Organizational Demographic Management: A System
Dynamics Model
Mahdi Bastan>*, Sareh Akbar Pour’, Saeid Delshad Sisi>
* Department of Industrial Engineering, Eyvanekey University, Eyvanekey, 35918-99888, Iran
» Department of Industrial Engineering & Management Systems, Amirkabir University of Technology, Tehran, 15875-4413, Iran
ABSTRACT
Nowadays, organizations need to pay much attention to the subject of human capital
management in order to progress and succeed in competitive and variable business
environment. One of the topics discussed in human capital management, which has an
important role in processes of organizational growth and development and resources allocation,
is the issue of organizational demographic composition and the study of its characteristics e.g.
size, aging chain structure, and staff education. This issue becomes more important especially
for service-based organization such as banks that manpower is their main capital. In order to
increase effectiveness and efficiency in these organizations, this issue should be analyzed with
a holistic approach. In a systemic view, Staff educational demographic should be distributed in
form of a chain of consecutive educational levels in which the number of people is determined
in balance with jobs and grows based on organization needs. Due to the influence of each
component of this dynamic chain on the next one, policies and decisions which could guarantee
their appropriate and balanced growth are of great importance. This research has been carried
out in a commercial bank in Iran as a case study to analyze the dynamics within its Staff
educational demographic, and for this purpose a dynamic model is developed based on system
dynamics. In this respect, various scenarios regarding different policies are simulated by
VENSIM software, and the results of adopting these policies are analyzed both qualitatively
and quantitatively.
Keywords: Organizational Demographic, System Dynamics, Human Resource Management, Staff
Educational Demographic
1. INTRODUCTION
Today, due to the existent variable business environment, the future of organizations is
somehow unpredictable, and managers are confronted with a lot of challenges. System
dynamics approach could help managers to pay attention to cause and effect relations, analyze
various decision scenarios, observe trends of system components in different time frames, find
out improvement policies, and finally achieve a better understanding of the system.
No one is unaware of the role of studying the structure of educational demographic in
economic and social planning in long term. Regarding the variable trends in monetary and
banking businesses, senior managers need to expand their knowledge about the quality and
quantity of human capital structure and its dynamics. By a systemic view, they would be able
to adopt right policies in connection with the development of organization human capital in
long term.
“Corresponding author. E-mail address: mbastan@ eyc.ac.ir
The most important action to do in the present situation is adopting a comprehensive policy
and holistic view in which more than just staff recruitment and promotion issues are noticed.
Here not only we should consider the rate of employment and its fluctuation during service
processes related to Staff educational demographic, but also we should regard the efforts to
improve the condition of human capital in terms of enhancing skills through education and
adopting reasonable policies to prevent disruptions in educational demographic.
In order to deal with current instabilities in the business environment, human resource
managers need to pay much attention to suitable approaches and staff’s educational dynamics
to cause any growth. Lacking a systematic view about educational demographic might even
lead to many challenges.
Structure of staff's composition in an organization can be studied via statistical
information within the organization; however, given the widespread growth of organizations
and their sophisticated architecture and dynamic conditions, it is necessary to do analysis and
increase awareness about staff's composition.
By adopting policies based on system dynamics, senior managers of human resource can
organize the educational demographic of their organization by dynamically evaluating and
analyzing different phenomena such as recruitment, training, education, and promotion in such
a way that they would be able to provide prerequisites for future development planning. In the
meantime, due to banks’ variable working conditions, it is important for their managers of
human resource to have a different perspective about their Staff educational demographic.
Regarding the significance of responses to changes, environment prediction, and making
effective decisions about the future, human resource management should have a holistic and
systematic view towards this issue.
This research offers a dynamic model for analyzing the organizations educational
demographic based on a structured approach. For this purpose, after reviewing the literature
and identifying status quo and processes related to current staff's structure, effective factor in
designing of a bank’s educational demographic model are identified. Then, the system
dynamics model is prepared according to real system structure, and the effect of using different
policies on the body of educational demographic are examined in accordance with decision
scenarios.
