S P R I N G 1 9 9 7

Hau L. LeeV. PadmanabhanSeungjin Whang

The BullwhipEffect in SupplyChainsDistorted information from one end of a supply chain to theother can lead to tremendous inefficiencies: excessiveinventory investment, poor customer service, lost revenues,misguided capacity plans, ineffective transportation, andmissed production schedules. How do exaggerated orderswings occur? What can companies do to mitigate them?

Vol. 38, No. 3 Reprint #3837 http://mitsmr.com/1phEOiM

93SLOAN MANAGEMENT REVIEW/SPRING 1997 LEE ET AL.

Not long ago, logistics executives at Procter &Gamble (P&G) examined the order pat-terns for one of their best-selling products,

Pampers. Its sales at retail stores were fluctuating, butthe variabilities were certainly not excessive. However,as they examined the distributors’ orders, the execu-tives were surprised by the degree of variability. Whenthey looked at P&G’s orders of materials to their sup-pliers, such as 3M, they discovered that the swingswere even greater. At first glance, the variabilities didnot make sense. While the consumers, in this case,the babies, consumed diapers at a steady rate, the de-mand order variabilities in the supply chain were am-plified as they moved up the supply chain. P&Gcalled this phenomenon the “bullwhip” effect. (Insome industries, it is known as the “whiplash” or the“whipsaw” effect.)

When Hewlett-Packard (HP) executives examinedthe sales of one of its printers at a major reseller, theyfound that there were, as expected, some fluctuations

over time. However, when they examined the ordersfrom the reseller, they observed much bigger swings.Also, to their surprise, they discovered that the ordersfrom the printer division to the company’s integratedcircuit division had even greater fluctuations.

What happens when a supply chain is plagued witha bullwhip effect that distorts its demand informationas it is transmitted up the chain? In the past, withoutbeing able to see the sales of its products at the distri-bution channel stage, HP had to rely on the sales or-ders from the resellers to make product forecasts, plancapacity, control inventory, and schedule production.Big variations in demand were a major problem forHP’s management. The common symptoms of suchvariations could be excessive inventory, poor productforecasts, insufficient or excessive capacities, poor cus-tomer service due to unavailable products or long back-logs, uncertain production planning (i.e., excessive revi-sions), and high costs for corrections, such as for expe-dited shipments and overtime. HP’s product divisionwas a victim of order swings that were exaggerated bythe resellers relative to their sales; it, in turn, createdadditional exaggerations of order swings to suppliers.

In the past few years, the Efficient Consumer Re-sponse (ECR) initiative has tried to redefine how thegrocery supply chain should work.1 One motivationfor the initiative was the excessive amount of invento-ry in the supply chain. Various industry studies foundthat the total supply chain, from when products leavethe manufacturers’ production lines to when they ar-rive on the retailers’ shelves, has more than 100 days of

The Bullwhip Effect in SupplyChains

Hau L. Lee • V. Padmanabhan • Seungjin Whang

Hau L. Lee is the Kleiner Perkins, Mayfield, Sequoia Capital Professorin Industrial Engineering and Engineering Management, and professorof operations management at the Graduate School of Business, StanfordUniversity. V. Padmanabhan is an associate professor of marketing, andSeungjin Whang is an associate professor of operations information andtechnology, also at Stanford.

Distorted information from one end

of a supply chain to the other can

lead to tremendous inefficiencies:

excessive inventory investment, poor

customer service, lost revenues,

misguided capacity plans, ineffective

transportation, and missed

production schedules. How do

exaggerated order swings occur? What

can companies do to mitigate them?

inventory supply. Distorted information has led everyentity in the supply chain — the plant warehouse, amanufacturer’s shuttle warehouse, a manufacturer’smarket warehouse, a distributor’s central warehouse,the distributor’s regional warehouses, and the retailstore’s storage space — to stockpile because of thehigh degree of demand uncertainties and variabili-

ties. It’s no wonder that the ECR reports estimated apotential $30 billion opportunity from streamliningthe inefficiencies of the grocery supply chain.2

Other industries are in a similar position. Computerfactories and manufacturers’ distribution centers, the

distributors’ warehouses, and store warehouses alongthe distribution channel have inventory stockpiles.And in the pharmaceutical industry, there are duplicat-ed inventories in a supply chain of manufacturers suchas Eli Lilly or Bristol-Myers Squibb, distributors suchas McKesson, and retailers such as Longs Drug Stores.Again, information distortion can cause the total in-ventory in this supply chain to exceed 100 days of sup-ply. With inventories of raw materials, such as integrat-ed circuits and printed circuit boards in the computerindustry and antibodies and vial manufacturing in thepharmaceutical industry, the total chain may containmore than one year’s supply.

