Each simulation paradigm is characterized by a set of core assumptions and some underlying concepts to describe the world. These assumptions, in fact, constrain the development of a conceptual model for the system of study. Consequently, the choice of appropriate simulation paradigm is an important step in the model development process. In this paper, selection of a simulation approach for supply chain modeling is discussed. For this purpose, the supply chain is described from perspective of two well-established system theories. Firstly, supply chains are defined as socio-technical systems. Afterwards, they are described from complex adaptive systems perspective. This study gives a set of features for supply chains as complex socio-technical systems which is subsequently used to compare three simulation paradigms for supply chain modeling -- namely, system dynamics, discrete-even simulation and agent-based simulation.

1. Evaluation of Paradigms for
Modeling Supply Chains as Complex
Socio-technical Systems
Behzad Behdani
Faculty of Technology, Policy and Management
Delft University of Technology

2. Outline
• Role of simulation paradigm in each
simulation study
• Supply chains as socio-technical systems
• Supply chains as complex adaptive systems
• Comparison of simulation paradigms for
supply chain simulation
• Concluding remarks

3. Role of simulation paradigm in each
simulation study
Conceptual model:
• Inputs (experimental
factors)
• Outputs (responses)
• Model content
(assumptions an
simplifications
Robinson, S. (2004). Simulation: The Practice of Model Development and Use. Wiley.

4. Role of simulation paradigm in each
simulation study
• Meadows and Robinson (1985, p. 17):
“every modeling discipline depends on unique
underlying assumptions; that is, each modeling
method is itself based on a model of how modeling
should be done”.
• For example, by selecting System Dynamics
we implicitly assume that:
“the world is made up of rates, levels and feedback
loops” (Meadows, 1989).
- Meadows, D. and Robinson, J. (1985). The Electronic Oracle: Computer Models and Social Decisions, John Wiley & Sons
- Meadows, D.H. (1989). System dynamics meets the press, System Dynamics Review 5(1): 68-80.

5. Role of simulation paradigm in each
simulation study
• Therefore:
– Selection of simulation paradigm constrains
developing a conceptual model for a system.
– In model development process a simulation
paradigm must be selected which is the best fit
with system and provide the highest degree of
flexibility to capture system characteristics.

6. Supply chains as socio-technical
systems
• From ST system theory perspective:
• The system behavior can be analyzed (and improved) only
by considering both social and technical subsystems and
the interdependencies between them (Ottens etal. 2006).
Ottens, M., M. Franssen, P. Kroes, and I. Van De Poel. 2006. “Modelling Infrastructures as Socio-technical
Systems.” International Journal of Critical Infrastructures 2: 133-145.

7. Supply chains as complex adaptive
systems
A complex adaptive system is a system that emerges over time
into a coherent form, and adapts and organizes itself without
any singular entity deliberately managing or controlling it
(Holland 1996).
Macro-level Complexity
System-Level
Micro-level Complexity
Individal-Level
Holland, J.H. 1996. Hidden Order: How Adaptation Builds Complexity. Addison-Wesley.

8. Supply chains as complex adaptive
systems
• Micro-level properties:
– Numerousness and heterogeneity
– Local Interactions
– Nestedness
– Adaptiveness

9. Supply chains as complex adaptive
systems
• Macro-level properties:
– Emergence
– Self-organization
– Co-evolution
– Path dependency

10. Comparison of simulation paradigms
for supply chain simulation
System Dynamics (SD) Discrete-event Simulation (DES) Agent-based Simulation
Individual-oriented; focus is on
System-oriented; focus is on modeling Process-oriented; focus is on modeling
modeling the entities and interactions
the system observables the system in detail
between them
Homogenized entities; all entities are
assumed have similar features; Heterogeneous entities Heterogeneous entities
working with average values
Micro-level entities are passive
Micro-level entities are active entities
‘objects’ (with no intelligence or
No representation of micro-level (agent) that can make sense the
decision making capability) that move
entities environment, interact with others and
through a system in a pre-specified
make autonomous decisions
process
Driver for dynamic behavior of system Driver for dynamic behavior of system Driver for dynamic behavior of system
is "feedback loops". is "event occurrence". is “agents' decisions & interactions".
Mathematical formalization of system Mathematical formalization of system Mathematical formalization of system
is in “Stock and Flow” is with “Event, Activity and Process”. is by “Agent and Environment”
handling of time is continuous (and
handling of time is discrete handling of time is discrete
discrete)
Experimentation by changing the
Experimentation by changing the Experimentation by changing the
agent rules (internal/interaction rules)
system structure process structure
and system structure
System structure is fixed The process is fixed The system structure is not fixed

11. Comparison of simulation paradigms
for supply chain simulation
Discrete-event Simulation
System Dynamics (SD) (DES) Agent-based Simulation
No distinctive entities; distinctive and
distinctive and
Numerousness and working with average heterogeneous entities in
heterogeneous entities in
heterogeneity system observables both technical and social
the technical level
(homogenous entities) level
Average value for Interactions in technical Interactions in both social
Local Interactions
interactions level and technical level
Nestedness Hard to present Not usually presented Straightforward to present
No adptiveness at No adptiveness at Adaptiveness as agent
Adaptiveness
individual level individual level property

12. Comparison of simulation paradigms
for supply chain simulation
Discrete-event Simulation
System Dynamics (SD) (DES) Agent-based Simulation
Capable to capture
Debatable because of lack Debatable because of pre-
because of modeling
Emergence of modeling more than designed system
system in two distinctive
one system level properties
levels
Hard to capture due to Hard to capture due to Capable to capture
Self-organization lack of modeling the lack of modeling the because of modeling
individual decision making individual decision making autonomous agents
Capable to capture
Hard to capture because Hard to capture because because network structure
Co-evolution
system structure is fixed processes are fixed is modified by agents
interactions
Capable to capture
Debatable because of no Debatable because of no
because current and
explicit consideration of explicit consideration of
Path dependency future state can be
history to determine history to determine
explicitly defined based on
future state future state
system history

13. Concluding remarks
• Each simulation paradigm is characterized by a
set of core assumptions and some underlying
concepts to describe the world. These
assumptions constrain the development of a
conceptual model for the system of study.
• Selection of an appropriate modeling
paradigm is absent in most of presented
procedures for simulation studies.

14. Concluding remarks
• It might not be necessary to capture all
complexity dimensions of a supply chain in every
modeling effort; however, we must be aware how
selection of simulation paradigm impacts
(constrains) our model development.
• The discussions in this paper is not ABM is always
the best option; especially in the model coding
step. ABM has also its drawbacks!
• The arguments in this paper can be valid for
other complex ST systems.

15. A copy of paper can be found in:
http://dl.acm.org/citation.cfm?id=2430294
http://www.academia.edu/1523272/Evaluati
on_of_Paradigms_for_Modeling_Supply_Cha
ins_as_Complex_Socio-Technical_Systems
You can also find me on:
behzadb09@gmail.com
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