In fact, it can said the most important purpose of this research is to identify the feedback
loops and leverage points in Staff educational demographic and also, to analyze the dynamic
and nonlinear outcomes of implementing different decision policies in form of scenarios which
would finally result in a decision support system for banking organizations’ human capital
management.
u. LITERATURE REVIEW
Extensive researches have been carried out regarding the studies of demographic structure
on human-centered and service organizations and also, human resource management and its
subdomains. Generally, the studies related to human resource management and personnel
training can be divided into two categories. The first group includes the researches in which
the main approach they apply in employees planning is based on mathematical and statistical
models. The most important feature of these types of models are their static behavior. In this
type of planning, training programs are designed and implemented commensurately with the
predicted number of jobs in the future.
The use of statistical prediction tools and the study of various trends leads to a behavioral
analysis of past historical data which would be consistent and appropriate as long as none of
the paradigms of business environment are changed, and also, no discontinuous mutation
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during the planning time window has occurred. In fact, it can be said that plans which are
designed based on this approach will be much event-oriented.
In another approach for planning in organizations, dynamic and flexible schemes are used.
Based on structural analyses, this method tries to attribute the behavior of a system to its
intrinsic structure. If the productive structure of dynamic behavior could be identified, then
control and management of the system would be much possible.
The main feature of educational planning models of these approaches are their dynamism
and flexibility. So that they have the capability to monitor changes over time and are also robust
and stable against the occurrence of unexpected events and changing of paradigms in business
environment in terms of acceptance and accountability towards flexible changes and also,
sustainability and reactiveness.
Ratna and Chawla [1] expressed definitions and applications of this approach in human
resource planning. Bahrami et al. [2] used system dynamics approach to determine guidelines
and factors influencing the evaluation of educational groups. By presenting a dynamic model
and changing its parameters, they evaluated the impact level of model variables on quality of
educational groups and generalized this model for the use of other organizations. Ahmad
Dardar et al. [3] examined the effect of training, job satisfaction, and availability of job
opportunities on the trade volume of petroleum companies in Libya. Aburawi and Hafeez [4]
examined dynamics of human resource and knowledge management in organizations through
system dynamics approach. They believe that human resource management in organizations is
a key sector of any organization and its related process and functions should be considered
holistically as a system. Also, it was demonstrated in this research that system dynamics
approach can be used to analyze issues in the field of human resource management such as
training, staff deficit or surplus, etc. Using this model, they were able to develop human
resource strategies and create optimal guidelines and reduce unfavorable scenarios related to
staff. In the field of human resource management, Pejic Bach et al. [5] presented a dynamic
model for personnel’s human resource management with abovementioned conditions in
knowledge-based organizations. In this research, system dynamics approach were used to help
make strategic decisions in providing intellectual services. Sveiby et al. [6] designed a flight
simulator model to develop knowledge-based strategies in A ustralian federal public service for
better understanding the dynamics of existent interactive relations, and they analyzed the
relation between organization profitability and investment on internal structure of organization
to make more competent employees to create value added capacity.
Tatagan et al. [7] named human resource as the main component of competition in today’s
global economy and considered quality and initiative of human resource as equally important
as computerization for economy. In this research, it has been mentioned that European
countries give much attention to continuous training of personnel and consider it as very
effective in organizations’ development. Narahari and Narasimha [8] analyzed dynamics of
enterprise resource planning in active organizations in the field of information technology.
Liu et al. [9] regard human resource management as a complex system, due to existence
of multiple information feedbacks and delays in its structure, and they believe that making
better judgments and decisions in this area requires an analytical view of this system. They
combined the principles and methods of implementing such systems in order to achieve a basic
model for making right decisions, and they also used the methodology of system dynamics to
face uncertainties. Many researches have been done on issues of optimization modeling [14],
(15], [16], [17], [18] and [19], [20], [25], [26]. Also using system dynamics approach for same
problem modeling is common [21], [22], [23], [24], [27], [28], [29].
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mm. RESEARCH METHODOLOGY
There are several approaches to review and analyze the dynamic behavior of systems
related to human resource, but the methodology chosen for this research is system dynamics,
because of its extensive application in the field of human resource management.