In a supply chain for a typical consumer product,even when consumer sales do not seem to vary much,there is pronounced variability in the retailers’ ordersto the wholesalers (see Figure 1). Orders to the manu-facturer and to the manufacturers’ supplier spike evenmore. To solve the problem of distorted information,companies need to first understand what creates thebullwhip effect so they can counteract it. Innovativecompanies in different industries have found that they

94 LEE ET AL. SLOAN MANAGEMENT REVIEW/SPRING 1997

Figure 1 Increasing Variability of Orders up the Supply Chain

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The ordering patterns share acommon, recurring theme: thevariabilities of an upstream

site are always greater than those of the downstream site.

can control the bullwhip effect and improve their sup-ply chain performance by coordinating informationand planning along the supply chain.

Causes of the Bullwhip Effect

Perhaps the best illustration of the bullwhip effect isthe well-known “beer game.”3 In the game, partici-pants (students, managers, analysts, and so on) playthe roles of customers, retailers, wholesalers, and sup-pliers of a popular brand of beer. The participantscannot communicate with each other and must makeorder decisions based only on orders from the nextdownstream player. The ordering patterns share acommon, recurring theme: the variabilities of an up-stream site are always greater than those of the down-stream site, a simple, yet powerful illustration of thebullwhip effect. This amplified order variability maybe attributed to the players’ irrational decision making.Indeed, Sterman’s experiments showed that human be-havior, such as misconceptions about inventory anddemand information, may cause the bullwhip effect.4

In contrast, we show that the bullwhip effect is aconsequence of the players’ rational behavior withinthe supply chain’s infrastructure. This important dis-tinction implies that companies wanting to control thebullwhip effect have to focus on modifying the chain’sinfrastructure and related processes rather than the de-cision makers’ behavior.

We have identified four major causes of the bull-whip effect:1. Demand forecast updating 2. Order batching 3. Price fluctuation4. Rationing and shortage gaming

Each of the four forces in concert with the chain’sinfrastructure and the order managers’ rational deci-sion making create the bullwhip effect. Understandingthe causes helps managers design and develop strate-gies to counter it.5

Demand Forecast UpdatingEvery company in a supply chain usually does productforecasting for its production scheduling, capacity plan-ning, inventory control, and material requirementsplanning. Forecasting is often based on the order histo-ry from the company’s immediate customers.

The outcomes of the beer game are the conse-quence of many behavioral factors, such as the players’perceptions and mistrust. An important factor is eachplayer’s thought process in projecting the demand pat-tern based on what he or she observes. When a down-stream operation places an order, the upstream man-ager processes that piece of information as a signalabout future product demand. Based on this signal,the upstream manager readjusts his or her demandforecasts and, in turn, the orders placed with the sup-pliers of the upstream operation. We contend that de-mand signal processing is a major contributor to thebullwhip effect.

For example, if you are a manager who has to de-termine how much to order from a supplier, you use asimple method to do demand forecasting, such as ex-ponential smoothing. With exponential smoothing,future demands are continuously updated as the newdaily demand data become available. The order yousend to the supplier reflects the amount you need toreplenish the stocks to meet the requirements of futuredemands, as well as the necessary safety stocks. The fu-ture demands and the associated safety stocks are up-dated using the smoothing technique. With long leadtimes, it is not uncommon to have weeks of safetystocks. The result is that the fluctuations in the orderquantities over time can be much greater than those inthe demand data.

Now, one site up the supply chain, if you are themanager of the supplier, the daily orders from the man-ager of the previous site constitute your demand. If youare also using exponential smoothing to update yourforecasts and safety stocks, the orders that you placewith your supplier will have even bigger swings. For anexample of such fluctuations in demand, see Figure 2.As we can see from the figure, the orders placed by thedealer to the manufacturer have much greater variabili-ty than the consumer demands. Because the amount ofsafety stock contributes to the bullwhip effect, it is in-tuitive that, when the lead times between the resupplyof the items along the supply chain are longer, the fluc-tuation is even more significant.

