Bullwhip Effect and variability in supply chains

Abstract

The bullwhip effect is an observed phenomenon prevalent in most supply chains It shows the amplification and exaggeration of actual downstream demand by supply chain actors as we move upstream in the supply chain. Thus as result of information asymmetry and lack of effective coordination amongst supply chain actors leading to variability in demand forecasting and uncertainty ,these propels an? oscillations which leads to an? amplification of the actual downstream demand situation, this reaction leads to the bullwhip effect.

This essay discusses and accentuates the bullwhip effect, its consequences on businesses, and causes of the effect and remedies to minimize or eliminate the bullwhip effect.?

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Keywords

Bullwhip effect, supply chain,demand,inventory,downstream,upstream, coordination, information sharing, uncertainty, amplification, variation, efficiency, optimization, total quality management.

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Introduction and description of the bullwhip effect.

The bullwhip is the increase in order fluctuation as one moves upstream. It normally starts with a small signal in the consumption of goods downstream. This signal is misinterpreted and misrepresented by actors in the supply chain leading to distortion in the actual demand; overtime businesses forecast based on this distorted and fictitious demand which leads to amplification and overstating demand as we move upstream; this oscillation is the bullwhip effect. ?The bullwhip effect refers to the amplification of upstream demand forecast based on a less than proportionate change in downstream demand forecast; that is an infinitesimal change in downstream demand situation leads to an exaggeration upstream by the supply chain actors of the variability in demand, this oscillation and huge swings in inventory leads to the bullwhip effect.? Thus as a result of lack of collaboration and cooperation amongst supply chain actors leading to substantial variability in demand forecast and uncertainty in the supply chain.

The bullwhip effect can be extensively explained and illustrated using the beer game simulation of traditional supply chain J forrester’s industrial dynamics (1961) also sometimes refers to as the Forrester’s effect. This simulation was first played by a group of professors MIT at Sloan school of management in the 1960s, which is used to analyse key issues in supply chain management. The game is normally played by a team of four players, every player makes an independent demand forecasting decision based on orders received from the immediate supply chain neighbour, only one player i.e the retailer receives the actual demand from the market, and there is no coordination and cooperation amongst the players in the game over the game period. This causes erratic behaviours and misgivings about the market situation o the players and ignited exaggeration of the actual market demand downstream. Therefore, at the end of the game there is huge variability and oscillations in demand forecasting? as we move upward the supply chain as each member orders and demand decision is solely hinged on their immediate supply chain neighbour which is an incomplete and limited information of the true demand situation along the supply chain, because there is no coordination amongst the players in the game, thus there is uncertainty,delays,long replenishment cycles ,these varying factors cause a huge variability in demand forecasting by the actors and they reacted by exaggerating the actual demand downstream this phenomenon is called the bullwhip effect.

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The beer game I played was a simulation of a typical traditional supply chain, with the primary objective to meet the demand of the customers only the retailer was privy to the actual demand?? information because of his proximity to customers in the supply chain. Thus as per the game requirement there should be no communication amongst partners; this barrier created a frantic and erratic order patterns amongst members as everyone was confused and tense in order to avoid penalties associated with excess inventory or a possible stock out. The results reveal that the variability of demand downstream was relatively constant over the period of the game. But because of lack of coordination and information sharing amongst us players there were astronomical amplification of downstream demand situations, because of the uncertainty, delays in receiving orders, signal processing etc our own demand forecast was therefore a function of our immediate downstream neighbour demand and our current inventory position before receiving the orders. Therefore it is evident that there were huge swings in our forecasting thus exaggerating the actual demand of customers because of lack of information and coordination amongst the supply chain actors this leads to the bullwhip effect.

This is an observed phenomenon in a forecast driven distribution channels. Since the oscillating demand magnification upstream a supply chain reminds someone of a cracking whip it became famous as the bullwhip effect.

The bullwhip effect is a well-known symptom of coordination problems in (traditional) supply chains.It refers to?the effect?that the amount of periodical orders amplifies as one moves upstream in the supply chain towards the production end. Even?in the face of stable customer demand small variations in demand at the retail end tend to dramatically amplify upstream the supply chain with the effect that order amounts are very erratic, and can be very high in one week and almost zero in the next week.

