Automated laboratories improve uptime with analytics
SAS and mayato partner for predictive service and maintenance solution
in system uptime
Predictive service and maintenance keeps Siemens Healthineers lab tests running on time
Laboratory testing of blood and other fluids has evolved from a manual, labor-intensive process to an automated, high-volume process that allows modern labs to test thousands of samples per day. Today’s clinical diagnostic systems optimize workflow and clinical performance by automatically reading labels, moving samples along a conveyer belt, running tests, returning vials to storage and compiling results.
Because laboratories rely on these systems to get results back to doctors, clinicians and researchers as quickly and accurately as possible, any amount of downtime is unacceptable.
That’s why Siemens Healthineers set out to build predictive service and maintenance capabilities into its Atellica Solution, an advanced system for immunoassay and clinical chemistry analysis. Now, malfunctions are a rare exception for users of the system.
In collaboration with data analytics and data science experts mayato, Siemens Healthineers developed the predictive service and maintenance solution to increase system availability and reduce service costs. The solution is based on the SAS® Platform.
The demands on maintenance and service are high for laboratory systems. SAS offers a flexible platform that enables us to analyze sensor data, generate forecasts and reduce downtimes and maintenance times. For our customers, this means higher system availability for their Atellica Solution – and we can optimize our service. Torben Scaffidi Head of Lab Operation Analytics Siemens Healthineers
Clinical laboratory diagnostics with a vision
Among the world’s leading companies in health care, Siemens Healthineers provides imaging for diagnosis and therapy, as well as clinical laboratory diagnostics. The Atellica Solution optimizes workflow and clinical performance by transporting samples along a bidirectional magnetic conveyer belt and tracking every sample with a multicamera vision system. It intelligently schedules and manages each sample throughout the testing process.
Using these innovative technologies, the system can perform up to 440 tests per hour, the highest productivity per square meter in the industry.
The predictive service and maintenance component of the system, designed using SAS, helps make sure throughput is uninterrupted and productivity remains as high as possible.
Proactive service on the SAS® Platform
Early in the development phase, product development engineers at Siemens Healthineers defined what data could be relevant for predictive maintenance. This meant necessary sensors were integrated and the system connected to the Siemens Healthineers database, to enable data processing.
Once the first development phase for the product series was complete, the next step was the dedicated development of the predictive service and maintenance solution. Here, Siemens Healthineers chose the SAS Platform, including SAS Data Management technologies, SAS Visual Analytics and SAS® Enterprise Guide®.
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lab tests completed per hour
system components monitored
service technician deployment
Project implementation with mayato
For the conceptual design and implementation, Siemens Healthineers chose SAS partner mayato GmbH, because they had already worked successfully together on earlier projects. The mayato team handled the complete process, including:
- Implementing the data loading points.
- Setting up the predictive service and maintenance framework.
- Preparing the data for reporting.
To work efficiently, the team took an iterative approach, where the basic requirements were first determined, and then modified during the project as needed.
“Because Siemens Healthineers uses the SAS Platform for data analysis, the cornerstone of the project was already set,” explains mayato Project Lead Paolo Vacilotto. “We were therefore able to focus completely on the sustainable design of the framework, to ensure high performance and comprehensive configuration options.”
Moving from reactive to proactive maintenance
The predictive service and maintenance capabilities inside the Atellica solution are fully automated and integrated into a system that Siemens calls the Guardian Program. As part of this program, country-level service organizations for Siemens Healthineers provide maintenance services and interact with the users.
Torben Scaffidi, Head of Lab Operation Analytics at Siemens Healthineers in Erlangen, Germany, explains: “Laboratory diagnostics systems transport many fluids and comprise many different components. That’s why the service and maintenance concept is highly complex. Our goal was to transform a service that had previously been predominantly reactive into a proactive concept.”
The predictive maintenance data is transmitted to respective country databases via the Siemens Healthineers Smart Remote Service following regional regulatory requirements. The data comes from various sources, including prestructured sensor data or log files.
The data is then transferred into SAS and analyzed using models built by Healthineers data scientists, to predict failure probabilities. The results are processed, visualized and made available via a web-based front end.
Automated warnings by email
To simplify and automate the process further, mayato developed an email alerting service that sends automatic warnings with detailed information as soon as anything requires action.
In addition, the solution uses incoming information to analyze key performance indicators and report results with SAS Visual Analytics. Initial results include:
- 58 system and process components are monitored proactively.
- 36% less system downtime compared to the reactive service.
- Optimized service technician deployment, because they know in advance which parts are needed at a particular location.
A successful project
Siemens Healthineers started with a goal of building predictive service and maintenance directly into its solutions. The SAS Platform provided an ideal way to integrate analytics into the product and scale as the system changes. And, finally, mayato’s structured implementation ensured tremendous flexibility.
The solution revolves around a continuous expansion of analytical data models, providing a modular structure and intuitive framework so the Siemens Healthineers team can make modifications on their own.
“We enjoy working with the mayato team,” says Scaffidi. “We value the competence and reliability of the consultants. Especially in such a dynamic environment, it’s essential to remain flexible, without losing sight of the structure – and we succeeded again in this project."
본 문서에 나오는 결과는 본 문서에 설명된 특정 상황, 비즈니스 모델, 데이터 입력 및 컴퓨팅 환경에 적합하게 되어 있습니다. 각 SAS 고객의 경험은 고유한 것으로, 비즈니스 및 기술적 변수에 따라 달라집니다. 따라서 모든 서술은 비전형적인 것이라는 점을 고려해야 합니다. 실제 절약, 결과 및 성능 특성은 개별 고객의 구성 및 조건에 따라 달라질 수 있습니다. SAS는 모든 고객이 비슷한 결과를 달성할 수 있다고 보증하거나 진술하지 않습니다. SAS 제품과 서비스에 대한 유일한 보증은 해당 제품 및 서비스에 대한 서면 계약의 보증서에 명시되어 있습니다. 본 문서의 어떠한 내용도 추가 보증을 구성하는 것으로 해석될 수 없습니다. 고객은 SAS 소프트웨어의 성공적인 구현에 따라 합의된 계약적 교환 또는 프로젝트 성공 요약의 일환으로 성공 사례를 SAS와 공유했습니다. 브랜드 및 제품 명칭은 각 기업의 상표입니다.