Order BatchingIn a supply chain, each company places orders with anupstream organization using some inventory monitor-ing or control. Demands come in, depleting inven-

95SLOAN MANAGEMENT REVIEW/SPRING 1997 LEE ET AL.

tory, but the company may not immediately placean order with its supplier. It often batches or accu-mulates demands before issuing an order. There aretwo forms of order batching: periodic ordering andpush ordering.

Instead of ordering frequently, companies mayorder weekly, biweekly, or even monthly. There aremany common reasons for an inventory system basedon order cycles. Often the supplier cannot handle fre-quent order processing because the time and cost ofprocessing an order can be substantial. P&G estimat-ed that, because of the many manual interventionsneeded in its order, billing, and shipment systems,each invoice to its customers cost between $35 and$75 to process.6 Many manufacturers place purchaseorders with suppliers when they run their material re-quirements planning (MRP) systems. MRP systemsare often run monthly, resulting in monthly orderingwith suppliers. A company with slow-moving itemsmay prefer to order on a regular cyclical basis becausethere may not be enough items consumed to warrantresupply if it orders more frequently.

Consider a company that orders once a monthfrom its supplier. The supplier faces a highly erraticstream of orders. There is a spike in demand at onetime during the month, followed by no demands forthe rest of the month. Of course, this variability ishigher than the demands the company itself faces.Periodic ordering amplifies variability and contributesto the bullwhip effect.

One common obstacle for a company that wantsto order frequently is the economics of transportation.There are substantial differences between full truck-

load (FTL) and less-than-truckload rates, so compa-nies have a strong incentive to fill a truckload whenthey order materials from a supplier. Sometimes, sup-pliers give their best pricing for FTL orders. For mostitems, a full truckload could be a supply of a monthor more. Full or close to full truckload ordering wouldthus lead to moderate to excessively long order cycles.

In push ordering, a company experiences regularsurges in demand. The company has orders “pushed”on it from customers periodically because salespeopleare regularly measured, sometimes quarterly or annu-ally, which causes end-of-quarter or end-of-year ordersurges. Salespersons who need to fill sales quotas may“borrow” ahead and sign orders prematurely. TheU.S. Navy’s study of recruiter productivity foundsurges in the number of recruits by the recruiters on aperiodic cycle that coincided with their evaluationcycle.7 For companies, the ordering pattern from theircustomers is more erratic than the consumption pat-terns that their customers experience. The “hockeystick” phenomenon is quite prevalent.

When a company faces periodic ordering by itscustomers, the bullwhip effect results. If all customers’order cycles were spread out evenly throughout the

week, the bullwhip effect would be minimal. The pe-riodic surges in demand by some customers would beinsignificant because not all would be ordering at thesame time. Unfortunately, such an ideal situation rarelyexists. Orders are more likely to be randomly spreadout or, worse, to overlap. When order cycles overlap,most customers that order periodically do so at thesame time. As a result, the surge in demand is evenmore pronounced, and the variability from the bull-whip effect is at its highest.

If the majority of companies that do MRP or dis-tribution requirement planning (DRP) to generatepurchase orders do so at the beginning of the month(or end of the month), order cycles overlap. Periodic

96 LEE ET AL. SLOAN MANAGEMENT REVIEW/SPRING 1997

Although some companies claim to thrive on high-low buying

practices,most suffer.

Figure 2 Higher Variability in Orders from Dealer toManufacturer than Actual Sales

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execution of MRPs contributes to the bullwhip effect,or “MRP jitters” or “DRP jitters.”

Price FluctuationEstimates indicate that 80 percent of the transactionsbetween manufacturers and distributors in the groceryindustry were made in a “forward buy” arrangementin which items were bought in advance of require-ments, usually because of a manufacturer’s attractiveprice offer.8 Forward buying constitutes $75 billion to$100 billion of inventory in the grocery industry.9

Forward buying results from price fluctuations inthe marketplace. Manufacturers and distributors peri-odically have special promotions like price discounts,quantity discounts, coupons, rebates, and so on. Allthese promotions result in price fluctuations. Addi-tionally, manufacturers offer trade deals (e.g., specialdiscounts, price terms, and payment terms) to the dis-tributors and wholesalers, which are an indirect formof price discounts. For example, Kotler reports thattrade deals and consumer promotion constitute 47percent and 28 percent, respectively, of their total pro-motion budgets.10 The result is that customers buy inquantities that do not reflect their immediate needs;they buy in bigger quantities and stock up for the fu-ture.

