The company, the second world’s largest aluminum company, founded in 2007, was a pioneer of innovation in the mining & metal industry from the very beginning. The company aimed to lower its carbon footprint, so implementing innovative and energy-saving technologies was crucial.
However, the green-oriented approach was not limited just to producing metal but also a logistic part of operations. Read about how the company managed to automate logistic workflows and increase efficiency thanks to implementing AI.
The company managed its shipping and inventory situation mostly manually. Managers used spreadsheets, entered data into complex SAP ERP systems, and exchanged tons of information about cargo and vessel nominations or notices of vessels’ ETAs via phone calls and emails. The process was simply slow, inefficient, and sensitive to mistakes that influence the whole operational performance:
AI-driven logistics optimization engine allows to automatically optimize routes for transportation by trains, vessels, and trucks. The developed unified supply chain management solution takes into account various specific business factors: loading rate, distance, average vehicle speed, sailing time, fuel consumption, MGO, order volume, material type, deadlines, and others. Additionally, the platform was enhanced by predictive modeling to forecast market vessel prices based on macroeconomic factors and seasonality.
The company’s goal was to reduce the challenges related to managing the logistics side of operations, avoid disruption, get an overall view of the factors that influence the supply chain, and – finally – reduce its costs and lower gas emissions.
Addepto team built a comprehensive platform enriched with predictive modeling that allows the company to efficiently manage its shipping schedule and inventory.
The company decided to seek an AI expert to see how to implement AI modules across departments to grasp the overall data flow and convert it into meaningful business insights and improve the efficiency of the supply chain.
It involves maritime and land routes, and requires multi-department cooperation across regions. Given that, controlling the entire end-to-end process is particularly challenging as it is highly sensitive to unpredictable events in geopolitics, rapid weather changes, or any other disruptions. Dysfunctions occurring on individual stages lead to collapsing the entire supply chain, both on a global and local level, causing a domino effect. Taking control of all these variables is almost impossible by using old-fashioned methods.
The company managed its shipping and inventory situation using spreadsheets, often combined with complex SAP ERP systems. Moreover, emails and phone calls are exchanged regularly between stakeholders to share operational information such as cargo and vessel nominations or notices of vessels’ ETAs. Relying on complex manual processes and data entry in multiple separate systems led to a lack of transparency and doomed Company to struggle with various difficulties in sourcing raw materials.
The project was very challenging not only from a technology perspective but from a business as well. To build a proper optimization system we had to gather very detailed requirements and understand each part of the company supply chain. Thanks to the close collaboration with the customer and its team we were to achieve the best results in optimization system and supply chain optimization that brings real money benefits to the business.
The logistics of homogeneous marine cargo loading and transportation due to the high complexity of an optimization problem is problematic. Still, the potential for reducing costs is enormous. The manual data processing was time demanding, the results inaccurate, and costly. Finding the optimal solution is impossible not only because of high computational complexity but simply due to unrealistic customer expectations. We are incredibly pleased with the solution found by Addepto. Heuristics implemented for us makes it possible to get the satisfactory assignment of orders to vessels and vessels to piers in loading and unloading ports. Thanks to this solution, we have a lot of savings both on the costs of loading, storage, and transport, but also on the work of our employees.
The developed supply chain management solution takes into account various specific business factors: loading rate, distance, average vehicle speed, sailing time, fuel consumption, MGO, order volume, material type, deadlines, and others.
Additionally, the platform was enhanced by predictive modeling to forecast market vessel prices based on macroeconomic factors and seasonality.
In order to be able to make decisions based on real factors, not assumptions; processes needed to be automated, scalable, repetitive, and bulletproof, so manual labor had to be reduced to a minimum.
After implementing the AI platform developed by Addepto, Company team was able to stay abreast of the live status of their inbound major raw material vessels. This always-up-to-date knowledge allows the company increases the efficiency of inventory planning, reduce/avoid demurrage, and – lastly – increase the general operational performance.
Thanks to automation, managers were able to quickly make the most cost-effective decisions, and so logistic expenses were reduced (transportation and stock costs were reduced by an average of $1.5 per ton) and the number of customer orders completed on time increased. The overall order handling process got faster and the delivery process was released from unexpected disruption.