scholarly journals Markov chain: a predictive model for manpower planning

2017 ◽  
Vol 21 (3) ◽  
pp. 557 ◽  
Author(s):  
V.O. Ezugwu ◽  
S Ologun
Keyword(s):  
Markov Chain ◽  
Predictive Model ◽  
Manpower Planning

Application of Markov Chain Model to Corporate Manpower Planning: A Nigeria Local Government Hub Example

2013 ◽  
Vol 824 ◽  
pp. 514-526 ◽  
Author(s):  
A.C. Igboanugo
Keyword(s):  
Markov Chain ◽  
Local Government ◽  
Markov Chain Model ◽  
Manpower Planning ◽  
Chain Model ◽  
Government Organizations ◽  
Absorbing Markov Chain ◽  
Long Run ◽  
Definite Pattern ◽  
Absorbing States

A corporate manpower planning study, seeking to gain insight into, and hence, attempt tounwrapthe wider meanings of a long-run manpower practice inherent in a set of data obtained from one of the 774 Local Government Organizations in Nigeria, was conducted. The data which spanned over a period of twenty years, relate to six states recruitment, staff stock, training, interdiction, wastage, and retirement and, in particular were found to possess Markov properties, especially stochastic regularity, and therefore had absorbing Markov Chain model fitted into the set. Our results suggest that staff habituate substantial number of times (47) among non-absorbing states before subsequent absorption into any of the two absorbing states. And, again, 52% of the workforce gracefully attain retirement while 48% regrettably get wasted. Agreeably, it seemed that the absorbing Markov Chain model employed has established a definite pattern of manpower flow in the organization as a sure-thing principle rather than a chance mechanism.

A Markov Chain application to academic manpower planning

1984 ◽  
Vol 9 (1) ◽  
pp. 27-42
Author(s):  
Adedoyin Soyibo
Keyword(s):  
Markov Chain ◽  
Planning Horizon ◽  
Manpower Planning ◽  
Military Government ◽  
Cost Structures ◽  
Manpower Supply ◽  
Salary Structure ◽  
The University ◽  
University Of Ibadan ◽  
Ibadan Nigeria

The application of Markov Chain modelling to manpower planning in military, government, and business is about two decades old. This paper discusses the application of Markov Chain to academic manpower planning in the University of Ibadan, Nigeria over a five-year planning horizon. It attempts to balance academic manpower supply forecasts with demand forecasts for each faculty⁄college of the university and recommends its planning implications. Under the assumption that there will be no drastic change in the promotion and recruitment policies as well as the salary structure of the university, the paper determines the corresponding cost structures of the forecast requirements.

LULC analysis of urban spaces using Markov chain predictive model at Ranchi in India

2017 ◽  
Vol 25 (3) ◽  
pp. 351-359 ◽  
Author(s):  
Firoz Ahmad ◽  
Laxmi Goparaju ◽  
Abdul Qayum
Keyword(s):  
Markov Chain ◽  
Predictive Model ◽  
Urban Spaces

scholarly journals DETERMINATION OF THE PERIOD OF MARKOV CHAIN’S TRANSITION MATRIX FOR MILITARY MANPOWER PLANNING IN INDONESIAN NAVY

2019 ◽  
Vol 5 (1) ◽  
pp. 34
Author(s):  
Agus Wahyuli
Keyword(s):  
Markov Chain ◽  
Human Resources ◽  
Human Resource ◽  
Transition Matrix ◽  
Moving Average ◽  
Manpower Planning ◽  
Chain Process ◽  
The Future ◽  
Military Manpower

<table width="605" border="1" cellspacing="0" cellpadding="0"><tbody><tr><td valign="top" width="416"><p> </p><p><strong>Abstract</strong></p><p> </p><div><p class="Els-history-head"> </p></div><p>Human resources are very important organizational assets thus it is only natural that every organization gives more attention to human resource management, including the Indonesian Navy (TNI AL). The approach to human resource planning in military organizations focuses on two aspects, including long term planning with strategic objectives and short term planning with operational objectives. This long term planning is oriented towards the continuation of the procurement, availability, and balance of the number of personnel (human resources) in each rank in the future. An analysis to find out and plan the condition of Navy personnel in the future is needed, which is called the analysis of the Markov chain. There are at least three stages of the Markov chain process, including determining the state, the transition matrix, and the initial vector value. The transition matrix describes changes from one state to another state in the next period of time. The transition matrix formed is the basis of the subsequent analysis, including the calculation of the number of personnel per rank in the future. Determining the length of the matrix period is done by comparing several transition matrices with various moving average orders. The result obtained suggests that the best transition matrix is the one with moving average orders six, including the transition matrix with the smallest value of Mean Square Error (MSE).</p><p> </p><div><p class="Els-keywords">Keywords: Markov Chain, Military Manpower Planning, Transition Matrix, Indonesian Navy</p></div><p> </p></td></tr></tbody></table>

scholarly journals Manpower planning using Markov Chain model

2014 ◽  
Author(s):  
Syafawati Ab Saad ◽  
Farah Adibah Adnan ◽  
Haslinda Ibrahim ◽  
Rahela Rahim
Keyword(s):  
Markov Chain ◽  
Markov Chain Model ◽  
Manpower Planning ◽  
Chain Model

Manpower Planning: A Markov Chain Application

1973 ◽  
Vol 2 (2) ◽  
pp. 133-144 ◽  
Author(s):  
Gordon L. Nielsen ◽  
Allan R. Young
Keyword(s):  
Markov Chain ◽  
Manpower Planning

Bayesian Applications in Auditory Research

2019 ◽  
Vol 62 (3) ◽  
pp. 577-586 ◽  
Author(s):  
Garnett P. McMillan ◽  
John B. Cannon
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Bayesian Methods ◽  
Prior Distribution ◽  
Interim Analysis ◽  
Iterative Process ◽  
Bayes Theorem ◽  
Sound Therapy ◽  
The Many ◽  
Auditory Research

Purpose This article presents a basic exploration of Bayesian inference to inform researchers unfamiliar to this type of analysis of the many advantages this readily available approach provides. Method First, we demonstrate the development of Bayes' theorem, the cornerstone of Bayesian statistics, into an iterative process of updating priors. Working with a few assumptions, including normalcy and conjugacy of prior distribution, we express how one would calculate the posterior distribution using the prior distribution and the likelihood of the parameter. Next, we move to an example in auditory research by considering the effect of sound therapy for reducing the perceived loudness of tinnitus. In this case, as well as most real-world settings, we turn to Markov chain simulations because the assumptions allowing for easy calculations no longer hold. Using Markov chain Monte Carlo methods, we can illustrate several analysis solutions given by a straightforward Bayesian approach. Conclusion Bayesian methods are widely applicable and can help scientists overcome analysis problems, including how to include existing information, run interim analysis, achieve consensus through measurement, and, most importantly, interpret results correctly. Supplemental Material https://doi.org/10.23641/asha.7822592

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