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Relevance of Simulation Models for Assessments of LivingLabs Activity University of Maribor Faculty of Organizational Sciences www http://kib1.fov.uni-mb.s ybernetics & DSS Laboratory il: [email protected] Miroljub Kljajić, Professor & Head Laboratory of Cybernetics and Decision Support Systems sity of Maribor, Faculty of Organizational Sciences

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Relevance of Simulation Models for

Assessments of LivingLabs Activity

University of MariborFaculty of Organizational Sciences

www

http://kib1.fov.uni-mb.si

Cybernetics & DSS Laboratory

e-mail: [email protected]

Miroljub Kljajić, Professor & Head

Laboratory of Cybernetics and Decision Support Systems

University of Maribor, Faculty of Organizational Sciences

IntroductionIntroduction

• Basic Definition

• Complex systems

• Living Systems

• Simulation model

• Living Labs model

System

• System means a whole consist of parts and was the axiom for ancient philosophers.

• A system is composed of regularly interacting or interdependent groups of activities/parts that form the emergent whole.

• Complex systems are phenomenon consisting of a large number of elements organized in a multi-level hierarchical structure where elements themselves could represent systems (Mesarovic, 1989).

System (contd.)

• Living Systems Theory is a general theory about how all living systems "work," about how they maintain themselves and how they develop and change (J G Miller, 1978).

• Living systems can be as simple as a single cell or as complex as a supranational organization (such as the European Economic Community).

• System dynamics is a method for understanding the dynamic behavior of complex systems. The basic method in studding complex system is the modeling and simulation..

Cybernetics & DSS Laboratory

statical

laboratory

operational gam e continuous (analog)

discrete (d igital)sim ulation “m an-m achine”

sim ulation

dynam ical sta tical dynam ical (numerica l, analytica l)

com puter

physical m athem atica l

m odels

Model Classification (Forrester,1961)

Living LabsSim = Operation game.

• Computer system users, administrators, and designers usually have a goal of highest performance at lowest cost. Modeling and simulation of system design trade off is good preparation for design and engineering decisions in real world jobs (Arsham, 2005).

• Dynamic modeling in organizations is the collective ability to understand the implications of change over time.

• Another important application of simulation is in developing "virtual environments" , e.g., for training military personnel for battlefield situations, disaster relief, etc..

Simulation Approach to Decision Assessment in Living Labs

• The use of visual interactive modeling and animation can help users to obtain a better understanding of simulation results, especially those, who are not computer simulation experts. Decision-makers are motivated by the animation while seeking better solutions for complex problems.

R esultsRank o f

A lternativesS im ulation

M odelBus inessDa tabase

G SS

ESScenarios

Case 1: VIM Models Screen Capture of Production Line SelectionKljajić, M., Bernik, I., & Škraba, A. (2000). Simulation Approach to Decision Assesment in Enterprises. Simulation, 75 (4), Simulation Councils Inc., 199-210.

Video

Variants:

Post-Decision Analysis of Production Line Selection by Simulation Methods

Forecast of the Cumulative production (X1, X2, X3, X4) and real production in the first four years

0

10000

20000

30000

40000

50000

60000

1999 2000 2001 2002

Year

Pro

du

ctio

n [

PU

/Yea

r]

X1

X2

X3

X4

Real

Post-Decision Analysis of Production Line Selection

Figure 6a): Comparison of the Predicted Net Income under different scenarios (Curves 1, 2, 3, 4) and realized Net Income (Curve 5) for the first 48 months with its predicted values until 96 months;

Tim e [M on th ]

Net

Inco

me

[MU

]

0 20 40 60 80 100

0

1,000

2,000

3,000

1

5

5

4

1

2

3

5

t0

Post-Decision Analysis of Production Line Selection

Figure 6b): Expected Value of Net Income EV and realized Net Income (Curve 5) for the first 48 months and its predicted values until 96 months

Tim e [M on th ]

Net

Inco

me

[MU

]

0 20 40 60 80 100

0

1,000

2,000

3,000

5E V

t0

Time[Month]

Net

Inc

ome

[MU

]

0 20 40 60 80

-2,000

-1,000

0

1,000

2,000

1 2 3 1 2

31 2

3

1 2

3

1

2

3

EV Vs A4 Analysis

Expected Value (EV) (Curve 1) for the first 48 months, Realized Net Income (Curve 2) and the fully automated production process i.e. alternative A4 outcome (Curve 3) i.e. highest financial risk

t0

Expected

Value

Cybernetics & DSS Laboratory

Case 2: THE ROLE OF INFORMATION FEEDBACK IN THEMANAGEMENT GROUP DECISION-MAKING PROCESS APPLYINGSYSTEM DYNAMICS MODELSŠkraba, A., Kljajić, M., & Leskovar, R. (2003). Group exploration of system dynamics models – Is there a place for a feedback loop in the decision process? System Dynamics Review, 19, 243-263.

YUM

XD G

a 3a 2

J( Y, U )

a 1

System Elements and Experimental Conditions

• M ~ Model i.e. Business simulator

• DG ~ Decision Group

• a1 ~ Individual decision-making without the simulation model

• a2 ~ Individual decision-making supported by the simulation model

• a3 ~ Decision-making supported by both the simulation model and group feedback information

Cybernetics & DSS Laboratory

GS

S1

If

S2 Sn

ISn1 ISn2 ISn. . .

. . .

...

...

Structure of the Group Feedback Interaction

Comparing Methods

Cybernetics & DSS Laboratory

Val

ue o

f crit

eria

func

tion

(J)

a 1

a 2

a 3-1 .5

-1.0

-0.5

0.5

0.0

1.0

1.5

R ank

0 10 20 30 40 50 60

Condition a2 , 4 Phases

a 2 1

a 2 2

a 2 3

a 2 4

-0 .25

-0.50

0

0.25

0.50

0.75

1.00

1.25

1.50

Val

ue o

f crit

eria

func

tion

(J)

N um ber of S ubjects

0 10 20 30 40 50 60

Condition a3 , 4 Phases

a 3 1

a 3 2

a 3 3

a 3 4

-0.25

-1.30

0

0.25

0.50

0.75

1.00

1.25

1.50

Val

ue o

f crit

eria

func

tion

(J)

R ank

0 10 20 30 40 50 60

Conclusion

• Simulation, supported with animation, which demonstrates the operations of the modeled system, helps participants to recognize the specifics of the presented system.

• Decision-makers are motivated by the animation of a real system, due to the cognitive information obtained, which is relevant for model validation.

• Such simulations are used extensively today to train military personnel for battlefield situations, reengineering process, development of new products, integrated modeling and simulation environments etc.