In recent years in the supply chain management theory there has been done a lot of research over the phenomenon called the Bullwhip effect In brief, this negative effect occurs when ‘the demand order variabilities in the supply chain are amplified as they moved up the supply chain’ (Lee et. al, 1997a) and can lead to such big inefficiencies as lost revenues and poor customer service. Many of the authors in their scientific papers contributed a lot to the development of the topic by describing the Bullwhip effect, explaining and evaluating its reasons, its implications and possible ways of its reduction, using higher mathematics (Chen et. al, 1999; Dejonckheere et. al, 2001; Warburton, 2004; Croson & Donohue, 2003) and specialized software (like SISCO) (Chatfield et al., 2004) to create simulation models. In our brief overview, however, we will not consider all of these particularized mathematical studies in detail, we will just try to focus on the more theoretical explanation of the negative implications of the Bullwhip effect, the possible reasons of its existence, and on the ways of its reduction. In this respect, we think that among all of the respected scientists that devoted their efforts to the Bullwhip effect research, the authors Hau L. Lee, V. Padmanabhan, and Seungjin Whang in their articles “The Bullwhip Effect in Supply Chains” (1997) and “Information Distortion in a Supply Chain: The Bullwhip effect” (1997) have not only most profoundly described and explained the notion of the phenomenon of the Bullwhip effect and the reasons of its existence, but also introduced the most valuable and applicable ways of how to diminish its negative consequences.
The essay consists of 3 main chapters: the first chapter describes the notion and the implications of the Bullwhip effect, in the second chapter the reasons for its existence are discussed, and the third chapter refers to the ways how to reduce the Bullwhip effect.
There are some different definitions and ways of understanding of so called Bullwhip effect (it can also be referred to as Whiplash effect or Whipsaw effect in some sources). This phenomenon was first established by Forrester (1961). The Bullwhip effect can appear in every industry and in every supply chain.
The Bullwhip effect is a consequence of one or a combination of the following four important aspects related to supply chain management, which are, according to Lee et al., demand forecast updating by supply chain partners; order batching; price fluctuation; rationing and shortage gaming. Small order variability on a customer level amplifies the orders for upstream players, such as wholesales and manufacturers, as the orders move up along a supply chain (Paik et al., 2007). As shown in the Figure 1, when consumer sales have really small fluctuations, the retailers demand fluctuates more, the wholesalers’ demand fluctuates more than the retailers demand and manufacturers demand fluctuates even more than wholesalers demand.
(Lee et al., 1997a).
According to Forrester, variability of customer orders is usually less than variability of manufacturing orders. To his opinion the main reason for this situation is the irrational behavior of participants involved in a supply chain (Paik et al., 2007).
The Bullwhip effect can be observed on different levels:
on macro level it shows up in inefficiency in production, scheduling, sourcing, distribution, revenue generalization and its realization (Ravichandran, 2008).
on operation level, it reflects in generation of more inventory and keeping it in inappropriate place, to meet a specified service level (Ravichandran, 2008).
on performance level, it can reduce the velocity of cash, destroy potential revenue and erode revenue realization (Ravichandran, 2008).
These are just some negative consequences of the Bullwhip effect (Carlsson & Fuller, 2001):
excessive inventory investments
poor customer service
the productivity capital in operations becomes substandard as revenues are lost
increasing in transportation costs and sub-optimal transportation pattern
demand variability may cause missed production schedule
The Bullwhip Effect is a result of behavior of supply chain members and is created by themselves as a result of their rational decision making in situation of short or not full information about real end-customer demand. The Bullwhip Effect is an internal effect. Companies can ignore the Bullwhip effect and suffer from it extra losses or can try to reduce it.
The good illustration of the Bullwhip effect is the “beer game”. In this experiment (it first took place in 1980’s) participants play four different roles: customers, managers, wholesales and supplies of one of the popular beer brand. It is not allowed to communicate with each other, so participants make decisions about ordering only based on orders from the next downstream player. The results of this experiment are variability and volatility of upstream levels that always exceed variability and volatility of downstream levels. The interpretation of this result can be different. On one hand it can be effect of irrational decision making on each level (Lee et al., 1997a). But on the other hand, if the Bullwhip effect is a summary of rational decision making, we can indentify different main causes, and then the Bullwhip effect appears because of problems in the supply chain structure.
