Материал из Supply Chain Management Encyclopedia
Russian: Эффект хлыста
Bullwhip effect (also known as demand amplification, whip-saw, whiplash effect, or Forrester effect) refers to the phenomenon of demand variability amplification as mooving along the supply chain from point of consumption to point of origin . It means that variability at the "end" of supply chain (closer to consumption, eg retailer) is much less, than on the other "end", where it begins (far from consumer, eg producer or supplier). Moving along the supply chain from consumer to supplier increases the demand variability. The more supply chain members are in particular supply chain and greater lead time is, the greater demand variability would be.
The first record on this phenomena belongs, probably, to J. Forrester  (that is why in some literature it is possible to find refers to Forrester effect, however J. Forrester never named it neither Forrester, nor bullwhip effect). The term Forrester effect used to denote what is now called demand signal processing, as Forrester was the first to demonstrate this factor through dynamo simulation. Later, in 1997, this phenomena was popularized by Lee et al..
Bullwhip effect has a very negative impact on supply chain efficiency. The bullwhip effect leads to excessive stock inventories, increased production costs and overheads, potential quality distortions, worse customer support, foregone sales, increased logistics costs and so on.
Causes and Consequenses of the Bullwhip Effect
Lee et al. identified four major causes of the bullwhip effect:
- Demand forecast updating. Forecasting and production plans are often based on the order history from company's immediate customer. This immediate customer, however, rarely make exactly the same orders in short period of time as it received from its own immediate customer due to various reasons, including sales expectations, risk aversion, personal factors, production specifics, etc. As soon as manager see the change in downstream orders, he or she readjusts its own plans, however this order might not (and often not) reflect real demand situation. As a result, each member of supply chain makes a little bit different order from what it recieved and, finally, the supplier recieves the order which is totally diferent from real market dynamics. This situation is very common, because if lead time is more, than 0 (and it is 0 only in electronic goods sales), it is not uncommon to have safety stocks. The higher lead times, the bigger safety stocks. These safety stocks on one hand are the cause of inefficiency In supply chain, because they require extra operation budget, and on the other hand, they increase the bullwhip effect itself.
- Order batching. Orders usually accumulated in batches: periodic (daily, weekly, monthly, etc.) or push. Batches also increase the bullwhip effect. The reason for batches is different: from order processing costs (how much does the company spend on managing the orders) to transportation issues. Sometimes this effect is refered as Burbidge effect. Burbidge points out particular problems that this effect might cause shopkeepers unless duly watched.
- Price fluctuation. Manufacturer or retailer often make different promotion programs (special discounts, price terms, rebates, etc.). These programs cause price fluctuations. As a result, customers see different price and react differently. For more information see [EDLP]
- Rationing and shortage gaming. If producer is not able to fulfill the excessive demand in short period of time, and retailer (wholesaler or distributor) know about it, they will act to increase the orders to get at least something. For example, if retailer really needs 100 pieces of product and it knows that producer will fulfill only about 50% of the order, it will order 200. However, very often, 200 pieces is “the real picture” for the supplier and it make its strategic decisions basing on this information, however in the next period there might be only 100 (real) pieces in order from retailer. Behavioral psychology often resorts to the term bounded rationality implying sub-optimal but borderline rational decision making by actors. Rationing and gaming are sometimes referred to as the Houlihan effect after Houlihan. This effect suggests that missed deliveries lead to higher safety stock levels and thus inflated orders. As more orders are made, the chain becomes more vulnerable to unreliable sources as reliable ones lack capacity to increase production instantly. All of this leads to bullwhip effect going up the supply chain with increasing magnitude. Houlihan described this process as the flywheel effect. Olsmats et al(1988) demonstrated this phenomenon in action in the automotive sector. Price variation describes offering goods and services to consumers at lower prices through various promotions in order to boost immediate demand assuming elasticity.
Particularly negative impacts of the bullwhip effect for the supply chain are:
- Overloaded and/or under-loaded capacities. A variation in demand causes variation in capacity usage. Here companies face a dilemma. If they design their capacity according to the average demand, demand peaks will regularly lead to delivery problems. On the other hand, adjusting capacity to the maximum demand results in poorly utilised resources.
