Fransoo and Wouters (2000) discussed that the effects of bullwhip defined the variability of the demand that increases further upstream in the supply chain, and concluded that the theory of measurement of the bullwhip effect in a practical setting had received limited attention.
Yu et al., (2001) studied that the research of the bullwhip effect had considered inter-organizational echelons, such as two echelons between companies.
McCullen (2001) studied that three/multi echelons between a sequence of companies (e.g.), in supply chains. There was therefore a need for research of the bullwhip effect on a company's internal inventories, e.g. sandwiched between a company's inbound flows and outbound logistics flows (i.e. two internal stocking levels). In some conditions a company maintains higher levels of stocks and inventories that were called as speculation, while in situation the company maintains lower levels of inventories and this condition was termed ad postponement, in the inbound and outbound logistics flows. The process of rational decision making was also influenced by the companies' business activities adding value in a value chain.
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Lee et al. (1997a) conclude that the bullwhip effect resulted from the rational decision making between the actors in a supply chain (i.e. inter-organizational echelons). This rational decision was making might also be based upon the relationship between actors within a company (i.e. intra-organizational echelons), such as the actors in charge of business activities dealing with procurement and physical distribution. The principles of postponement and speculation previously stated that a bullwhip effect between a company's inbound and outbound logistics flows should indicated a higher level of inventories in the inbound logistics flows than in the outbound logistics flows, e.g. caused by insufficient market data, deficient forecasts or other uncertainties.
Alderson and Bucklin (1950) also studied that could also be explained by the effects or consequences of the principle of postponement and the principle of speculation.
Mentzer et al. (2001) emphasized on the coordination of the systemic and strategic functions in the conventional business and the plans transversely these business functions within a picky company and crosswise businesses for the motive of enhancing the continuing routine of the individual companies and the supply chain as an entire.
Lummus et al. (2001) also took account of the logistics stream, client order administration, the manufacture procedure, and the information flows essential to observe all the activities at the company's stage of the commerce.
Lee and Billington (1992) gave the association of manufacturing and distribution sites that the procurement of the starting or raw materials, transform them into intermediate or medium and finished products, and finally distributes to the finished products to customers business activities lessen the risk by moving the differentiation nearer to the time of exchange. The authors had also provided a point of departure for a critical scrutiny to enhance the performance of companies' business activities, and for a possible diminishing or reduction of the bullwhip effect in a company's level.
Stevens (1990) emphasized the management of the stream of substances from dealer, through the value-adding procedure and the channels of distribution to end users.
Ellram and Cooper (1990) worked on the philosophy to handle the whole stream of a sharing channel from supplier to final purchaser.
Houlihan (1988) covered the stream of commodities from trader through manufacturer and distributor to the end user.
Jones and Riley (1985) dealed with the total flow of materials from supplier were right through to the end users.
Oliver and Webber (1982) worked on the marketing channel should have been seen as an integrated single entity. The disequilibrium between the points of inventory in a supply chain might be caused by the value adding process in companies' different business activities. Therefore, the occurrence of the bullwhip effect did not necessarily have to do with demand variability. It could be explained by the effects or consequences of the value chain concept.
Porter (1985) studied that the value chain concept was a guide or tool for recognizing different ways of creating customer value the value chain disaggregates a firm into its strategically relevant activities. Generally, the value chain concept showed that the value chain may be useful in terms of identifying and understanding fundamental aspects to reach competitive or core strengths on the market.
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Weld (1916) concluded that the idea of the value-added process was recognized "At each step an increment of value was added by those who handle or transform the product". The value-added approach contributes in part to the understanding of the bullwhip effect between a company's inbound and outbound logistics flows. As per the journal of the International distribution of physical and logistics, bullwhip effect was also defined by the reliance or dependencies between actors, activities and resources that could cause negative consequence when variability occurs upstream or downstream.
