Abstract
Recent transitions from the industrial to knowledge economy suggest an immediate and wholesale retraining scenario so that many organisations can remain at the cutting edge of technology. The dynamics of the job market is creating a challenge for many organisations in recruiting and retaining their core staff. In fact, many companies are in fear of losing critical business knowledge when their employees leave. In this paper, systems dynamics is employed to illustrate the relationship between recruitment, training, skills, and knowledge in a causal loop form. Strategies for human resource management are developed by conducting time-based dynamic analysis. We anticipate that systems dynamics modelling would help organisations to devise efficient human resource management strategies.
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Appendix: Transfer function of SKPM
Appendix: Transfer function of SKPM
Notation
Figure A1 shows the block diagram representation of the key variables of the model and their interactions.
A block diagram representation of SKMP.
Equations (1) to (5) outline the main structure of SKPM in terms of its variables. Equation (1) calculates the skill gap as the difference between a fixed or constant desired level of skill and the actual level of skill pool.
Equation (5) shows the forecast skill loss rate as a smoothing function, [1/(1+TaS)], of the present skill loss rate. The latter is then used to derive the scheduled training rate in Equation (2). The schedule aims to meet the forecast skill loss rate but adjusts this target to take into account the current skill gap. The adjustment is given by function (1/Ti), representing a control algorithm as shown in Equation (3). The training completion rate is given as the result of the delaying function [1/(1+TrS)] of the schedule training rate in Equation (3). Finally, in Equation (4) the actual level of skill level is shown as the accumulation onto its previous level, function (1/S) of the training completion rate less present skill loss rate. These equations can be written down directly from Figure A1 using the difference equations.
Equations
where SKG=current skill gap; DLSKP=desired level of skill pool; and ALSKP=actual level of skill pool.
where: TRATE=training rate; and FSKLR=forecast skill loss rate.
where TCRATE=training completion rate.
where PSKLR=present skill loss rate.
Equations (1) to (5) are solved to develop the actual level of the skill pool/present skill loss rate transfer function (Equation 6), and actual level of skill/training completion rate (Equation 7) as shown in the following:
Equations (6) and (7) are extremely useful in understanding how the two parameters Ti and Ta, to be set by the system designer, interact and affect the actual level of skill loop dynamic recovery pattern. If the feed-forward component is removed, so that the control law is actual level of skill pool, the application of the Final Value Theorem shows that there is a steady-state skill deficit of Ti for a sudden unit change in present skill loss rate. With the feed-forward component added, it may be similarly shown that this deficit is eliminated. Equations (6) and (7) are the form required if the recovery is to be calculated via standard Laplace Transform Tables.
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Hafeez, K., Abdelmeguid, H. Dynamics of human resource and knowledge management. J Oper Res Soc 54, 153–164 (2003). https://doi.org/10.1057/palgrave.jors.2601513
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DOI: https://doi.org/10.1057/palgrave.jors.2601513