Table of Contents
22nd International Conference of the System Dynamics Society, Oxford July 25 — 29, 2004
HUMAN RESOURCE MODELLING USING SYSTEM DYNAMICS
Khalid Hafeez, Izidean Aburawi and Allan Norcliffe
School of Computing and Management Sciences, Sheffield Hallam University
Howard Street Sheffield S1 1WB, UK.
Abstract
Effective human resource planning allows management to recruit, develop and deploy
the right people at the right places at the right times to fulfil both organizational and
individual objectives. Firms are constantly looking out for strategies to cope with staff
shortage which is particularly acute in the “knowledge intense” industries due to high
staff turnover.
This paper describes how System dynamics may be used as a tool to model and
analyse the human resource planning problems associated with staff recruitment, staff
surpluses and staff shortages. An integrated system dynamics framework is discussed.
The Inventory and Order Based Production Control System (IOBPCS) construct has
been introduced to develop various feedback and feed forward paths in the context of
human resource management. The model is mapped onto an overseas petrochemical
company's staff recruitment and attrition situations and subsequently tested using real
data. Strategies for HRP are developed by conducting time based dynamic analysis.
Optimum design guidelines are provided to reduce unwanted scenario of staff surplus
and/or shortage. We anticipate that system dynamics modelling would help the
decision maker to devise medium to long term efficient human resource planning
strategies.
Keywords: human resource planning, system dynamics, simulation, decision support system
22nd International Conference of the System Dynamics Society, Oxford July 25 — 29, 2004
HUMAN RESOURCE MODELLING USING SYSTEM DYNAMICS
Introduction
Human resource planning (HRP) needs to respond to a greater demand for new
talent due to increased competition in the knowledge economy. Walker (1974) has
suggested that, through HRP, management is able to develop and deploy the right
people at the right places at the right times to fulfil both organizational and individual
objectives. Firms are constantly looking out for strategies that will help them to cope
with competition and diversification agenda through building a linkage between human
resource planning and the corporations’ long-term business objectives. Most
organization feel the need to predict future human resource levels in order to forecast
recruitment and training needs to ensure that sufficient experienced people are rising
through the rank to fill vacancies at higher levels, (Brian, 1996).
The dynamics of market forces and job opportunities is becoming a challenge for
many organizations to retain their core staff. Companies are losing critical business
knowledge as employees walk out from their doors. Also, the recent transitions from
the industrial market to the knowledge economy dictate an immediate and wholesale
retraining scenario for many organizations to remain at the cutting edge of technology.
An efficient human resource or intellectual capital investment strategy demand a good
understanding of the dynamics of recruitment and training issues.
Skill, knowledge and competence, as a measure of improvement, cannot be bought
and delivered instantly. It takes a considerable amount of time to develop and support
these infrastructures. Human resource planning (HRP) is an effort to improve morale
and productivity and therefore, help minimise staff turnover. HRP helps to facilitate
companies make effective use of employee skills, provide training opportunities to
enhance those skills, and boost employee satisfaction with their job and working
conditions. Training includes employer sponsored efforts to improve the skill and
competences of employees through education, work-shadowing, and apprenticeship
programmes for personal development. On the other hand, human resource planning
concerns forward looking analysis of current and future human resource development
needs, issue and challenges facing a particular occupation such as the supply and
22nd International Conference of the System Dynamics Society, Oxford July 25 — 29, 2004
demand of skilled people, the impact of changing technology, the need of skill
upgrading and the efficiency of the existing training.
System dynamics
Jay Forrester (1961) conducted some pioneering work by combining the fields of
feedback control theory, computer and management sciences as early as 1961 in
order to shape the systems dynamics discipline. System dynamics is a method for
developing management “flight simulators” to help us learn about dynamic
complexity and understand the sources of resistance to design more effective
policies (Sterman, 2001). The method allows us to study and manage complex
feedback systems by creating models representing real world systems. System
dynamics is part of management science that deals with the controllability of
managed systems over time, usually in the face of external shocks. However,
successful intervention in complex dynamic systems requires technical tools and
mathematical models. This process is fundamentally interdisciplinary, because it
concerned with the behaviour of the complex system, and is based on the theory
of non-linear dynamics and feedback control developed in mathematics and
engineering (Coyle, 1996). On the other hand, it is a modelling approach that
considers the structural system as a whole, focusing on the dynamic interactions
between components as well as behaviour of the system at large.
