SYSTEM DYNAMICS SIMULATION MODELLING OF
ORGANISATIONAL BUSINESS SYSTEM OF
MANAGEMENT OF MATERIAL AND INFORMATIONAL
FLOWS IN PRODUCTIVE COMPANY
Josko Dvornik
Ante Munitic
Frane Mitrovic
University of Split
Maritime Faculty of Split
Zrinsko- frankopanska 38
21000 Split, Croatia
e-mail: josko@ pfst.hr
ABSTRACT
System Dynamic Simulating Modeling is one of the most appropriate and successful
scientific dynamics modeling methods of the complex, non - linear, natural, echnical and
organizational systems. The methodology of this method, together with use of digital
computer, showed its efficiency in practice as very suitable means for solving the problems
of management, of behavior of sensibility, of flexibility of behavior dynamics of very
complex systems. All this is made by computer simulating, i.e. “in laboratory”, which mean
without any danger for observed realities.
System Dynamics simulation models of organizational business system of management of
material (raw- materials, orders, money, labor, personnel, population, capital equipment:
tools, units and factories e.t.c.) and informational flows in productive company will be
presented in this paper. Organizational business-production system is simulated by
effective scientific discipline System Dynamic and realized by Dynamo (PD4) and
PowerSim program packages also.
Due to complexity and extensiveness of business management of organizational business
process, or productiondistribution system global simulation models of companies are
presented on the modular way, i.e. with seven relevant sub systems:
Production inventory sub system;
Credits sub system;
Debits sub system;
Sub system of productive capacities;
Sub system of Cash Flow;
Gross income- net income sub system;
OO FwWNr
7. Sub system of demand for organization products,
which are common structural characteristic in every productive business organization
These sub system are modeled according to its specific quality.
Keywords: System Dynamics, Modeling, Heuristics optimization, Continuous and Discrete
simulation, Business System.
1, INTRODUCTION
For one productive organization which is made up of series of cause-consequence
dependent sub systems, i.e. modules, which represents division based on functionality, can
be told that this is complex process with large numbers of feedback loops, which are
necessary to take into consideration. This interdependence sometimes effects very strongly
on the final result of behavior dynamics of organizational business system. The result of
dynamics behavior of business- production process can be manifested with fluctuation of
relevant business variables, such as: speed of supplying raw materials, speed of arriving the
raw materials, speed of finishing the final products, state of unfinished production, state of
finished goods - inventory, speed of shipment, state of productive capacities), state of:
credits, debt, cash-flow, gross income, net income, speed of investment new capacities
police, etc.
Previously it was mentioned that business production process, i.e. production organization
business production system, is made up of seven sub system (sub- models), which have
direct or indirect flows influence on some or even all listed indicators i.e. production
relevant variables. Meaning, it is necessary to have a priori knowledge of this business-
production process in order to define relationship between these indicator variables and
between every single module. Furthermore, it is possible to detect ineffective parts of such
business organization system by necessary knowledge of this business-production
processes and continuous modeling with System Dynamics. Further, with simulation of
dynamics processes of production organization different behavior of this organization can
be predicted, as response to different stimulus, i.e. test functions. For stimulus (known test
functions), i.e. inputs in such processes in consequence consideration can be taken: changes
in the markets, such as increase or decrease in credits for sale products or debit of this
organization, introduction of new production equipment, change of supplier of components
or materials, etc... Subjecting the production organization to different scenarios which are
stipulated with changes in the market production organizationcan become more flexible,
adaptive and robust. In this paper SD-continuous model of such production organization
will be presented, and also a possibility of application System Dynamics methodology for
simulation of this kind of business- production systems. The paper is conceived as follows:
sub systems of business production organization entire model of productive organization
system and itssimulation, conclusion and used references.
2. PRODUCTION SUBSY STEM
2.1, Mental-verbal model of the production subsystem
The “order speed” of material (NM ) is influenced by demand for organization products
(EXPR) which can be described as exponential average of the demand for products in last
36hrs (see graph “Demand for products” in subsystem of demand for organization
products). With a larger exponential average (EXPR), the orders of the material (NM ) are
also larger (+), consequently with the increase of the order, the state of unfinished
production will also increase (+). Increasing the quantity of finished production(ZGR), the
quantity of unfinished production (NP) is decreasing ¢). With a higher speed of product
finishing, the supply of finished products will be higher (+) with the consequence of the
increase of quantity of delivered products (+). When quantity of delivered products (IR) is
higher, the quantity of finished products (ZGR) in supply is lower (-), which gives a
negative (-) sign to the feedback link of FBL 1. The quantity of delivered products (IR)
will, of course, depend on demand for products on the market (TRAZNJA). When the
delay of the ordered material occurs, it could be described with macro function DELAY 3,
which as arguments takes a variable for which we describe the delay of the material flow of
III order, and as a time delay parameter KP. The structural diagram and flow diagram are
presented on Figures 1 and 2.