These are the main causes of the Bullwhip Effect:
1. Demand forecasting updating (Lee et al., 1997a).
Every company on each level of supply chain makes forecasting for production, capacity, inventory, material requirements and demand levels. Demand forecasting is usually based on the order history from the company’s immediate customers, i.e. on what the company actually observes (Lee et al., 1997a). By using simply forecasting methods, for example exponential smoothing (forecasting of future demand based on new daily demand and it updating when new data is received) the order that is sent to the suppliers is a reflection of safety stock plus amount that is needed to satisfy future demand. The result is that the variability of amount of orders will increase during going on supply chain from the end-customer to the end supplier.
2. Order batching (in two forms – periodic ordering and push ordering) (Lee et al., 1997a).
There often appears a situation when companies order once a week, once in two weeks, once a month, instead of ordering every day or every few days. This situation appear because sometimes suppliers cannot satisfy frequent ordering or transportation costs are too high (there is a big difference between full load and less than truckload rate, suppliers may even provide customers with discounts for full-truck loads) or time for processing orders is too long. Companies want to make advantages on economies of scale but amount of ordering varies during the time (people order more on the end of the week, end of the months, holidays etc.) The Bullwhip effect decreases when order cycles decrease.
3. Price fluctuations (Lee et al., 1997a).
The bullwhip effect also appears when quantity of goods that customers buy doesn’t reflect their current needs. This is a result of customers buying in advance more than they need and stock some quantity because of “attractive” prices (it can be periodic discounts or promotions events when product prices are low). When level of prices becomes normal, customers stop buying products until they have it in stock. In this case buying structure doesn’t reflect the consuming structure, as a result the fluctuation of buying amount is more tremendous than the variation of consuming amount (Lee et al., 1997a). Such discounts and promotions influence negatively the supply chain. It seems like manufactures and distributors create these price fluctuations themselves, and it means that they set up a bullwhip effect themselves.
4. Rationing and shortage gaming (Lee et al., 1997a).
There are situations when demand is bigger than supply. In this case customer needs can be satisfied only partly. So customers order bigger amounts than they actually need, and when the situation becomes stable (demand is equal to supply) orders suddenly get cancelled. This means that customers give wrong information about their real demands to the suppliers, and this effect is referred to as gaming (Lee et al., 1997a). This is a common situation for a market.
5. Material and information delays (Paik et al., 2007).
According to Towill and his co-authors, material and information delays might be a major contributing factor to the Bullwhip effect (Paik et al., 2007).
6. Supply variability (Paik et al., 2007).
According to Taylor, supply variability (machine reliability problems and quality problems) is one of the possible causes of the Bullwhip effect. Output of the unreliable machines fluctuates and it pushes the variability of demands of the upstream members. Variability in production level is thus the initial trigger of demand variability, which in turn triggers the Bullwhip effect (Paik et al., 2007).
7. Number of echelons (Paik et al., 2007).
According to Towill and his co-authors and to Ackere, reducing number of one or more intermediates lead to significant reducing of the Bullwhip effect (Paik et al., 2007).
In the previous chapter we described the reasons for existence of the Bullwhip effect. Understanding of these reasons gives a very good base to understanding of how to counteract the negative consequences of the Bullwhip effect. Many companies developed their own successful mechanisms of fighting the outcomes of this effect, and Hau L. Lee, V. Padmanabhan, and Seungjin Whang suggest to divide these various initiatives into three categories: “Information sharing”, “Channel alignment”, and “Operational efficiency” (see Table 1).
Table 1 (Lee et al., 1997a)
Demand forecast update
understanding system dynamics
use POS data
electronic data interchange
computer-assisted ordering (CAO)
discount for information sharing
echelon-based inventory control
discount for truck-load assortment
reduction in fixed cost of ordering by EDI or electronic commerce
Continuous replenishment program (CRP)
Everyday low cost (EDLC)
Everyday low price (EDLP)
Activity-based costing (ABC)
The above mentioned categories imply the following:
Information sharing: the information about actual customers’ demand is transmitted from the downstream site to the upstream;
Channel alignment is about coordination of different business activities (as pricing, transportation, planning etc.) between the upstream and the downstream sites in the supply chain, and
Operational efficiency implies the set of activities that help to improve performance, such as to reduce the lead-time.
