About the Customer

The client is a leading global manufacturing group with more than 60 operative subsidiaries and production facilities in 12 countries. 


Azure Stream Analytics
Azure Event Hub

HD Insights
Azure SQL Data Warehouse
Power BI Embedded


Business Challenge

  • An increasing need to monitor manufacturing equipment on customer sites.
  • Recurring machine problems.
  • Predict manufacturing equipment problems before it impacts production.
  • Real-time monitoring of equipment and units.
  • Automate troubleshooting.
  • Alerting field executives immediately after equipment issues are discovered.

The SNP Solution

SNP’s predictive analytics solution offers the simplicity and self-service the client needed while meeting the governance and automation requirements of their IT teams.


The solution was offered in 2 phases:

Phase 1: Source Analysis & Requirement Gathering

  • Analyze the overall platform for a full-blown predictive analytics implementation.
  • Implement the Azure-based solution to prove the concepts of the cloud and predictive analytics.
  • Create a preventive maintenance dashboard from the log files.
  • Create an Azure channel to extract data files from various machines.
  • Implement an ETL tool and import it into the data model to create requisite dashboards.
  • Track activity by location.

Phase 2: Roadmap for Predictive Analytics Solutions 

  • Set up real-time alerts and data monitoring across complex manufacturing environments.
  • Analyze high volume streaming data to get real-time insight into the Power BI analytics dashboard.
  • Enable data correlation to give clarity on how variability in one area will impact another.
  • Detect and resolve any errors or unruly patterns among departments, machines, processes or individuals.
  • Set up predictive maintenance during streaming forecasting on data and alert when thresholds are surpassed.
  • Use streaming data from sensors and devices to recognize warning signs (e.g., predict equipment failure) and perform maintenance before equipment breakdown occurs.
  • Send notifications to field executives or customers when errors or warnings occur.
  • Set up advanced preventive analytics for periodic machine health check and historical analysis.
  • Establish customer satisfaction and SLA monitoring.
  • Develop exception reporting for failure and error analytics.


Customer Benefits

  • Real-time monitoring of performance.
  • Detect equipment failures before they happen and fix them using smart sensors and real-time data.
  • Remote diagnosis by gathering and transforming data from sensors and systems.
  • Replace legacy ETL technique with modern IoT workflows that enable event hubs for real-time analytics and notifications.