Realizing a Prescriptive Maintenance Solution using Data Analytics as a Service

Today maintenance and optimization of manufacturing equipment is largely via manual inspection and vendor recommendations. More specifically: machine data is manually recorded on paper and spreadsheet. And despite rigorous maintenance, failures are largely random.

Unplanned outages of manufacturing equipment cause serious disruption to production & revenues and imperceptible changes in efficiency can dramatically affect output and quality. On top of this, many assets are not utilized to the fullest potential and availability of manufacturing experts is decreasing, resulting in knowledge loss. 

Manufacturing data analytics can be used to build a prescriptive maintenance solution that will reduce unplanned downtime and maximize profitability and equipment reliability. Manufacturers however struggle to adopt and realize such solution as data specialists are rare, deploying the right tools is complex & takes time while IT operations is typically unable to support data analytics (from development to production).

HPE data analytics as a service provides a cloud-like self-service portal for deployment of data analytics tools in containers and linked to the right data sources. As such this solution will help to attract, retain and free up time of scarce data specialists, simplify data analytics tools delivery and quickly move a data analytics solution from an agile development environment into production.

This demo will show how a sample prescriptive maintenance solution (for reducing unplanned downtime) can be build and deployed using HPE data analytics as a service based on Dataiku's Data Science Studio for orchestration, HPE's BlueData Elastic Private Instant Clusters (EPIC) for deployment and various other data analytics tools.