Production Process Optimization and Efficiency Improvement
Ganesh Chandra
Microsoft Power BI
Python
Snowflake
Data Integration and Processing with Python:
•
Leveraged Python to integrate and process
large volumes of production data from disparate sources, ensuring
scalability and efficiency.
•
Implemented robust data pipelines to cleanse, transform, and
harmonize production data for further analysis.
Predictive Analytics and Process Optimization using KNIME:
•
Utilized KNIME for advanced data analytics and predictive modeling to
identify process inefficiencies and optimize production workflows.
•
Engineered predictive maintenance models to anticipate equipment
failures and optimize maintenance schedules, minimizing downtime and
improving overall equipment effectiveness (OEE).
Data Analysis and Visualization with Power BI:
•
Designed and developed interactive dashboards using Power BI to
visualize key performance indicators (KPIs) such as production output,
yield rates, and defect rates.
•
Provided real-time insights to stakeholders, enabling data-driven
decision-making and proactive management of production operations.
SQL and Snowflake for Data Warehousing:
•
Employed SQL queries to extract and analyze data stored in Snowflake
data warehouse, facilitating comprehensive analysis and reporting of
production metrics.
•
Ensured data integrity and consistency within Snowflake by
implementing effective data governance and quality control measures.
Results:
•
Achieved a 5% increase in production output, 3% reduction in
downtime through the implementation of data-driven process
optimization strategies.
•
Streamlined production workflows and reduced cycle times, resulting in
enhanced operational efficiency and cost savings.
