Life Sciences Suppliers

SpringML specializes in “what-if” models for clinical trials.

Supply Chain Responsiveness

Our predictive forecasting engine – deployed on a responsive, high-speed cloud platform – senses supply networks in real-time. Using sophisticated linear regression models to constantly evaluate current and historic inventory levels, supply vendor schedules, and other critical factors, your CMO or CRO supply chain operation is able to anticipate potential disruptions on virtually any time scale, whether its four months to four hours before a disruption occurs.

Anticipate and Avoid Disruptions and Delays

Whether you’re a contract research organization (CRO) conducting clinical trials for a promising new drug or medical device, or a contract manufacturing organization (CMO) making the new drug or device, your mission-critical supply chain operation is only increasing in complexity.

As a result, unforeseen supply disruptions are on the rise, making successful execution of highly-choreographed clinical trials, procedures, and production runs even more challenging. What’s more, regulatory requirements are on the rise as well, making timely and compliant reporting even more difficult to achieve.

Use cases

Raw Material QA/QC

For CROs provisioning and implementing clinical trials, you can use ML algorithms to find patterns that can accurately pre-assess raw material quality and identify quality issues before committing materials for clinical trial use. You can meet the strictest standards for quality and precision formulation to better ensure patient safety, and avoid financial losses due to improper formulation of raw materials during clinical trials

Predictive Maintenance

Whether you’re a CRO monitoring critical laboratory equipment in an R&D setting or a CMO monitoring precision pharmaceutical manufacturing equipment in a production setting, you can keep your equipment up and running longer and always in compliance with strict performance and calibration requirements. Combine our ML analytics capabilities with Internet of Things technology to get more insight out of equipment sensor data. You can compare past equipment data with current data (along with base data from equipment makers) to detect patterns that indicate part failures or failures in equipment calibration before they actually occur.

Clinical Trial Modelling

Minimize the safety and financial risks of real-world clinical trials by creating robust “virtual” models ahead of time. Use ML to analyze demographic, epidemiological, and genomic data to identify the ideal patient candidates for a clinical trial, as well as analyzing personnel records and research documentation to make the ideal staffing choices. Once the actual clinical trial begins, you can use live data to continue to run simulations so you can model outcomes, test alternate hypotheses, or improve critical decision making to address emerging safety or efficacy concerns.

Raw Material QA/QC

For CROs provisioning and implementing clinical trials, you can use ML algorithms to find patterns that can accurately pre-assess raw material quality and identify quality issues before committing materials for clinical trial use. You can meet the strictest standards for quality and precision formulation to better ensure patient safety, and avoid financial losses due to improper formulation of raw materials during clinical trials

Clinical Trial Modelling

Minimize the safety and financial risks of real-world clinical trials by creating robust “virtual” models ahead of time. Use ML to analyze demographic, epidemiological, and genomic data to identify the ideal patient candidates for a clinical trial, as well as analyzing personnel records and research documentation to make the ideal staffing choices. Once the actual clinical trial begins, you can use live data to continue to run simulations so you can model outcomes, test alternate hypotheses, or improve critical decision making to address emerging safety or efficacy concerns.

Predictive Maintenance

Whether you’re a CRO monitoring critical laboratory equipment in an R&D setting or a CMO monitoring precision pharmaceutical manufacturing equipment in a production setting, you can keep your equipment up and running longer and always in compliance with strict performance and calibration requirements. Combine our ML analytics capabilities with Internet of Things technology to get more insight out of equipment sensor data. You can compare past equipment data with current data (along with base data from equipment makers) to detect patterns that indicate part failures or failures in equipment calibration before they actually occur.

Customer Successes

Learn about how our expertise has helped customers advance their Analytics and AI journey.

Thought Leadership

Check out our recent blogs and videos on best practices and implementation approach to help the healthcare community.

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We’re ready to help you seize the advantage with Machine Learning in Healthcare and Life Sciences.

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