Adexin developed an intelligent supply chain management system that provides real-time visibility, predictive analytics, and automated decision-making across procurement, inventory, and logistics operations.
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Technologies
AI-powered supply chain management system
- CountryNetherlands
- Duration2025 - 2026
- Services
- Industry
Building a data-driven supply chain ecosystem
This case study demonstrates how an AI-powered supply chain platform transformed fragmented logistics and procurement operations into a unified, data-driven system.
The solution integrates real-time data from suppliers, warehouses, and logistics providers, enabling end-to-end visibility and more efficient supply chain coordination. By combining predictive analytics with automation, the platform improves demand forecasting, reduces inefficiencies, and supports faster decision-making.
The platform is actively used by operations and procurement teams to monitor stock levels, manage supplier orders, and respond to demand changes in real time. Based on AI-driven forecasts, it recommends when and how much to reorder at the SKU level, helping prevent stockouts while avoiding overstock.
As a result of the implementation:
- Real-time visibility reached 93% system-wide coverage, reducing data silos by 85%
- Stockouts decreased by 32%, and overstock by 27%
- Forecast accuracy improved by 38% with AI-driven predictions
- Supplier efficiency increased by 40%
- Operational costs reduced by 22%
- Decision-making speed improved by 50%
- Manual workload reduced by 35%
Operational challenges in supply chain digitalization
Modern supply chains involve multiple stakeholders, disconnected systems, and constantly changing data across procurement, inventory, and logistics. In the client’s case, operations were spread across several tools, limiting real-time visibility and slowing down decision-making.
The main challenge for Adexin was to unify these workflows into a single platform while ensuring real-time data synchronization across supplier systems, warehouses, and logistics providers. This required handling different APIs, data formats, and update frequencies, while maintaining data consistency for inventory, orders, and shipments.
At the same time, the system needed to support accurate demand forecasting, scale with growing transaction volumes, and present clear, actionable insights. The final solution had to act as a central intelligence layer without disrupting existing supply chain processes.
Client overview
The client (operating under NDA) is a mid-sized European logistics and distribution company operating across retail and manufacturing supply chains. The organization manages a distributed network of suppliers, warehouses, and third-party logistics providers, handling procurement, inventory, and delivery operations across multiple regions. Before the project, many processes were partially digitized but remained fragmented across different systems, limiting visibility and operational efficiency.
The company’s goal was to centralize supply chain operations, improve forecasting accuracy, and reduce manual coordination between teams and partners. A key focus was on increasing transparency across inventory and shipments, while enabling faster, data-driven decision-making to support business growth and operational scalability.
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We built an AI-driven supply chain intelligence platform
- Real-time supply chain visibility. The platform aggregates data from warehouses, suppliers, and logistics providers, giving users a live overview of inventory levels, order statuses, and shipment tracking.
- AI-powered demand forecasting. Machine learning models analyze historical data, seasonality, and external factors to predict demand and optimize inventory planning.
- Automated procurement workflows. The system automatically generates purchase recommendations based on stock levels, demand forecasts, and supplier lead times.
- Supplier performance tracking. Built-in analytics evaluate supplier reliability, delivery times, and pricing trends to support better sourcing decisions.
- Logistics optimization. AI algorithms suggest optimal shipping routes and delivery schedules, reducing transportation costs and delays.
- Centralized analytics dashboard. Decision-makers can access actionable insights through intuitive dashboards, enabling faster and more informed decisions.
- Scalable architecture. The platform is designed to grow with the business, supporting increasing data volumes and expanding supply chain networks.
Adexin designed and implemented a scalable SCM platform that acts as a central intelligence hub for supply chain operations. The system connects procurement, inventory, and logistics into a unified ecosystem powered by AI and real-time analytics.
Technology stack
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Our clients say “They delivered exactly what we were looking for and stayed within the budget.” Eli Edri COO at GAMP