Lee et al. introduced a set of efficient countermeasures that were designed to minimize the negative effects of the Bullwhip effect (Lee et al., 1997a, b):
Avoid multiple demand forecast updates
Since the main reason of existence of the Bullwhip effect is the fact, that every member of the supply chain makes its own demand forecasting based on the data provided to it by its immediate downstream member, the one evident way to avoid this repetitive processing of demand data in a supply chain is to make the real consumption data (that is known at a downstream site) available at all of the upstream sites. This would allow all of the enterprises in a supply chain (from downstream to upstream) to make and update their forecasts based on the same raw data.
Data sharing can be implemented, for instance, by the use of the electronic data interchange (EDI) systems.
But the practice shows that in some cases even though all of the organizations in a supply chain use the same demand data to make their forecasts, the differences in forecasting methods and/or buying practices may still lead to fluctuations in the orders placed with the upstream sites.
Break order batches
The main idea here is to avoid another reason of appearance of the Bullwhip effect – order batching – by developing the strategies that lead to smaller batches and thus more frequent supply. One of the reasons of large order batches and low order frequencies is the high cost of processing the orders, which can be avoided, for example, by the use of electronic document circulation instead of paper-based.
The other reason of large order batches is the transportation costs: the differences in the costs of full truckloads and less-than-truckloads are very high, and this makes companies to “wait” for the full truckloads and thus stretch the replenishment times, which also creates order batching. This problem can also be avoided by inducing by the manufacturers their distributors to order assortments of different products at a time (a truckload from the same producer may contain different products instead of full load of the same product) and thus significantly increase the order frequency. This can be stimulated by offering discounts by manufacturers to their distributors if they order mixed loads. The other effective way to solve the problem of order batching is the use of third-party logistics companies: these companies allow economies of scale by combining loads from different suppliers situated near each other and delivering these loads to different companies, what is especially very useful for small companies, for which full truckload replenishment times are very long.
A very straightforward way of eliminating the Bullwhip effect caused by forward buying is for the manufacturers to reduce the levels and frequencies of wholesale discounts. One of the most effective ways of doing it is implementing the everyday low price (EDLP) pricing strategy. The practice shows that this strategy is effective both for the suppliers and for the customers since it helps to decrease costs of inventory, storage, transportation etc. for every participant. Though with use of the conventional accounting systems the benefits of the EDLP strategy compared to wholesale price discounting strategy are not evident for the buyer, ABC systems in most cases explicitly show the advantages of EDLP strategy.
Eliminate gaming in shortage
The aim of this measure is to deprive buyers of the incentives to exaggerate their orders in hope of the partial satisfaction of these orders by the suppliers. One of the simple ways to get rid of this reason of Bullwhip effect appearance is as following: in case of shortage the supplier can allocate products to the customers not based on their orders, but in proportion to past sales records. Also the buyers’ desire for gaming may be lessened if the supplier shares its capacity and inventory information with them. The other way of fighting with buyers’ “gaming desire” is to use strict supply contracts that restrict buyer’s flexibility in ordering unlimited quantities of goods and free cancelling of orders.
However, we have to admit that the above mentioned measures of reduction of the Bullwhip effect are not exhaustive and cannot fully eliminate the existence of this effect. A number of scientific papers mathematically prove that the Bullwhip effect still exists even when demand information is shared by all stages of the supply chain and all stages use the same forecasting technique and inventory policy (Chen et al., 2000), and even if almost all of the above described causes (like batching, price fluctuations etc.) are removed (Croson & Donohue, 2003). This gives us the understanding that the Bullwhip effect problem still needs to be closely scrutinized and other ways of reducing this effect are still need to be developed.
In our concise but, we hope, substantial overview we tried to reflect different approaches to the understanding of the phenomenon called “The Bullwhip effect” and the negative effects that it brings to the members of a supply chain, and to describe the most sound, to our opinion, ways of reducing this effect, that were introduced by the researchers during the past two decades.
We also found that though during the last years to the investigation of this matter a lot of scientists devoted a lot of their efforts, the problem of getting rid of the Bullwhip effect in a supply chain has not yet been solved completely: a number of scientific papers mathematically prove that the Bullwhip effect still exists even when almost all of the discovered (so far) causes of its appearance (like batching, price fluctuations etc.) are removed. This means that the Bullwhip effect problem still needs to be closely scrutinized and other ways of reducing this effect are still need to be developed.
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