This methodology was first created by J. W. Forrester at MIT in 1950. After a while, the
application of this method extended to be used in other sectors of industry. System dynamics
can pattern different aspects of a problem, and it is an effective method of analyzing a system
by simulation [10]. It can also elucidate the unspecified or unexpected outcomes of a decision
and help us with understanding complex systems. System dynamics may be used for testing
different scenarios with a systemic view of the problem. So, it allows the decision maker to
simulate and test his proposed policies and to see long-term outcomes of implementing each
policy before making his final decision [11].
In order to solve a problem by means of system dynamics we need to pursue five steps
below:
. Identifying and defining the problem
. Mapping causal loop diagrams
. Developing the mathematical model (stock and flow diagram)
Model simulation and validation
. Scenarios generation and evaluation, then selecting and implementing the most
appropriate solution
Forrester believed that system dynamics has joined the human mind capability to modem
computers’ power. In the first steps of developing the model, in order to specify the appropriate
variables and possible feedback iterations, we need creativity of a human mind. Computers are
employed to elucidate the unexpected outcomes emerged from complexity and dynamic
behavior of the system; because predicting the feedbacks and nonlinear impacts of decision
variables in complex systems would be too difficult for humans. People often consider the
relations among variables as linear in order to predict the outcomes which can clearly lead to
wrong inferences [30].
System dynamics is often employed to analyze complex social and economic systems; as
these systems dynamically change due to many unknown causes. Sterman [12] describes the
steps of system dynamics modelling according to figure 1.
OPW
1-Problem Articulation
5-Policy
Formulation &
Evaluation
—————— A
4-Testing 3-Formulation
Figure 1-Iterative and cyclic feedback process of modelling based on system dynamics methodology [12]
As it can be seen from figure 1, this modelling is not a linear sequence of steps, but it is a
feedback process. This modelling process is iterative and cyclic, so it improves our
understanding of the problem in each iteration by providing more and more feedbacks [12].
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Wolstenholm [13] believes that through analyzing human systems by system dynamics
approach, one can gain a good insight of their performance even without help of computer
software.
This approach is a continuous simulation method which make it possible for model
developers to see system’s behavior and changes of values, quantitative data, or state variables
over a specific period of time under different policies. System dynamics models use what if
approach to determine optimal policies for human systems. This methodology helps managers
with identifying important rules of decision making and shows them how to continuously
improve policies to achieve their long-term goals in human resource management [14].
1v. DYNAMIC MODELLING
4.1, Problem Statement & Dynamic Hypothesis
The new structure and recent changes of our case study, the commercial bank, including
the sale of shares in stock market, acceptance of corporate governance, improvements in
productivity and other performance indicators, competition in national monetary and banking
market, move towards modem banking e.g. e-banking, corporate banking, private banking,
virtual banking, as well as active presence in international currency market have all led to many
new open positions and new jobs in the organization.
In response to these wide range of changes, and in order to make suitable arrangements
for new defined jobs, there is an obvious necessity for appropriate training and educational
planning for the staff in all sectors of organization. Plus, new effective mechanisms are needed
in order to increase organizational learning. Additionally, as a result of shift from a branched
service-oriented retail bank to a great service corporation, the set of employees’ skills must be
expanded. If financial majors and specialties sufficed before to manage bank affairs, now with
expansion in the scope of bank’s activities, graduates from new interdisciplinary fields seem
to be required. In the past, according to the type of activities in branches, lower levels of
education were sufficient for the established positions or jobs e.g. high school diplomas,
associate’s degrees, or bachelor’s degrees. However now, regarding the emerging new
paradigms in banking industry, there is a need for the presence of highly-educated people with
master or even PhD degrees.
Considering the trend of higher education system during the recent years, we find that due
to the heavy number of graduates in recent years, the proportion of the graduates with a master
or PhD degree to the graduates with an associate’s or bachelor’s degree has increased. However
in most of organizations, despite fundamental changes in the nature of activities, jobs suitable
to abilities and skills of new employees are not designed, and allocation of personnel is often
disproportional which has had a significant negative impact on organizational growth and its
productivity.
