This document discusses various techniques for human resource forecasting, including trend analysis, Markov analysis, and regression analysis. Trend analysis uses past trends to predict future needs based on historical attrition rates. Markov analysis tracks employee movement within an organization using transition matrices to forecast internal labor supply. Regression analysis develops mathematical equations to analyze the relationship between dependent and independent variables, such as using sales and new customers to predict future full-time employees needs. These techniques help organizations project future human resource demands and how to meet staffing needs.

1. HR FORECASTING TECHNIQUES
BY,
JENIL V VORA

2. CONTENTS
• Introduction
• Trend Analysis
• MARKOV Analysis
• Regression Analysis

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. 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. 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. 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. 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. 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. 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. 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. Thank You…!!!