How To Manage the Bullwhip Effect

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Maddy Osman
Maddy Osman

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In an ideal supply chain, operations flow smoothly and predictably from one link to the next. But that’s not always the case in the real world. 

How to manage the bullwhip effect

You may be familiar with the term butterfly effect, where a small shift in one place can cause a major change in another. 

This phenomenon also happens in supply chain management, and it’s called the bullwhip effect.

What is the bullwhip effect in the supply chain?

The bullwhip effect occurs when a small change at the retail level of a supply chain amplifies as it moves upstream and causes major disruptions at the manufacturing and supply end. 

The disruptions occur as each member of the supply chain continues to escalate their response to demand changes.

As a result, businesses can experience:

  • Excess inventory or product shortages
  • Negative customer interactions
  • Disrupted transportation plans
  • Missed production deadlines
  • Revenue loss

Bullwhip effect examples 

To better understand the bullwhip effect, here are two examples.

Example 1: Drop in consumer demand

Say a clothing retailer experiences a 20% drop in consumer demand for windbreakers. 

As a result, the retailer makes two assumptions: 

  • Future windbreaker sales will be lower. This is because they’re using past demand information to predict future sales.
  • They will have too much inventory. Based on the assumption that sales will decrease, the retailer assumes they’ll end up with excess products. 

Given this information, the retailer cuts orders to the wholesaler by 40% — they have already ordered buffer stock and want to avoid having too much inventory.

The wholesaler in turn sees a 40% reduction in windbreaker orders and makes the same assumptions. They predict lower sales and assume they have too much inventory. 

However, the wholesaler sells to several retailers and assumes that those stores will begin to reduce their orders, too. So the wholesaler cuts windbreaker orders with the manufacturer by 60%. 

What began as a 20% decrease in consumer orders for one retailer has escalated to a 60% order reduction for the manufacturer. 

And this pattern will continue up the supply chain until it reaches the raw material suppliers.

Example 2: Rise in consumer demand

The bullwhip effect can also work in the opposite direction.

Say there’s a coffee shop that normally sells 200 cups of coffee per day. For some reason, they sell an average of 250 cups per day (a 25% increase) for one month. 

The coffee shop owner makes the following assumptions: 

  • Consumer demand is increasing. This assumption occurs because the seller uses past data to inform demand planning and sales forecasts.
  • They will need more inventory. If demand is going up, the seller will need to accommodate it.

So, the shop owner puts in a purchase order for 50% more coffee beans from the distributor. 

When the distributor sees this increase, they make the same assumptions. Say this distributor is a national seller that provides coffee beans to stores and cafes across the country. They assume that customer demand is increasing across the board and that they need more inventory.

Based on these assumptions, the distributor orders 75% more inventory from the supplier. A 25% bump in coffee orders at one shop has escalated into a 75% order increase at the supplier level. 

While a response to changes in consumer demand causes the bullwhip effect, the response may have been the incorrect interpretation.

For instance, what if the 25% increase was temporary?

Imagine that holiday travel brought additional customers to the coffee shops in this area for a short period of time. Instead of continuing to rise, demand returns to normal levels after the holidays, leaving the coffee shop owner with excess inventory. 

Implications of the bullwhip effect

The chain-reaction element of the bullwhip effect means that small changes in consumer demand can have large impacts on wholesalers, manufacturers, and suppliers. As such, there tends to be more market volatility as you go further up the supply chain.

Causes of the bullwhip effect

It’s helpful to understand why the bullwhip effect happens. Here are four of the top causes of this phenomenon. 

High sensitivity to consumer demand

As you can see from the examples, overreactions to fluctuations in customer demand can create more volatility as they move up the supply chain. 

Retailers that take a single data point and change their orders may create a wavelike effect that dramatically impacts manufacturers and suppliers.

Lack of communication in the supply chain

The bullwhip effect can be more frequent (and stronger) if each supply chain member does their own demand forecasting. Problems arise when there’s a change in demand at the consumer level, but one or more members don’t understand what caused it. 

