Development of big data assisted effective enterprise resource planning framework for smart human resource management

Area of Science:

• Operations Management
• Human Resource Management
• Business Analytics

Background:

• Inefficient resource allocation and skill-task misalignment decrease organizational productivity and operational efficiency.
• Poor resource utilization negatively impacts company production and financial performance.

Purpose of the Study:

• To develop and evaluate a Placement-Assisted Resource Management Scheme (PRMS).
• To enhance resource allocation, usage, operational efficiency, and productivity in organizations.

Main Methods:

• PRMS utilizes data-driven expertise, business requirements, and processes.
• It matches resources to tasks based on aligned capabilities and timely performance needs.

Main Results:

• PRMS demonstrated superior performance metrics compared to existing methods.
• Achieved a high success rate (0.9328%), minimal swapping (12.052%), reduced resource mitigation (4.098%), and faster decision times (5.414s).
• PRMS improved success and data analysis by 9.98% and 8.2%, respectively, while reducing key ratios and decision time.

Conclusions:

• PRMS is an effective corporate improvement strategy focused on productivity.
• The scheme optimizes resource allocation to meet business demands efficiently.