More recently, tools such as systems thinking have made many gains in soft
systems problem structuring as advocated by Senge (1994). In other examples,
Morecroft (1999) has used system dynamics to examine the management
behavioural resource system to analyse a diversification strategy based on core
and non-core business. Winch (1999) has used system dynamics to introduce a
skill inventory model to manage the skill management of key staff in times of
fundamental change. Coyle (1999) has used system dynamics to manage and
control assets and resources in major defence procurement programmes. Warren
(1999) defines tangible and intangible resources for system dynamics model
development. Hafeez (1996) has used system dynamics modelling to re-
engineering a supply chain. Mason-Jones et al (1995) have extended the work of
Hafeez et al (2000), to show its applicability in an Efficient Consumer Response
(ECR) environment by linking it to point of sale inventory triggers.
22nd International Conference of the System Dynamics Society, Oxford July 25 — 29, 2004
Hafeez (2003) has developed a skill pool model (SKPM) based on “Inventory and
Order Based Production Control Structure” (IOBPCS) as described by Coyle (1977),
to help understand the dynamics of skill acquisition and retention, particularly during
times when a company is going through some major change. The model, which is
based on system dynamics principles, relates with the organization environment to
show how new (or improved) skills could enhance organization productivity and
innovations. Also, it aims to respond to the future training and learning needs, as a
result of present skill loss rate, by incorporating a feed forward path. It aims to
properly manage the skill pool level and recruitment and training performance by
incorporating a goal seeking (feed back) loop.
An integrated system dynamics framework
The model presented in this paper is constructed by adopting an integrated system
dynamics framework developed by Hafeez et al. (1996), which is illustrated in
Figure (1). The framework has been successfully used for the modelling and
analysis of a number of supply chains. Essentially, it consists of two overlapping
phases, namely qualitative and quantitative. The quantitative phase is associated
with the development and analysis of the simulation model. The main stages
involved in the qualitative phase are system input-output analysis, conceptual
modelling, and block diagram formulation. The first step towards the quantitative
model building is to transform the conceptual model into a block diagram. The
simulation model is to be verified by relevant personnel and validated against the
field data ( Hafeez et al., 1996 ).
Qualitative system dynamics is based on creating cause and effect diagrams and to
create and examine feedback loop structure of the system using resource flows,
represented by level and rate variables and information flows. It provides a
qualitative assessment of the relationship between system process and system
behaviour and enables the system modeller to postulate strategy design changes to
improve behaviour. System dynamics is centred on the use of diagrams as a medium
for transmitting mental models and discussing change. System thinking and system
22nd International Conference of the System Dynamics Society, Oxford July 25 — 29, 2004
dynamics modelling help leaders make good decision based on sound data-driven
models. The greatest advantages in adopting system dynamics as an analytical tool
is that it take into account many interrelationships that influence the behaviour of a
complex system.
Real world supply chain
Business objective
System input-output
analysis,
Problem
Conceptual model
Block diagram
formation
Control theory Computer simulation Statistical
techniques techniques techniques
ae a
Verification/validation
e Phase
Dynamic analysis
‘ ae
Fine Tune wre
existing Structural redesign hat if business
scenarios
)
Figure 1: Integrated system dynamics framework for supply chain management
( Source Hafeez et al. International Journal of Production Economics 1996).
22nd International Conference of the System Dynamics Society, Oxford July 25 — 29, 2004
Influence diagram representation of SKPM in Ithink
The influence diagram for Skill Pool Model is shown in Figure 2 using the
standard Jthink software package, which allows anyone with elementary control
theory knowledge to construct an equivalent model to present time-based
dynamics. In order to anticipate the staff leaving replacement requirements, some
kind of averaging is useful. We have used exponential smoothing function to
average the present staff leaving rate over time Ta and added back to the original
recruitment rate to reflect the staff loss history in the recruitment planning.
Based on IOBPCS structure, the company recruitment rate comprises two parts,
one the staff gap (staff deficit), and the other forecast staff leaving rate.
