Siemens Healthineers predicts lab analyser failures on its Atellica solution, improving uptime by 36%.
Diagnostic lab systems cannot tolerate downtime, so the company wanted proactive rather than reactive maintenance. With mayato it built predictive service on SAS Data Management, Visual Analytics and Enterprise Guide, analysing sensor data from 58 components. The page reports a 36% improvement in system uptime and optimised technician and parts deployment.
SAS is an analytics and AI software vendor, used across pharma, hospitals and medtech for clinical trial programming, prediction and decision support
This sits in the Scale & Govern stage of the AI use-case lifecycle: Named examples from the tools pharma teams use to monitor, govern and keep validating an AI system once it is already live in production.
Published by SAS on its own site — read the original case study on sas.com ↗
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