HR Demand Flashcards
HR demand
The firm’s future need for human capital, types of jobs, and the number of positions that must be filled to implement firm strategy
Ways to improve the accuracy of forecasts
- Include factors that contribute to changes in demand
- Like seasonal changes- let demand
forecasts follow sales changes with the
seasons
- Like seasonal changes- let demand
- Do research and analysis on several factors affecting demand- requires a longer time frame and more data sources
Quantitative Methods
Trend/Ratio Analysis
Time Series Models
Regression Analysis
Structural Equation Modelling
Trend analysis
Way of forecasting using historical changes in one or more organizational indices
- looks at employment changes in past
- more practical in static business environments
Ratio Analysis
method of
projecting HR demand
by analyzing the historical relationship
between an operational
index and the number
of employees required
-Compares the historical index to employees required
- Uses ratio as a base for future predictions
Steps in doing effective ratio analysis
- SELECT THE APPROPRIATE BUSINESS/OPERATIONAL INDEX- 1. one known to directly influence demand for labour and 2. requires future forecasting due to the normal business planning process
- TRACK THE OPERATIONAL INDEX OVER TIME- look back at least 4-5 years/ more than decade to get index levels over time
- TRACK THE WORKFORCE SIZE OVER TIME
- look at total number of employees/ amount of direct and indirect labour for same period as index - CALCULATE THE AVERAGE RATIO OF THE OPERATIONAL INDEX TO THE WORKFORCE SIZE
- level of index for year/number of employees required to produce the level of that index - CALCULATE THE FORECASTED DEMAND FOR LABOUR
annual forecast/average employee requirement ratio for each future year arrive
Forecast for future year/ average employee requirement for years past (can be most recent ratio or average of previous ratios)
Examples of Operational Indices
Sales
1) the number of units produced,
(2) the number of clients serviced, and
(3) the production (i.e., direct labour) hours. (so using hrs, it would be hours spent/number of employees in workforce to estimate hours per worker- which could determine full time vs part time)
Time series models
- uses past data to predict future demand
Regressional Analysis
Presupposed a linear relationship between one or more independent variables- causal/predictor variables predicted to affect the dependent variable(criterion variable)
- best shows the relationship between predictor and criterion
Variability- looking at how changes in one variable match up/can be explained by changes in the other variable
So x change in V1 explains/results in x change in V2
Regression value ranges from 0-1
Which variables to add?
Make an informed guess (using indices i suppose) until you can account for most of the variability
So if r=0.5 for one variable, find another. That other variable may have r=.3, and then another variable=.2 and so on. go until you can account for the variables
Avoiding redundant variables
- Can be spotted in the r result and the relationships between predictors
- So if two variables add up to the r value of 1 factor/ r value is very similar and are related, then one is unnecessary. chose the one that would include the other variable
Limitations of regression
- only good for linear relationships- straight increase and decrease. Not useful for when relationship between variables not clear cut
- require significant amounts of historical data- not very feasible for new/smaller firms
Structural Equation Modelling
-Like regression modelling but allows you to use more variables and creates more complex models.
- so it’s like creating a model with the variables and sub variables(that lead to main variables) that directly impact demand for labour
Disadvantage of SEM
requires more data and observations than regression
Qualitative Forecasting Techniques
Management Survey
Scenario Planning
Delphi Technique
Nominal Group Technique