The term was first coined around 1990 when Procter&Gamble?perceived erratic and amplified order patterns in its supply chain for baby diapers. The effect is also known by the names whiplash or whipsaw effect.

?2007-2012?Dr. Kai Riemer,?THE UNIVERSITY OF Sydney &?The University of Münster - Department of Information Systems (IOS work group)?

Fig1.0

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Fig1.1

Despite the constant order quantities for the entire game, the results are very surprising — this is what demonstrates the Bullwhip Effect:

Fig1.2

From the above diagram even though the order quantities were relatively constant for the entire period under observation at 8 units from figure 1.1, there were swings and fluctuation upstream as a result of marginal or little change in consumers’ demand in the downstream. These fluctuations are clearly depicted by the graphs above in fig 1.2 which shows variation and fluctuations in orders and stock quantities in response to the change in downstream demand .Thus depicting that actors in the supply chain reacting differently upstream based on limited information downstream consumer demand pattern.

Probably the most well known demonstration of the bullwhip e?ect was carried out by

Sterman with the well known “beer distribution game”. The game is a role-playing

simulation that portrays the supply chain of beer. It consists of four sectors: retailer,

wholesaler, distributor and factory. The game is played in teams of four persons and each person manages one sector. Each week a card is drawn that represents customer demand. The retailer ships the beer requested out of its inventory and orders new beer from the wholesaler, who ships the beer requested out of its inventory and orders beer from the distributor, who orders and receives beer from the factory. So there is a downstream ?ow of physical goods and an upstream ?ow of demand information, as displayed in Figure 2.1. At each stage there are order and receiving delays that represent the time required to receive, process, ship, and deliver orders. The objective is to minimize total company costs that consists of inventory holding costs and stockout costs.

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Figure 1.0 From Metters Flow of goods and information in the beer game.

The game has been played all over the world by thousands of people ranging from high school students to chief executive o?cers and government o?cials. All of these trials show the following results. Orders and inventory are both subject to instability and oscillation. In almost all cases, the inventory levels of the retailer decline, followed by a decline in inventory of the wholesaler, distributor and factory. As inventory falls, players tend to increase their orders. As additional beer is brewed, inventory levels grow and in many cases overshoot the desired level. As a reaction, orders fall of rapidly. In addition, the amplitude and variance of orders increases as one moves from retailer to factory and the order rate tends to peak later at each stage. Figure 1.1shows the e?ects in four typical trials. The oscillation, ampli?cation and phase lag e?ects are clearly visible. The bullwhip e?ect is not solely observed in theory and simulations; it has been recognized in many markets. Procter & Gamble (P&G) found strange order patterns for diapers. The sales at retail stores were ?uctuating, but the ?uctuations were relatively small compared to the large variability of orders placed by the distributor. The distributors orders to their suppliers were even larger. This did not make sense, because the babies consumed diapers at a steady rate, whereas the variations in demand were ampli?ed as they moved up the supply chain. P&G called this phenomenon the bullwhip e?ect. The same phenomenon was observed by executives of Hewlett-Packard in the supply chain for laser printers. Also many examples can be found in the grocery industry and automotive industry.

?(1)J. D. Sterman. Modelling managerial behavior: Misperceptions of feedback in a

dynamic decision making experiment. Management Science, 35(3), 1989.

R. Metters. Quantifying the bullwhip e?ect in supply chains. Journal of Operations

(2)Management, 15, 1997.

(3)H. L. Lee, V. Padmanabhan, and S. Whang. The bullwhip e?ect in supply chains.

Sloan Management Review, 38(3), 1997.

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https://www.shmula.com/the-bullwhip-effect/310/(Accessed 20/09/2014)

Consequences of the bullwhip effect

The bullwhip effect has numerous adverse consequences for businesses and their operations as it shows inefficiency in managing activities across the supply chain which affects profitability and reputation of a going concern and has far reaching consequences if not checked and minimized, the following are some of the most noticeable consequences of the bullwhip effect:

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Increased safety stock levels: as a result of the of demand variability and uncertainties of downstream demand situation, coupled with demand forecast and varying and sometimes long cycles times., firms are compelled to increase their safety stock levels to meet any demand requirement that may arise in the short run and as such has an associated holding cost which leads to sub optimization of inventory.

