Radheshyam Mohapatra

Building Model for fault detection by using classification model.

• Analyzed large datasets to extract meaningful insights and support decision-making processes.
• Utilized SQL for data querying and management, ensuring data integrity and accuracy.
• Created visualizations and dashboards using Spotfire to communicate complex data trends and patterns.
• Conducted descriptive analysis to summarize data and identify key performance indicators.
• Identify, analyze and interpret trends or patterns in complex data sets in python and spotfire.
• Collaborated with team members to address data-related challenges and improve analytical methodologies.
• Interacted with clients to understand the problems and prepare road map to provide analytical solution.
• Support clients to find the revenue leakage and insight generation for decisions making.
• Pre-support to business development team

• Predict failure of machines by analyzing data remotely by developing forecasting technique
• Building Model for fault detection by using classification model
• Identify, analyze and interpret trends or patterns in complex data sets
• Build and used machine learning techniques like regression, clustering, time series forecasting and more to identify early fault in high risk equipment and improved the reliability by forecasting remaining useful life
• Identify, analyze and interpret trends or patterns in complex data sets
• Complex analysis and dashboard preparation in spotfire ,power bi,and python.
• Help giant manufacturing companies (Power,Steel,oil refinary) by giving insights about the industry machine health status and reducing the breakdowns to control the loss.

• Analyze monthly production report from SAP for different Plant all across India and insight generation from the same.
• Analyze the breakdown reports and preventive checks data to find if the breakdown happened due to avoid of preventive checks.
• Quarterly sales and production report preparation for cxo level presentation.
• Help HR department for different analysis to find the performance issue in company,departments and employees.
• Collecting, organizing,analyzing,interpreting the data and presenting data in comprehensive manner for different talent management initiatives.

• Collection and interpretation of data by using statistical methods to analyze data and generate useful business reports
• Analyze asset performance, identify bottlenecks, improve reliability
• Continuous improvement of solutions implemented on a frequent basis
• Model preparation to optimize energy consumption and reduce maintenance cost
• Develop visualization of data to reveal hidden insights
• Monitor pollution and its environmental effect
• Action plan to minimized pollution and research for renewable energy
• Classify faults of machine by machine learning technique
• Raw material optimization by developing models to get best output
• Productivity and cost related improvements based on the results generated from Analytics solutions/modelling.