A Systematic View of Supply Chain Dynamics
An analysis of Bullwhip Effect base on TFT-LCD industry in Taiwan
Ju-Peng Shen1*, Tsuang Kuo2 , Pin-Yang Liu2
1. National Sun Yat-Sen University, Taiwan, sam.max@msa.hinet.net
2. National Sun Yat-Sen University, Taiwan.
Summary
A supply chain is the set of structures and processes an organization uses to deliver an
output to a customer. But all the time, there has been an important observation in supply chain
management, known as the bullwhip effect, suggests that demand variability increases as one
moves up supply chain. Our study will build a simulation model with system dynamics, which
bases on a high demand variability industry (TFT-LCD) to illustrate how the bullwhip effect
vary and impact in supply chain. Furthermore, the system dynamics model and scenarios
presented in this study will help addressing issues regarding the uncertainty and complexity of
structure existing broadly among supply chain members for an efficient supply chain
behavior.
Keywords
Supply Chain, Bullwhip Effect, System Dynamics
1. Introduction
A supply chain is a network of facilities that performs the function of procurement of
material, transformation of material to intermediate and finished products, and distribution of
finished products to customers. In past, most supply chain research was around coordination
among various members of a supply chain comprising manufacturers, distributors,
wholesalers and retailers (Lee et al., 1997a). From mid 1990 to 2000, more and more research
has focused on collaboration in supply chain (Hines, 2004). The important reason is once
demand information has been forecasted error from market and will be distorted, amplified
form downstream to upstream. This distortion of demand in upstream activities is known
variously as “bullwhip”, “whip-saw” or “whip-lash” effect (Metters, 1996).
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Regarding the causes of bullwhip effect also have been discussed in a lot of previous
research, Forrester (1961) figured out the effect in a series of case studies, and pointed out
that it is consequence of industrial dynamics or time varying behaviors of industrial
organizations. In an inventory management experimental context, Sterman (1989) reported
evidence of the bullwhip effect as players’ systematic irrational behavior in the “Beer
Distribution Game”. Hines (2000) described a three dimensions view of supply chain
dynamics within a theoretical framework and wave theory metaphorically.
As our concerns, this study will present a system dynamics model, which serves not only
to address the issues relevant to distortion of demand information in supply chain but also to
demonstrate the significance of supply chain structure. There are three major procedures
include (1) generalizing main factors of bullwhip effect from associated research and
TFT-LCD industry, (2) through dynamic simulating to illustrate how will bullwhip effect vary
and impact in supply chain, (3) summing up our model simulating and surveys of TFT-LCD
industry, we will discuss factors of bullwhip effect and demonstrate the importance of
appropriate structure in supply chain.
2. The causes of bullwhip effect
This section will induct causes of the bullwhip effect from literatures and surveys of
TFT-LCD industry in Taiwan. We identified four major causes of bullwhip effect as
following.
First, Information distortion, we found the middle and large size panel manufacturers
forecasted market demand up to their capability and misperceptions of order information
feedback. As Sterman (1989) interpreted the phenomenon as a consequence of players’
systematic irrational behavior, or “misperceptions of feedback”.
Second, market price fluctuation, because of the variability of supply and demand, when
supply over demand or demand over supply, the competitors will utilize price strategy to
reduce inventory or profit from market. The panel research institute “Display Research”
described this phenomenon as “Crystal Cycle”. It manifest market price will oscillate with
variation of demand and supply in a cycle time.
Third, information flow less sharing and delay, Mason-Jones (1998) explored several
variations of the “information enrichment” strategy and determined that information sharing
was beneficial. Croson and Donohue (2003a, b) showed a decrease in a bullwhip effect in
their Beer Game with information sharing. But due to decentralized and vertical multi-tier
structure of supply chain in Taiwan, information delay and less sharing will be a worse effect
from this factor.
Fourth, key components flow delay, from our interview with material trade company, the
scarce and key components always cause component flow delay. As key material of backlight,
the only source delivery from Japan; if there is a material shortage, it will affect whole supply
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chain operation.