Now, given the existent paradigms and great desires in staff for studying in higher
education levels, we need to see whether today’s organizations have a robust plan to expand
knowledge of their employees, if they have mechanisms and policies to control their
educational demographic, and whether they are able to reconcile their educational demographic
with requirements of new jobs in long-term and to be ready to face the changes in business
environment?
In fact, obtaining a proper and proportionate educational demographic for playing new
roles in the future is the main subject of this research, and in this regard, a decision support
system for simulating different scenarios of changes in educational demographic would be very
effective in organization policy-making.
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To achieve this goal, we backtrack to five years ago and calculate necessary rates using
reference charts based on historical data and then, results of adopting different policies would
be simulated in a twenty-year horizon until 2031.
Followed by problem identification and statement during an appropriate time window, the
dynamic hypothesis is theorized. The dynamic hypothesis is called dynamic as it should
represent the dynamic features of the problem according to important feedbacks, state structure,
and flow of the system. It is also called a hypothesis as it is always temporary and might be
revised based on model developer’s learning from the modeling process and the real world.
Briefly, dynamic hypothesis is an elaboration (of the type of a closed system) with a dynamic
and systematic perspective.
The dynamic hypothesis for analyzing the issue of Staff educational demographic in a
bank can be defined as follows. At first, a number of staff were hired by the bank with diploma,
associate and bachelor’s degrees. Employment is an iterative feedback mechanism for each of
these levels, and factors e.g. layoff, retirement, mission, transfer, and morality are part of a
balancing mechanism of the system. Personnel with diploma degrees can continue their
education for two or four years to achieve associate and bachelor degrees. A percentage of
personnel with bachelor’s degrees can become master graduates with two years of delay, and
then again get their PhD with another five years of delay. Of course, organization positions
which require higher qualifications can be filled both through activation of the mechanism of
old staff continuing their education to higher levels and also, through direct employment of
new personnel with higher degrees. At each level of qualification with regard to the number of
available jobs, a desirable level is assumed, which specifies the surplus or deficit of personnel
at each level. Then the organization needs to adjust this level to the desired level in a reasonable
time. In figure 11, schematic illustration of the dynamic hypothesis is shown in a causal loop
diagram.
4.2. Data Analysis and Reference Modes
Overall structure of Staff educational demographic of the bank’s human resource in 2011
is shown in figure 2.
2.42
2.38__ 0.21
| m Lower than Diploma
Diploma
m Associate's
™ Bachelor's
™ Master
mPhD
Figure 2-Staff educational demographic in the bank in 2011
Also, personnel dynamics of each level from 2011 to 2015 based on reported data is as
figure 3 and 4.
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2000
—— Diploma Degree
1500 e
jate's Degree
1000 — Bachelor's Degree
500
0
2011 2012 2013 2014 2015
Figure 3-Trend of demographic change in personnel with diploma, associate's and bachelor's degrees
150
100 —Master Degree
——PhD Degree
50
0 LAN a
2011 2012 2013 2014 2015
Figure 4-Trend of demographic change in personnel with master and PhD degrees
The most significant factors influencing the dynamism of educational demographic are
employment, continuing education, transfer, retirement, mission and layoff. Trends of their
changes in the five-year period is shown at figure 5.
5.00
4-00 —— Diploma Degree
3.00 — Associate's Degree
pe —— Bachelor's Degree
—— Master Degree
1.00
——Ph0 Degree
0.00 es
2011 2012 2013 2014 2015
Figure 5-Trend of personnel employment rate at different levels
Based on historical data, geometric mean of personnel absorption or employment rate at
each level is calculated and the results are reported in table 1.
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Table 1-Average precentage of personnel employment at each educational level during five years
Educational Level Geometric Mean of Personnel Employment Rate
High School Diploma 0.30
Associate’s Degree 0.73
Bachelor’s Degree 2.71
Master Degree 0.23
PhD Degree 0.05
Furthermore, factors leading to reduction in number of personnel such as death, retirement,
mission, layoff, and resignation are all integrated to calculate the leaving rate of personnel.