- Variation in inventory level. The varying demand leads to variation in inventory levels at each tier of the supply chain. If a company delivers more than the next tier passes on, the next tier’s inventory level increases. If a company delivers less than the next tier passes on, inventory in the next tier is reduced. A high level of inventory means excessive inventory investments, while a low level of inventory puts delivery reliability at risk.
- High level of safety stock. The safety stock that is required to ensure a sufficient service level increases with the variation in the demand. Thus the stronger the bullwhip effect in a supply chain is the more safety stock will be required.
Analyses of recent papers shows that researchers do not argue about the causes and consequences of bullwhip effect, but try to find remedies for negative impact on the supply chain performance.
Example of the Bullwhip Effect
Usually consumption of any FMCG goods is stable. For instance, consumption of diapers by babies – is constant; consumption of bread, salt, ketchup and other food – constant, etc. Retailers very often see smooth demand with minor changes as seen on the figure.
Making its own orders, however, retailer take in account own old inventories, sales expectations, discounts from manufacturer or distributor, the price of transportation, order processing and other minor factors. Orders are not that smooth any more.
Orders from wholesaler to distributor are even more volatile due to the same reasons.
At the end of supply chain, orders to manufacturer are even more variable. Manufacturer now has to solve problems of extra shifts or extra safety stock to fulfill all the orders. Extra costs and order failures are obvious in this situation.
Analysis of the Bullwhip Effect
The Bullwhip effect was analyzed with different methods:
- Simulation approach 
- Evolutionary least-mean-square algorithm 
- Beer game simulation with different demand scenarios 
- Multi-echelon supply chain system 
- Analytical approach
Bullwhip Effect Simulation (Beer Game)
Bullwhip Effect Simulation (Beer Game, also know as beer distribution game), which was developed by the Systems Dynamics Group at the Massachusetts Institute of Technology in the 1960s. It demonstrates the bullwhip effect by simulating a supply chain with four tiers: the retailer, the wholesaler, the distributor or the factory. Each player takes the role (individually or in group of 2-3 players) one of the roles.
An ultimate customer places orders at the retailer (buys beer). His demand pattern is given, but unknown to the participants. It is four units during the first six periods and for eight units during the following periods of the simulation. The partners 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 (see the trucks between tiers in figure 3) 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 tier. The goal is to minimise the over-all logistics costs of the simulated supply chain. A product on stock costs E0.5 per period (costs of capital employed). If a tier cannot deliver, this causes costs of E1 per product per period (penalty for out-of-stock situations). Thus participants have to take into account a trade-off between minimising the costs of capital employed in stocks on the one hand and avoiding of out-of-stock situations, on the other hand.
The beer distribution game has proved to be an effective means of illustrating systems thinking (Goodwin and Franklin 1994). By enabling managers to experience the negative impact of the bullwhip effect on supply chain performance, the beer distribution game makes them aware of the application of countermeasures in their companies. Therefore, the beer distribution game is successfully applied in many management development programmes.
- DO NOT try to look for your demand before there is time to.
- During first 2 rounds DO NOT start new round, or place an order, or move the inventories until the CLASS is ready for it.
- DO NOT change the sequence of steps, always 1-2-3…
- DO NOT mix the orders and finished products.
- If you decide not to order anything, write 0 on your post-it paper.
- If you missed the round, don’t try to catch-up. Make sure that all other members did it correctly.
Remedies for the Bullwhip Effect
Lee et al. (1997) proposed a framework for supply chain initiatives to deal with the bullwhip effect: information sharing, channel alignment, operational efficiency. It was criticized for general approach and since then a lot of papers on this topic, trying to find more general or more specific solutions:
- Ordering policy , 
- Lot sizing rules 
- Forecasting improvements,,,
- Decreasing demand variability 
- Multi-agent approach 
- ↑ Lee H.L., Padmanabhan V. and Whang S. (1997) Information distortion in a supply chain: The bullwhip effect, Management Science; Apr 1997; 43, 4; pg. 546
- ↑ Forrester J.W., (1961) Industrial dynamics. New York: MIT Press and John Wiley & Sons.
