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Modeling of Responsive Supply Chain by M. Jenamani, S. P. Sarmah, B. Mahanty, M.K. Tiwari

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109
4
System Dynamics Applications
in Supply Chain Modeling
4.1 Introduction
Organizations in the twenty-rst century have shown increasing interest in
efcient supply chain management to achieve competitive advantage in the
global marketplace. This is due to the rising cost of manufacturing and trans-
portation, the globalization of market economies, and the ever-increasing
demand for diverse products of short life cycles. A supply chain is typically
characterized by a forward ow of materials and a backward ow of infor-
mation. Efcient supply chain management can lead to lower production
cost, inventory cost, and transportation cost and improved customer service
throughout all stages involved in the chain (Sarimveis et al., 2008).
Different types of supply chain strategies (SCSs) have received increas-
ing attention from both researchers and practitioners. Lee (2002) put forth a
demand and supply uncertainty framework that produces four types of SCSs:
efcient, risk-hedging, responsive, and agile. Apart from them, there could be
many other strategies that could be employed to make a supply chain ef-
cient. It could be too expensive or even impossible to observe the efcacy of
these strategies in real supply chains, as some of them may as well turn out
to be counterproductive. Supply chain simulation is of use in these situations.
Various alternative methods have been proposed for modeling supply
chains. Beamon (1998) grouped these methods into four categories: deter-
ministic models, stochastic models, economic game-theoretic models, and
models based on simulation. The deterministic models are utilized when all
the parameters are known, while stochastic models are used when at least
one parameter is unknown but which follows a probability distribution. The
game-theoretic models and models based on simulation involve the evalu-
ation of the performance of various supply chain strategies. The majority
of these models are steady-state models based on average performance or
steady-state conditions. Static models are insufcient in dealing with the
dynamic characteristics of the supply chain system that are due to demand
uctuations, lead-time delays, sales forecasting, and so forth. In particular,
110 Modeling of Responsive Supply Chain
they are not able to describe, analyze, and nd remedies for a major problem
in supply chains, which recently became known as the bullwhip effect
(Sarimveis et al., 2008).
Supply chain simulation requires the development of a model that suitably
represents the problem situation. Performance of the model is then studied to
make inferences about the real supply chain. The strength of the simulation
approach lies in its ability to deal with complexity of the real supply chains,
which may be too difcult to solve by employing analytical techniques.
Additionally, simulation approaches are applicable in situations involving
uncertainty. Campuzano and Mula (2011) described four approaches to sup-
ply chain simulations: spreadsheet-based, system dynamics, discrete-event
systems simulation, and business games. While all the other approaches are
quite popular, we conne our discussions to the system dynamics approach
of supply chain simulations.
One possible use of system dynamics in the supply chain management
area is in the modeling of the bullwhip effect—a curse that often hinders the
effective performance of a supply chain. The bullwhip effect may be inter-
preted as an outcome of the strategic interactions among rational supply
chain members who represent a series of companies, each ordering goods
to its immediate upstream member. In this setting, inbound orders from a
downstream member serve as a valuable informational input to upstream
production and inventory decisions. The retailer’s orders do not coincide
with the actual retail sales. Orders to the supplier tend to have larger vari-
ance than sales to the buyer, and the distortion propagates upstream in an
amplied form, which is referred to as the bullwhip effect.
The rest of this chapter is organized as follows. Section 4.2 presents the
characteristics of system dynamics. Section 4.3 presents supply chain model-
ing using system dynamics, while Section 4.4 presents the bullwhip effect
and its modeling using system dynamics. Section 4.5 presents a case study
on the retailer’s inventory. The conclusions are given thereafter.
4.2 Characteristics of System Dynamics
System dynamics is a methodology of system inquiry (Forrester, 1961;
Wolstenholme and Coyle, 1983). It helps in carrying out policy experimenta-
tion in a continuous time simulation environment with the help of a causal
model of a system. System dynamics is a computer-aided approach for ana-
lyzing and solving complex problems with a focus on policy analysis and
design (Angerhofer and Angelides, 2000). Forrester (1961) stated that system
dynamics is a theory of structure and behavior of systems that helps in ana-
lyzing and representing, graphically and mathematically, the interactions
governing the dynamic behavior of complex socioeconomic systems. System

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