Based on the reported data, the trend of personnel leaving rate at each educational level in the
five-year period was examined which is shown in figure 6.
1.20
1.00
—— Diploma Degree
0.80
——Associate's Degree
0.60 —— Bachelor's Degree
0.40 Master Degree
0.20 PhD Degree
0.00
2011 2012 2013 2014 2015
Figure 6- Trend of personnel leaving rate at each educational level
Based on historical data, geometric mean of personnel leaving rate at different levels is
calculated and the results are reported in table 2.
Table 2-Average precentage of personnel leaving the bank at each educational level during five years
Educational Level | Geometric Mean of Personnel Leaving Rate
High School Diploma 0.69
Associate’s Degree 0.59
Bachelor’s Degree 0.80
Master Degree 0.07
PhD Degree 0.02
The number of jobs requiring each educational level and also the number of these jobs
which are already occupied are shown in charts of figure 7. Using this figure one can determine
the number of personnel deficit or surplus at each level.
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b2
2000
1500
1000
i 500
0
PhD Degree "Mails —“Bathaloe'a! Accadiuiats ce -500 a
Degree Degree _Degree | o -1000
-1500
-2000 _* Diploma Degree = Associate's Degree
mnumber of jobs m occupied jobs m deficit or surplus » Bachelor's Degree: Master: Degree:
= PhD Degree
Figure 7-percentage of Defined jobs, defined and occupied number of jobs at each level of education
4.3. Causal Model
Causal loop diagram of the system is the result of interactions and feedback
communications among different subsystems involved in the problem and is presented at figure
8.
a ___ Number of Defined
Desired’ Posttions for Associate's:
Associate's Level evel
Leaving of Associate's
Associate's Level
Employment of a
Diploma Level Personnel with xe iz
‘ ving of PAD
aa i oe aid She Person oy ea Level Personnel
, Personnel with Master Degee Ge
eae Employment sencaser /
Desired Diploma a, Level Personnel
Personnel with
Bachelor's D Leaving of Master Employment of PAD
= Level Personnel Level Personnel.
Number of Defined
Positions for Diploma Tearing of Dinka Desired Master
cee evel Personnel _ tel
Leaving of Bachelor's Bachelor's Level
Level Personnel Personnel Desired PhD Level:
‘Number of Defined
Desired Positions for Master
Bachelor's Level Naked Level Number of Defined
Positions for Bachelor's Positions for PHD Level
Level
Figure 8-Causal loop diagram of educational demographic model
4.4. Stock and Flow Diagram
After determining the stock and flow structure, the final Stock and flow model was
achieved based on figure 8. Some of existent variables in the flow model are described in table
3
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Table 3-Some of existent variables in system stock and flow model
Variable Name Variable Description Nadable
Type
Personnel with Diploma Degree Personnel with diploma degree Level
Personnel with Bachelor's Degree Personnel with bachelor’s degree Level
Personnel with Master Degree Personnel with master degree Level
Personnel with PhD Degree Personnel with PhD degree Level
Personnel with Associate's Degree Personnel with technical diploma degree Level
D-Hiring Rate Employment rate of staff with diploma degree Rate
B-Hiring Rate Employment rate of staff with bachelor’s degree Rate
‘A-Hiring Rate rate of staff with Associate's Degree Rate
M-Hiring Rate Tate of staff with master degree Rate
PhD-Hiring Rate rate of staff with PhD degree Rate
D-Edu Rate 1 Rate of continuing education from diploma to associate's degree Rate
D-Edu Rate 2 Rate of continuing education from diploma to bachelor’s degree Rate
A-Edu Rate Rate of continuing education from Associate's Degree to bachelor’s Rate
B-Edu Rate Rate of continuing education from bachelor’s to master degree Rate
M-Edu Rate Rate of continuing education from master to PhD degree Rate
D-Out Rate Leaving rate of personnel with diploma degree Rate
B-Out Rate Leaving rate of personnel with bachelor’s degree Rate
‘A-Out Rate Leaving rate of personnel with Associate's Degree Rate
M-Out Rate Leaving rate of personnel with master degree Rate
PhD-Out Rate Leaving rate of personnel with PhD degree Rate
D-Hiring Fraction percentage of personnel with diploma degree Constant