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Increased lead times – because of variability in demand and uncertainty? as to the actual requirement downstream created by the bullwhip effect, it affects material planning ,increases back orders, and causes delay in production and meeting the demand of customers.

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Reduced Service Levels: owing to the gaps in information and material flows, delays, forecasting and uncertainty issues customer demand requirement cannot be met in a timely manner, which impacts negatively on business relationship and affect profitability and the bottom line of the company.

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Inefficient allocation of resources: actors in the supply chain will not be able to execute their intermediary role of matching demand to supply in the business environment, thus leading to inefficient allocation of scare resources because of the variability in demand situations which propels an unwarranted response of suppliers upstream leading to a sub optimal condition in the market.

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Over investment in inventory/stock out – due to the bullwhip effect businesses over invest in inventory because of the oscillating nature of demand forecasting and the uncertainty of downstream activities; this leads to soaring holding costs and in some cases a possible stock out because of varying stock levels maintained due to the variability in customers demand.

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Poor relationship – the bullwhip ruins the relationship between and amongst suppliers along a supply chain, as every supply chain member makes decision based on their individual interest and business model and as such makes coordination difficult. This affects the trust and effective coordination amongst supply chain actors, which is a critical factor in eliminating the bullwhip effect.

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Increased Transportation Cost: numerous orders in varying quantities over the period increases the transportation cost of the company, which affects the logistical efficiency of the company and reduces profitability.

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Increase in cost of production and Manufacturing - the bullwhip effect compels companies to respond to streams of amplified orders above the demand capacity downstream, this increases the cost of producing additional units because of the inflated demand thus increasing the cost of manufacturing across the supply chain as there is parallel response in the amplification as we move upstream. This makes firm to either hold excess inventory or increase production capacities to meet the teeming demand of its customers. This can be illustrated graphically as shown.

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https://sinaslogisticsblog.blogspot.no/2010/04/bullwhip-effect.html 22/9/2014

The ?figure above you can see clearly the variability in demand as we move upstream in the supply chain starting from customer to end supplier in the supply chain, which increases as we move upward in the supply chain thus leading to the bullwhip effect.

Loss to business: one major impact of the effect it has far reaching cost implication in the entirety of an entity, which reduces revenue, leads to loss of market share, increased operating cost, reduces working capital, reduces profitability and adversely affect the bottom line of the company.

The impact of the bullwhip effect on different performance measures is summarized in the table below:

Neil K., “Consequences of bullwhip effect”, https://yourbusiness.azcentral.com/consequences-bullwhip-effect-14826.html. (Accessed: 24/9/2014)

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Table 1? The Performance Impact of bullwhip effect on Supply Chain

Chopra, S., and P. Meindl. 2007. Supply chain management – Strategy, planning and operations,3rd ed. Upper Saddle River: Prentice Hall, P 501.

From the table above it is evident that the bullwhip effect affects the efficiency and continuity of any going concern and as such must be mitigated against to enhance operational efficiency ,total quality management and? maximizing the bottom line of the company

?Chopra, S., and P. Meindl. 2007. Supply chain management – Strategy, planning and operations,3rd ed. Upper Saddle River: Prentice Hall, P 499.

Causes of the bullwhip Effect

There are numerous causes attributed to the bullwhip effect, primarily Forester and Sterman attributed behavioural reasons that are the irrational behaviours of actors in the supply chain for the effect. Further researches by (Lee, Padmanabhan, and Whang, 1997) reveal operational causes for the effect as well.

Behavioural Causes

Forrester in his beer game simulation of supply chain management postulated that actors in the supply chain tend to overreact?? to customer demand variability because of their lack of information on the actual demand situation downstream, an increase or decrease in demand will affect the response of the immediate upstream neighbour, who orders based on the anticipation of an increase in demand downstream, and the next level actor order to meet the customer demand? and the actor further order to? replenish his inventory based on orders received from the immediate downstream neighbour, this amplification will further continue in the upstream supply chain. This continues as customer demand varies because of gaps in both information and material flows amongst actors in the supply chain which further exacerbated the oscillations in the upstream supply chain movement. Bu?a and Miller (1979) illustrated this with the following example. Imagine a product with constant deterministic demand that is delivered through the supply chain of the beer game, depicted in Figure 1.1. The retailer sees a permanent 10% drop in sales on day 1, but consistent with a reorder point policy, does not place an order until say day 10. Accordingly, the wholesaler notes the 10% decrease on day 10, but does not place an order until day 20. As this process moves up the supply chain, the ?rm furthest upstream may not discover the decline in demand for several weeks. However, during this time they are producing at the old rate, which is 1/0.9 = 111% of the new consumer requirement. Consequently, excess production of 11% per day would have accumulated since day 1. Due to the overstock position, production may be cut back substantially more than 10%, which is an exaggerated reaction to the actual demand decrease.

