Which analysis method is commonly used to forecast manpower requirements?

Regression analysis is a statistical method used to identify the relationship between a dependent variable, which in this case is manpower requirements, and one or more independent variables that could influence it, such as business demand, turnover rates, or economic indicators. By analyzing historical data, regression analysis helps in predicting future manpower needs based on the trends and patterns identified.

This method allows organizations to make informed decisions about hiring, training, and resource allocation by providing a quantitative basis for estimating future workforce requirements. It utilizes historical trends and data correlations, making it particularly advantageous in dynamic environments where workforce needs can change rapidly based on various external factors.

In contrast, other methods like cost-benefit analysis focus more on evaluating the financial implications of decisions rather than forecasting needs, SWOT analysis is aimed at strategic planning through assessing strengths, weaknesses, opportunities, and threats without specifically addressing manpower forecasting, and benchmarking compares performance metrics with best practices without delving into predictive modeling. Thus, regression analysis stands out as the most applicable method for forecasting manpower requirements due to its predictive power and reliance on historical data trends.