Say a retailer doubles their order out of nowhere, and there’s no communication in the supply chain. The wholesalers and distributors are left to guess what’s happened and then try to project future demand.

Inaccurate assumptions

Flawed assumptions tend to happen around demand forecasting. Someone sees a change in demand and assumes it will last, so they adjust their orders accordingly. 

It’s possible to mistake a temporary spike or drop in demand for a more permanent change. Supply chain members who don’t properly analyze what’s going on in the market can make decisions that cause the bullwhip effect.

Inaccurate data

Sometimes data collection errors or a retailer accidentally ordering the wrong number of items can cause a disturbance as they work their way up the supply chain. 

Even the simple act of placing an incorrect order amount can cause someone else to assume what it means for future demand and subsequently change their orders.

How to reduce the bullwhip effect

While it’s impossible to completely eliminate the bullwhip effect, you can take steps to reduce its occurrence and total impact.

Improve data analysis and forecasting methods

Demand forecasting is a challenging but important part of supply chain management. New technologies can help you make smarter data-driven decisions and improve the overall accuracy of your projections.

Predictive analytics tools that include artificial intelligence (AI) features are an effective way to improve demand planning accuracy. AI can use several factors (such as order history, upcoming weather predictions, and even social media data) to inform demand projections. 

According to McKinsey, companies that leverage these new technologies often reduce demand forecasting errors by 30%-50%.

In addition to leveraging technology, it’s also important to consider the context of any changes in demand. Several events may cause temporary demand shifts, such as weather conditions, local events, and even sales and discounts. 

Supply chain members will be less reactive to temporary demand fluctuations when they better understand why consumer purchases shift and when these shifts signal an actual change. 

Enable supply chain communication and data sharing

Streamlined communications and real-time data sharing throughout the supply chain can improve decision-making. 

The 2022 Interos global supply chain report found that 82% of companies believe collective responsibility is necessary to protect against disruptions.

Zach Klempf, founder of used-car dealer software firm Selly Automotive, says companies “should think about collaborating with vendors on data analysis monthly.” It’s easier to stay accountable when it’s a regular part of the supplier relationship.

When one supply chain member changes their orders for a temporary reason, they can share the reasoning with other members, preventing overreactions down the chain. 

Use smaller order quantities

Placing frequent small orders instead of occasional large ones can help reduce the impact of the bullwhip effect. 

The practice gives everyone more flexibility, and you can respond better to demand changes without throwing off the entire chain. 

Also, you can  explore more demand-driven production methods with shorter lead times, such as just-in-time (JIT) and vendor-managed inventory (VMI).

JIT is a form of inventory management where you work closely with suppliers to ensure raw materials arrive as production is about to begin, but not sooner. 

In contrast, VMI is when the buyer (retailer) shares inventory data with the vendor (manufacturer). Then, the manufacturer recommends orders for the buyer.

Leverage technology and automation

Technology and automation can increase data accuracy and speed up communications.

For example, reducing the use of paper orders and invoice sheets, which have to be faxed or mailed.

Enoch Musaasizi, founder of marketing firm E.S Group Digital, advises companies to “use electronic data interchange (EDI), which allows companies to exchange business documents electronically with the use of encryption to improve confidentiality and data integrity.” 

EDI reduces the time it takes to process orders and improves the flow of information in the supply chain. 

Other useful supply chain technologies include: 

    • Cloud computing: Delivering computing services over the internet, which lets teams access information from multiple places and scale operations, such as inventory control, freight, warehousing, and fulfillment.
    • Internet of Things (IoT): Embedding physical objects with sensors to track them throughout the supply chain, which can help identify logistics and transportation bottlenecks.
  • Machine learning: A type of artificial intelligence (AI) that can be used to support real-time visibility, more accurate forecasts, and data-driven decision-making in the supply chain. Providers include companies like Havi and Echo Global Logistics.
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