Such promotions can be costly to the supply chain.11

What happens if forward buying becomes the norm?When a product’s price is low (through direct discountor promotional schemes), a customer buys in biggerquantities than needed. When the product’s price re-turns to normal, the customer stops buying until it hasdepleted its inventory. As a result, the customer’s buy-ing pattern does not reflect its consumption pattern,and the variation of the buying quantities is much big-ger than the variation of the consumption rate — thebullwhip effect.

When high-low pricing occurs, forward buyingmay well be a rational decision. If the cost of holdinginventory is less than the price differential, buying inadvance makes sense. In fact, the high-low pricingphenomenon has induced a stream of research onhow companies should order optimally to take ad-vantage of the low price opportunities.

Although some companies claim to thrive onhigh-low buying practices, most suffer. For example,a soup manufacturer’s leading brand has seasonal

sales, with higher sales in the winter (see Figure 3).However, the shipment quantities from the manufac-turer to the distributors, reflecting orders from thedistributors to the manufacturer, varied more widely.When faced with such wide swings, companies oftenhave to run their factories overtime at certain timesand be idle at others. Alternatively, companies mayhave to build huge piles of inventory to anticipate bigswings in demand. With a surge in shipments, theymay also have to pay premium freight rates to trans-port products. Damage also increases from handlinglarger than normal volumes and stocking inventoriesfor long periods. The irony is that these variations areinduced by price fluctuations that the manufacturersand the distributors set up themselves. It’s no wonderthat such a practice was called “the dumbest market-ing ploy ever.”12

Using trade promotions can backfire because of theimpact on the manufacturers’ stock performance. Agroup of shareholders sued Bristol-Myers Squibbwhen its stock plummeted from $74 to $67 as a resultof a disappointing quarterly sales performance; its ac-tual sales increase was only 5 percent instead of the an-ticipated 13 percent. The sluggish sales increase wasreportedly due to the company’s trade deals in a previ-ous quarter that flooded the distribution channel withforward-buy inventories of its product.13

Rationing and Shortage GamingWhen product demand exceeds supply, a manufactureroften rations its product to customers. In one scheme,the manufacturer allocates the amount in proportionto the amount ordered. For example, if the total supplyis only 50 percent of the total demand, all customers

97SLOAN MANAGEMENT REVIEW/SPRING 1997 LEE ET AL.

Figure 3 Bullwhip Effect due to Seasonal Sales of Soup

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receive 50 percent of what they order. Knowing thatthe manufacturer will ration when the product is inshort supply, customers exaggerate their real needswhen they order. Later, when demand cools, orderswill suddenly disappear and cancellations pour in. Thisseeming overreaction by customers anticipating short-ages results when organizations and individuals makesound, rational economic decisions and “game” thepotential rationing.14 The effect of “gaming” is thatcustomers’ orders give the supplier little informationon the product’s real demand, a particularly vexingproblem for manufacturers in a product’s early stages.The gaming practice is very common. In the 1980s,on several occasions, the computer industry perceiveda shortage of DRAM chips. Orders shot up, not be-cause of an increase in consumption, but because ofanticipation. Customers place duplicate orders withmultiple suppliers and buy from the first one that candeliver, then cancel all other duplicate orders.15

More recently, Hewlett-Packard could not meet thedemand for its LaserJet III printer and rationed theproduct. Orders surged, but HP managers could notdiscern whether the orders genuinely reflected realmarket demands or were simply phantom orders fromresellers trying to get better allocation of the product.When HP lifted its constraints on resupply of theLaserJets, many resellers canceled their orders. HP’scosts in excess inventory after the allocation periodand in unnecessary capacity increases were in the mil-lions of dollars.16

During the Christmas shopping seasons in 1992and 1993, Motorola could not meet consumer de-mand for handsets and cellular phones, forcing manydistributors to turn away business. Distributors likeAirTouch Communications and the Baby Bells, an-ticipating the possibility of shortages and acting de-fensively, drastically overordered toward the end of1994.17 Because of such overzealous ordering by retaildistributors, Motorola reported record fourth-quarterearnings in January 1995. Once Wall Street realizedthat the dealers were swamped with inventory andnew orders for phones were not as healthy before,Motorola’s stock tumbled almost 10 percent.

