Scania is a world leading provider of transport solutions and is leading the shift towards sustainable transport systems. In 2018 it delivered 88,000 trucks, 8,500 buses as well as 12,800 industrial and marine engines to customers. Research and development is in Sweden, with branches in Brazil and India. Production takes place in Europe, Latin America and Asia, with regional production centres in Africa, Asia and Eurasia.
Scania was looking to make operations as efficient as possible, understand exactly where the inefficiencies lie, and consequently how to solve the problems causing them. The company needed to move towards a data-driven mindset to make use of the information coming from the 300,000-strong connected fleet. It saw the need of creating a data product with the ability to transform position data into business insights in a reproducible, reliable and scalable way. This was beyond the capability of traditional ways of working with data and new processing paradigms and tooling were needed.
The streaming paradigm in combination with the processing power of the data lake proved to be a success factor. Also, a common data platform facilitated cross-functional collaboration between the business, data scientists and data engineers. Cloudera and Scania worked together on the data processing which was done using Apache Kafka and Apache Spark to translate the desired business outcome into data language. By using practises from DevOps and Continuous Delivery an end-to-end data pipeline was created. Scania has a verifiable, reliable and repeatable streaming big data application.
Scania was able to gather those within the business that understood the problem and outcome they needed, with those that could manipulate the relevant data to achieve mission-critical business outcomes. By jointly analysing seemingly disparate datasets such as truck size, location and time, for example, Scania can paint the most accurate picture to adequately inform prompt decision making, whether it is a case of deploying a certain type of truck or creating a diversion to the route based on live streaming traffic information. This created a much more efficient and sustainable model of analysis, development and improvement.