For users of high-throughput clinical laboratory systems, unplanned downtime is more than an operational inconvenience. It disrupts patient testing schedules, delays diagnoses, and strains the clinical teams who depend on continuous results. Our client, a global leader in medical technology, recognized this reality and engaged CBTW to lead a predictive analytics implementation that shifted their global service model from reactive to proactive, monitoring 58 system components in the field and reducing system downtime by 36%.
Predictive Maintenance · Medical Devices · SAS Analytics · IoT Sensor Data · Remote Service · Data Automation
Context and Challenges: Complex Devices, High Stakes, and a Reactive Service Model
Our client is a global leader in medical technology, with operations spanning major regions worldwide. Their flagship laboratory diagnostics platform sets the benchmark in the field, combining immunoassay and clinical chemistry analysis in a single high-throughput system designed for continuous clinical use.
The platform is also highly complex. It manages reagents, biological fluids, and individual samples through dozens of interdependent mechanical, fluidic, and software components.
At the time, the client operated a predominantly reactive service model. The service and maintenance concept were highly complex, and the goal was to transform a service that had previously been predominantly reactive into a proactive concept. This approach carried two direct operational consequences:
- Unplanned outages were disruptive and costly for hospital labs running continuous sample batches.
- Service technicians often arrived without full visibility into what had failed or which parts were needed, leading to multiple visits.
The business case for change was clear. What was needed was a predictive analytics implementation capable of turning raw, multi-source sensor data into actionable intelligence, at scale, across a globally distributed installed base spanning multiple regional service organizations.
Our Approach: Predictive Analytics Implementation on the SAS Platform
Designing for data quality from the start
The foundation for this project was established during the development of the platform itself. The client’s engineers identified the sensor data points relevant for predictive maintenance early in the product development process and ensured the necessary sensors were integrated at hardware level, with connections provisioned to the client’s central database. This meant the analytical work had clean, purpose-built inputs from the outset.
CBTW’s role as SAS analytics partner
CBTW was engaged to architect and deliver the full predictive framework, covering data pipeline design, analytical model development, and operational reporting. Our work unfolded in three main phases:
- Platform design and data ingestion: CBTW designed the data architecture on the SAS Data Analytics Platform, using the SAS Data Integration Server and SAS Data Integration Studio to build and manage the ingestion pipelines. Device data is collected from field installations worldwide, transmitted via the client’s remote service network into country-level databases, and consolidated into the central SAS environment. Data sources include pre-structured sensor readings and log files from across the platform’s component systems.
- Analytical modelling and scoring: Working alongside the client’s team of data scientists and domain engineers, CBTW developed analytical models that evaluate incoming sensor and operational data to estimate component failure probabilities. These models evaluate incoming device data as it is collected, producing scored risk outputs that service teams can act on. Results are processed, visualized, and made available via a web-based interface. SAS Visual Analytics drives the reporting and dashboard layer; SAS Enterprise Guide supports deeper analytical work.
- Automated alerting and reporting: CBTW designed and built an automated email notification service that simplifies and automates the process for service teams. When new information about a component is received that requires action, the relevant personnel receive a structured alert with enough contextual detail to prepare the correct parts and plan the site visit in advance. All incoming data also feeds a set of key performance indicators processed through SAS Visual Analytics, giving service leadership an operational view of platform health across the global installed base.
Delivery model and continuous improvement
The project was delivered by a team of three data scientists and engineers plus one project manager over six months, using an iterative approach that allowed requirements to be refined as field data and service feedback accumulated. The modular framework was designed for continuous evolution, allowing new analytical models to be introduced as additional components and operational data become available. The client’s own team can make these modifications independently, which matters for a service operation running across multiple countries and regional organizations.
Key Benefits: Measurable Outcomes Across Service and Operations
- 36% reduction in system downtime: Interim analyses from the first system installations show that the predictive model produces 36% less downtime compared to reactive service. For clinical labs running continuous sample batches, fewer unplanned outages translate directly into more reliable diagnostic output.
- Multiple system and process components monitored proactively: The framework tracks 58 distinct system and process components across each deployed unit, turning the globally distributed installed base into a continuously updated health dashboard.
- Optimized field service deployment: With early visibility into components approaching failure, technicians arrive at site with the correct parts. The costs and delays associated with diagnosis on arrival are substantially reduced.
- A scalable, extensible framework: The modular architecture enables the client to extend the framework independently as operational requirements evolve, supporting long-term adoption across multiple regional service organizations.
Technology Stack
SAS Data Integration Server · SAS Data Integration Studio · SAS Visual Analytics · SAS Enterprise Guide · Remote Service Network
From Reactive to Proactive: What This Project Demonstrates
The starting point for this project was a clear operational ambition: to transform a service that had previously been predominantly reactive into a proactive concept. Achieving that required sensor infrastructure built into the product from the start, analytical models capable of assessing failure probabilities across dozens of components, and an automation layer that puts the right information in front of service teams before they travel to site.
The result is a solution that evolves continuously. As new field data accumulates and new components are added, the analytical models can be extended, and the client’s own team can make those modifications independently. Higher system availability for end users and a more efficient, better-informed service operation are the outcomes that follow.
For organizations facing similar complexity in their service and maintenance operations, the evidence from this project is clear: sensor data, when properly collected and analyzed at scale, can shift the entire service model from response to readiness.