In October 1994, IBM’s new Aptiva personal com-puter was selling extremely well, leading resellers tospeculate that IBM might run out of the product be-fore the Christmas season. According to some analysts,

IBM, hampered by an overstock problem the previousyear, planned production too conservatively. Other an-alysts referred to the possibility of rationing: “Retailers— apparently convinced Aptiva will sell well and afraidof being left with insufficient stock to meet holidayseason demand — increased their orders with IBM,believing they wouldn’t get all they asked for.”18 It wasunclear to IBM how much of the increase in orderswas genuine market demand and how much was dueto resellers placing phantom orders when IBM had toration the product.

How to Counteract the Bullwhip Effect

Understanding the causes of the bullwhip effect canhelp managers find strategies to mitigate it. Indeed,many companies have begun to implement innovativeprograms that partially address the effect. Next we ex-amine how companies tackle each of the four causes.We categorize the various initiatives and other possibleremedies based on the underlying coordination mech-anism, namely, information sharing, channel align-ment, and operational efficiency. With informationsharing, demand information at a downstream site istransmitted upstream in a timely fashion. Channelalignment is the coordination of pricing, transporta-tion, inventory planning, and ownership between theupstream and downstream sites in a supply chain.Operational efficiency refers to activities that improveperformance, such as reduced costs and lead time. Weuse this topology to discuss ways to control the bull-whip effect (see Table 1).

Avoid Multiple Demand Forecast UpdatesOrdinarily, every member of a supply chain conductssome sort of forecasting in connection with its plan-ning (e.g., the manufacturer does the production plan-ning, the wholesaler, the logistics planning, and so on).Bullwhip effects are created when supply chain mem-bers process the demand input from their immediatedownstream member in producing their own forecasts.Demand input from the immediate downstream mem-ber, of course, results from that member’s forecasting,with input from its own downstream member.

One remedy to the repetitive processing of consump-tion data in a supply chain is to make demand data at adownstream site available to the upstream site. Hence,

98 LEE ET AL. SLOAN MANAGEMENT REVIEW/SPRING 1997

both sites can update their forecastswith the same raw data. In the com-puter industry, manufacturers requestsell-through data on withdrawn stocksfrom their resellers’ central warehouse.Although the data are not as completeas point-of-sale (POS) data from theresellers’ stores, they offer significantlymore information than was availablewhen manufacturers didn’t know whathappened after they shipped theirproducts. IBM, HP, and Apple all re-quire sell-through data as part of theircontract with resellers.

Supply chain partners can use elec-tronic data interchange (EDI) to sharedata. In the consumer products indus-try, 20 percent of orders by retailers ofconsumer products was transmittedvia EDI in 1990.19 In 1992, that fig-ure was close to 40 percent and, in1995, nearly 60 percent. The increas-ing use of EDI will undoubtedly fa-cilitate information transmission andsharing among chain members.

Even if the multiple organizationsin a supply chain use the same source demand data toperform forecast updates, the differences in forecastingmethods and buying practices can still lead to unnec-essary fluctuations in the order data placed with theupstream site. In a more radical approach, the up-stream site could control resupply from upstream todownstream. The upstream site would have access tothe demand and inventory information at the down-stream site and update the necessary forecasts and re-supply for the downstream site. The downstream site,in turn, would become a passive partner in the supplychain. For example, in the consumer products indus-try, this practice is known as vendor-managed inven-tory (VMI) or a continuous replenishment program(CRP). Many companies such as Campbell Soup,M&M/Mars, Nestlé, Quaker Oats, Nabisco, P&G,and Scott Paper use CRP with some or most of theircustomers. Inventory reductions of up to 25 percent arecommon in these alliances. P&G uses VMI in its dia-per supply chain, starting with its supplier, 3M, and itscustomer, Wal-Mart. Even in the high-technology sec-

tor, companies such as Texas Instruments, HP, Motorola,and Apple use VMI with some of their suppliers and, insome cases, with their customers.

