Monday, October 2, 2017
Cargotec Transforms into a Digital Leader in Intelligent Cargo Handling with IoT Platform Powered by Cloudera
Cloudera, Inc., the modern platform for machine learning and analytics, optimized for the cloud, announced that CargotecOyj, a leading provider of cargo and load handling solutions, is using Cloudera Enterprise Data Hub to power its Internet of Things (IoT) offering to enable predictive maintenance and develop insightful, data-driven services. Cloudera, together with Tata Consultancy Services (TCS), built a cloud-based IoT-as-a-Service platform that will usher in a new era of digital connectivity for the company’s cargo handling operations and enable operational excellence using machine learning.
Cargotec offers solutions and services through various businesses in the areas of cargo and load handling that ensure its customers continuous, reliable and sustainable performance. Operating in more than 100 countries, and with sales of 3.5 billion EUR in 2016, the company and its subsidiaries have delivered half a million loader cranes to customers. They have also moved every fourth container in the world with terminal and port operation solutions, and offered engineering solutions and services for half of the world’s ships to make transport by sea safe and reliable.
The IoT-as-a-Service solution uses machine learning to derive insights from streams of data across the thousands of cargo handling equipment and machinery to enable remote monitoring and predictive maintenance. Using data, cargo handling operation is improved, maintenance becomes predictive and anomalies are detected in real-time. This empowers Cargotec to increase their competitive edge by offering new types of intelligent services and solutions with embedded artificial intelligence to customers. Once collected and cross-utilized, enriched data will be used for driving and boosting new types of ecosystems by exposing the data through API’s in a robust and controlled way to leverage data assets in new contexts.
“It is our goal to be a leader in intelligent cargo handling by 2020,” said Soili Mäkinen, chief information officer at Cargotec. “With our scalable IoT platform, we can offer our customers data-based insights that many of these industrial companies have never seen before. We use IoT data and machine learning to help customers recognize how their cargo handling equipment are performing in different weather conditions, understand how usage relates to failure rates, and even detect anomalies in transport systems.”
To support Cargotec’sIoT goals, Cloudera, together with TCS, built a cloud-based, sensor data analytics framework that helps collect, store, analyze and correlate sensor data streams with data from internal, external and third-party data sources. The advanced analytics and machine learning platform, based on the flagship product Cloudera Enterprise Data Hub, will ingest data from equipment and fleet management, pull in weather patterns and forecasts, and contrast geography — to perform key analysis for remote monitoring, predictive equipment maintenance and anomaly detection, all in real-time.
“We are pleased to enable Cargotec with the Digital Reimagination of its business processes and build a strong data backbone through our solutions and IP,” said Dinanath Kholkar, global head of analytics and insights at TCS. “Our solution allows for real time sensor data acquisition to establish diagnostics and facilitate predictive maintenance. The established big data backbone is scalable, robust and cost effective, and will empower agility in data driven strategies and business growth.”
With Cloudera Enterprise Data Hub, Cargotec derives fuel efficiency and route optimization which end users like ports or ships can purchase to improve their operational efficiencies. This creates new revenue models for Cargotec and keeps its focus on being an intelligent cargo handling solution. Additionally, the Cargotec data science team uses Cloudera Data Science Workbench, a collaborative hub and integrated development environment capable of running Python, R or Scala with support for Apache Spark to build machine learning solutions.