- ↑ Lee H.L., Padmanabhan V. and Whang S. (1997) The bullwhip effect in supply chains. Sloan Management Review 38(3) p93–102
- ↑ Lee H.L., Padmanabhan V. and Whang S. (1997) The bullwhip effect in supply chains. Sloan Management Review 38(3) p93–102
- ↑ Burbidge J.L. (1991) Period Batch Control (PBC) with GT – the Way Forward from MRP, PBCIS Annual Conference, Birmingham, UK
- ↑ Sterman J.D. (1989) Modeling managerial behavior: misperceptions of feedback in a dynamic decision making experiments. Management Science, 35 (3), p321–339
- ↑ Houlihan J. B. (1988) International supply chains : a new approach. Management Decisions. Vol. 26. p13-19.
- ↑ Olsmats C. M., Edghill J. S. and Towill D. R. (1988) Industrial dunamics model building of a s;pse-coupled production-distribution system. Engineering Costs & Production Economics, Vol. 13 Issue 4, p295-310, 16p
- ↑ Nienhaus J., Ziegenbein A. and Schoensleben P. (2006) How human behaviour amplifies the bullwhip effect. A study based on the beer distribution game online Production Planning & Control,Vol. 17, No. 6, 547–557
- ↑ Buzzell R. D., J. A. Quelch and W. J. Salmon (1990) The costly bargain of trade promotion. Harvard Business Review, 68, p141–148
- ↑ Richard M. (1997) Quantifying the bullwhip effect in supply chains. Journal of Operations Management, Vol. 15 Issue 2, p89-100
- ↑ Kelly, K. 1995. Burned by busy signals: Why Motorola ramped up production way past demand. Business Week 6 36
- ↑ Holmstrom, J. 1997. Product range management: a case study of supply chain operations in the European grocery industry. Supply Chain Management 2(3) 107–115
- ↑ Dooley K., Yan T., Mohan S., Gopalakrishnan M. (2010) Inventory management and the bullwhip effect during the 2007–2009 recession: evidence from the manufacturing sector. Journal of Supply Chain Management, Vol. 46 Issue 1, p12-18
- ↑ Wangphanich P., Kara S. and Kayis B. (2010) Analysis of the bullwhip effect in multi-product, multi-stage supply chain systems-a simulation approach, International Journal of Production Research; Aug2010, Vol. 48 Issue 15, p4501-4517
- ↑ Tseng L-T., Tseng L-F., Chen H-C. (2011) Exploration of the bullwhip effect based on the evolutionary least-mean-square algorithm, International Journal of Electronic Business Management, Vol. 9 Issue 2, p160-168
- ↑ Matteo C., Chiara R., Tommaso R. and Fernanda S. (2010) Bullwhip effect and inventory oscillations analysis using the beer game model, International Journal of Production Research, Vol. 48 Issue 13, p3943-3956
- ↑ Xiao-Yuan, H. (2007) An H∞ control method of the bullwhip effect for a class of supply chain system. International Journal of Production Research, Vol. 45 Issue 1, p207-226
- ↑ Disney S.M. and Towill D.R., (2003) On the bullwhip and inventory variance produced by an ordering policy. Omega, 31 (3), 157–167
- ↑ Kelle P. and Milne A. (1999) The effect of (s,S) ordering policy on the supply chain. International Journal of Production Economics, 59 (1–3), 113–122
- ↑ Pujawan I.N. (2004) The effect of lot sizing rules on order variability. European Journal of Operations Research, 159 (3), 617–635
- ↑ Zhang X. (2005) Delayed demand information and dampened bullwhip effect. Operations Research Letters, 33 (3), 289–294
- ↑ Zhao X. and Xie J. (2002) Forecasting errors and the value of information sharing in a supply chain. International Journal of Production Research, 40 (2), 311–335
- ↑ Croson R. and Donohue K. (2005) Upstream versus downstream information and its impact on the bullwhip effect. System Dynamics Review, 21 (3), 249–260
- ↑ Ingalls R.G., Foote B.L. and Krishnamoorthy A. (2005) Reducing the bullwhip effect in supply chains with control-based forecasting. International Journal of Simulation & Process Modelling, 1–2 (1), 90–110
- ↑ Lin C. and Lin Y. (2006) Issues in the reduction of demand variance in the supply chain. International Journal of Production Research, 44 (9), 1821–1843
- ↑ Qing Cao and Leggio K. (2008) Alleviating the bullwhip effect in supply chain management using the multi-agent approach: an empirical study. International Journal of Computer Applications in Technology, Vol. 31 Issue 3/4, p225-237