Sterman also identi?ed a number of behavioural causes of the bullwhip e?ect. First,

Members of the supply chain do not adequately account for the delays between order placement and order delivery. These misperceptions of time lags lead to continued over and under ordering in the intervening periods. Moreover, members of the supply chain do not use optimal stock levels and only try to optimise their own element of the chain.

Also the behavioural cause of the bullwhip effect was further elucidated by research conducted by:

J?rg Nienhaus, 2002) attempted to analyze the difference between human and computer behaviour in a study based on online Beer Game. (Figure 2) shows the role of human behaviour in the bullwhip effect. This shows a fantastic insight into human behaviour (panic, being erratic, irrational choices n behaviour, fear, greed, self interest etc)as a cause of bullwhip effect .

J?rg Nienhaus,"What is the Bullwhip Effect caused by?", Supply Chain World Europe 2002,28-30 October, 2002, Amsterdam,p15.

https://www.beergame.lim.ethz.ch/Bullwhip_Effect.pdf (Accessed: 18/9/2014)

Operational causes

Lee et al.? further elucidated that the bullwhip e?ect is not holistically a function of irrational behaviours of supply chain actors, but they argued that also as a? consequence of the operational issues within the supply chain infrastructure. Lee et al. identi?ed four major causes of the bullwhip e?ect:

1. Demand signal processing

2. Order batching

3. Price ?uctuation

4. Rationing and shortage gaming

?Demand Signal Processing

This refers to a situation where a firm demand forecast is a function of another player in the supply chain demand situation. Using the beer game simulation of supply chain, each actor uses the demand information of the immediate downstream neighbour as a basis of forecasting their own demand, thus this signal has a reactionary effect in a greater proportion? moving upward in? the supply chain this oscillations based on forecasting demand based on downstream demand history Lee et all refers to as demand signal processing i.e the signal or sign of change in demand downstream is being processed by the nest level upward neighbour in the supply chain which normally leads to an exaggeration of the demand situation downstream.

For example, suppose that a manager uses exponential smoothing to determine how

much to order from a supplier. Exponential smoothing is commonly used in practice.

It is a forecast technique whereby demands are continuously updated as the new daily demand data become available. The forecasts of future demands and associated safety stocks are updated using the smoothing technique. With long lead times, it is not uncommon to have high levels of safety stocks. The ?uctuations in order quantities can therefore be much greater than those in demand data. It is intuitively clear that longer lead times lead to greater ?uctuations. As explained above, safety stock contributes to the bullwhip e?ect. With longer lead times the need for safety stock will be greater.

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Order Batching.

Most businesses nowadays, as a result of numerous factors do batch and accumulate demand from customer or downstream neighbour, as they will choose to order periodically to enjoy and maximize certain benefits and economies of periodic ordering, mostly associated with quantity discounts, reduce cost of transportation, complete order received from customers, these are all geared towards reducing the operational cost of ordering and maximize profit. However, this delay and long cycles will hugely magnify the demand variability in the downstream supply chain which causes amplification of demand forecasting moving upstream thus causing bullwhip effect.?

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Price Fluctuation

Because of fluctuations and volatility in prices, companies tend to respond to lower prices offered for them to meet future demand schedule ,which does not reflect the current material requirement of the company on a just in time basis; thus companies can hold stock in advance of the holding cost is lower than the price ,which makes business sense.However,holding large amount of stocks compels business to postpone buying decision until inventory reaches certain levels, the time lags and variation of order quantities based on the forecasted demand situation leads to the bullwhip effect.

An example of sale and order patterns of chicken noodle soup from Lee et al. shows how high-low buying practices can lead to high variability in shipments from ?manufacturer to distributors, see Figure 2.1 Such wide swings often force companies to run their factories overtime at certain times and to be idle at others. Alternatively, companies may have to build huge piles of inventory to anticipate on big swings in demand. On the other hand, if the manufacturer would not do price discounting, a competitor who does, might take over its business.??????? ?????????????????

