Kai WaehnerSiemens Healthineers, a global leader in medical technology, delivers solutions that improve patient outcomes and empower healthcare professionals. As part of the Siemens AG family, Siemens Healthineers stands out with innovative products, data-driven solutions, and services designed to optimize workflows, improve precision, and enhance efficiency in healthcare systems worldwide. A significant aspect of their technological prowess lies in their use of data streaming to unlock real-time insights and optimize processes. This blog post delves into how Siemens Healthineers uses data streaming with Apache Kafka and Flink, their cloud-focused technology stack, and the use cases that drive tangible business value such as real-time logistics, robotics, SAP ERP integration, AI/ML, and more.
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Siemens Healthineers: Shaping the Future of Healthcare Technology
Who They Are
Siemens AG, a global powerhouse in industrial manufacturing, energy, and technology, has been a leader in innovation for over 170 years. Known for its groundbreaking contributions across sectors, Siemens combines engineering expertise with digitalization to shape industries worldwide. Within this ecosystem, Siemens Healthineers stands out as a pivotal player in healthcare technology.
With over 71,000 employees operating in 70+ countries, Siemens Healthineers supports critical clinical decisions in healthcare. Over 90% of leading hospitals worldwide collaborate with them, and their technologies influence over 70% of critical clinical decisions.
Their Vision
Siemens Healthineers focuses on innovation through data and AI, aiming to streamline healthcare delivery. With more than 24,000 technical intellectual property rights, including 15,000 granted patents, their technological foundation enables precision medicine, enhanced diagnostics, and patient-centric solutions.
Siemens Healthineers and Data Streaming for Healthcare and Manufacturing
Siemens is a large conglomerate. I already covered a few data streaming use cases at other Siemens divisions. For instance, the integration project from SAP ERP on-premise to Salesforce CRM in the cloud.
At the Data in Motion Tour 2024 in Frankfurt, Arash Attarzadeh (“Apache Kafka Jedi“) from Siemens Heathineers presented various very interesting success stories that leverage data streaming using Apache Kafka, Flink, Confluent, and its entire ecosystem.
Why Data Streaming with Apache Kafka and Flink?
Healthcare and manufacturing processes generate massive volumes of real-time data. Whether it’s monitoring devices on production floors, analyzing telemetry data from hospitals, or optimizing logistics, Siemens Healthineers recognizes that data streaming enables:
- Real-time insights: Immediate and continuously action on events as they happen.
- Improved decision-making: Faster and more accurate responses.
- Cost efficiency: Reduced downtime and optimized operations.
Healthineers Data Cloud
The Siemens Healthineers Data Cloud serves as the backbone of their data strategy. Built on a robust technology stack, it facilitates real-time data ingestion, transformation, and analytics using tools like Confluent Cloud (including Apache Kafka and Flink) and Snowflake.
This combination of leading SaaS solutions enables seamless integration of streaming data with batch processes and diverse analytics platforms.
Technology Stack: Healthineers Data Cloud
Key Components
- Confluent Cloud (Apache Kafka): For real-time data ingestion, data integration and stream processing.
- Snowflake: A centralized warehouse for analytics and reporting.
- Matillion: Batch ETL processes for structured and semi-structured data.
- IoT Data Integration: Sensors and PLCs collect data from manufacturing floors, often via MQTT.
Many other solutions are critical for some use cases. Siemens Healthineers also uses Databricks, dbt, OPC-UA, and many other systems for the end-to-end data pipelines.
Diverse Data Ingestion
- Real-Time Streaming: IoT data (sensors, PLCs) is ingested within minutes.
- Batch Processing: Structured and semi-structured data from SAP systems.
- Change Data Capture (CDC): Data changes in SAP sources are captured and available in under 30 minutes.
Not every data integration process is or can be real-time. Data consistency is still one of the most underrated capabilities of data streaming. Apache Kafka supports real-time, batch and request-response APIs communicating with each other in a consistent way.
Use Cases for Data Streaming at Siemens Healthineers
Siemens Healthineers described six different use cases that leverage data streaming together with various other IoT, software and cloud services:
- Machine monitoring and predictive maintenance
- Data integration layer for analytics
- Machine and robot integration
- Telemetry data processing for improved diagnostics
- Real-time logistics with SAP events for better supply chain efficiency
- Track and Trace Orders for improved customer satisfaction and ensured compliance
Let’s take a look at them in the following subsections.
