A Remedy for Bullwhip Effect

In a supply chain, the consumers consume products at an almost constant rate. With this trend, one would expect the orders to be constant as one goes upwards on the supply chain. However, this is rarely the situation with most industries. Even though retailers experience very small variations in their sales, distributors place very varying orders at different times of the year (Lee, Padmanabhan, & Whang, 1997). In addition, manufacturers place orders for their materials with variations greater than the distributors. Procter & Gamble was the first firm to realize the amplification of these variations in orders for their Pampers, which is the firms best-selling product. Consequently, Procter & Gamble called this amplification the bullwhip effect. One cause for bullwhip effect is demand update forecasting.

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Demand update forecasting happens in each company in order to schedule production, plan for capacity, control inventory and plan for material requirements (Lee, Padmanabhan, & Whang, 1997). In order for a firm to forecast on any of these areas, it uses data from its immediate customers. Therefore, upstream managers use downstream orders as signals for a products future demand. For instance, these managers could choose to use a method such as exponential smoothing to determine how much order to place. During this process, managers consider amount of products needed for downstream customers to replenish their stocks and the appropriate safety stocks. The latter is dependent on the lead times. With this knowledge, it is obvious that one remedy for bullwhip effect is avoiding demand forecast updating at different supply levels (Lee, Padmanabhan, & Whang, 1997).

The fact that each member of the supply chain uses order data from the immediate downstream supply chain member to do his forecasts introduces errors that lead to the bullwhip effect. However, it is possible to eliminate this compounding error as one goes up the supply chain. In order to do this, each member of the chain should request for raw demand data from the downstream chain member. This data provides a better image for demand than the orders from a downstream chain member. When data is processed this way, distributors and their suppliers will use the same data. Even though data from distributors is not as accurate as data from a point of sale, it helps in reducing compounded errors above a supply chain, which leads to bullwhip effects.

Some companies have already made this realization. Therefore, companies such as IBM and HP ensure that they get into a contractual agreement with their resellers for purposes of getting sell-through data from the resellers (Lee, Padmanabhan, & Whang, 1997). With such information, these firms forecast their production schedules depending on how much the resellers have distributed the produced goods. Luckily, sharing of sales information between different supply chain members is easy due to the availability of electronic data interchange methods. With electronic transmission, information is able to reach upstream chain members almost instantly, and its analysis is equally easy.

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Even though the availability of demand data from downstream supply chain members reduces compounded forecast errors, there is still a chance of demand forecast fluctuations with the suppliers. This is due to the use of different methods to forecast by suppliers. However, upstream supply chain members can reduce such fluctuations by controlling resupply to downstream members. This way, some downstream chain members become passive, which eliminates the bullwhip effect. Some companies in the consumer products industry use a method called continuous replenishment program in order to control supply to downstream chain members. These companies include Nestle, Proctor & Gamble, and Scott Paper among others.

Apart from controlling supplies to downstream supply chain members, a firm can avoid the bullwhip effect by bypassing its resellers and distributors (Lee, Padmanabhan, & Whang, 1997). In this strategy, a manufacturer sells its products directly to consumers. Therefore, such a firm is able to have accurate data of its products consumption and demand. With such data, a manufacturer is able to schedule production appropriately. Some of the companies that have taken up this strategy are Apple and Dell.

Finally, it is also possible to remedy the bullwhip effect by reducing resupply lead times (Lee, Padmanabhan, & Whang, 1997). With long resupply lead times, downstream supply chain members will always place orders that will provide them with sufficient safety stocks. This way, even when new supplies delay, they are still able to carry on with their business. Considering that there are many resellers, the intention of having safety stocks leads to bullwhip effect. Therefore, in order to avoid this situation, manufacturers and other upstream supply chain members should increase efficiency in their operations. Consequently, they will reduce supply lead times and eliminate the need for safety stocks among the downstream supply chain members. For instance, if manufacturers usually resupply their distributors after one month of placing an order, then these distributors will always want to have a safety order for that month before an order is delivered. However, if this lead-time is reduced to less than a week, distributors will not need to include safety stocks in their orders. Therefore, with reduced lead times, it becomes possible to reduce the bullwhip effect.

In conclusion, one remedy for bullwhip effect is avoiding multiple forecast updates for demand. It is possible to achieve this remedy in the following ways: using demand data from downstream members rather than their orders, controlling downstream supplies, reducing resupply lead-times, and bypassing distributors and resellers.

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