Increasing Challenges in Global Supply Chain Management
The client is the second world’s largest aluminum company. Founded in 2007, they were pioneers of innovation in the mining & metal industry from the very beginning.
The client operates one of the world’s largest aluminum production and distribution networks, managing highly complex logistics operations across multiple transportation channels and global supply chain environments. As operational scale increased, the organization faced growing challenges in optimizing transportation workflows and reducing operational inefficiencies.
- Manual and fragmented operational workflows: Shipment and inventory management relied heavily on spreadsheets, SAP ERP systems, phone calls, and email-based coordination. Disconnected workflows created communication inefficiencies, duplicated data, and increased exposure to human error across logistics operations.
- Poor data quality and inconsistent operational visibility: Critical supply chain data was distributed across Excel files, flat files, and multiple disconnected systems, resulting in incomplete, outdated, and inconsistent information. The lack of a centralized operational view limited real-time decision-making and reduced planning accuracy.
- Limited ability to predict disruptions across global logistics networks: The multimodal supply chain environment was highly vulnerable to external factors such as weather conditions, geopolitical events, and market volatility. Without predictive AI capabilities, the organization struggled to anticipate vessel delays, logistics bottlenecks, and operational disruptions effectively.
- Rising operational costs and inefficiencies: Delayed responses to disruptions increased the risk of demurrage costs, missed delivery deadlines, and higher CO₂ emissions across transportation operations.
A unified AI-powered supply chain management platform was required to centralize operational intelligence, automate logistics workflows, and enable predictive decision-making across complex global transportation networks.
“Our newly developed system enables all the microservices required for PDF generation to operate in parallel, dramatically reducing the time needed to generate large batches of PDFs while improving scalability and resilience.”
Piotr Danielczyk
Senior Data Engineer – Addepto
Developing an AI-Powered Unified Supply Chain Platform
To address these challenges, the client needed a platform capable of processing operational data dynamically, optimizing multimodal transportation workflows, and supporting predictive logistics decisions at scale was essential.
Our team engaged as a strategic engineering partner to design and implement a scalable AI-driven supply chain optimization platform tailored to large-scale industrial logistics operations.
1. Building a Unified AI-Driven Supply Chain Platform
Addepto developed a centralized logistics platform that consolidated shipping schedules, inventory data, and order management workflows into a single operational environment.
The platform enabled the organization to coordinate transportation planning across vessels, trains, and trucks while eliminating data silos and improving operational visibility across supply chain operations.
A unified operational architecture established a scalable single source of truth for logistics planning and decision-making.
2. Implementing Predictive Modeling for ETA and Market Pricing
Machine learning models were developed to forecast vessel ETAs using historical shipment patterns, real-time operational updates, and third-party logistics APIs.
The solution also incorporated predictive pricing algorithms capable of estimating vessel market prices based on macroeconomic indicators, seasonal trends, and changing market conditions.
Predictive operational intelligence improved transportation planning accuracy while helping optimize procurement and logistics costs.
3. Automating Logistics Planning and Optimization Workflows
Addepto designed an AI optimization engine capable of evaluating multiple operational constraints simultaneously, including loading rates, sailing times, fuel consumption, incoterms, delivery deadlines, and customer preferences.
The platform automatically grouped orders into optimized shipment batches, determined ideal dispatch schedules, and continuously updated operational plans whenever new data became available from systems such as SAP.
Automated planning workflows improved responsiveness while significantly reducing manual coordination effort across logistics operations.
4. Integrating Internal and External Operational Data Sources
The solution seamlessly integrated internal ERP data with third-party services providing ship tracking, weather intelligence, and real-time transportation updates.
Integrated operational visibility enabled supply chain teams to monitor logistics conditions continuously, respond to disruptions faster, and reduce unnecessary transportation costs.
5. Designing a Scalable and Future-Ready Supply Chain Architecture
The platform was engineered to scale across additional regions, terminals, transportation routes, and product categories as operational demand evolved.
A flexible and modular architecture enabled continuous improvements and future expansion without disrupting existing logistics workflows or operational stability.
Accelerating Global Logistics Operations Through AI
The partnership between Addepto and the client transformed fragmented and intuition-driven logistics workflows into a scalable AI-powered supply chain optimization platform. Operational teams can now optimize transportation planning more efficiently while improving visibility across multimodal logistics operations.
The platform reduced transportation and stock management costs by an average of $1.5 per ton while also improving planning efficiency and delivery performance.
Before
- Manual and fragmented data processing
- Slow and inefficient information exchange
- High exposure to human error
- Elevated logistics costs and carbon emissions
- Limited ability to predict disruptions
After
- Automated and streamlined data processing
- Unified operational data environment
- Predictive disruption forecasting powered by historical data
- Reduced logistics costs and operational losses
- Lower carbon footprint across supply chain operations
The intelligent optimization workflows also enabled supply chain teams to focus more on strategic operational improvements rather than repetitive planning and coordination tasks.
As part of KMS Technology, Addepto continues to help organizations modernize complex logistics operations through scalable AI platforms, predictive analytics, and intelligent supply chain optimization solutions.
Ready to optimize your supply chain with manufacturing-based AI? Contact us today!