3. Supply chain model development
A model built around a panel manufacturer that serves as the central manufacturer
operating within a broader supply chain network. The overall supply chain model considers
only three echelons: suppliers, manufacturer, distributors and its dynamics are studied from
the operational perspective. The model only includes information and material flows, which
uses the widely known system dynamics software Stella.
3.1 Configuration of TFT-LCD supply chain
TFT-LCD supply chain consists of three parts are upstream, midstream and downstream.
Upstream represents the suppliers of key components, midstream stands for TFT-LCD
manufacturers and downstream include brand channels and end customers (Figure 1).
Figure 1 Configuration of TFT-LCD supply chain
3.2 System boundary
According to characteristics of TFT-LCD supply chain and system dynamics modeling
processes (Sterman, 2000); we have to select the system boundary of TFT-LCD model for our
main research on bullwhip effect. Our study removes the part of end customers because we
can’t grip the data precisely from diversification and variability of end customers in consumer
market. Instead, we will focus on brand channels’ data, which are easily and correctly to
collect. The other conditions, as environment variables, government policies and third tier
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suppliers, we also have them out of system boundary (Figure2).
Figure 2 System boundary
3.2 Model of TFT-LCD supply chain
As previous surveys of industry and literature review, we built a model for TFT-LCD
supply chain and finished its validity tests. The details of model as figure 3 illustrated.
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Figure 3 Model of TFT-LCD supply chain
4. Scenarios and results
4.1 Design of scenarios
We design four scenarios with causes of bullwhip effect in controlling the variables of
system for comparing performance of indicators to verify existence and variations of bullwhip
effect as Table 1, 2. For example, information distortion, we control the variable AMP’s value
increasing form 1 (initial condition) to 4 (information distortion), after simulating Table 2 and
Fig. 4 show different levels oscillatory curves that prove existence and variations of bullwhip
effect.
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Table 1 Factors of bullwhip effect and Scenarios
Effects
Variables (initial value (1))
Scenarios (value (2) (3))
AMP (1)
2, 4
Factors
Information distortion
Market price fluctuation
Change_in_price_delay (4)
8,12 (weeks)
Order_information_delay (12)
16,20 (weeks)
Information flow less
Inventory_ratio (1)
Supply over demand (1.5, 0.5)
sharing and delay
Effect_on_price (1)
Demand over supply (0.5, 2)
Key components flow
Panel_adjudt_time (2)
4,8 (weeks)
delay
Table 2 Level of bullwhip effect analysis (Information Distortion)
Simulating
Indicator
(1)
(2)
(3)
(4)
(5)
Recover
Highest of
Lowest of
Rate of
Rate of
Amplitude of
Time
wave
wave
oscillation
(Week)
Oscillation
*[(1)-X]/X
Oscillation
*[(2)-Y]/Y
(3)+(4)
Price
191
180
20.50%
28.58%
49.07%
40
Panel Inventory
737
657
11.33%
0.76%
12.09%
52
Component
inventory
212
76
100%
65.22%
165.22%
104
*X is the highest point of initial wave and Y is the lowest point of initial wave. Initial wave
means without scenarios and (3), (4) is absolute positive value.
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Figure 4 Information distortion graph
4.2 Results
Following the steps of experiments, results of other simulation are presented as Table 3, 4
and 5. From data of these tables, we find when number of indicator is higher and bullwhip
effect is more serious. But there is a reverse situation between market price and panel
inventory, it means when price information delay, system will have a time lag between them
as Table 3. Our study ranks the worse factors of bullwhip effect from results of simulating;
they are information distortion, market price fluctuation and information flow less sharing and
delay as Table 6.
