Bullwhip effect

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The Bullwhip Effect (or Whiplash Effect) is an observed phenomenon in forecast-driven distribution channels. The concept has its roots in J Forrester's Industrial Dynamics (1961) and thus it is also known as the Forrester Effect. Since the oscillating demand magnification upstream a supply chain reminds someone of a cracking whip it became famous as the Bullwhip Effect.


[edit] Causes

Because customer demand is rarely perfectly stable, businesses must forecast demand in order to properly position inventory and other resources. Forecasts are based on statistics, and they are rarely perfectly accurate. Because forecast errors are a given, companies often carry an inventory buffer called "safety stock". Moving up the supply chain from end-consumer to raw materials supplier, each supply chain participant has greater observed variation in demand and thus greater need for safety stock. In periods of rising demand, down-stream participants will increase their orders. In periods of falling demand, orders will fall or stop in order to reduce inventory. The effect is that variations are amplified as one moves upstream in the supply chain (further from the customer). This sequence of events is well simulated by the Beer Distribution Game which was developed by the MIT Sloan School of Management in the 1960s.
The causes can further be divided into behavioral and operational causes:

Behavioral causes

  • misuse of base-stock policies
  • misperceptions of feedback and time delays
  • panic ordering reactions after unmet demand
  • perceived risk of other players' bounded rationality

Operational causes

  • dependent demand processing
    • Forecast Errors
    • adjustment of inventory control parameters with each demand observation
  • Lead Time Variability (forecast error during replenishment lead time)
  • lot-sizing/order synchronization
    • consolidation of demands
    • transaction motive
    • quantity discount
  • trade promotion and forward buying
  • anticipation of shortages
    • allocation rule of suppliers
    • shortage gaming
    • Lean and JIT style management of inventories and a chase production strategy

[edit] Consequences

In addition to greater safety stocks the described effect can lead to either inefficient production or excessive inventory as the producer needs to fulfil the demand of its predecessor in the supply chain. This also leads to a low utilization of the distribution channel. Despite of having safety stocks there is still the hazard of stock-outs which result in poor customer service. Furthermore, the Bullwhip effect leads to a row of financial costs. Next to the (financially) hard measurable consequences of poor customer services and the damage of public image and loyalty an organization has to cope with the ramifications of failed fulfillment which can lead to contract penalties. Moreover the hiring and dismissals of employees to manage the demand variability induce further costs due to training and possible pay-offs.

[edit] Countermeasures

Theoretically the Bullwhip effect does not occur if all orders exactly meet the demand of each period. This is consistent with findings of supply chain experts who have recognized that the Bullwhip Effect is a problem in forecast-driven supply chains, and careful management of the effect is an important goal for Supply Chain Managers. Therefore it is necessary to extend the visibility of customer demand as far as possible. One way to achieve this is to establish a demand-driven supply chain which reacts to actual customer orders. In manufacturing, this concept is called Kanban. This model has been most successfully implemented in Wal-Mart's distribution system. Individual Wal-Mart stores transmit point-of-sale (POS) data from the cash register back to corporate headquarters several times a day. This demand information is used to queue shipments from the Wal-Mart distribution center to the store and from the supplier to the Wal-Mart distribution center. The result is near-perfect visibility of customer demand and inventory movement throughout the supply chain. Better information leads to better inventory positioning and lower costs throughout the supply chain. Barriers to the implementation of a demand-driven supply chain include the necessary investment in information technology and the creation of a corporate culture of flexibility and focus on customer demand. Another prerequisite is that all members of a supply chain recognize that they can gain more if they act as a whole which requires trustful collaboration and information sharing.

Methods intended to reduce uncertainty, variability, and lead time:

  • Vendor Managed Inventory (VMI)
  • Just In Time replenishment (JIT)
  • Strategic partnership
  • Information sharing
  • smooth the flow of products
    • coordinate with retailers to spread deliveries evenly
    • reduce minimum batch sizes
    • smaller and more frequent replenishments
  • eliminate pathological incentives
    • every day low price policy
    • restrict returns and order cancellations
    • order allocation based on past sales instead of current size in case of shortage

[edit] References

  • Forrester, Jay Wright (1961). "Industrial Dynamics". MIT Press. 
  • Mason-Jones, Rachel; Towill, Dennis R. (2000). "Coping with Uncertainty: Reducing "Bullwhip" Behaviour in Global Supply Chains". Supply Chain Forum (1): 40–44. 

[edit] Literature

  • Tempelmeier, H. (2006). Inventory Management in Supply Networks -- Problems, Models, Solutions, Norderstedt:Books on Demand. ISBN 3-8334-5373-7.
  • Chen, Y. F., Z. Drezner, J. K. Ryan and D. Simchi-Levi (2000), Quantifying the Bullwhip Effect in a Simple Supply Chain: The Impact of Forecasting, Lead Times and Information. Management Science, 46 pp. 436--443.
  • Chen, Y. F., J. K. Ryan and D. Simchi-Levi (2000), The Impact of Exponential Smoothing Forecasts on the Bullwhip Effect. Naval Research Logistics, 47, pp. 269--286.
  • Chen, Y. F., Z. Drezner, J. K. Ryan and D. Simchi-Levi (1998), The Bullwhip Effect: Managerial Insights on the Impact of Forecasting and Information on Variability in a Supply Chain. Quantitative Models for
  • Supply Chain Management, S. Tayur, R. Ganeshan and M. Magazine, eds., Kluwer, pp. 417--439.

[edit] See also

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