1. Machine Monitoring and Predictive Maintenance in Manufacturing
Goal: To ensure the smooth operation of production devices through predictive maintenance.
Using data streaming, real-time IoT data from drill machines is ingested into Kafka topics, where it’s analyzed to predict maintenance needs. By using a TensorFlow machine learning model for infererence with Apache Kafka, Siemens Healthineers can:
- Reduce machine downtime.
- Optimize maintenance schedules.
- Increase productivity in manufacturing CT scanners.
Business Value: Predictive maintenance reduces operational costs and prevents production halts, ensuring timely delivery of critical medical equipment.
2. IQ-Data Intelligence from IoT and SAP to Cloud
Goal: Develop an end-to-end data integration layer for analytics.
Data from various lifecycle phases (e.g., SAP systems, IoT interfaces via MQTT using Mosquitto, external sources) is streamed into a consistent model using stream processing with ksqlDB. The resulting data backend supports the development of MLOps architectures and enables advanced analytics.
Business Value: Streamlined data integration accelerates the development of AI applications, helping data scientists and analysts make quicker, more informed decisions.
3. Machine Integration with SAP and KUKA Robots
Goal: Integrate machine data for analytics and real-time insights.
Data from SAP systems (such as SAP ME and SAP PCO) and machines like KUKA robots is streamed into Snowflake for analytics. MQTT brokers and Apache Kafka manage real-time data ingestion and facilitate predictive analytics.
Business Value: Enhanced machine integration improves production quality and supports the shift toward smart manufacturing processes.
4. Digital Healthcare Service Operations using Data Streaming
Goal: Stream telemetry data from Siemens Healthineers products for analytics.
Telemetry data from hospital devices is streamed via WebSockets to Kafka and combined with ksqlDB for continuous stream processing. Insights are fed back to clients for improved diagnostics.
Business Value: By leveraging real-time device data, Siemens Healthineers enhances the reliability of its medical equipment and improves patient outcomes.
5. Real-Time Logistics with SAP Events and Confluent Cloud
Goal: Stream SAP logistics event data for real-time packaging and shipping updates.
Using Confluent Cloud, Siemens Healthineers reduces delays in packaging and shipping by enabling real-time insights into logistics processes.
Business Value: Improved packaging planning reduces delivery times and enhances supply chain efficiency, ensuring faster deployment of medical devices.
6. Track and Trace Orders with Apache Kafka and Snowflake
Goal: Real-time order tracking using streaming data.
Data from Siemens Healthineers orders is streamed into Snowflake using Kafka for real-time monitoring. This enables detailed tracking of orders throughout the supply chain.
Business Value: Enhanced order visibility improves customer satisfaction and ensures compliance with regulatory requirements.
Real-Time Data as a Catalyst for Healthcare and Manufacturing Innovation at Siemens Healthineers
Siemens Healthineers’ innovative use of data streaming exemplifies how real-time insights can drive efficiency, reliability, and innovation in healthcare and manufacturing. By leveraging tools like Confluent (including Apache Kafka and Flink), MQTT and Snowflake and transitiing some workloads to the cloud, they’ve built a robust infrastructure to handle diverse data streams, improve decision-making, and deliver tangible business outcomes.
From predictive maintenance to enhanced supply chain visibility, the adoption of data streaming unlocks value at every stage of the production and service lifecycle. For Siemens Healthineers, these advancements translate into better patient care, streamlined operations, and a competitive edge in the dynamic healthcare industry.
To learn more about the relationship between these key technologies and their applications in different use cases, explore the articles below:
- Data Streaming with Apache Kafka and MQTT – A Blog Series about Use Cases
- Apache Kafka and Snowflake – Friends, NOT Enemies
- Machine Learning / AI and Data Streaming in the Healthcare Sector
Do you have similar use cases and architectures like Siemens Healthineers to leverage data streaming with Apache Kafka and Flink in the healthcare and manufacturing sector? Let’s connect on LinkedIn and discuss it! Stay informed about new blog posts by subscribing to my newsletter.
