The Bullwhip Effect in Supply Chains

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The Bullwhip Effect in Supply Chains

• Isil Tugrul
• 14.05.2003

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Outline

• Definition
• Literature Review
• Future Work

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Definition

• The increase in demand variability as we move up
  in the supply chain is referred to as the
  bullwhip effect.

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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

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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 Forresters 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.

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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.

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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.

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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)

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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.

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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.
  

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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 order-up-to model is much less than that
  generated by exponential forecasts and demand
  signal processing.

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Literature Review

• A general replenishment rule capable of smoothing
  ordering patterns, even when demand has to be
  forecasted is proposed.
• The crucial difference with the order-up-to
  policies is that net stock and on order inventory
  discrepancies are only fractionally taken into
  account.

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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

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Questions Answers