A hybrid Markov chain model of manpower data

A hybrid model based on Markov chain and data interpolation is proposed for evaluating the manpower recruitment policy in higher learning institution.The model is developed and analysed on Excel spreadsheet.Based on the model, the new estimation of the states transition matrix for each category of manpower driven by interpolation technique is devised.The recruitment policy changes require some data needs modification while other data remains.This dataset are numerically intractable.But the revised transition matrix of Markov chain can be substituted by an interpolated data for which a revised transition probability matrix can be used as an equation solver to calculate mean time estimation for each category of manpower.The hybrid model results are then compared to the classical Markov chain result for both old and new policies by means of mean time estimation.Two scenarios were considered in the study; scenario 1 was based on historical data pattern between year 1999 – 2014 and scenario 2 was based on RMK 9 policies.The results showed the possibility average length of stay by position and probability of loss for both scenarios.The greater impact is expected for average length of stay of senior lecturers compared to other faculty position considering the new policy