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

PRODUCTS & SERVICES

• 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.

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.

• 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.