Forecasting Future Staffing Needs
Problem
Accurately predicting the future staffing requirements is challenging due to varied demands across skill sets and geographic locations.
Consequences of inability to anticipate precise staffing requirements are:
- Financial implications due to misallocation of resources.
- Client dissatisfaction stemming from insufficient or excessive staffing.
- Inefficient allocation of resources impacting operational efficiency.
Complexity of the problem:
- Varying demands across skill sets and geographies.
- Lack of historical data analysis affecting accuracy in forecasts.
- Inability to incorporate real-time changes and external factors.
Solution Overview
Complexity of the problem:
Accurately predicting the future staffing requirements is challenging due to varied demands across skill sets and geographic locations.
Feature Engineering
Derive Seasonality, Historical Demand, Economic Indicators, Business Trends, Population demographics, local events.
Modeling
Time Series (ARIMA, Prophet) | Machine Learning (GBM, RF)
Validation and Evaluation
Split data into training and validation sets. Validate the models using appropriate performance metrics such as RMSE, Accuracy, etc.
Deployment and Monitoring
Deployment for future predictions. Continuously monitor model performance and retrain periodically with updated data to improve accuracy.
