What is meant by statistical cost planning?
"Statistical cost planning" refers to the use of statistical methods and historical data to predict and plan future costs in a company. This approach utilizes past values, trends, and statistical analyses to create cost estimates for upcoming periods that are as accurate as possible.
Typical software functions in the area of "statistical cost planning":
- Data Analysis: Evaluation of historical cost data to identify patterns and trends.
- Trend Extrapolation: Projection of recognized cost trends into the future.
- Regression Analysis: Determination of relationships between cost factors and influencing variables.
- Monte Carlo Simulation: Conducting simulations to account for uncertainties in cost planning.
- Time Series Analysis: Examination of seasonal fluctuations and cyclical patterns in cost data.
- Forecasting Models: Application of various statistical prediction models for cost forecasting.
Examples of "statistical cost planning":
- Material Cost Forecast: Prediction of future material costs based on historical price fluctuations and consumption quantities.
- Personnel Cost Trend: Extrapolation of personnel costs considering tariff increases and staff turnover.
- Energy Cost Simulation: Monte Carlo simulation to estimate possible energy costs considering market volatilities.
- Maintenance Cost Analysis: Regression to determine the relationship between equipment age and maintenance costs.
- Seasonal Cost Planning: Consideration of seasonal fluctuations in planning production costs.
- Overhead Cost Forecast: Statistical prediction of overhead cost development based on various influencing factors.