2.2. Structural model of the production subsystem
According to the described mental verbal model it is possible to determine the system
dynamic structural model of observed ——s~
a,
KPD 1.(-)
EXPR
TRAZN) oO
Figure 1. SD-structural flow diagram of the production subsystem
2.3. Structural flow diagram of the Production subsystem
In a similar way it is possible to present SD-simulation structural flow diagram of
production model.
D3 7 ToKeo
NP ZP ZGR 3
Or ey aye
/
TRAZNJ A }’
Figure 2. SD- structural flow diagram of production
2.4, SD - quantitative simulation model of the production subsystem
AGES GOOG OKOKER ACRAORAORK
* PRODUCTION SUBSY STEM
AGERE UI OR SAORI AK AR AK HK
R NM.KL=EX PR.K
*
L NP.K=NP.J+(DT)*(NM JK-ZP.JK)
*
N NP=20000
*
R ZP.KL=DELAY 3(NM JK,KP)
*
C KP=3
*
L ZGR.K=ZGRJ+HDT)*(ZP JK-IRJK)
*
N ZGR=4000
*
R IR.KL=CLIP*
(TRAZNJA.K,ZGR.K,ZGR.K,TRAZNJA.K)
*
3, DEMAND SUBSY STEM
3.1. Mental-verbal model of the Demand subsystem
The demand depends on the quantity of the delivered invoices (FI) meaning the higher the
quantity, the higher the state of the demand (POT) (+). The value of the delivered invoices
(FI) is influenced by the price of the product jC P) and the quantity of the delivered
products (IR), and the larger are those sizes the bigger is the value of the delivered invoices
(+). When delivering invoices, a material delay of the III order occurs, and can be
described by macro function DELAY 3. The bigger the delay is, the quicker is the speed of
charging the demand (SPOT) on the behalf of production organizations (+). The quicker
the speed of charging the demand (SPOT ) means reduction of the state of demand (POT),
ite. the negative sign (-). Figures 3. and 4. present structural diagrams and flow diagram of
demand subsystem.
3.2. Structural model of the Demand subsystem
According to the described mental verbal model it is possible to determine the system
dynamic structural model of observed subsystem.
Figure 3. Structural diagram of demand subsystem
3.3. Structural flow diagram of the Demand subsystem
In a similar way it is possible to present SD- simulation structural flow diagram of demand
model.
D3
CQ} x PoT| SPOT a}
f
f 4
f \
KPO
x
R ‘| yep
Figure 4. SD- Structural flow diagram of demand subsystem
4. DEBIT SUBSY STEM
4.1, Mental-verbal model of the Debit subsystem
The debit of production organization (DUG) depends on the speed of invoice arrival (PRF)
and also the speed of payment of the debits to the supplier GDUG). The quicker the
invoice arrival is, the state of debit is also higher (+). The quicker the payment of the debits
to the supplier is, the state of debit is lower (-). There is a material delay between invoice
arrival and payment of the debit to the suppliers and it can be described by macro function
DELAY 3. The higher the delay is, the speed of the payment of the debit to the supplier
reduces ¢). The speed of invoice arrival is directly influenced by production expenses
(TRP) which are: acquisition of the material for the production (NM), variable production
expenses (VTR ) and fixed expenses (FTR). With the increase of all of these expenses, the
production expenses, those that directly influence the invoice arrival (+), increase, as well.
Based on such verbal model the structural diagram and flow diagram can be shown in
Figures 5. and 6.
4.2. Structural model of the Debit subsystem
According to the described mental verbal model it is possible to determine the system
dynamic structural model of observed subsystem.
fo"
TRP. \ cata)
iii via PTR
Figure 5. Structural diagram of debit subsystem
4.3. Structural flow diagram of the Debit subsystem
In a similar way it is possible to present SD-simulation structural flow diagram of debit
model.
D3
OQ Duc] spuG C)
PRF vkD
Ee
Figure 6. SD- structural flow diagram of debit subsystem
5. PRODUCTION CAPACITY SUBSYTEM
5.1. Mental-verbal model of the Production Capacity subsystem
Desired production capacity will depend on exponential average of demand (EXPR) and
singular value of production capacities (JVPK), and that size can be mathematically
determined by product of multiplication of last two. The higher exponential average of
demand and singular capacity value means the increase of the states of desired capacities
(+). Discrepancy (RZKIS, i.e. the difference between desired capacity state ZELJK and
the real capacity state SKAP) will be higher when the desired capacity state is higher (+);
increasing the real capacity state by investing in new capacities, the discrepancy reduces,
i.e. by higher investment in new capacities, the real state of capacity increases (+) and the
discrepancy reduces (-). The acquisition of new capacities (NKAP) will naturally depend
on the state of existing, i.e. the writing off of the expired capacities FOT). This link
between acquisition of the new and the expiration of the existing can be modulated by
macro function DELAY 3.
