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August 31, 2015
Optimize your supply chain Bullwhip Effect
By: Mark Wahl & Jeff Keene

Supply chain professionals are quite familiar with the concepts of both Multi Echelon Inventory Optimization (MEIO) and the Bullwhip Effect, but the relationship between the two is rarely discussed. We would like to introduce some ideas around another type of analysis—"optimizing a supply chain with respect to the bullwhip effect."


To start, let us first define terms. When we talk about MEIO, we are referring to the process of modeling an organization’s end-to-end supply chain and then optimizing for both cost and customer service. This is an involved process that requires a significant investment of time and resources, but the payoffs can be significant; we find that most organizations can save as much as 30 percent in inventory costs after completing an MEIO analysis. In addition, they gain an understanding of their inventory drivers and can better manage their resources.

The Bullwhip Effect, on the other hand, is a pretty straightforward operations concept representing the trend of ever-increasing swings in inventory in response to changes in customer demand as one looks further back through the supply chain. In other words, changes in customer demand are relatively easy to see for the customer-facing nodes of the supply chain (the retail store, for example), but as you move upstream in the supply chain to the second-tier distribution centers and manufacturers, those changes in demand can be drastically over-stated, causing difficulty in planning and execution.

In most MEIO analysis, the objective is to strike the appropriate balance between minimizing inventory costs and maximizing customer service (i.e. maximize fill rate of finished goods to customer nodes) given the existing constraints. These constraints may be in the form of shipping lane restrictions (e.g. plant A only ships to DC X and plant B only ships to DC Y) or inventory restrictions (e.g. must always hold Z units of inventory at facility C). The bullwhip effect is almost never modeled directly when setting up constraints.

Note that in a one or two echelon supply chain, the bullwhip effect is implicitly modelled, as the optimal inventory solutions are typically where the demand variability of the overall system is minimized. However, for complex supply chains of more than three echelons with greater uncertainty, constraining the maximum bullwhip effect between nodes may be an important concern.

We can measure the Bullwhip in a system by comparing the coefficient of variation (standard deviation of demand over the mean of demand) from an upstream facility to a downstream facility. We call this the Bullwhip Effect Ratio, and when it is greater than one, the downstream node is introducing additional volatility into the system. When the ratio is less than one, the downstream node is relatively more stable than the upstream node.

Let’s look at a simplified (but real life) client example. The following is a graphical representation of the Bullwhip Effect Ratio:

This graphic highlights areas where this company is doing well and where they are doing poorly with respect to the Bullwhip Effect. In the cases highlighted in red, we can see that the Bullwhip Effect Ratio is significantly higher than one, which suggests that the downstream nodes are introducing high levels of variability to the system, which has a ripple effect on the entire supply chain through increased costs and decreased service levels to customers. This analysis helped our client understand where their pain points are with respect to the Bullwhip Effect.

The bullwhip effect is created by the behavior of participants in the supply chain but there are many ways to mitigate it through better understanding of inventory drivers, typically achieved with an MEIO analysis.

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