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?????????????????????????????????????????????????? , p97. (Accessed: 22/09/2014) Figure 1 Bullwhip effect due to Seasonal Sales of Soup

Hau L. Lee, V. Padmanabhan and Seungjin Whang, "The Bullwhip Effect in Supply Chains", MIT, spring 1997, Volume 38, Number3

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Rationing and Shortage Gaming???????????????????

One major cause of the bullwhip effect is rationing and shortage gaming, in real life it is situation suppliers cannot adequately cater for varying demand of customers as a result of lack of effectively collaboration amongst supply chain partners. Thus, they are compelled to ration the available resources based on the competing needs of their customers. For instance say a retailer faced with such exigency situation may tend of potential stock out will tend to amplify the current demand situation of the product. When there is an eventual fall in demand after his demand has been met, then it will lead to small orders and cancellations from the retailer, as most times they place multiple orders to edge against running out of stock.

Lee et al call this overreaction by customers, rationing and shortage gaming. This “gaming” results in misleading information on the product’s real demand. To illustrate the e?ects of rationing gaming on the variance ampli?cation, consider a supply chain consisting of a manufacturer,

Multiple wholesalers and multiple retailers. If the manufacturer appears to be in short of supply, wholesalers will play the rationing game to get a large share of the supply.

Assessing a possibility of the wholesaler not getting enough from the manufacturer,

retailers also play the rationing game. The e?ect is that demand and its variance are

ampli?ed as one moves up the supply chain. In practice, there are many examples of

this rationing and shortage gaming. One example is the shortage of DRAM chips in

the 1980’s, from Lee et al. In the computer industry, orders for these chips grew

fast, not because a growth in customer demand, but because of anticipation. Customers placed duplicate orders with multiple suppliers and bought from the ?rst one that could deliver, then cancelled all other duplicate orders.

Also the following additional supply chain phenomena can lead to the bull whip effect;

Pushing of orders:?by sales people, by giving temporary incentives so that they can meet their quarterly targets, causes excess supply at the end of the quarter.

Introduction of new products?in the market: creates a sharp increase in orders from the retailers and distributors. This distorts the actual demand for the product which leads the manufacturer to produce even more. After the initial excitement of the product launch.

Promotions and Discounts increase purchases temporarily and this sends a wrong signal to the distributors and manufacturers that the consumption has increased

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4.J. W. Forrester. Industrial dynamics. MIT Press, Cambridge, MA, 1961.

5. H. L. Lee, V. Padmanabhan, and S. Whang. The bullwhip e?ect in supply chains.

Sloan Management Review, 38(3), 1997.

6. H. L. Lee, V. Padmanabhan, and S. Whang. Information distortion in a supply

chain: The bullwhip e?ect. Management Science, 43(4), 1997.

7.J. D. Sterman. Modeling managerial behavior: Misperceptions of feedback in a

dynamic decision making experiment. Management Science, 35(3), 1989.

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How to reduce or eliminate the bullwhip effect.

Lee et al postulated the operational causes of the bullwhip effect and as such proposed remedies based on these causes to remedy or manage a bullwhip effect situation. These remedies are summarily explained below:

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Remedy to Demand Signal Processing

This is majorly as a result of processing demand signals downstream by the immediate upstream neighbour, thus overstating downstream activities and being overwhelmed in their actual demand forecast since their own demand forecast is a function of another actor’s demand situation; this variation moving upstream causes the bullwhip effect as elaborated in the previous chapter of this essay. However the problem of demand signal processing can be overcome essentially by: Collaboration, replenishment smoothing and operational efficiency which when fully implemented.?

1. Collaboration.

This is obvious remedy and crucial to a large extent for all supply chain complexities is the problem is collaboration between the supply chain members. Canella et al. de?ne collaboration in a supply chain as “transforming suboptimal solutions of individual links into a comprehensive solution through sharing customer and operational information”. Several authors have shown how collaboration could reduce the ampli?cation of orders in the upstream direction (Chen et al. ,Disney et al., Chat?eld et al. ), reduce inventory holding costs (Shang et al. ,Kelepouris et al. ), and improve customer service levels (Hosada et al.).A framework for di?erent supply chain structures based on the degree of collaboration was provided by Holweg et al.? In their model they use the collaboration on inventory replenishment and the collaboration on forecasting as dimensions, From Holweg et al. Supply chain collaboration framework.

