How Behavior Change Within the Enterprise Can Mitigate the Bullwhip Effect

In 1956, considerable fluctuations in production, inventories and profit baffled managers in General Electric’s household appliance division. Despite supervisory efforts, the variations endured. Traditionally, managers blamed these types of fluctuations on external causes, like business cycles.

Jay Forrester, a systems scientist at MIT, spoke with the individual managers and observed their departments. He discovered these fluctuations were not the result of exogenous events, but rather actions taken by the managers themselves. By responding to whatever information was locally available, managers at each link of the supply chain altered supplier orders to compensate for order and inventory variations. Each was seemingly rationally doing their best to control their piece of the organization.

The results, though, were changes in orders, production, hiring and more. And this fed back to alter inventories, backlogs, prices and advertising, heightening instability for the system as a whole.

Later termed the “bullwhip effect”, this important supply chain phenomenon continues today. But new research suggests there’s a simple solution for alleviating one of the major causes of this destabilizing effect.

Understanding the bullwhip effect

As demand moves up the supply chain, from consumers to stores to suppliers, it becomes more and more unpredictable. A small change in downstream demand near the end consumer ripples back through the tiers of supply with increasingly greater magnitude.

In their 1997 groundbreaking paper, Stanford Professors Hau L. Lee, V. Padmanabhan, and Seungjin Whang theorized four different factors that might be driving the bullwhip effect:

  • Demand forecast updating: Traditionally, companies in a supply chain forecast demand by looking at historical data on their own direct customers. Since the upstream chains see fluctuations in demand from the bullwhip effect downstream, those members order accordingly, creating greater swings for the upstream suppliers 
  • Order batching: The companies that place orders on upstream suppliers tend to do so periodically. To last several days or weeks, they’ll order a batch of an item, reducing transportation and transaction costs. This creates larger demand fluctuations further up the chain
  • Price fluctuation: A common practice in the grocery industry, frequent price changes lead buyers to purchase large quantities when prices are low, and avoid buying when prices are high. This wreaks havoc on the supply chain upstream 
  • Rationing and shortage gaming: Some industries face varying periods of oversupply and undersupply. When buyers know that a shortage is forthcoming, they increase their order size to ensure they’ll have what they need

The influence of human behavior on the bullwhip effect is clear, and the fear of loss, or of being wrong is still a key factors issue in supply-chain management.

Alleviating inventory runs

Just like the bank runs that caused much of the economic damage in the Great Depression, when a retailer sees that a supplier is on the verge of running out of an item, they order an inordinate amount in the hopes of keeping their shelves well stocked. 

In a new study, Robert Bray, Yuliang Yao, Yongrui Duan and Jiazhen Huo, found the first ever hard evidence of what they call “inventory runs.” Analyzing data from a major Chinese grocery chain, the researchers found that each store manager placed their orders on a computer system that also let them see the current inventory in the distribution center’s warehouse. The store managers used this information when deciding how much of a product to order, and when the distribution centers looked like they were about to stock out of an item, the managers all increased their order quantities. These inventory runs contributed significantly to the bullwhip effect for this supply chain.

The researchers simulated what would have happened if the stores had not been able to see the inventory level of the distribution center, and they found the distribution center would have seen 11% less fluctuation in day-to-day orders.

Sharing information tends to lead to better, more efficient outcomes for everyone in a supply chain, and since much of the source of the bullwhip effect is rooted in batching due to an unclear picture of downstream or end-consumer demand, it is hard to argue for less visibility. So, what lessons can we draw from these findings. 

It's important to note researchers Bray, Yao, Duan and Huo were looking at behaviors occurring inside a particular organization’s distribution network, consisting of distribution centers and retail stores.

Major CPG companies and retailers are colossal organizations with massive volumes of good running through the different tiers of their internal distribution networks. 11% fewer fluctuations within these large, if internal, supply chain networks, would contribute significantly to mitigation of bullwhip effect in the entire upstream supply chain. 

While hiding distribution center inventory levels from retail store managers (as the authors suggest) might work in the short run, we suspect that in the absence of visibility and predictability of supply, the risk averse overordering behavior would spread to all SKUs over time, thereby raising store inventory.

A better approach would be to consider the organization as a whole, rather than leave store managers to operate independently, in isolation. Better enterprise-wide outcomes are founded on:

  • Reliably better demand forecasts, considering the effect of promotions and other demand drivers at the store-SKU level
  • Enterprise-wide inventory optimization, considering stock at all tiers simultaneously, and
  • Setting up an allocation policy framework for constrained supply situations, and tracking compliance of stores to the recommended inventory levels

Antuit can help you guide behavior

When organizations recognize the underlying reasons behind process bottlenecks and other failures, they can more effectively allocate resources for better business outcomes.

Our team of data scientists, technologists and domain experts has worked with enterprises across various industries to drive behaviors that are optimal for an organization overall by way of processes, incentives, and UX design that make certain behaviors more convenient and frictionless, and others less so.


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