CASE STUDY
Improving operational efficiency and optimizing costs with machine learning.
OVERVIEW
India’s largest energy utility aimed to leverage machine learning and deep analytics to enhance operational efficiency and optimize costs and revenue.
CHALLENGES
- Identifying the optimal area for implementing machine learning and deep analytics.
- Ensuring organization-wide buy-in and adoption for the analytics initiatives.
- Partnering with a project execution team to assess data availability from integrated SAP systems.
- Addressing data integration across multiple locations to facilitate successful implementation and achieve meaningful outcomes.
Solution Implemented:
Prakat implemented an SAP data analytics framework that included:
- Identified a key organizational need for effective data analytics profiling through vendor assessment and rating.
- Analyzed existing SAP systems for data analysis, tool-based profiling, and data extraction.
- Developed a comprehensive data analytics dashboard and machine learning platform to capture, extract, and present data from various systems.
- Successfully implemented a vendor rating model utilizing SAP application data to evaluate supplier performance based on responsiveness, quality, delivery, and post-delivery metrics.
Technologies Used:
- SAP Analytics Cloud: For real-time data analysis and visualization.
- Data Warehousing Solutions: To consolidate data from multiple sources.
Business Impact:
- Improved decision-making through actionable insights.
- Enhanced operational efficiency and data accuracy.
Results Achieved:
- Achieved organization-wide adoption of the newly implemented Vendor Rating System.
- Identified and quantified efficiencies and cost savings resulting from automation and enhanced decision support systems.
- Continuous improvement in procurement outcomes across vendor performance parameters, including pre-award and post-award quality, delivery, response time, completeness, deviations, and overall timeliness in accepting purchase orders.
Conclusion:
Prakat’s implementation of the Vendor Rating System significantly enhanced the energy utility corporation’s operational efficiency and decision-making capabilities. By leveraging advanced data analytics and machine learning, the organization achieved greater transparency in vendor performance, leading to improved procurement outcomes and cost savings. This successful integration not only streamlined processes but also fostered a culture of continuous improvement, positioning the corporation to adapt effectively to future challenges and opportunities.