Abstract
The use of Mathematical models for manpower planning has increased in recent times for better manpower planning quantitatively. In respect of organizational management, numerous previous studies have applied Markov chain models in describing title or level promotions, demotions, recruitments, withdrawals, or changes of different career development paths to confirm the actual manpower needs of an organization or predict the future manpower needs. The movements of staff called transitions are usually the consequences of promotions, transfer between segments or wastage and recruitment into the system. The objective of the study is to determine the proportions of staff recruited, promoted and withdrawn from the various grades and to forecast the academic staff structure of the university in the next five years. In this paper, we studied the academic staff structure of university of Uyo, Nigeria using Markov chain models. The results showed that there is a steady increase in the number of Graduate Assistants, Senior Lecturer and Associate professors, while, there is a steady decrease in the number of Assistant Lecturer, Lecturer II, Lecturer I, and Professor in the next five years. The model so developed can only be applied when there is no control on recruitment but the research can be extended to include control on recruitment. The model can also be applied in school enrollment projection.Keywords: Markov Chain, Transition Probability Matrix, Manpower Planning, Recruitment, Promotion, Wastage
Highlights
A Markov chain (Discrete Time Markov Chain, DTMC), named after a Russian Mathematician, Andrey Markov in 1907, is a random process that undergoes transition from one state to another on a state space
From the pooled academic staff flow based on recruitment, promotion and withdrawal, the transition p p probability matrix P, the wastage probabilities, and the recruitment probabilities are obtained using i0
This could be attributed to their age and closeness to retirement, while grades and 3 (Assistant Lecturer and Lecturer II) have the highest recruitment probability
Summary
A Markov chain (Discrete Time Markov Chain, DTMC), named after a Russian Mathematician, Andrey Markov in 1907, is a random process that undergoes transition from one state to another on a state space. The workforce planning, on the basis of established process, requires a good knowledge of those deployed in the establishment, as well as entry, dropout ant promotion of employees in order to reach a future plan fit and desired administration in determining the future policies of the workforce system, Touama, (2015) applied Markovian models and transition probability matrix to analyze the movement of the workforce in Jordan productivity companies. The approach to manpower planning in Nigerian universities are guided by the traditional method of putting the right number of people in the right place at the right time or arranging a suitable number of people to be allocated to various jobs usually in a hierarchal structure This method is difficult in that it does not offer computational tools that will enable administrators to determine possible line of action to be taken nor provides tools to generate alternative policies and strategies. The objective of this study is to determine the proportions of staff recruited, promoted and withdrawn from the various grades and to forecast the academic staff structure of the university in the five years
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