CASE STUDY
Food Import Logistics & Supply Chain Management System
Problem
Company operating warehouses in 3 countries and serving regional customers with a mixed fleet of full-size articulated trucks (for inter-warehouse and large customer deliveries) and smaller vans (for last-mile local distribution). Demand fluctuated significantly by season and customer, supply chains stretched across multiple countries and product shelf life created hard constraints on stock rotation.
The operation was managed largely manually: route planning by phone, inventory tracked in country-specific spreadsheets, demand forecasting based on gut feel from sales managers. The results were predictable — sub-optimal route loading, spoilage from poor stock rotation, and cash tied up in excess warehouse inventory.
Solution
We build an end-to-end logistics and supply chain platform: AI demand forecasting, optimized routing for both full-size trucks and last-mile vans (including multi-stop truck loads across 2–3 locations), warehouse inventory with spoilage tracking, and a full financial management layer covering all costs and revenue.
The platform covers the full supply chain cycle — from forecasting what customers will order next week to generating the invoice and recording the margin. Customer order pattern analysis: flags customers whose ordering behavior is deviating significantly from their historical pattern – early warning for churn or opportunity to upsell. Carrier performance tracking: on-time delivery rates, damage rates and cost per km per carrier — gives the procurement team objective data for carrier negotiations.
Key Features
AI Demand Analysis and Forecasting
ML model forecasts demand per customer per SKU (eggs by size/grade, flour by type, oil by pack size) using order history, seasonality, customer growth trends and external signals (local market events, holidays). 14-day rolling forecast updated daily.
Full-Size Truck Route Optimization
Optimizes loads for articulated trucks delivering between central warehouses and to large customers. One truck can serve 2–3 drop locations if the combined load fills the vehicle and the route is efficient. Accounts for time windows, driver hours regulations and product compatibility constraints.
Last-Mile Van Routing
Daily route optimization for local delivery vans from regional warehouses to smaller customers. Customer time window constraints, vehicle capacity and driver familiarity scoring. Dynamic re-routing when orders change or a delivery fails. Integration with Google Maps for live traffic-adjusted ETAs.
Warehouse and Inventory
Real-time stock levels across all warehouses by product, lot, arrival date and expiry date. FEFO (First Expired First Out) picking rules enforced by the WMS layer — prevents avoidable spoilage. Inter-warehouse transfer recommendations when one location has surplus and another is at risk of stockout.
Spoilage & Loss Tracking
Every loss event recorded: breakage (eggs), moisture damage (flour), oxidation (oil), returns. Loss categorized by cause, location and responsible party. Supplier claims automatically generated for transit damage. Loss cost allocated to P &L and flagged for operational investigation above configurable thresholds.
Financial Management
Full landed cost calculation per shipment: product cost, customs, freight, warehouse handling, last-mile delivery. Real-time gross margin per SKU, per customer and per country. Accounts payable (suppliers, carriers, warehouse operators) and accounts receivable (customers).
Key Achievements
Food Import Logistics & Supply Chain Management System within 6 month lead to results listed below.
-17%
Total transport cost through route optimization and load consolidation
-25%
Product spoilage and waste losses through FEFO and better forecasting
-20%
Warehouse inventory value through AI-driven replenishment planning
Why Food Import Logistics & Supply Chain Management System become successful
Following the implementation of our custom system, the company transitioned from manual, fragmented spreadsheets to a unified digital ecosystem connecting warehouses across all three countries. By automating route planning for the mixed fleet and deploying AI-driven demand forecasting, the platform maximized truck load capacity and minimized product spoilage within strict shelf-life constraints. Ultimately, this eliminated excess inventory, unlocked trapped working capital, and streamlined both inter-warehouse logistics and last-mile distribution.
Managing logistic and distribution?
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Stack & Architecture
Frontend
React + TypeScript
All UI for Food Import Logistics & Supply Chain Management System
Backend
Python / FastAPI
Business logic, routing engine, forecasting model serving
AI / ML
Prophet + OR-Tools
Demand forecasting + Google OR-Tools for VRP optimization
Database
PostgreSQL
Single source and data storage
Maps
Google Maps Platform
Geocoding, distance matrix, live traffic ETAs
Infrastructure
AWS
Hosted SaaS solution