B-Hiring Fraction percentage of personnel with bachelor’s degree Constant
‘A-Hiring Fraction percentage of personnel with Associate's Degree Constant
‘M-Hiring Fraction percentage of personnel with master degree Constant
PhD-Hiring Fraction D .ge of personnel with PhD degree Constant
D-Out Fraction Leaving net percentage of personnel with diploma degree Constant
B-Out Fraction Leaving net percentage of personnel with bachelor’s degree Constant
‘A-Out Fraction Leaving net percentage of personnel with Associate's Degree Constant
M-Out Fraction Leaving net percentage of personnel with master degree Constant
PhD-Out Fraction Leaving net percentage of personnel with PhD degree Constant
TAD1 Duration of continuing education from diploma to Associate's degree ‘Auxiliary
TAD 2 Duration of continuing education from diploma to bachelor’s degree ‘Auxiliary
TAD 3 Duration of continuing education from technical diploma to bachelor’s ‘Auxiliary
TAD4 Duration of continuing education from bachelor’s to master degree ‘Auxiliary
TAD 5 Duration of continuing education from master to PhD degree ‘Auxiliary
B-Out Farction M-OutFarcton
ating D-Out Farction cal apie
Fraction D-Edu Fraction2 ss Vitae idee M-Bau Fraction n-04
Personne with
Dag Rate edars bee [ae
ae seet doef)
sacl aN
‘D-Edu Fraction 1 ‘M-Hiring memes
—] me od
. |____sloa Pau Kate Fron
"iting Rate
ro
fae ADuf Rate
‘A-Out Farctlon
Figure 9-Stock and flow diagram of educational demographic of human resource
10|Page
4.5. Levrage Points Identification and Scenario Generation
In order to generate scenarios, leverage points of the problem should first be identified.
Considering the structure of staff educational demographic, ideas of human resource managers,
and existent variables in the casual model, leverage points of proposed model would be as
follows:
A) Number of personnel with bachelor’s degree
B) Duration of educating at each level
C) Rate of continuing education from bachelor’s to master level
Scenarios are designed based on identified leverage points in staff educational
demographic chain. Meantime, current situation of educational demographic is considered as
the base case, and changes resulting from adopting different scenario and decision policies is
compared with the current situation and the results of simulating the model in each scenario
are analyzed.
Based on identified leverage points in the dynamic model of educational demographic of
human resource, following scenarios were implemented to predict the behavior of educational
demographic:
1) First scenario: prolonging current situation of educational demographic
This scenario means that no changes would be made in current trends of staff educational
demographic in the organization. In this scenario, it is assumed that procedures of personnel
employment and attending continuing education programs are similar to the estimates made
based on historical data of the recent five-year period (the base case).
2) Second scenario: changing time duration parameter
Since one of the leverage points of the model is duration of education at each level, second
scenario can be designed based on it. This variable is exogenous and uncontrollable if we
assume that employees must attend formal courses at universities like other full or part time
students; however, the organization do not necessarily have to acquire the required level of
knowledge for achieving a good performance in jobs by having their employees attend the
continuing education program out of organization. Instead, organizations can simply adopt
equivalent intra- organizational service training policies. Since compression of training periods
in this type of training is easily possible, here we can assume that the time duration parameters
are endogenous, and can be reduced as desired.
3) Third scenario: changing employment rates
The nature of jobs requiring associate’s degree is different from the jobs which need
bachelor’s degree. So, we can employ fewer staff with diploma and associate’s degree, and
increase employment rate of staff with master or PhD degree after 2015. For this purpose, we
can outsource jobs relating to personnel with diploma and associate’s degree, and perform jobs
relating to master and higher qualified personnel internally.