Sterman (1989) demonstrated that the misleading or misperception about any information may lead to the over reaction of any human. Variability in the business environment was therefore troublesome to handle in a managerial context.
Lee et al. (1997a) stated that the variability could be symptoms of excessive inventory, deprived product prediction, inadequate or extreme capacities, poor client service due to out of stock products or long backlogs, unsure production planning and lofty costs for corrections.
Lee et al. (1997b) identified four major reasons of the bullwhip effect, namely demand forecast updating, order batching, rise and fall of price, and rationing and scarcity betting.
Xu et al. (2001) presented that when the forecasting errors were occurred by the manufacturer's and was greater than those of the retailer's before co-ordination or collaboration, co-ordination becomes effective in decreasing the manufacturer's safety stocks.
Lee and Billington, Towill, Fransoo and Wouters (2000) concluded that the bullwhip effect could be diminished by reducing the lead times, looking again the reordering procedures, controlling the price fluctuations, and the incorporation of planning and performance measurement.
Baljko (1999) said that the bullwhip effect may be get rid of through measures such as: shared knowledge with suppliers and customers to better gauge demand strictly, co-operation and coordination with supply chain partners to determine what information was causing an overreaction, and the use of web based technology that was internet-enabled technology and the application of the web to speed up the communication among different customers and the improvement of response time.
Lee et al. (1997a) discussed the occurrence of factors that causes the bullwhip effect also the possibilities of reducing the bullwhip effect based upon the co-ordination mechanism in terms of information in sequence, alignment of the channel, and efficiency of the operations. Demand information at a downstream site was conveyed to the upstream with information sharing. The harmonization of costing, shipping, supply scheduling, and possession between the upstream factors and downstream factors refers to channel alignment. Improved performance, e.g. reduced costs and shortened lead times, may be accomplished through increased operational effectiveness and efficiencies.
Chen et al. (2000) quantified the bullwhip effect in two different stages which consist of a retailer and a manufacturer that includes two factors, namely demand forecasting and order lead times. This research exemplified that the bullwhip effect could be decreased by centralizing demand information.
Kelle and Milne (1999) studied the bullwhip effect and considered the three basic elements namely; the procured sort of entity seller, the collective orders of the vendor, and the dealer way of ordering/producing policy. This research demonstrated that how one could decrease the demand variability by taking orders. It was concluded that the unconstructive effect of high variability and improbability could be reduced by small regular orders.
Xu et al. (2001) worked on the development of supply chain co-ordination through additional effectual information exchange and constant forecasting. The outcome demonstrated the negative impact that independent activities performed by actors of a traditional supply chain have on order release volatility and forecast error volatility. The author emphasized on how to and when to control the fluctuations in the order and the collaboration or coordination with in the actors in the mechanism. As per the journal of the International distribution of physical and logistics, the bullwhip effect depends upon the gap between speculation and postponement of business activities. In a managerial context, the bullwhip effect diminished or eliminated if there was no space between the level of speculation and rescheduling of business activities that might not be an ideal situation.
Swenson (2002) found that there were three generic categories of dependencies between buyers and sellers in the marketplace of interest for the typology of the bullwhip effect, namely:
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(1) Time dependence;
(2) Functional dependence; and
(3) Relationship dependence
Forrester (1961) said that the ``bullwhip'' was a rising variability of required demand further upstream. Providing the supplier upstream with EPOS (electronic point of sale) data could significantly reduced this bullwhip effect. Such information cuts short all kinds of information distortions which often lead to a bullwhip effect. The first research to extensively study the amplification of demand information in a supply chain was reported by Forrester studied the seminal book Industrial Dynamics. The author basically reduced the problems of this demand amplification to two types of holdup, namely the holdup of conveying stipulated information and the setback of the transferring the physical products through the supply chain (lead times).
Jan C. Fransoo and J.F. Wouters (1986) also worked on the other improvements that could reduce the bullwhip effect and included the reduction of lead times, revising reorder procedures, price fluctuations limitations, and the merging or integration of planning and performance measurement. As per Jan C. Fransoo & J.F. Wouters many problems were due to the limitations of information systems.
