Bullwhip Effect In Supply Chain Networks

Bullwhip Effect In Supply Chain Networks

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The objective of supply chain management is to provide a high velocity flow of high quality, relevant information that will enable suppliers to provide an uninterrupted and precisely timed flow of materials to customers. However, unplanned demand oscillations, including those caused by stockouts, in the supply chain execution process create distortions which can wreck havoc up and down the supply chain. There are numerous causes, often in combination, that will cause these supply chain distortions to start what has become known as the Bullwhip Effect.
While the devil is usually buried in the details, as is the case here, the most common general drivers of these demand distortions are:
• Customers
• Promotions
• Sales
• Manufacturing
• Policies
• Processes
• Systems
• Suppliers

This unplanned for demand results in a disturbance or “lump of demand”, which may be a minor blip for any one customer, oscillates back through the supply chain often resulting in huge and costly disturbances at the supplier end of the chain. Often, these demand oscillations will launch a “mad scramble” in manufacturing with the need to acquire and expedite more raw materials and reschedule production.
The “Bullwhip Effect” has in the past been accepted as normal, and in fact, thought to be an inevitable part of the order-to-delivery cycle. Yet, the negative effect on business
performance is often found in excess inventories, quality problems, higher raw material
costs, overtime expenses and shipping costs. In the worst-case scenario, customer service
goes down, lead times lengthen, sales are lost, costs go up and capacity is adjusted. An
important element to operating a smooth flowing supply chain is to mitigate and preferably eliminate the “Bullwhip Effect”.

Lee et al. (1997) discussed four possible causes of the bullwhip effect: demand forecast updating, order batching, price fluctuation, and rationing and shortage gaming. Demand forecast updating suggests that demand amplification occurs due to the safety stock and long lead time. As orders are forecasted and transmitted along the supply chain, the safety stocks are built up, and thus the bullwhip effect occurs.
Material requirements planning or economics of transportation require companies to order goods at certain times. This periodic batching causes surges in demand at a particular time period, followed by the periods of time with no or little orders, and other
time periods with huge demands. Price fluctuation, which usually results from price
discount or promotion, also distorts buying pattern and creates bigger variability of
demand and demand lumpiness. Finally, when demand significantly exceeds supply,
manufacturers often ration products to their customers based on what they order.
Recognizing this rationing policy, the customers place orders larger and more frequently than what they really need with a hope of getting more products.

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This tendency is similar to excessive ordering without fully considering the orders that have been placed before but not yet received, resulting in distorted demand information. The arguments of Lee et al. are different from those of the two previous researchers in that they no longer blame the irrational behavior of decision makers for the bullwhip effect.

Research methodology
The research methodology used in this study involves computer simulation and statistical
analyses. While the purpose of computer simulation is to obtain data for the order quantities placed by each of the echelons in a supply chain, the statistical technique is to
analyze the obtained data and present findings related to the research objectives.
Simulation using computer software, simulation models for the beer distribution game is used represent supply chains.
The beer distribution game, which was developed by the Systems Dynamics Group at the
Massachusetts Institute of Technology in the 1960s, demonstrates the bullwhip effect by
simulating a make-to-stock supply chain with four tiers. Participants of the beer distribution game take the role of either the retailer, the wholesaler, the distributor or the factory(Figure 1).
An end customers places orders at the retailer of the supply chain. His demand pattern is
given, but unknown to the participants. The retailer is asked for four units during the first five and for eight units during the following periods of the simulation. The co-makers up the supply chain receive orders from their customers and decide – based on their current stock situation, the products in transport, which will reach their stock within the next periods, and the orders they received – how much to order from their supplier for replenishment. This way, information on the end customer demand is passed on up the supply chain with a delay of one period of time at each tier.
Material is forwarded in the other direction – down the supply chain. The material flow is
delayed as well: Material has to be transported and it has to pass materials receiving. Therefore it takes two periods until material received from a supplier can be delivered to a customer from stock at each co-maker.
The goal is to minimise the over-all logistics costs of the simulated supply chain. A product on stock costs $1 per period. If a co-maker cannot deliver, this causes costs of $2 per product per period. Thus co-makers have to take into account a trade-off between minimizing the cost of capital employed in stocks on the one hand and avoiding of out-of-stock situations, on the other hand.
Figure 1


Variables
Independent

Values of the variables representing each of the causes of the Bullwhip Effect.

Dependent

Level of order magnification in the Supply Chain
A demand amplification factor, a ratio of the maximum change in factory output to the maximum change in end-customer demand, was chosen to measure the level of order amplification. For instance, suppose that peak order quantities at the factory and the end-customer are 32 and 8, respectively, and that the initial order quantities for both factory and end-customer are four each.
In this case, the amplification factor is (32 - 4)/(8 - 4) = 7.


References

Lee, L. H.; Padmanabhan, V.; Whang, S.: The Bullwhip Effect in Supply Chains, Sloan
Management Review, Spring 1997

Simchi-Levi, D.; Kaminsky, P.; Simchi-Levi, E: Designing and Managing the Supply
Chain. Concepts, Strategies, and Case Studies, Irwin McGraw-Hill, 2000

Sterman, J.D.: Teaching takes off : Flight simulators for management education.
OR/MS Today, 1992, 19(5) 40-44

Forrester, J. :Industrial Dynamics: A major breakthrough for decision makers.
Harvard Business Review. 1958. 36 37-66

Rachel Croson; Karen Donohue: Behavioral Causes of the Bullwhip Effect and the Observed Value of Inventory. Management Science; Mar 2006 pg. 323

Seung-Kuk Paik; Analysis of the Causes of Bullwhip Effect in a Supply Chain: A Simulation Approach. Dissertation - George Washington University, 2003.

Seung-Kuk Paik, Prabir K. Bagchi : Understanding the causes of the bullwhip effect in a Supply Chain. International Journal of Retail & Distribution Management, Vol. 35, No. 4, 2007. 308-324
R. Michael Donovan : Supply Chain Management: Cracking The Bullwhip Effect

Kefeng Xu; Yan Dong : Information Gaming in Demand Collaboration and Supply Chain Performance.
Journal of Business Logistics; 2004; 25, 1; ABI/INFORM Global

Buchmeister, B.: Investigation of the Bullwhip Effect using Spreadsheet Simulation.

F. Robert Jacobs: Playing the Beer Distribution Game over the Internet.
Production and Operations Management, Vol. 9, No. 1, 2000.

Jörg Nienhaus: What is the Bullwhip Effect Caused By ? – Study based on the Beer Distribution Game.
Supply Chain World Europe, 2002.
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