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Hr forecasting techniques


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TREND Analysis
MARKOV Analysis

Published in: Recruiting & HR
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Hr forecasting techniques

  2. 2. CONTENTS • Introduction • Trend Analysis • MARKOV Analysis • Regression Analysis
  3. 3. Human Resource Forecasting • Process of projecting the organization’s future HR needs (demand) and how it will meet those needs (supply) under a given set of assumptions about the organization’s policies and the environmental conditions in which it operates. • Without forecasting, we cannot assess the disparity between supply and demand nor how effective an HR program is in reducing the disparity. INTRODUCTION
  4. 4. TREND ANALYSIS • Trend analysis involves collecting and evaluating data to identify patterns of information that might impact the future. • A method of forecasting that assumes past trends and ratios in employee movement are stable and indicative of future trends and ratios in employee movement. • One of the simplest methods of forecasting future HR supply • For example, an organization reviewing historical data may realize that every year, approximately five percent of their staff retire, six percent resign, and three percent are dismissed. • Using a simple trend analysis, future HR supply forecasts can be established by assuming an average reduction in internal HR supply of 14 percent per year.
  5. 5. MARKOV ANALYSIS • This is named after Russian mathematician Andrei Andreyevich Markov. • Markov Analysis is the statistical technique used in forecasting the future behaviour of a variable or system whose current state or behaviour does not depend on its state or behaviour at any time in the past in other words, it is random. • Analysis that helps to predict internal employee movement from one year to another by identifying percentages of employees who remain in their jobs, get promoted or demoted, transfer, and exit out of the organization. • By tracking and predicting employment movement within an organization, the Markov analysis allows for the development of a transition matrix to forecast internal labour supply.
  6. 6. MARKOV ANALYSIS (contd..) • 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. • To determine the probabilities of job incumbents remaining in their jobs for the forecasting period.
  7. 7. A Sample Transition Matrix Part A: Personnel Supply Estimated Personnel Classification in Year T + 1 (%) Classifications in Year T P M S Sr A Exit Partner .70 .30 Manager .10 .80 .10 Supervisor .12 .60 .28 Senior .20 .55 .25 Accountant .15 .65 .20 Part B. Staffing Levels Estimated Personnel Availabilities in Year T + 1 (%) Beginning Classifications in Year T Levels P M S Sr A Exit Partner 10 7 3 Manager 30 3 24 3 Supervisor 50 6 30 14 Senior 100 20 55 25 Accountant 200 30 130 40 10 30 50 85 130
  8. 8. REGRESSION ANALYSIS • The statistical technique with an objective to analyse the relationship among quantitative variables. • It is one of the extensively used techniques for forecasting variables. • It involves developing mathematical equations to analyze the relationship between dependant variable and independent variable.
  9. 9. REGRESSION ANALYSIS The equation of a straight line is key to the concept of regression analysis: where: X = the independent variable Y = the dependent variable β1 = the slope β0 = a constant (the “Y” intercept) ε = Random error The Four Assumptions • The relationship between X and Y is linear. • Y is distributed normally for every X value (the bell-shaped curve). • The variance of Y at every value of X is the same (“homogeneity of variances”). • The observations are independent of one another.
  10. 10. REGRESSION ANALYSIS • Statically identify historical predictor of workforce size Example: FTEs = a + b1 sales + b2 new customers • Predict future HR requirements, using equation Example: (a) FTEs = 7 + .0004 sales + .02 new customers (b) Projected sales = $1,000,000 Projected new customers = 300 (c) HR requirements = 7 + 400 + 6 = 413
  11. 11. Thank You…!!!