4) Fourth scenario: restructuring staff educational demographic
Previous scenarios were based on changing the parameters of the system. But the behavior
of the system do not only depend on its parameters, but also, we can lead system’s behavior
towards a desired change by changing its structure. In this scenario, we change the current
structure of staff educational demographic and try to eliminate personnel surplus or deficit in
11|Page
a three-year period according to the current statistics of number of defined jobs and number of
available personnel. With regards to personnel’s desire to pursue higher educational levels, this
scenario tries to find out if the current educational demographic is able to respond to the shocks
occurred as the result of an increase in the number of favorable jobs for master and PhD levels
given the changing nature of activities in the bank by 2025. According to this scenario, the
stock and flow structure of the bank educational demographic will be as figure 9.
B.Out Fartion ‘M-Out Farction
ey D-OutFaretion
me Rate
anit tion
Dba Fraction2 /
\ Out Rate
a true Deda eS \ /
Bachelor's Degree aoe weet
‘Deda Rat Eda Ra
ie: | ay 7
iT ) \
1AD2 Lota Rate TAD4 , \ |
ati Hing Rate mys
Tine 1-3
D-Hiring Rate
ry
\
‘Thine to Adjust \
sda Fraction 1
—
Jon -Bau Rate.
.
a _
— % TAD 3—
AO Faretion
“
Figure 10-Stock and flow diagram of restructured system
In this scenario, instead of using annual employment rate, desired levels of staff with each
degree are considered, and necessary actions to eliminate personnel surplus or deficit at each
level is planned in a three-year horizon.
4.6. Model Validation & Scenarios Simulation
Regarding the standard methods for the validation of system dynamics models, presented
model has been tested against conformity with structural behavior of the real system. On the
other hand, given the structure of staff educational demographic, parameters and model
equations have been obtained in accordance with existent facts of the system. So, we can claim
that numerical behavior of model is largely valid. In order to validate the proposed model,
different tests have been carried out. The presented model was tested for adopting the structural
behavior of the real world system considering the system dynamics approach. Additionally, in
order to validate the model following tests have been carried out:
e Extreme Condition Test: the consistency and significance of variable’s behavior was
tested by setting the parameters to their extreme values.
e Boundary Adequacy for Structure Test: the adequacy level of the model boundaries was
confirmed by asking for the ideas of five experts.
e Structural Behavior Test: the compatibility level of behavior generated by the model
was determined by the reference variables’ behavior.
e Dimensions Consistency Test: the dimensions of all variables in all equations was
reviewed and it was determined that the dimensions of two sides of equations were in
balance.
After validation, different scenarios were generated and simulated by VENSIM software,
and the results of adopting different policies to the problem were evaluated and analyzed.
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1) Results of first scenario simulation
As itis clear in figures 11 and 12, share of personnel with diploma and associate’s degrees
are descending and a decreasing goal-seeking behavior is being witnessed. System’s behavior
for staff with bachelor’s degree is ascending, and the number of people with master and PhD
degrees are increasing exponentially. These behaviors are logical regarding the historical data.
0
2011 2013 2015 2017 2019 2021 2023 2025 2027 2029 2031
Personnel with Diploma Degree : $¢212110———
Personnel with Associate's Degree : $c€12110-——
Figure 11-Simulated effects of prolonging current situation of educational demographics on the number of personnel with
diploma and associate’s degrees
2011 2013 2015 2017 2019 2021 2023 2025 2027 2029 2031
Time (Year)
Personnel with Bachelor's Degree : S210 ———
Personnel with Master Degree : Scenario
Personnel with PhD Degree : SOenai0 ———— et
Figure 12-Simulated effects of prolonging current situation of educational demographics on the number of personnel with
bachelor's, master, and PhD degrees
2) Results of second scenario simulation
In the second scenario, in order to control the duration time of educating and to better
managing staff educational demographic, instead of encouraging and supporting personnel for
attending external continuing education programs, the bank signs contracts with universities
and scientific research centers for developing intra-organizational equivalent programs of
master and PhD which are more fit to bank’s processes but take less time from personnel in
order to get their degrees. After graduation, valid certificates will be issued for personnel by
these universities and scientific research centers. If this could reduce the necessary time for
studying master and PhD courses by half, then we would witness variable changes shown in
figures 12 and 13.