Recruitment rate is therefore effectively controlled via the average time to
determine the forecast staff leaving rate (Ta), and the time over which the present
staff gap is to be recovered (Ti). The difference between the present staff leaving
rate and recruitment rate is accumulated to give the present actual staff level in
the pool. Therefore the model as shown in Figure 2 consists of two parts; feed-
forward control based on the forecast staff leaving rate, and feedback control
based on the staff gap. In order to analyse the dynamic response of the SKPM,
recruitment process delay is represented by a time delay Tr (recruitment lead
time) and the time over which staff leaving rate is averaged by Ta.
Towill (1982) suggests using exponential delay for industrial dynamics
simulation. We have used the discrete time feed forward and feedback difference
equations giving the relationship between the major variables are presented in
equations | to 5 in the skill pool model. Furthermore, it is important to recognise
how to manage the actual staff level of the pool. To reach the target value, a
simple and appropriate policy is proportional control, where information
concerning the magnitude of the actual staff level is fed back to control the
recruitment rate. The recruitment demand rate is calculated by dividing the
discrepancy between the target level and actual level by a time factor, which
represents the average delay in performing the recruitment rate.
22nd International Conference of the System Dynamics Society, Oxford July 25 — 29, 2004
Actual staff
Recruitment pecritess Recruitment level Present stat
rate completion rate leaving rate
Oo Vas O83
Predict staf
leaving rate
Figure 2: Influence diagram of the SKPM
Skill Pool Model (SKPM)
We have used the Skill Pool Model as described by Hafeez et al. (2003), and tested it
using staff pool data from a large overseas petrochemical company. The company
operates in a relatively stable “push market” with low staff turn over. Due to lack of
opportunities the majority of the workforce, more or less, assume a “job for life”.
However, there is a tendency of employing a pool of contract worker requiring manual
to specialists skills for various projects. A block diagram representation of the case
company recruitment and training system is given in Figure 3. In this format the skill
pool model is developed to improve our understanding of the dynamics of staff turn
over in a company when it is operating in a steady state. Also it allows us to see the
impact going through some major changes, this model is implicitly link with the
organization environment to develop new policy, also it aims to respond the training
and hiring needs as a result of present staff leaving rate (feed forward) as well as actual
stall level and staff training completion rate (feedback). Therefore the main aim of
using system dynamics model in HRP is to find the optimum polices to manage
company recruitment and training policies effectively in the face of shocks experienced
due to changes in its external environment.
22nd International Conference of the System Dynamics Society, Oxford July 25 — 29, 2004
Forecast staff
leaving Rate ¢———
Present staff
leaving rate
Recruitment
Staff completion rate,
Target staff Recruitment Actual
level KD demand rate & staff level
y 4 GX) us ->
Figure 3: A block diagram representation of the SKPM
It is customary to use abbreviations for the various rates, level, and operations met in
planning dynamics simulation. Those used in Figure (3) are defined in Table (1).
Equations (1) to (5) outline the main construct of Skill Pool Model and help to
established feed forward and feedback structures and associated transfer functions.
FSLR 4; = FSKR , + a, (PSLR jy. - FSLR,) ---(1)
Where oa= 1 / (1+ T, *S)
SG a1 = DSL gut - ASL gu ----(2)
RDR a1 = SGyy / T; + FSLR k+1 -----(3)
RCR kai = RCR x + Or ( RDR 41 - RCR x) --(4)
Where, or = 1 / (1+ Tp*S)
ASL yay = ASL, + RCR 41 - PSLR is (5)
22nd International Conference of the System Dynamics Society, Oxford July 25 — 29, 2004
Dynamic Behaviour Analysis
As mentioned earlier, the SKPM model and simulation analyses presented in this
paper relate to an overseas petrochemical company. The main purpose of this analysis
was to find optimum policy parameters for the company to maintain its target staff
pool. The experiments were designed to set parameters Ti, Ta, Tr triplets in a given
range to observe and record the dynamic response in order to determine their optimum
setting. Once selected, the system would determine staff recruitment automatically
governed by Ta and Ti according to a present staff leaving rate and staff gap. Table 2
shows the performance index of the SKPM and describe the related system behaviour.
Figure 4 gives a five-year record of staff leaving rate for the company. On average
company is expecting about 7% turn over at any time. Also the data reveals a step
increase in the staff leaving rate. Therefore, the SKPM is tested using real data.
Experiments were designed to study the system behaviour against the given design
parameters Ti, Ta and Tr as explained earlier.