Inventory researchers have long recognized thatmulti-echelon inventory systems can operate betterwhen inventory and demand information from down-stream sites is available upstream. Echelon inventory— the total inventory at its upstream and downstreamsites — is key to optimal inventory control.20

Another approach is to try to get demand informa-tion about the downstream site by bypassing it. AppleComputer has a “consumer direct” program, i.e., itsells directly to consumers without going through thereseller and distribution channel. A benefit of the pro-gram is that it allows Apple to see the demand patternsfor its products. Dell Computers also sells its productsdirectly to consumers without going through the dis-tribution channel.

Finally, as we noted before, long resupply lead timescan aggravate the bullwhip effect. Improvements inoperational efficiency can help reduce the highly vari-

99SLOAN MANAGEMENT REVIEW/SPRING 1997 LEE ET AL.

Table 1 A Framework for Supply Chain Coordination Initiatives

Causes of Information Channel OperationalBullwhip Sharing Alignment Efficiency

Demand • Understanding • Vendor-managed • Lead-time reductionForecast system dynamics inventory (VMI) • Echelon-basedUpdate • Use point-of-sale • Discount for infor- inventory control

(POS) data mation sharing• Electronic data • Consumer direct

interchange (EDI)• Internet• Computer-assisted

ordering (CAO)

Order • EDI • Discount for truck- • Reduction in fixedBatching • Internet ordering load assortment cost of ordering by

• Delivery appoint- EDI or electronicments commerce

• Consolidation • CAO• Logistics out-

sourcing

Price • Continuous • Everyday low priceFluctuations replenishment (EDLP)

program (CRP) • Activity-based• Everyday low cost costing (ABC)

(EDLC)

Shortage • Sharing sales, • Allocation based Gaming capacity, and on past sales

inventory data

able demand due to multiple forecast updates. Hence,just-in-time replenishment is an effective way to miti-gate the effect.

Break Order BatchesSince order batching contributes to the bullwhip effect,companies need to devise strategies that lead to smallerbatches or more frequent resupply. In addition, thecounterstrategies we described earlier are useful. Whenan upstream company receives consumption data on afixed, periodic schedule from its downstream cus-tomers, it will not be surprised by an unusually largebatched order when there is a demand surge.

One reason that order batches are large or order fre-quencies low is the relatively high cost of placing anorder and replenishing it. EDI can reduce the cost ofthe paperwork in generating an order. Using EDI,companies such as Nabisco perform paperless, com-puter-assisted ordering (CAO), and, consequently, cus-tomers order more frequently. McKesson’s Economostordering system uses EDI to lower the transactioncosts from orders by drugstores and other retailers.21

P&G has introduced standardized ordering termsacross all business units to simplify the process and dra-matically cut the number of invoices.22 And GeneralElectric is electronically matching buyers and suppliersthroughout the company. It expects to purchase at least$1 billion in materials through its internally developedTrading Process Network. A paper purchase order thattypically cost $50 to process is now $5.23

Another reason for large order batches is the cost oftransportation. The differences in the costs of fulltruckloads and less-than-truckloads are so great thatcompanies find it economical to order full truckloads,even though this leads to infrequent replenishmentsfrom the supplier. In fact, even if orders are made withlittle effort and low cost through EDI, the improve-ments in order efficiency are wasted due to the full-truckload constraint. Now some manufacturers inducetheir distributors to order assortments of different prod-ucts. Hence a truckload may contain different prod-ucts from the same manufacturer (either a plant ware-house site or a manufacturer’s market warehouse)instead of a full load of the same product. The effect isthat, for each product, the order frequency is muchhigher, the frequency of deliveries to the distributorsremains unchanged, and the transportation efficiency

is preserved. P&G has given discounts to distributorsthat are willing to order mixed-SKU (stock-keepingunit) loads of any of its products.24 Manufacturerscould also prepare and ship mixed SKUs to the distrib-utors’ warehouses that are ready to deliver to the stores.

“Composite distribution” for fresh produce andchilled products uses the same mixed-SKU concept tomake resupply more frequent. Since fresh produce andchilled foods need to be stored at different tempera-tures, trucks to transport them need to have varioustemperatures. British retailers like Tesco and Sainsburyuse trucks with separate compartments at differenttemperatures so that they can transport many productson the same truck.25

The use of third-party logistics companies also helpsmake small batch replenishments economical.26 Thesecompanies allow economies of scale that were not fea-sible in a single supplier-customer relationship. Byconsolidating loads from multiple suppliers locatednear each other, a company can realize full truckloadeconomies without the batches coming from the samesupplier. Of course, there are additional handling and

administrative costs for such consolidations or multi-ple pickups, but the savings often outweigh the costs.