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Personnel with Master Degree
2000
1500
1000
500
pacer tt
Jee
2011 2013 2015 2017 2019 2021 2023 2025 2027 2029 2031
Time (Year)
Personnel with
Personnel with
Scenario
Figure 13-Simulated effects of reducing time duration parameter on the number of staff with master degree
Personnel with PhD Degree
200
AT |
Lor | | er
2011 2013 2015 2017 2019 2021 2023 2025 2027 2029 2031
Time (Year)
Personnel with PhD Degree : Scenario 3
Personnel with PhD Degree : Scenario $$
Figure 14-Simulated effects of reducing time duration parameter on the number of staff with PhD degree Simulated effects of
reducing time duration parameter on the number of staff with PhD degree
In the second scenario, it can be seen that by reducing duration time of studying courses
by half, the number of master and PhD graduated personnel could increase to double.
3) Results of third scenario simulation
In the third scenario it is demonstrated that if we increase employment rate of personnel
with master and PhD degrees to double after 2015, system’s behavior would not respond to
these changes.
LL! | |
Figure 15-Simulated effects of changing employment rates on number of staff with diploma, associate's, master, and PhD
degrees
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Pema wt FAD Dee
Figure 16-Simulated effects of changing employment rates on the number of staff with PhD degree
4) Results of fourth scenario simulation
Based on the fourth scenario, the structure of personnel employment would change. In a
three-year horizon, necessary planning are implemented to eliminate personnel surplus or
deficit in each level. The results of adopting this policy are shown at charts of figure 17.
amine ape eat ge
Figure 17-Simulated effects of restructuring staff educational demographic on the number of personnel with all degrees
By applying this scenario and changing bank’s demographic structure, the extra number
of personnel with diploma would reduce over time. But in other levels, due to deficit of work
forces, we witness an increase in number of personnel. The number of personnel with a
bachelor’s degree would reach a stable level after 2015. The same issue would happen for
personnel with master degree after 2020. For the PhD, an equilibrium would be achieved near
the end of horizon.
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v. CONCLUSION AND FUTURE RESEARCH
Given the purposes of this research and with regards to dynamic structure of staff
educational demographic, it can be said that the proposed model, with a high level of flexibility
in policy making and decision making, is able to represent the dynamic behavior of the system,
and can be introduced as a dynamic model for educational demographic of an organization.
Additionally, the most important feedback loops of this system are related to personnel with
bachelor’s and master degrees which have major effects on dynamic behavior of the system.
Furthermore, variables of duration of study, employment rate, and number of personnel with
associate’s and bachelor’s degrees were identified as leverage points of staff educational
demographic. This model facilitates the process of decision making in areas related to human
resource for managers of banks or other similar organizations.
This investigation tries to present a dynamic model as a flexible tool to design and
implement policies in systems which involve human resource with continuous and complex
causal relations. The developed model, regarding its features, is able to simulate different
scenarios from which the most important ones were chosen and results of adopting them were
predicted.
In the first scenario, results of prolonging current situation of the system was simulated.
This policy resulted in an increase in the number of personnel with master and PhD degrees.
In the second scenario, policy of reducing the duration time needed for training personnel and
its effects on existent variables of the system was simulated and the results were analyzed.
Results showed that system is highly sensitive to changes of this parameter. In the third
scenario, by increasing the employment rate of master and PhD personnel, it was shown that
educational demographic is not sensitive to this parameter, as there were no significant
reaction. Ultimately, in the fourth scenario, the consequences of eliminating personnel surplus
or deficit were studied. This scenario would sooner or later lead to balanced conditions in all
levels.
In this model, dynamic behavior of staff educational demographic was studied over a
simulation period of 20 years, including educational levels of high school diploma to PhD. It
is recommended for future studies to develop dynamic models with variable rates and
parameters over time and to consider more various and detailed parameters in the model of
staff educational demographic; e.g. role of compensation and reward systems, productivity, etc.
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