Based on Sterman’s beer game simulation of a simple and traditional supply chain, the only information available to the upstream neighbours are purchase orders from the downstream actor which they integrate into their demand planning forecasting to meet their requirement and stock replenishment .Thus as there is evident lack of coordination and collaboration amongst actors even with the highest sense of rationality might still lead to the bullwhip effect.

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Information Exchange – this is complements collaboration critical to eliminate the bullwhip effect, as information exchange and real time collaboration of information should be amongst actors across the supply chain which reduces or minimize significantly forecasting errors and variability in actual customer demand downstream. Therefore, upstream supply chain actors should not based their predictability of demand exclusively on the immediate downstream member orders .Also there should be visibility across the supply chain of all members activity to increase response time and efficiency in meeting customers demand, thus eliminating long cycles delays and uncertainties created by inadequate information across the supply chain. thus making meeting customers’ need in a timely manner more feasible and eliminate the bull whip effect.

Vendor Managed Inventory is an effective collaborative tool to eliminate the bottlenecks associated with the bullwhip effect of uncertainty, delays, inadequate communication. Thus the VMI models compels supplier to manage replenishment and inventory levels by giving them the responsibility improves service levels and minimize the bullwhip effect.

Vendor Managed Inventory(VMI), also often referred to as Vendor Managed replenishment(VMR) o?ers two sources of bullwhip reduction; one layer of decision making is eliminated and the delays in information ?ow are reduced.

Synchronised Supply – when there is effective synchronization of the supply chain process amongst all players, replenishment decision becomes relatively easy for suppliers as they have visibility of information on direct downstream activities. This enables suppliers to synchronize the downstream information real time in their production and material planning. Thus such collaboration reduces the variability and uncertainty of demand thus eliminating the bullwhip effect

Replenishment Smoothing

In real world situations; this is an edging model used by firms to achieve optimum inventory by avoiding over or under stocking with it associated effects, thus they used the smoothing technique to determine stock holding and replenishment requirement at any given time; thus it serves as an effective mechanism to edge on inventory investment, thus maximizing working capital, grow revenue and increase profitability. This commonly used periodic review policy is the (S,R) order policy. At each review period R the inventory is reviewed and a quantity O is ordered to bring the level of the available inventory to the order-up-to level S. The available inventory consists of the inventory on hand plus the inventory on order but not yet arrived (Work-in Progress or Pipeline Inventory).A smoothing replenishment rule is an (S,R) policy in which the entire gap between the level S and the available inventory is not recovered in one review period. Instead, for each review period R the quantity O is ordered to recover only a fraction of the gap between the target on-hand inventory and the current level of on-hand inventory, and a fraction of the gap between the target pipeline inventory and the current level of pipeline inventory. The fractions to recover are regulated by decision parameters known as proportional controllers. The proportional controller Ty modulates the recovery of the on-hand inventory gap. The proportional controller Tw determines the recovery of the work in progress inventory gap. Smoothing replenishment is an e?ective remedy to demand signal processing, because it limits the over-reaction/under-reaction to changes in demand. In the literature it is shown that properly tuning the value of the proportional controllers can reduce the bullwhip e?ect (Disney and Towill , Disney et al. ). However, smoothing replenishment rules may have a negative impact on customer service (Dejonckheere et al.).This has only been shown in supply chains with no collaboration thus accentuating the need for effective collaborations amongst all supply chain members.

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Operational Efficiency? one fundamental setback that causes the bullwhip effect as agreed by most scholars and leading proponent are extenuating lead times in the supply chain which when curtailed by operational efficiency can minimize bullwhip effect.

?(Lee et al. , Chen et al. ). These lead times can be reduced by improving the operational e?ciency along the supply chain. An effective way to achieve operational efficiency and total quality management is adopting the JIT (just in time)management, this model focuses on the complete elimination of waste measurable of any kind across the supply chain through operational excellence and optimization in meeting end users demand. This is in total contrast with traditional supply chain with long resupply times and safety stocks that causes the bullwhip effect. With the implementation of this lean manufacturing philosophy it creates effective collaboration across and amongst players in the supply chain thus eliminate the bullwhip effect, as this? the model is geared towards total quality management of the entire supply chain system.