160
155 4
© 150 4
S
8
— 1454
s
o
zz 140 4
3
oO
< 135 4
130 + + r r r r
0 10 20 30 40 50 60
Time (Months)
staff leaving rate
Figure (4) Plot of the data collected on Staff leaving rate
Figure 5 examines the step response of the actual staff level and staff recruitment
completion rate for varying recruitment lead times (Tr). As shown in Figure 5(a) the
increasing recruitment delay Tr would increase system oscillation. As shown in Figure
5(b), reducing the value of Tr improves the staff pool deficit. Figure 6(a) and 6(b),
22nd International Conference of the System Dynamics Society, Oxford July 25 — 29, 2004
respectively, show the response of actual staff level, and recruitment completion rate
for the range of Ti values. The larger Ti values lead to larger droop in the staff pool,
indicating the company is unable to recover from the staff shortages over a period of
time. In a worst-case scenario (Figure 6(b)), the company faces staff shortages for
about 42 months for Ti=18 months. On the other hand, small Ti values lead to large
oscillation over staffing about the required staff pool system over a longer period.
Clearly, in control theory terminology, this is a bad system design. In reality, this
shows a very aggressive hiring and firing human resource policy for the case company.
Figure 7 examines the system response of the staff level and staff recruitment
completion for varying values of Ta. Ta is gradually varied between | month to 18
months, for fixed values of Tr and Ti. As shown in Figure 7(a), increasing Ta slows
down the recruitment process slightly. However, as shown in Figure 7(b) it would
means the company would make from a short period of over staff to a relatively
prolonged period of staff shortages.
Table 3 gives the overall summary of the effect of varying Ti, Ta and Tr on the human
resource polices. Furthermore, by inspection on Table 2, the values of Ta, Ti and Tr
have been varied from 1 month to 18 months and by checking the simulation results
SKPM suggests that setting Ti=2 months, Tr=2 months and Ta = 4 months is a good
design, since unnecessary fluctuations in staff deficit have been avoided and the time
to recover the target staff level is not excessively long. Also, the large value of Ti
gives relatively high droop in inventory level. Therefore, at Ti=2 months, Tr=2
months and Ta=4 months is closer as an optimum design, indicating minimum initial
staff level droop for a minimum period of staff pool shortages.
10
22nd International Conference of the System Dynamics Society, Oxford July 25 — 29, 2004
Staff recruitment completionrate
(Units/Month)
Actual staff level (Units)
(b)Staff level behaviour (Ti=2 and Ta=4 months)
22nd International Conference of the System Dynamics Society, Oxford July 25 — 29, 2004
Figure 5 Step response of SKPM for varying values of Tr
142-4 SO
pu
Staff recruitment completion rate (Unit/Month
a
it
ct
mn 8 -
91 ° R SS
3 ~
aaa - ;
sono: AN “
. tht |
P 1990] WAT ii
2 | ee |
zen | nT ‘ae
- |i Hg |
g 1970] ‘ail Nu! |
3 KN |
8 “ast
8 |
1950-| ; L - |
10 Fn |
oo my
- 2 ey “a
ce ml wo
ime Nonwisy mn eu ry ee og
“8 Bae -_ we
(b) Staff level behaviour (Tr=2 and Ta=4 months)
22nd International Conference of the System Dynamics Society, Oxford July 25 — 29, 2004
Figure 6 Step response of SKPM for varying values of Ti
Staff recruitment completion rate (Units/Month)
iy
Vi
a
Actual staff level (Units)
(b) Staff level behaviour (Ti=2 and Tr=Ti=2 months)
Figure 7 Step response of SKPM for varying values of Ta
22nd International Conference of the System Dynamics Society, Oxford July 25 — 29, 2004
Conclusion
Human resource planning needs to respond to a greater demand of ‘talent’ due to
increased competition in the global market. The current developments in the resource
based and core competence theories (Hafeez, et al. 2002) have made practitioners
increasingly aware of the importance of maintaining soft “core” skills within the
company oppose to traditional asset based strategies. Therefore, management need to
understand the dynamics of human resource policy within the company. Systems
dynamics modelling can provide management with a tool to explore the impact of
different human resource policies and to determine the key influencing parameters.
