What is the bullwhip effect? Why small changes in retail sales hit suppliers hard

A supplier to a UK retailer plans from purchase orders. Some retailers also share their sales, stock and forecast figures, but the order is what you ship against. When a retailer's replenishment rule reacts to a change in sales (the stock replenishment guide shows how those rules turn into orders), its order to you can move by more than the sales did. Your own planning then reacts to that order. Every business in the chain can act sensibly, and the swing still grows.

What is the bullwhip effect?

The bullwhip effect is demand distortion that grows as it travels upstream. Orders to a supplier vary more than the buyer's sales, and the gap widens at each step back. Lee, Padmanabhan and Whang put it this way in Management Science in April 1997: "the variance of orders may be larger than that of sales, and the distortion tends to increase as one moves upstream". CIPS, the UK procurement body, says the same in one line: "The bullwhip effect is the demand distortion that travels upstream in the supply chain."

The same authors' practitioner article, in MIT Sloan Management Review (Spring 1997), opens with Procter & Gamble's nappies. Babies used them at a steady rate, yet "the demand order variabilities in the supply chain were amplified as they moved up the supply chain." The article notes that some industries call it the whiplash or whipsaw effect. Wikipedia adds a third name, the Forrester effect, after Jay Forrester's Industrial Dynamics (1961).

The idea is older than the name. As the 1997 paper records, "Forrester (1961) illustrates the effect in a series of case studies", and Sterman reported it in 1989 in the Beer Distribution Game, a four-player supply chain in which each player orders without consulting the others. Lee and his co-authors went further. They showed the effect appears even when every business in the chain is "rational and optimizing".

An illustrative example: how a 10-unit change in sales becomes an 80-unit swing

The table below is an illustrative example. It uses no retailer's data. The rule is simple. Each business keeps one week of stock cover based on the latest week of demand it sees, so each week it orders what it sold plus the change in the stock it wants to hold:

order this week = demand this week + (demand this week - demand last week)

Week 1 starts settled at 100 units. There is no lead time, to keep the sums simple.

Shoppers bought 10 extra units in one week, then went back to normal. The retailer's orders moved across a range of 30. A supplier planning production from those orders moved across a range of 80, with a week at 140 followed straight away by a week at 60. Over the five weeks every column adds up to 510 units: five steady weeks of 100, plus the 10 extra units shoppers bought, and not one more. Only the shape of the orders changed.

The last column shows what happens when the retailer shares its shelf sales and the supplier plans from them instead. The supplier's own layer of amplification goes. The retailer's layer stays, so the range is 30 where the shelf's was 10. Sharing data damped the swing. It did not remove it.

Real chains also have lead times. The 1997 paper found that "a long lead time in replenishing orders from upstream tends to aggravate the distortion even further." For the business upstream, the swings show up as what the MIT Sloan Management Review article calls "excessive inventory, poor product forecasts, insufficient or excessive capacities".

What causes the bullwhip effect?

Four things, according to the 1997 paper: "demand signal processing, rationing game, order batching, and price variations." CIPS uses plainer labels for the same four: demand forecast update, order batching, price fluctuations, and rationing and gaming.

Demand forecast updating. Retailers, the 1997 paper says, routinely treat the sales they see as a signal of future demand, and each business upstream forecasts from the orders it receives. Wikipedia describes the extra step that amplifies it: "a member of the chain who is ordering will add safety stock to the amount actually needed." The example above is this cause on its own.

Order batching. Buyers save demand up into larger, less frequent orders. Wikipedia calls it "the preference of most companies to accumulate demand before ordering, with the intent of reducing cost and simplifying logistics", and notes that "Consolidation of orders in this way creates an artificial variability in demand". The paper explains why it is routine: buyers "are constantly trying to gain economies in pricing (i.e., volume discounts) and transportation."

Price variations. Promotions and discounts pull demand forward. The paper calls price promotions "endemic in mature product categories". Wikipedia describes the result: "large spikes of demand followed by longer periods without orders while the excess stock is sold off".

Rationing and shortage gaming. A buyer that expects to be rationed orders more than it needs, then cancels. The paper traces this to "strategic decisions by the retailer who assesses the possibility of being placed on allocation by the manufacturer", and adds that "an unrestricted choice of order quantities, free return and generous order cancellation policies all contribute to gaming."

None of the four needs anyone to behave badly. The paper's central finding is that each business can be ordering sensibly for itself, and that "companies attempting to gain control of the bullwhip effect are better served by attacking the institutional and inter-organizational infrastructure and related processes." The paper's target is how orders, prices and allocation rules are set up.

The four causes, and what damps each:

Why does it hit suppliers harder than retailers?

Because the supplier sees the order, not the sale. The retailer at least watches its own tills. A supplier planning from purchase orders sees demand only after the retailer's forecasting, batching and promotions have shaped it, and in the 1997 definition the distortion "tends to increase as one moves upstream". In the example, the shelf moved by 10 and the supplier's plan by 80.

What the supplier carries is the cost of that gap. Wikipedia notes the effect "can lead to either inefficient production or excessive inventory". The MIT Sloan Management Review article lists excess stock, poor forecasts and capacity that is too high or too low.

Is the bullwhip effect good or bad, then? It is a cost, and an understandable one. Each order on its own makes sense to the business that placed it. The fixes sit in shared information and in the rules both sides trade by, so a supplier has some say over it.

How can the bullwhip effect be reduced?

By giving each business better information about real demand, and by changing the rules that make orders lumpy. The 1997 paper's main prescription reads: "Sell-through data and information on inventory status at downstream nodes are keys to improving channel coordination and dampening the bullwhip effect." In its section on order batching, the paper describes a manufacturer using that data to build "a production schedule that is determined by sales as opposed to orders". The last column of the example does the same.