Lee et al. (1997a, 1997b) had identified four major causes of the bullwhip effect:
(1) Update on the demand forecast: Future demand forecast and expecting the resulted in creating the links in the supply chain about future demand.
(2) Order batching: When demands were coming there would be depletion in the inventories.
(3) Price fluctuations: Price fluctuations were created because of the promotions and trade deals which could increases the variability of demand. Here lee et al. emphasized that when the product's price was low, then a customer buys in bigger quantities than needed and when the price returns to the normal situation, the customer bought less than needed that deplete its inventory. So, stabilizing prices and decreasing the number of promotions was a way of reducing this effect.
(4) Rationing plus scarcity betting: When manufactured goods demand goes up supply, a dealer needs to ration its product to customers. Knowing that, customers might order more than would really want. After, when there was no scarcity, orders vanish. Introducing rationing methods based on past sales rather than on orders placed takes away the incentive for customers to inflate order sizes. A bullwhip effect caused by price fluctuations rarely happened and concluded that this was due to the short shelf life of the product and a as a result increased risk for the buyer of ordering based on price.
Lee et al., (1997) studied that a renowned and well known example of supply chain dynamics was the bullwhip effect, which was a term derived by the logistics executives of Procter and Gamble, called because small order variability at the customer level amplifies the orders for upstream players, such as wholesalers and manufacturers, as the order moves up along a supply chain, even when consumer sales show relatively constant demands, the demand/order placed by a retailer to a wholesaler was likely to fluctuate more than the actual demand perceived by that retailer. The order of the wholesaler to the manufacturer and manufacturer order to the supplier fluctuate even more. This increase in the variability of orders at each stage in a supply chain was often called as the effect of the bullwhip. Such effects results in the high variability in different orders points all through the system in the supply chain. This move to and fro was also probable to be higher in this system.
Forrester (1961) illustrated that the order variability to the manufacturer was usually far greater than the variability of the actual consumer demand.
Sterman (1989) also found out that the effect due to bullwhip was by the decision about the irrational making of the participants. After examining the results of the well known role playing game, the beer distribution game, author concluded that the participants of the game underestimated the order delays and more importantly, that did not take the opportunity of the entire supply chain inventory in to account while placing orders. The poor decision was deemed to come from difficulties in evaluating the complex feedback loops in conjunction with the delay of the time.
Lee et al. (1997) studied four various possible reasons of the effect of the bullwhip that were updating the demand forecast and orders, cost variation and dividing and short of the materials (gaming). This forecasted demand would update that demand magnification. The orders were forecasted and conveyed, then the safety stocks were made up, and thus the bullwhip effect occurs.
Lee et al. (1997) also discussed the material procurement and planning and the transportation required companies to order goods at a particular time. This episodic batching causes rush forward in demand at a particular time period, followed by the periods of time with no or little orders, and other time periods with enormous or huge demands. Lee also discussed the price fluctuation also created bigger inconsistency of demand and demand roughness. Finally, when demand got exceed then the supply, manufacturers often ration products to their customers based on what would be the order.
Towill (1999) studied the bullwhip effect by using a computer simulation model. As a benchmark, this research was based in the Forrester's simulation model consisting of the retailer, a distributor, a factory warehouse and a factory. At last the research depicted that the delay in the information and material delays might be one of the major contributing factors that causes the bullwhip effect. Author also showed that if the production lead reduces then the production reduction of the bullwhip effect occurs.