The model employed in this study is a skill pool model (SKPM) to study the
dynamics of the staff pool by tuning the design parameters associated with
recruitment time, recruitment averaging time and a proportional control parameter to
reduce the staff pool shortages. Based on the defined performance indices, the
decision maker can choose to minimise the current and future staff shortages by
selecting an appropriate recruitment policy. This study confirms that the dynamic
analysis based on simulation model greatly improves the understanding of human
resource system behaviour. Furthermore, such model can guide management to
develop improved human resource policies by reducing the "hiring" and "firing" rules,
which is proven to be costly and have negative impact on staff morale.
Finally the use of the Skill Pool Model (SKPM) has been tested and discussed with
relevant people in our case company. The company has _ validated the results and they
interested in to implement this model in their company, because its use should enable
them to better plan the recruitment and training required, as well as predicting the
future manpower needs.
14
22nd International Conference of the System Dynamics Society, Oxford July 25 — 29, 2004
Appendix: Transfer function of SKPM
In the classical control theory, the transfer function of a system represents the
relationship describing the dynamics of the system under consideration. It
algebraically relates a system input and system output. Figure 3 show the block
diagram representation of the key variables of the SKPM model and their interactions.
Equation | calculates staff gap as the discrepancy between target staff and actual staff
level, Equation 2 calculates the forecast staff leaving rate as the smoothing function
@, of the present staff leaving rate and Equation 3 shows the schedule recruitment rate
which aims to meet the forecast staff leaving rate, in order to meet this target we need
to undertake some adjustment in staff gap as given by function (1/T;).
Equation 4 calculates the recruitment completion rate and it is given as a result of
delaying function of the schedule recruitment rate @,, and the actual staff level
calculated in equation 5 accumulated over its previous level. Function (1/S) of the
recruitment completion rate less present staff leaving rate.
Equation | to 5 are used to develop associated transfer functions. Using the block
diagram (Figure 3), Furthermore, the transfer function can be derived as (actual staff
leaving rate / present staff leaving rate) and (staff recruitment completion rate /
present staff leaving rate), these transfer functions are shown in equations A and B
respectively.
Actual.staff level _ ~Til(Tr+Ta).S + TrTa.S| (A)
Present.staff leaving.rate (1+ Ta.S)(1+Ti.S +TiTr.S*)
Recruitment.completion.rate _ 1+ (Ti+Ta).S 8)
Present.staff leaving .rate ~ (14+ Ta.S)(1+Ti.S +TiTrS*)
15
22nd International Conference of the System Dynamics Society, Oxford July 25 — 29, 2004
Equation A and B are useful in understanding how the parameters T;, T,, T,, to be set
by the system designer to study the time response behaviour and determine human
resource management policy guidelines.
Rates and levels appear as abbreviations at the start and finish of the arrow link lines.
The signs associated with the arrow tips are extremely important in establishing the
correct behaviour of the system, especially with regard to stability.
Terms Abbreviations Description
Present staff leaving | PSLR The units of staff leaving rate are staff unit/month and
rate it is refers to present staff leaving rate
Forecast staff FSLR It is the time average of staff leaving rate and it is
leaving rate refers predicts staff leaving rate. The units of staff
leaving rate are staff units/month
Target staff level | DSL It is the level of target staff level. The unit of target
staff level is staff unit.
Staff gap SG It is the difference between desired staff level and
actual staff level. The unit of staff gap ff unit.
Recruitment rate | SRR It is the demand recruitment rate and it is refers to
staff gap. The units of recruitment rate are staff
units/month.
Recruitment SRCR Staff recruitment completion rate it is refers to the
completion rate acquired staff and it is units are staff /month
Actual staff level | ASL It is the actual number of staff which company needs
to run its work. The units of actual staff level are staff
unit.
Ti Time to reduce staff gap to zero
Ta Time over which staff leaving rate is averaged
Tr Recruitment process delay
1/T; VT; It is the proportional constant to deal with the
discrepancy between target staff and actual staff level
/S W/S This represent the actual staff level accumulated over
time through the recruitment and __ training
development and is affected by the present staff
leaving rate
1/ (1+ T, *S) Oa Multiplier used in simulation to take account of Ta to
average the staff leaving rate over the demand average
time
1/(1+T,*S) Or Multiplier used in simulation to take account of Tr,
and it is the recruitment process to acquire staff during
recruitment session
Table 1: Glossary of terms used in the SKPM block diagram
16
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22nd International Conference of the System Dynamics Society, Oxford July 25 — 29, 2004
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