Now Reading
Predictive and prescriptive analytics aim to transform operational data into actionable information
0

Predictive and prescriptive analytics aim to transform operational data into actionable information

Posted by Anita PodsiadloOctober 17, 2014

Savi®, a company specialising in sensor technology and sensor analytics solutions that create operational intelligence from the Internet of Things, has announced that new predictive and prescriptive analytics-based scenarios have been added to Savi Insight™ to uncover previously undetected operational and supply chain patterns.

These capabilities empower organisations to measure and monitor Key Performance Indicators (KPIs), enhance operations with predictive analytics that spot opportunities and avoid problems and benefit from prescriptive analytics that recommend actions to improve future outcomes.

By ingesting, correlating and analysing vast amounts of historical and real-time machine-generated data, Savi Insight quantifies and forecasts risk, performance and efficiency, helping clients improve supply chain visibility, optimise operations and prevent loss of high-value assets.

risk-map“Savi Insight is a SaaS analytics solution that captures data from sensors and other sources, correlates multiple variables including time, temperature and location and applies logic that turns data into actionable intelligence. Savi Insight is “tag agnostic,” and therefore able to ingest and correlate data from nearly any source, whether it’s weather, an RFID tag, barcode, ERP system or even Twitter. The award-winning solution empowers organisations to achieve operations excellence and reduce risk by continually taking in real-time data to update models, significantly improving prediction accuracy and strategically prescribing more relevant actions.

Savi Insight not only transforms data into operational intelligence but also derives Key Risk Indicators (KRIs) that identify and benchmark against potentially harmful situations to mitigate risk.  By tapping into existing data sources that many organisations are already collecting, Savi Insight delivers new intelligence from existing data without the need for human intervention. Savi Insight’s pre-packaged scenarios accelerate time to value for clients and address business challenges around assets in motion, risk management and compliance, operations excellence, risk maps and consigned assets.”

“The rise in big data has contributed to an increased complexity in data analysis and fueled the growth and evolution of purpose-driven supply chain analytic solutions that use predictive technology to collect, organise and analyse data. These solutions must address the growing requirements of operational managers to get timely and precise information and operationalise and deliver actionable intelligence for global organisations,” said Ann Grackin, CEO at ChainLink. “Savi Insight’s pre-packaged scenarios bring organisations the predictive and prescriptive analytics required to harness existing sensor data to drive operational efficiency and strategic actions for measured impact against KRIs and KPIs.  These capabilities are the cornerstone of a growing market trend in supply chain and global industrial applications.”

return-stock

“Savi Insight was designed to provide intelligence to organisations leveraging sensors and other machine-generated data and answer the most common and business-impacting supply chain related questions,” said Bill Clark, president & CEO of Savi. “Savi Insight allows our clients to quickly measure, assess and predict performance based on their own data and to rapidly benefit from a growing list of pre-packaged scenarios that address their top business challenges.  We are now working with nearly 600 commercial clients to answer questions such as how well assets are being utilised, how to optimise fleet performance, when goods will arrive and the safest (or riskiest) routes. The answers to these questions provide operational intelligence that increase supply chain confidence, decreases risk and improves operational efficiency.”

About The Author
Anita Podsiadlo

Leave a Response