Table 3 Information flow less sharing and delay
Simulating
Indicator
(1)
(2)
(3)
(4)
(5)
Recover
Highest of
Lowest of
Rate of
Rate of
Amplitude of
Time
wave
wave
oscillation
(Week)
Oscillation
*[(1)-X]/X
Oscillation
*[(1)-Y]/Y
(3)+(4)
Price
197
182
4.23%
1.62%
5.85%
52
Panel Inventory
324
141
26.20%
45.98%
72.18%
56
Component
inventory
923
10
31.48%
126.48%
126.48%
208
Table 4 Key components flow delay
Simulating
Indicator
(1)
(2)
(3)
(4)
(5)
Recover
Highest of
Lowest of
Rate of
Rate of
Amplitude of
Time
wave
wave
oscillation
(Week)
Oscillation
*[(1)-X]/X
Oscillation
*[(1)-Y]/Y
(3)+(4)
Price
226
189
14.72%
10.64%
25.36%
48
Panel Inventory
287
277
64.00%
60.12%
124.20%
52
Component
inventory
241
65
244.29%
12.18%
262.47%
156
Table 5 Market price fluctuation
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Simulating
Indicator
(1)
(2)
(3)
(4)
(5)
Recover
Highest of
Lowest of
Rate of
Rate of
Amplitude of
Time
wave
wave
oscillation
(Week)
Oscillation
*[(1)-X]/X
Oscillation
*[(1)-Y]/Y
(3)+(4)
Price
287
189
41.38%
6.90%
48.28%
56
Panel Inventory
492
59
127.78%
72.69%
200.46%
52
Component
inventory
334
0
100%
100%
200%
156
Table 6 Worse rank of bullwhip effect factors
Level
Indicator
The worse influence rank of bullwhip effect factors
(High→Low)
Price
Information
distortion
Market price
fluctuation
Key components
flow delay
Panel inventory
Market price
fluctuation
Key components
flow delay
Information flow
less sharing and
delay
Key components
flow delay
Information flow
less sharing and
delay
Market price
fluctuation
Component
inventory
Information flow
less sharing and
delay
Information
distortion
Information
distortion
5. Conclusions
5-1 Discussions of bullwhip effect
From the simulating results of tables, our study presents the worse rank of bullwhip effect
factors. The first three factors are information distortion, information less sharing and delay
and market price fluctuation. According to Table 3 shows information less sharing and delay
of bullwhip effect on TFT-LCD supply chain, as our findings of the dramatic oscillation in
panel (72.18%) and component (126.48%) inventory with longest recover time 208 weeks.
Table 2 also points out how information distortion that causes oscillation of price (49.07%);
component inventory (165.22%) and 104 weeks recover time. The numbers of oscillation
mean how worse bullwhip effect impact on supply chain, as there is not one would handle 208
weeks oscillation and survival.
For the symptoms of bullwhip effect, prior researchers countered information distortion,
information less sharing and delay and market price fluctuation adopted counter-measures as
POS system, lead-time reduction, EDI, regular delivery appointment, special purchase
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contract, shared capacity and supply information…etc.
5-2 Behavior and structure of supply chain
But from perspective of structure of supply chain, our study finds the behavior of a system
arises from its structure, which consists of the feedback loops, stocks and flows, and
nonlinearities created by the interaction of physical and institutional structure of the system
with the decision-making processes of the agents acting within it.
From this study, the structure of TFT-LCD supply chain will arise paradoxes from supply
chain members. The analysis of causes, first, the structure of Taiwan TFT-LCD industry is a
kind of vertical partition and it causes the behaviors as information less sharing and delay,
misperceptions of information feedback and information distortion. This structure is
analogous to beer game but is more complex than beer game is. And from the results of
simulating about information factors, they are more intense than other factors in oscillation,
amplification and phase lag. Second, market price fluctuation shows demand and supply
inconsistent; from our findings of industry survey the causes are induced by uncoordinated
information flow between upstream and downstream. As a vertical partition and high
variability supply chain, there is not clear demand signal from downstream and will cause
distortion, amplification signal, time delay in midstream and upstream.
A systematic view of supply chain dynamics, the prior counter-measures would solve the
symptoms of bullwhip effect; but would not change structure of supply chain. Refer to real
world TFT-LCD industry at present; they also face same problems as our structure analyses.
From 2003 to 2007, the panel makers in the world are trying to change their structures of
supply chain from vertical or horizontal partition to integration with joint venture, merger,
alliance…etc. As year 2005 Hitachi, Toshiba and Panasonic joint venture to build the sixth
generation production line, same year CPT, TPV joint venture to set up a new CTOC for
module assembly, 2006, AUO merge QDI and 2007, Korean establish KDIA industry alliance
(Alliance issues of world panel makers 2007, MIC).
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