Similarly, a third-party logistics company can utilizea truckload to deliver to customers who may be com-petitors, such as neighboring supermarkets. If eachcustomer is supplied separately via full truckloads,using third-party logistics companies can mean mov-ing from weekly to daily replenishments. For smallcustomers whose volumes do not justify frequent fulltruckload replenishments independently, this is espe-cially appealing. Some grocery wholesalers that receiveFTL shipments from manufacturers and then shipmixed loads to wholesalers’ independent stores use lo-

100 LEE ET AL. SLOAN MANAGEMENT REVIEW/SPRING 1997

The simplest way to control thebullwhip effect caused by

forward buying and diversionsis to reduce both the frequency and the level of wholesale price

discounting.

gistics companies. In the United Kingdom, Sainsburyand Tesco have long used National Freight Companyfor logistics. As a result of the heightened awarenessdue to the ECR initiative in the grocery industry, weexpect to see third-party logistics companies that fore-cast orders, transport goods, and replenish stores withmixed-SKU pallets from the manufacturers.

When customers spread their periodic orders or re-plenishments evenly over time, they can reduce thenegative effect of batching. Some manufacturers coor-dinate their resupply with their customers. For exam-ple, P&G coordinates regular delivery appointmentswith its customers. Hence, it spreads the replenish-ments to all the retailers evenly over a week.

Stabilize PricesThe simplest way to control the bullwhip effect causedby forward buying and diversions is to reduce both thefrequency and the level of wholesale price discounting.The manufacturer can reduce the incentives for retailforward buying by establishing a uniform wholesalepricing policy. In the grocery industry, major manufac-turers such as P&G, Kraft, and Pillsbury have movedto an everyday low price (EDLP) or value pricing strat-egy. During the past three years, P&G has reduced itslist prices by 12 percent to 24 percent and aggressivelyslashed the promotions it offers to trade customers. In1994, P&G reported its highest profit margins in twenty-one years and showed increases in market share.27 Simi-larly, retailers and distributors can aggressively negotiatewith their suppliers to give them everyday low cost(EDLC). From 1991 to 1994, the percentage of tradedeals in the total promotion budget of grocery productsdropped from 50 percent to 47 percent.

From an operational perspective, practices such asCRP together with a rationalized wholesale pricingpolicy can help to control retailers’ tactics, such as di-version. Manufacturers’ use of CAO for sending or-ders also minimizes the possibility of such a practice.

Activity-based costing (ABC) systems enable com-panies to recognize the excessive costs of forward buy-ing and diversions. When companies run regionalpromotions, some retailers buy in bulk in the areawhere the promotions are held, then divert the prod-ucts to other regions for consumption. The costs ofsuch practices are huge but may not show up in con-ventional accounting systems. ABC systems provide

explicit accounting of the costs of inventory, storage,special handling, premium transportation, and so onthat previously were hidden and often outweigh thebenefits of promotions. ABC therefore helps compa-nies implement the EDLP strategy.28

Eliminate Gaming in Shortage SituationsWhen a supplier faces a shortage, instead of allocatingproducts based on orders, it can allocate in proportionto past sales records. Customers then have no incentiveto exaggerate their orders. General Motors has longused this method of allocation in cases of short supply,and other companies, such as Texas Instruments andHewlett-Packard, are switching to it.

“Gaming” during shortages peaks when customershave little information on the manufacturers’ supplysituation. The sharing of capacity and inventory infor-mation helps to alleviate customers’ anxiety and, conse-quently, lessen their need to engage in gaming. Butsharing capacity information is insufficient when thereis a genuine shortage. Some manufacturers work withcustomers to place orders well in advance of the salesseason. Thus they can adjust production capacity orscheduling with better knowledge of product demand.

Finally, the generous return policies that manufac-turers offer retailers aggravate gaming. Without apenalty, retailers will continue to exaggerate theirneeds and cancel orders. Not surprisingly, some com-puter manufacturers are beginning to enforce morestringent cancellation policies.

