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.
Problem illustration

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.

Solution illustration