Cargotec Uses Data and ML to Optimize Cargo Flow and Drive Sustainable Solutions


Cargotec Oyj (Cargotec), a global provider of cargo- and load-handling solutions, is a 1.5°C company, meaning it has announced the goal of cutting its carbon dioxide emissions in half between 2019 and 2030. To achieve the goal, Cargotec drives efficiency and sustainability by providing customers with electric solutions as well as collecting data with its Internet of Things (IoT) solution. While aiming for digital transformation of cargo- and load-handling, Cargotec’s mission is to provide smarter cargo flow for better everyday life.

Enabling data analytics is key for the future of the transportation and logistics industry, as well as for a global company like Cargotec. One of Cargotec’s strategic business units, Kalmar, provides solutions that are involved in nearly 800 million container moves globally every year. And three out of four ships in global shipping carry equipment from MacGregor, another of Cargotec’s business units. Building an IoT architecture that would capture data from all of Cargotec’s solutions and then analyze it for insights would be a challenge.

To build that IoT and data analytics solution, Cargotec turned to Amazon Web Services (AWS). Cargotec’s data-driven services team used Amazon SageMaker—which can be used to prepare, build, train, and deploy high-quality machine learning (ML) models quickly—to create models to support data-driven digital services. Using Amazon SageMaker and other AWS services, Cargotec turns its data into insights that have led to more efficient, sustainable, and cost-effective operations.


Using AWS solutions, we distill information from service data, maintenance data, and equipment-usage data to improve customer operations and provide better uptime for our customer equipment.”

Pekka Mikkola
Director of Data Driven Services, Cargotec Oyj

Going All In on AWS

Cargotec provides cargo- and load-handling solutions for ships, ports, terminals, and inland logistics operators in more than 100 countries by means of its four business units: Kalmar, Hiab, MacGregor, and Navis. In 2020, its sales totaled approximately €3.3 billion.

In 2015, Cargotec began building an IoT and data analytics system on AWS to better serve its customers worldwide. “We wanted to better understand our customers and their operational challenges with IoT and data collection,” explains Pekka Mikkola, director of data-driven services at Cargotec. Together with its customers, the company was able to develop intelligent services using data in new contexts. These IoT and data analytics approaches would also support Cargotec’s newly electrified solutions—for instance, by improving charging scenarios to optimize power operations. According to a Cargotec blog, “data-driven methods such as artificial intelligence are crucial for making the transition to electrically powered fleets possible in a judicious, factual manner instead of through speculation.”

Since 2018, the company has been using AWS services, including Amazon Simple Storage Service (Amazon S3)—an object storage service that offers industry-leading scalability, data availability, security, and performance—which Cargotec uses to store hundreds of terabytes of raw data.

Mikkola says that Cargotec chose AWS because of its flexibility and speed of innovation: “We can be quick in demonstrating value to our customers. We have a diverse customer base and a broad range of solutions that require true modularity from the services we use, and using AWS has supported that need.” 

Turning Hundreds of Terabytes of Raw Data into Actionable Knowledge on AWS

Cargotec built a pipeline that collects data from equipment using Amazon Kinesis Data Firehose, a simple way to reliably load streaming data into data lakes, data stores, and analytics services. The streaming data is stored in Amazon S3, which also houses other types of data, such as business systems data. Cargotec’s data scientists then use Amazon Athena, a serverless interactive query service, to analyze data in Amazon S3 using standard structured query language. They can then input data from Amazon Athena tables to Amazon QuickSight, a scalable, serverless, embeddable, ML-powered business intelligence service that enables the team of experts to create and publish interactive dashboards with ML-powered insights for wider audiences. The company also uses AWS Lambda, a serverless compute service that lets users run code without provisioning or managing servers, creating workload-aware cluster scaling logic, maintaining event integrations, or managing runtimes. “We are able to tune our core services and match the compute with the demand every day for flexibility and scalability,” says Mikkola.

Using Amazon SageMaker, the data-driven services team has developed and deployed ML models that perform predictive analytics on Cargotec equipment. “Using Amazon SageMaker enables our data scientists to be productive and to access and explore hundreds of terabytes of stored data from the machines,” says Mikkola. “We don’t need to have dedicated people for data manipulation. The data scientists can access the data by themselves for processing. We are especially proud of our fully serverless ML operations pipeline, which manages data ingestion and model serving and everything in between.” A serverless architecture is not only efficient; it’s cost effective.

One ML model is used in the company’s newly introduced energy-saving guarantee: a cutting-edge, eco-efficient sales initiative that enables customers to estimate operational costs and save on emissions when transitioning to electric machines like Kalmar’s electric forklift trucks. Cargotec uses an ML model to understand how much energy the cargo-handling equipment will consume in various scenarios based on the operating conditions, the driving distances, and the weight of the loads. Then Cargotec can ask customers how they plan on using the equipment and can then predict the energy consumption. If customers exceed the predicted amount, Cargotec promises to reimburse them. “Customers are very happy: because of this offering, a company can take what was previously a variable cost and make it a fixed cost,” says Mikkola.

Data analytics are also used for enhancing equipment maintenance operations, such as predicting when equipment might fail or need service. This information can orchestrate service operations and bring new insights. “Using AWS solutions, we distill information from service data, maintenance data, and equipment-usage data to improve customer operations and provide better uptime for our customer equipment,” Mikkola says.

Growing Closer to Fulfilling Its Sustainability Promise on AWS

By going all in on AWS, Cargotec built an IoT and data analytics solution that helps its customers make their operations safer and more efficient, sustainable, and cost effective. Customers can use the AWS-powered Cargotec solutions to optimize their daily operations—providing a smarter cargo flow for better everyday life.

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About Cargotec Oyj

Headquartered in Finland, Cargotec Oyj is a provider of cargo-handling machinery for ships, ports, and terminals. Operating in more than 100 countries, Cargotec provides equipment and logistic solutions for intelligent container handling.

Benefits of AWS

  • Uses ML to analyze hundreds of terabytes of data
  • Scales infrastructure up and down to meet demand
  • Predicts energy consumption on machinery
  • Makes operations more efficient and sustainable
  • Improved cost efficiency by adopting serverless technologies 

AWS Services Used

Amazon SageMaker

Amazon SageMaker is a fully managed service that provides every developer and data scientist with the ability to build, train, and deploy machine learning (ML) models quickly. 

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Amazon Kinesis Data Firehose

Amazon Kinesis Data Firehose is the easiest way to reliably load streaming data into data lakes, data stores, and analytics services.

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Amazon Athena

Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL.

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Amazon QuickSight

Amazon QuickSight is a scalable, serverless, embeddable, machine learning-powered business intelligence (BI) service built for the cloud. 

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