2

We contend that the bullwhip effect results from ration-al decision making by members in the supply chain.Companies can effectively counteract the effect by thor-oughly understanding its underlying causes. Industryleaders like Procter & Gamble are implementing inno-vative strategies that pose new challenges: integratingnew information systems, defining new organizationalrelationships, and implementing new incentive andmeasurement systems. The choice for companies isclear: either let the bullwhip effect paralyze you or finda way to conquer it. ◆

References

1. This initiative was engineered by Kurt Salmon Associates but pro-

101SLOAN MANAGEMENT REVIEW/SPRING 1997 LEE ET AL.

pelled by executives from a group of innovative companies like Procter& Gamble and Campbell Soup Company. See:Kurt Salmon Associates, “ECR: Enhancing Consumer Value in theGrocery Industry (Washington, D.C.: report, January 1993); andF.A. Crawford, “ECR: A Mandate for Food Manufacturers?” FoodProcessing, volume 55, February 1994, pp. 34-42. 2. J.A. Cooke, “The $30 Billion Promise,” Traffic Management, volume32, December 1993, pp. 57-59. 3. J. Sterman, “Modeling Managerial Behavior: Misperception ofFeedback in a Dynamic Decision-Making Experiment,” ManagementScience, volume 35, number 3, 1989, pp. 321-339. 4. Sterman (1989); andP. Senge, The Fifth Discipline: The Art and Practice of the LearningOrganization (New York: Doubleday/Currency, 1990).5. For a theoretical treatment of this subject, see:H.L. Lee, P. Padmanabhan, and S. Whang, “Information Distortionin a Supply Chain: The Bullwhip Effect,” Management Science, 1997,forthcoming. 6. M. Millstein, “P&G to Restructure Logistics and Pricing,” Super-market News, 27 June 1994, pp. 1, 49.7. V. Carroll, H.L. Lee, and A.G. Rao, “Implications of SalesforceProductivity, Heterogeneity and Demotivation: A Navy Recruiter CaseStudy,” Management Science, volume 32, number 11, 1986, pp. 1371-1388. 8. Salmon (1993).9. P. Sellers, “The Dumbest Marketing Ploy,” Fortune, volume 126, 5October 1992, pp. 88-93. 10. P. Kotler, Marketing Management: Analysis, Planning, Implementation,and Control (Englewood Cliffs, New Jersey: Prentice Hall, 1997).11. R.D. Buzzell, J.A. Quelch, and W.J. Salmon, “The Costly Bargainof Trade Promotion,” Harvard Business Review, volume 68, March-April 1990, pp. 141-148.

12. Sellers (1992).13. Ibid.14. Lee et al. (1997).15. L. Lode, “The Role of Inventory in Delivery Time Competition,”Management Science, volume 38, number 2, 1992, pp. 182-197.16. Personal communication with Hewlett-Packard.17. K. Kelly, “Burned by Busy Signals: Why Motorola Ramped upProduction Way Past Demand,” Business Week, 6 March 1995, p. 36. 18. Rory J. O’Connor, “Rumor Bolsters IBM Shares,” San Jose MercuryNews, 8 October 1994, p. 9D. 19. M. Reid, “Change at the Check-Out,” The Economist, volume334, 4 March 1995, pp. 3-18. 20. A. Clark and H. Scarf, “Optimal Policies for a Multi-EchelonInventory Problem,” Management Science, volume 6, number 4, 1960,pp. 465-490.21. E.K. Clemons and M. Row, “McKesson Drug Company — AStrategic Information System,” Journal of Management InformationSystems, volume 5, Summer 1988, pp. 36-50. 22. Millstein (1994).23. T. Smart, “Jack Welch’s Cyber-Czar,” Business Week, 5 August1996, pp. 82-83. 24. G. Stern, “Retailers of P&G to Get New Plan on Bills, Shipment,”Wall Street Journal, 22 June 1994.25. Reid (1995).26. H.L. Richardson, “How Much Should You Outsource?,” Trans-portation and Distribution, volume 35, September 1994, pp. 61-62. 27. Z. Schiller, “Ed Artzt’s Elbow Grease Has P&G Shining,” BusinessWeek, 10 October 1994, pp. 84-86.28. R. Mathews, “CRP Moves Towards Reality,” Progressive Grocer,volume 73, July 1994, pp. 43-44.

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