A Stochastic Model For The Prediction Of Manpower Using Incomplete Manpower Data In Tamilnadu Software Industry

In any organization it usually happens that whenever the policy decisions regarding pay, perquisites, promotion and targets of work or sales to be achieved are revised, then, there will be exit of personnel, which in other words is called the wastage. In manpower planning, one of the most important variables is Completed Length of Service (CLS) on leaving a job, since it enables us to predict staff turnover. The data on CLS are often incomplete due to left truncation as well as right censoring. Right censoring occurs when a number of people have not yet left when data collection is terminated. Left truncation arises when some people are already in service at the commencement of data collection. For such data much of work has been done on both non-parametric and parametric estimation. In this paper, a Stochastic model for prediction of manpower using incomplete data in Tamilnadu software industry has been discussed.