed current supply chain, identified inefficiencies

PHASE 2

Data Integration

Connected to ERP, WMS, TMS, mapping systems

PHASE 3

Optimization Engine

Built route, inventory, and allocation optimizers

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What Improved

WEBOPS Limited looked at our supply chain with fresh eyes and immediately found millions in optimization opportunities. Their AI system is running our logistics better than we ever could manually. This transformed our profitability.

$3.8M
Saved/Year
+34%
Route Eff.
+18%
On-Time
30% reduction in logistics spend through optimization
Better route planning reduced miles driven
Optimization improved on-time delivery rate
Better forecasting reduced inventory imbalance
Automated planning vs. manual (weeks to hours)
Better routing saved $1.4M in fuel annually
business_center

FOUNDER BRIEF

INDUSTRY
Logistics
TIMELINE
10 weeks
CLIENT TYPE
Regional logistics operator
terminal

DELIVERY STACK

PythonOptimization algorithmsMachine learningPostgreSQLGoogle Maps APIERP/WMS/TMS APIsReal-time trackingAWS

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