Effects of Prioritized Input on Human Resource Control in Departmentalized Markov Manpower Framework

Ossai, E. O.; Madukaife, Mbanefo S.; Udom, Akaninyene Udo; Nduka, U. C.; Ugah, T. E.

doi:10.1007/s11009-023-10011-8

Cited by 1 publication

(7 citation statements)

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“…The vector containing the manpower overall stock in each department and each grade at is represented by . The vector is a classified stock vector in that it can be expressed as , where all members in are in active class or service in the system and all members in are in nonactive class or limbo class [25]. The non-active position of members in can be characterized in different ways.…”

Section: Model Specificationmentioning

confidence: 99%

“…The proportion of inputs from both sources and the choice of which source is considered first differ according to system needs or management decision. For more on the vacancy analysis, proportion and priority of input see Ossai et al [25], Georgiou & Tsantas [12] and Dimitriou & Tsantas [26]. For the current paper, is the number of available vacancy in the active class, ψ is the proportion of intake from the external environment, and are the probability vectors for input from the external environment and non-active class respectively.…”

Section: Model Specificationmentioning

confidence: 99%

“…To ensure tractability, as stated above, the more tractable Markov chain is employed to build a homogeneous Markov manpower model which incorporates the various state transitions arising from the different structural extensions. One of such structural extensions is the non-active or limbo class [25], which holds members who face wastage, but are still useful, in a non-active and limbo position. According to Guerry [13,14], wastage analysis is very essential in manpower planning because wastage governs, to a large extent, the need for promotion and recruitment.…”

Section: Introductionmentioning

confidence: 99%

“…According to Guerry [13,14], wastage analysis is very essential in manpower planning because wastage governs, to a large extent, the need for promotion and recruitment. According to Ossai et al [25], the inclusion of the limbo class is a form of wastage control. It also ensures two recruitment channels, one from the non-active class and the other from the external recruitment pool or environment.…”

Section: Introductionmentioning

confidence: 99%

“…It also ensures two recruitment channels, one from the non-active class and the other from the external recruitment pool or environment. The steady state or maintainability condition [25] on the manpower structure shall then be considered along these two channels of recruitment into the active class.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Steady State Conditions in Tractable Markov Manpower Model for an Extended Manpower System

Ossai

2023

AJPAS

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In this work, a formulation of manpower structure in discrete-time homogeneous Markov model is done for a multilevel manpower system. The structure of the manpower system is first extended in a departmentalized framework and the features of the extended structure utilized to create a scenario of personnel membership in three classes: the active, non-active and external classes. This allows for the inclusion of different units of the system in the model. Specifically, a pool of members in absorbing states with respect to intra-class transitions is included, which forms a second channel of recruitment. The first channel of recruitment is from the external class and all recruits go to the active class. All states of the active class are non-absorbing and give rise to various intra-class and inter-class transitions. Different probability matrices that form the main components of the Markov manpower model are constructed from probabilities of these transitions. One-step steady state condition on the manpower structure is considered, based on the formulated model, and established to be invariant with respect to varying proportion of recruits into the system.

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Section: Model Specificationmentioning

confidence: 99%