Forecasting HR Demands
Posted 03-07-2008 at 07:55 PM by humanitics
Gone are the days when, HR merely had to extrapolate a standard growth percentage for forecasting demand and start recruiting. Forecasting demand for Human Resources is uncertain both in terms of FTEs and the profiles required. With shorter business demand cycles, it is very hard, if not impossible to predict future demand for human resources in any business. Cost of going wrong with the forecast is unfortunately significant both in terms of lost business due to under hiring and lost bench time due to over hiring.
There are two ways in which businesses try to forecast demand for human resources. One- the traditional way of statistical extrapolation adjusted for business growth factors, economic factors, seasonal factors, technology change factors etc. And two- the modeling based approach, where businesses try to evolve a model based on product/service demand ratio to human resource demand and arrive at an empirical rule for staffing positions before the demand for these resources arrives. Typically they give a gap of 3 to 6 months as a lag for training and job fitments for the new recruits.
It is certain that any type of forecasting is better than no forecasting or trying to firefight the recruitment pressure all the time. Our insight is that time and money spent in building a robust forecasting model certainly pays off very quickly by reducing cost of under hiring or over hiring.
Some of more sophisticated methods available in operations research, artificial intelligence (more precisely heuristics sciences) and simulation technologies can be put to use while predicting future demand for human resources. Humanitics has built a set of tools to help achieve this.- Humanitics
There are two ways in which businesses try to forecast demand for human resources. One- the traditional way of statistical extrapolation adjusted for business growth factors, economic factors, seasonal factors, technology change factors etc. And two- the modeling based approach, where businesses try to evolve a model based on product/service demand ratio to human resource demand and arrive at an empirical rule for staffing positions before the demand for these resources arrives. Typically they give a gap of 3 to 6 months as a lag for training and job fitments for the new recruits.
It is certain that any type of forecasting is better than no forecasting or trying to firefight the recruitment pressure all the time. Our insight is that time and money spent in building a robust forecasting model certainly pays off very quickly by reducing cost of under hiring or over hiring.
Some of more sophisticated methods available in operations research, artificial intelligence (more precisely heuristics sciences) and simulation technologies can be put to use while predicting future demand for human resources. Humanitics has built a set of tools to help achieve this.- Humanitics
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