5.2. Structural model of the Production Capacity subsystem
According to the described mental verbal model it is possible to determine the system
dynamic structural model of observed subsystem.
KPD 2.(-)
NKAP
oO Sore
DELAY
ZEUK
FOT
expr ‘J VPK
Figure 7. Structural diagram of the production capacity subsystems
5.3. Structural flow diagram of the Production Capacity subsystem
In a similar way it is possible to present SD-simulation structural flow diagram of
production capacity model.
D3
q a
Kap, FOT Q
NKAP voK
7 )
————-|vek
CG 6
Figure 8. SD- structural model of flow diagram of the production capacities
6. MONEY ON TRANSFER ACCOUNT SUBSYSTEM
6.1. Mental-verbal model of the Money on transfer account subsystem
The amount of money on transfer account NNZR) depends on deposits of money on
transfer account (UNZR) and on payment from transfer account (SZR). Payments from
transfer account depend on debits state GDUG) and the acquisition of new capacities
(NKAP), i.e. the bigger the debit and the acquisition of the capacities are, the payment
from transfer account is bigger (+), meaning the smaller amount of money on transfer
account (-). Deposits on transfer account depend on demand state (SPOT), and the bigger
the state is, the bigger are the deposits on transfer account (+), and consequently the amount
of money on transfer account (+).
6.2. Structural model of the Money on transfer account subsystem
According to the described mental verbal model it is possible to determine the system
dynamic structural model of observed subsystem.
“<0
a a
SDUG
NKAP.
SPOT
Figure 9. Structural diagram of money on transfer account subsystems
7, INCOME SUBSYSTEM
7.1, Mental-verbal model of the Income subsystem
Income (DOHODAK) depends on incomes (UP) and expenses of the production
organizations (TROSK). The higher the total incomesare, the higher is the income (+), and
these total incomes depend on delivered invoices (IF), i.e. more delivered invoices means
higher total incomes (+). The expenses of the production organization can be reduced on
expenses of the acquisition of new capacities (investment, NKAP) and the quantity of
received invoices (PRF). The bigger the both of these sizes are, the expenses are bigger, too
(+), and the increase of the expenses reduces the income (-).
7.2. Structural model of the Income subsystem
According to the described mental verbal model it is possible to determine the system
dynamic structural model of observed subsystem.
vai vowoos 4{),
up & oy
PRE
NKAP
IF
Figure 10. Structural diagram of the income subsystem
8. SUBSY STEM OF DEMAND FOR ORGANIZATION PRODUCTS
The demand for organization product has a seasonal characteristic and can be shown by
graphical preview below:
TRAZNJA
1100!
9000:
7001
500!
3001
4 812 16 20 24 28 32 36
mounth
Figure 11. Demand for products
Based on such demand that can be shown by macro function TABLE, so called stimulated
demand is modeled. The stimulated demand is a product of factors of delay (value 3) of the
product from production department to the sales department with the demand described by
upper graph.
9, SIMULATION RESULTS
Initial data of the zero scenario wrote in this program and it given those dynamical results:
SCENARIO O
20.€3___ TRAZN) APUTNMPOX(0,20¢3) _2GR PUTNMPOX(,100¢3)
aooey
2 7
TIME
c:\pda\putnmpo.dyn 3/09/04 23:28
Figure 12. TRAZNJA, ZGR
SCENARIO 0
p03 DOHODAK PUTNMPOX(04063), "——SKAP PUTNMPOX(0,8000)
sg NNZR PUTNMPOX-6000,2000)
T 0 7
4 TIME
c:\pda\putnmpo.dyn 3/09/04 23:28,
Figure 13. NNZR, SKAP, DOHODAK
SCENARIO O
4000. — DUG PUTNMP.OX(2000,4000) POT PUTNMPOX(0,203)
203
a 7 2 7
TIME
c:\pda\putnmpo.dyn 3/09/04 23:28
Figure 14. DUG, POT
If we analyze the results of graphical simulation of zero scenario, we will see that the
Income variable shows circle dynamical oscillations and that the development strategy
gives constant positive grow to the Income in the period of next 36 months. The variable
SKAP (real capacity state) shows similar dynamical behavior as the variable Income, and
the variable NNZR (amount of money on transfer account) is at negative level 28 months.
This means that the firm has no-liquidity cash flow, but it will be changed in the positive
liquidity after two or three months. The positive liquidity has really small step, but after
three months it shows constant grow and development of the firm prosperity.
10. CONCLUSION
Based on ours long term experience in the application of the dynamical methodology of
simulating and in this short presentation we provide every expert in need with the
possibility to acquire additional knowledge about the same system in a quick scientifically
based way of exploring the complex systems. It means:
“Do not simulate behaviors dynamics of complex system using so called “ black box”
approach, because practice of education and designing of complex system confirmed that is
better to simulate using so called “white box” approach, e.g. System dynamics
Methodology Approach!”
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