As early as 1997, the paper gave EDI two jobs. One is carrying demand data upstream: "These systems facilitate quick and easy transmission of demand data to upstream members of the channel." The other is making each order cheaper to send: "EDI-based order transmission systems are a big help in reducing the ordering costs and batch size." When orders cost less to place, buyers can order smaller and more often, which takes some of the lumps out of order batching.

The other measures in the paper:

• Shorter lead times. The authors call shortening the lead time "a direct and effective counter-measure".
• Steadier pricing. Reduce "the frequency as well as depth of manufacturer's trade promotions". The paper ties this to the shift then under way among major manufacturers such as P&G, Kraft and Pillsbury to an Every Day Low Price strategy.
• Fairer allocation. Allocate scarce supply "in proportion to the retailer's market share in the previous period". Imaginary shortages "can be prevented to a degree if the manufacturer shares production and inventory information with downstream members of the supply chain."

CIPS gives UK readers the same direction: "The bullwhip effect can be reduced by sharing your knowledge with your suppliers and customers." Its list of measures starts with reducing lead times and includes "Limit price fluctuations where possible". Wikipedia describes collaborative planning, forecasting and replenishment (CPFR) as an approach built on shared information and "joint visibility and replenishment of products throughout the supply chain".

No single measure does it alone. The same paper says "Access to a common data set for forecasting purposes is not the total solution", and that "a combination of activities are necessary to counter the bullwhip effect." Stanford Graduate School of Business, summarising Lee and Whang's follow-up study in 1999, put the condition plainly: "By sharing information with its supplier, a retailer can lessen information distortion" and "information sharing has to be timely and accurate." Late or wrong figures do not meet that test.

What can a supplier do from its own side?

Start with any data your customers already share. Four UK retailer supplier manuals show how it reaches suppliers:

• Marks & Spencer (ambient food logistics handbook, April 2019), for long life ambient products and beers, wines and spirits: "M&S provides Suppliers with 16 week forecasts of expected demand, these are sent on a weekly basis via email." Finalised purchase orders go by EDI. The handbook also asks suppliers to "Work with your Supply Chain Planner to review orders, intake profiles, improvement plans and seasonal & promotional peaks."
• Wickes (supplier manual, January 2022): "Forecasts are available for you to obtain in i-Supply", alongside sales out and current stock by product and location.
• Travis Perkins (supplier manual, October 2021): its extranet gives suppliers "access to shared information about their stock and sales out with the Group."
• Ocado (supplier manual, September 2024): suppliers use its portal to "Review delivery forecasts and order schedules" and to "Give Ocado advance warning of PO shortages".

The sales and stock figures Wickes and Travis Perkins share are the sell-through and stock data the 1997 paper asks for. M&S, Wickes and Ocado share forecasts by email or through a portal; at Travis Perkins, suppliers request a purchase plan from their Supply Chain Analyst. M&S also asks for "Early and clear communication of any Purchase Order amends with the forecast and supply chain teams" to keep its forecasts accurate. The data runs both ways.

Read the forecast as a plan. Wickes and Travis Perkins both give their forecasts "in good faith, but without any liability or obligation". As the stock replenishment guide puts it: "A forecast shared by a retailer is a plan, not an order: suppliers plan with the forecast and ship against the purchase order."

Know the standard messages. GS1's EANCOM standard has a message for each kind of data. The sales data report (SLSRPT) carries "basic sales data related to products or services, including the corresponding location, time period, product identification, pricing and quantity information." The inventory report (INVRPT) carries "information related to held inventories and planned or targeted inventories." The delivery schedule (DELFOR) carries "short term delivery instructions and/or medium to long term product/service forecast for planning purposes". GS1 also says the sales message should never replace business transactions such as purchase orders, delivery schedules or inventory reports. The guide to EDI transactions sets these beside the core order documents: "Around them sit changes, confirmations, product data and forecasts."

Keep the lead time honest. As the lead time guide puts it: "Your customer holds the lead time you agreed, and the due date on each order comes from it." Shorter lead times are one of the measures the paper names.

Answer each order accurately. The purchase order is the signal that gets distorted, and the order response is your answer to it. In the purchase order guide's words: "An order response can acknowledge an order, accept it, propose changes or decline all or part of it, and each customer sets whether one is required." An accurate response, sent early, tells the customer what you will supply against that order before the delivery is due.

The despatch advice (ASN) does the same for the goods: it "is an EDI message sent when goods leave the supplier, listing what is coming, how it is packed and when it will arrive." The OTIF score then records how the delivery matched the order: it is "the share of a customer's orders delivered on the due date or inside the booked slot, and in the quantity ordered."

How Transalis fits

Transalis is a managed outcomes provider for B2B integration, EDI and eInvoicing. Its part of this picture is the order traffic between you and your retail customers: orders arriving as data rather than on paper, and order responses going back. The EDI page describes three ways in:

• Web EDI: "A managed web portal to receive orders, send invoices and manage compliance in a browser."
• Integrated EDI: "EDI wired directly into your ERP, accounting or warehouse system."
• Enterprise EDI: "A scalable, enterprise-grade platform for high transaction volumes across thousands of trading partners, countries and systems."

The web EDI guide draws the line between the first two: "With web EDI a person reads each order and keys each invoice, while integrated EDI connects an ERP, accounting or warehouse system so documents flow without rekeying."

Transalis also sends order responses and receiving advice messages for its customers.

Book a 30-minute call with a Transalis retail supply chain specialist to walk through EDI between your systems and your retail customers.