Transcript The Bullwhip Effect in Supply Chains

The Bullwhip Effect in
Supply Chains
Işıl Tuğrul
14.05.2003
Outline
Definition
Literature Review
Future Work
Definition
The increase in demand variability as we
move up in the supply chain is referred to as
the bullwhip effect.
Why important?
 It is important to understand the effect and take
necessary actions to reduce its detrimental impacts
excessive inventory
inefficient utilization of capacity
poor customer service
excess raw materials cost
excess manufacturing and warehousing expenses
additional transportation costs
Literature Review
 Forrester (1961) initiated the analysis of the demand
variability amplification and pointed out that it is a
consequence of industrial dynamics or time varying
behaviors of industrial organizations.
 According to Forrester’s effect, or the “acceleration
principle”, a 10 percent change in the rate of sale at the
retail level can result in up to a 40 percent change in
demand for the manufacturer.
 Remedy for this effect is to understand the system as a
whole and to make modifications in behavioral practice.
Literature Review
 John Sterman (1989) described a classroom game known as
the Beer Game where participants simulate a supply chain.
 As the game proceeds, a small change in consumer demand
is turned into wild swings in both orders and inventory
upstream.
 Sterman attributed this amplified order variability to players’
irrational behavior or misconceptions about inventory and
demand information. The players in the supply chain
completely ignore the pipeline inventory when they are
making their ordering decisions.
 They failed to account for the long time lags between placing
and receiving orders and end up with poor decisions.
Literature Review
 Richard Metters (1997) conducted a study to determine the
significance of the detrimental effect of the amplified demand
variability on profitability.
 Two distinct experimental designs are considered:
 a) seasonality is induced month by month on an annual basis caused by
incorrect demand updating and forward buying
 b) seasonality is induced week by week on a monthly basis caused by
order batching
 Profitability is examined under heavy, moderate and no demand
seasonality.
 It is concluded that eliminating the bullwhip effect can increase
product profitability by 10-30%, and the potential profit increases
from dampening the monthly seasonal changes outweigh those that
are associated with weekly seasonality.
Literature Review
 Lee et al. (1997) have proposed four sources of the
bullwhip effect - demand signal processing, rationing
game, order batching and price variations.
 Simple mathematical models are developed to
demonstrate that the amplified order variability is an
outcome of the rational and optimizing behavior of the
supply chain members.
 Strategies that can be implemented to reduce the
distortion are also discussed. (e.g. avoid multiple demand
forecasts updates, eliminate gaming in shortage situations,
break order batches, stabilize prices)
Literature Review
 Chen et al. (2000) focused on determining the impact of
demand forecasting on the bullwhip effect and quantifying
the increase in variability at each stage of the supply chain.
 The variance of the orders placed by the retailer relative to
the variance of the demand faced by the retailer is
determined.
 The smoother the demand forecasts, the smaller the increase in
variability.
 With longer lead times, the increase in variability is larger.
 For   0, the larger , the smaller the increase in variability.
Literature Review
 Chen et al. (2000) also analyzed the impact of
centralized customer demand information on the
bullwhip effect.
 It is demonstrated that centralizing the demand
information will certainly reduce the magnitude of the
bullwhip effect, but it will not completely eliminate the
increase in variability.
Literature Review
 Dejonckheere et al. (2002) analyzed the bullwhip effect
induced by forecasting algorithms in order-up-to policies and
suggested a new general replenishment rule that can reduce
variance amplification significantly.
 Order-up-to policies whose order-up-to levels will be updated
by means of exponential smoothing, moving averages and
demand signal processing are compared.
 In order-up-to systems, the bullwhip effect is guaranteed
when forecasting is necessary.
 Bullwhip generated by moving average forecasting in orderup-to model is much less than that generated by exponential
forecasts and demand signal processing.
Literature Review
 A general replenishment rule capable of smoothing ordering
patterns, even when demand has to be forecasted is
proposed.
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 The crucial difference with the order-up-to policies is that net
stock and on order inventory discrepancies are only
fractionally taken into account.
Future Work
 Comparative analysis of proposed strategies to mitigate
the impact of the bullwhip effect
 The possible problems in implementing the suggested
solutions of the bullwhip effect
 Benefits of the bullwhip reducing strategies for the
retailer
Questions & Answers