Taylor (2000) also discussed the variability of the supply could be a possible cause of the bullwhip effect. Supply variability could include problems in machine reliability and quality problems. When Outputs from unreliable machines fluctuate then the fluctuation triggers the variability of demands at the upstream members from that machine. So the variability at the production level was thus the initial prompt of demand variability, which in turn created the bullwhip effect. In addition to these possible causes, author also discussed the downstream members' stock policy aimed at minimizing their inventories. Author argued that the bullwhip effect could be caused by simply passing inventory holding responsibility to the upstream members. As per the International Journal of Retail & Distribution Management the following diagram was as follows:
As shown in Figure 2.4, there were nine possible causes of the bullwhip effect that were studies in the research.
Sterman, Disney and Towill, (2003) demonstrated and incorporated the variables and studied the relationship among the variables. The authors also presented the flow as follows:
Forrester (1961) studied that whenever an order, consisting of the amount of stocks to meet the future demands and its associated safety stock, was forecasted and transmitted along the supply chain, order quantities were increased as the safety stock builds up in the supply chain. Therefore, the order quantities placed on a factory was much larger than the actual consumer demand. The author found out that breakdown of machine was also measured one of the probable factors of the bullwhip effect. So if there was a breakdown or problem in machine then could cause the delays in production and ultimately leads to the bullwhip effect.
The same author also studied that in price discounts like in sales promotion campaign had any effect on customers, took the form of dropping the average time gap before buying. In other words, a price discount plays an important role in reducing the delay between the time that a consumer becomes liable to promotion and the time at which a purchase was actually made. Because of this reason, purchasing delay was related to the rate of consumption. The author concluded that the transportation delay and mail delay could lead to the order delay and the increased material transit lead-time and information delay, such as order preparation and processing time, contributed to the demand amplification. Bullwhip effect generates the greatest inefficiency on the upper echelons in a supply chain. However, all the involved companies in the relevant supply chain contribute to the effect and need to work together to lessen it.
Holmstrom (1997) conducted a case study of supply chain operations in the European grocery industry. The author found wholesalers and the retailers were the main causes of the bullwhip effect in creating the changeability. The variability increase was partly due to a slow, inaccurate demand information flow in the supply chain.
Lee, Bagchi, Skjoett-Larsen, Disney and Towill, (2003) studied that the use of the most recent information technology not only decreases the material and information delays among supply chain members, but also makes possible accurate and transparent sharing of actual customer demands across a supply chain. Lack of coordination or collaboration among each stage of the supply chain may lead to actions that increase variability and reduce total supply chain profits. The authors also found out that by eradicating or reducing the intermediaries, partners in a supply chain may be able to prevent unclear demand information and to understand the buying pattern of genuine customers.
Stein (1998) found that in modern living there had been immense boost in the superiority and amount of information shared crosswise supply chains. This boost was drive in fraction by improvement in the technology accessible for gathering and giving out statistics. The introduction of venture logistics software, such as SAP, permitted corporation to sustain and share stock information for various deliver points on a widespread record.
Forrester (1958) studied that the former to spot out this outcome and its likely causes amplified difference was a concern for allocation chains in view of the fact that lead to amplified costs in the shape of amplified stock necessities, expedite, or client shortage.
Lee et al. (1997) studied that the fluctuation factors that could cause were the demand indication processing, stock share, order batching, and value variation.
Chen et al. (1998) showed that traditions to improve operational troubles consist of enhanced order forecasting technique capability allotment schemes.
Cachon (1999) showed that the spread over a phase of time order batching and on a daily basis low pricing.
Kaminsky, Simchi-Levi and Steckel et al. (2004) stated that the control for demand signal processing inaccuracy by distribution information of the retail demand allocation with all participant. In this logic, side of game was associated to the stationary beer game in recent times.
Chen et al. (1998) had discussed the main causes of the bullwhip effect. In this paper, to reduce the bullwhip effect using information sharing strategies (centralized information) and breaking order batches (changing the frequency of reordering using two inventory control policies).
Seung-kuk Paik and Prabir K. Bagchi (2006) identified that the potential causes of the bullwhip effect could be the price fluctuation or variation, supply shortages, demand forecasting update, delay in information flow, production, material, purchasing and transportation.