HR Supply Forecasting - succession analysis - Markov Analysis

- Supply and demand of jobs.
- literacy rate of nation.
- rate of population
- industry and expected growth rate and levels
- technological development.
- compensation system based on education, experience, skill and age.

The most important techniques for forecasting of human resource supply are Succession analysis and Markov analysis.

Succession analysis

Once a company has forecast the demand for labour, it needs an indication of the firm's labour supply. Determining the internal labour supply calls for a detailed analysis of how many people are currently in various job categories or have specific skills within the organization. The planner then modifies this analysis to reflect changes expected in the near future as a result of retirements, promotions, transfers, voluntary turnover, and terminations.

Demand forecasting helps in determining the number and type of personnel/human resources required in future. The next step in human resource planning is forecasting supply of human resources. The purpose of supply forecasting is to determine the size and quality of present and potential human resources available from within and outside the organisation to meet the future demand of human resources. Supply forecast is the estimate of the number and kind of potential personnel that could be available to the organisation.

*Estimating Internal Labor Supply for a Given Unit*

The above figure illustrates that internal supply forecasting can be estimated based on the following:

(a) Current Staffing Level

(b) Projected Outflows This Year

(c) Projected Inflows This Year

(b) Projected Outflows This Year

(c) Projected Inflows This Year

**Markov Analysisâ€”transition probability matrix is developed to determine the probabilities of job incumbents remaining in their jobs for the forecasting period.**

The technique is named after Russian mathematician

**Andrei Andreyevich Markov,**

**A transition matrix, or Markov matrix,**can be used to model the internal flow of human resources. These matrices simply show as probabilities the average rate of historical movement from one job to another. Figure 2-12 presents a very simple transition matrix. For a line worker, for example, there is a 20% probability of being gone in 12 months, a 0% probability of promotion to manager, a 15% probability of promotion to supervisor, and a 65% probability of being a line worker this time next year. Such transition matrices form the bases for computer simulations of the internal flow of people through a large organization over time.