Solution to Order Batching

Lee et al accentuated that, large order batches and low order frequencies contribute to the bullwhip e?ect, and the e?ect is ampli?ed if the order periods of di?erent retailers are correlated.

However to remedy this situation is to have adequate and real time interface from downstream to upstream players through electronic data interchange (EDI), Point of sale data etc and other real time software that gives current information about the need and demand along the supply chain. Also, this system enables orders to be placed at a much lower cost, considering the cost of replenishment can be relatively high and impacts profitability. All these considerations make company to adopt different ordering strategies to minimize operational and logistical cost in their operations. In a nutshell, these system could us determine order size and frequencies to avoid distortion and exaggeration of downstream demand thus reducing variability and effectively managing customers demand which minimizes the bullwhip effect.

Elimination and minimization Price Fluctuation

Many companies make buying decisions on variety of objectives and models, mostly companies can buy in advance to benefit from price or quantity discounts, coupons etc thus overstating their actual need at that material time, which has some adverse business implications in the future most of the times. As arguably research shows that the long run cost outweighs the benefit overtime. However companies can edge against price and currency exchange fluctuation by negotiating a forward price with supplier to make prices stable over a reasonable period of time, thus this stability serves as a cushion for proper planning and gives the true and fair view of your actual requirement, thus eliminating the uncertainty associated with fluctuations this reduces overtrading and the possibility of bullwhip effect.

How to eliminate or minimize Rationing and Shortage Gaming

In real world situation there is most times supply and demand mismatch, thus suppliers are compelled by the business environment to ration available resources amongst the competing needs of clients. Most times retailers tend to overstate their demand when they face the potential of running out of stocks and can place multiple orders to different suppliers; only to cancel later some of the orders placed, such behavioural tendency can lead to the bullwhip effect. A remedy proposed by Lee et al? is to allocate in proportion to past sales records, instead of allocating in proportion to the amounts ordered. Customers then have no incentive to exaggerate their orders.

Nonetheless, if demand drops later, companies can easily cancel their orders due to the generous return policy of most manufacturers. Without a penalty, retailers will continue to exaggerate their needs and cancel their orders in case they ordered too much. Lee et al. therefore propose that manufacturers enforce more stringent cancellation policies regime, so that retailers are more cautious with exaggerating their orders and be more realistic in their estimations.

Conclusion

In a nutshell, the bullwhip effect can be minimized or effectively eliminated if both behavioural and operation causes are fully mitigated against by actors in the supply chain. Therefore the need for effective? collaboration and information sharing, synchronized supplies and operational efficiency cannot be overemphasized to achieve total quality management and operational optimization of? the entire supply chain system ,thus eliminating the bullwhip effect.

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List of References.

(1)J. D. Sterman. Modelling managerial behavior: Misperceptions of feedback in a

dynamic decision making experiment. Management Science, 35(3), 1989.

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R. Metters. Quantifying the bullwhip e?ect in supply chains. Journal of Operations

(2)Management, 15, 1997.

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(3)H. L. Lee, V. Padmanabhan, and S. Whang. The bullwhip e?ect in supply chains.

Sloan Management Review, 38(3), 1997.

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4.J. W. Forrester. Industrial dynamics. MIT Press, Cambridge, MA, 1961.

5. H. L. Lee, V. Padmanabhan, and S. Whang. The bullwhip e?ect in supply chains.

Sloan Management Review, 38(3), 1997.

6. H. L. Lee, V. Padmanabhan, and S. Whang. Information distortion in a supply

chain: The bullwhip e?ect. Management Science, 43(4), 1997.

7.J. D. Sterman. Modeling managerial behavior: Misperceptions of feedback in a

dynamic decision making experiment. Management Science, 35(3), 1989.

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8.H. L. Lee, V. Padmanabhan, and S. Whang. The bullwhip e?ect in supply chains.

Sloan Management Review, 38(3), 1997.

9.H. L. Lee, V. Padmanabhan, and S. Whang. Information distortion in a supply

chain: The bullwhip e?ect. Management Science, 43(4), 1997

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12.S. M. Disney and D. R. Towill. A discrete transfer function model to determine

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