Client: NDA

Unified Supply Chain Management

Case study details


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.



Challenge


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:

  • A lot of information is stored in Excel and flat files
  • Bad data quality and inconsistencies in data
  • Multiple data sources
  • Customizability
  • Short data history
  • Additional data sources were not identified by business


Approach


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.



Goal


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.



Outcome


Addepto team built a comprehensive platform enriched with predictive modeling that allows the company to efficiently manage its shipping schedule and inventory.


Challenge

Mining and manufacturing sectors grapple with significant logistics costs


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.


The order handling process is very long and complex


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's managers were not aware of the full potential of AI


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.


Our team expert opinion







Approach

AI-driven engine automates route optimization for trains, vessels, and trucks


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.


During project development, our team worked on:


  • Addepto team built a comprehensive platform that allows the company to efficiently plan and manage their shipping schedule and inventory across terminals and commodities in one unified solution.
  • AI algorithms were implemented to predict ETAs of the particular vessels to inform customers and stakeholders with higher arrival time accuracy.
  • The solution is using different 3rd party data sources, integrating internal data, and helping to track ships.
  • The system groups orders into batches and plans a calendar for 3 months in advance. Plans are updated on a daily basis if information about orders changes in the SAP software.
  • The system automatically chooses the best day of dispatch for each batch of orders and considers deadlines, loading speed, incoterms, and client preferences.
  • The logistics platform has been additionally enriched with predictive modeling.
  • The implemented algorithm predicts market vessel prices based on macroeconomic factors and seasonality.
  • Implemented machine learning models help to choose the period when the price of the vessels will be the lowest.

Goal

Company managers needed to get data-grounded business insights


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.




Unified Platform for Managing Supply Chain


Predictive AI Modules


Decision-making is based on intuition, not on data


Decreasing logistics costs and losses


Reducing the amount of manual data processing


Adding scalability and automation processes


Outcome

Transportation and stocks costs were reduced by an average of $1.5 per ton


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.



Before


  • Manually data processing
  • Slow and unscalable information exchange
  • Processes sensitive to human mistakes
  • Huge logistics costs, losses, and high gas emissions
  • Inability to predict events and possible disruptions


After


  • Automated data processing
  • Unified data environment (one source of truth)
  • Forecasting potential disruptions based on historical data
  • Reducing logistics costs, losses
  • Lowering carbon footprint

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