Inventory Optimization

How to Best Understand Multi Echelon Inventory Optimization

Executive Summary

  • Multi echelon inventory optimization MEIO is one of the significant methods of supply planning.
  • MEIO is related to effective lead time.
  • Multi-echelon versus DRP for deployment.

Introduction: What Multi Echelon Answers

The short answer is that multi-echelon inventory optimization answers the question of “where” in the supply network should inventory be located. Multi-echelon is a supply positioning model. You will learn how multi echelon planning differs from standard planning.

See our references for this article and related articles at this link.


One can see the different supply positioning model that is created with multi-echelon functionality. Stock in different locations is no longer treated as islands. 

The Companion Math to Inventory Optimization

Multi echelon Inventory Optimization is the companion technology or math to inventory optimization within inventory optimization and multi echelon (MEIO) software. Multi-echelon is the less well-known of the two parts of multi echelon inventory optimization software. (A definition of inventory optimization is listed in this article.)

Two Technologies in One Application

Multi echelon Inventory Optimization is an innovative use of two separate forms of optimization: inventory optimization and multi-echelon inventory optimization (but which I refer to as multi-echelon planning to reduce confusion). Each answer separate supply chain planning questions. Inventory optimization answers the question of how much to keep in stock, while multi-echelon planning answers the question of where to keep inventory in the supply network. Unlike supply planning techniques that use sequential processing or calculation, MEIO calculates the service level impact of carrying one additional item at every product location combination. It then sorts the list of options by their contribution to service levels and selects the best contributor.

The mathematics of inventory optimization and multi-echelon planning are combined to improve the inventory and service planning, creating a supply positioning model of the supply network; the graphic on the following page illustrates where they t into the larger supply chain software context.

Multi Echelon Inventory Optimization Mathematics

The mathematics of multi-echelon planning allows for the planning of a supply network in a way that uses assumptions on how the locations in a supply network interact with one another that is different from any other supply planning method (MRP/DRP, heuristics, allocation or cost optimization)

Unlike these methods, software with multi-echelon capability assumes that the various locations (specifically parent and child locations) are interrelated. That is, the stocking at a parent location impacts both the stocking decision and service level at the child location.

Understanding Effective Lead Time

For anyone who wants to understand multi-echelon planning, it is necessary to comprehend the concept of effective lead time. A post on effective lead-time is in this article.

Tests for Multi-Echelon Inventory Optimization Capability

The software must pass two tests to determine whether it is, in fact, multi-echelon. These tests are necessary because some vendors like SAP have written white papers that have given the distinct impression that their software contains the mathematics of multi-echelon planning, when, in fact, it does not.

  1. When the stocking position at the parent location changes, does the effective lead time change? (not the lead time, that stays the same, but the effective lead time)
  2. Is the inventory in the supply network being managed as a “pool?” That is, does the software have the underlying mathematics to perform all the complex calculations that are necessary to make optimal stock location decisions across the supply network?

Multi-Echelon Versus DRP for Deployment

DRP is the deployment supply planning method that most people are familiar with. I thought it would be interesting to compare DRP to MEIO deployment. I will begin with a quotation from the white paper “Multi Echelon Inventory Optimization” by Calvin Lee Ph.D., where he describes the important distinction between DRP v.s multi-echelon enabled planning.

“DC-level demand forecasts are first used to develop gross product requirements. These forecasts are combined with safety stock requirements and stock status information to arrive at net requirements at the DCs. This is analogous to an MRP master schedule. The time-phased dependent demand at the RDC is calculated by offsetting the DC net requirements by the RDC to DC lead times and summing over corresponding time periods. The RDC uses these “pass-up” demands to replenish itself. The DRP approach has several major shortcomings. The No. 1 weakness is the deterministic perspective vis-à-vis pass-up demands and lead times. An immediate consequence of this is the subjective way in which the RDC safety stock is usually determined. Because the requirements passed up to the RDC include no uncertainty, there is no rigorous method for determining safety stock. This is why enterprises that use this replenishment method generally use rules of thumb for the RDC safety stock; this unscientific approach leads to excess inventory. It is not surprising that safety stock determination is somewhat loose—DRP has its roots in manufacturing, where production and transportation costs are of greater concern than inventory costs. Like the sequential approach, DRP fails to exploit visibility up the demand chain and lacks a network view of inventory optimization.”Calvin Lee PhD

DRP’s Limitations

DRP is an older method of deployment that dates back to the early 1980s. It is essentially a modification of MRP. MRP can only plan the production and procurement for each location and does not perform the deployment. DRP only creates stock transfer requisitions but can see the valid location to location combinations. MEIO is a much more sophisticated way of performing deployment but requires that the company assign service levels. This can be something new for companies that are used to set the DRP system as either push or pull and using fair share logic to perform the allocation.

There is flexibility in how this service level is assigned, and the benefit of MEIO is that the same service levels drive both inbound and outbound logistics. For instance, depending on the vendor, they can be assigned to the product location combination or the location, or the customer.


Multi echelon Inventory Optimization is one of the most sophisticated technologies available in supply planning. It allows a supply positioning model to be created that can determine the best source of supply among many options, and it creates pools of inventory rather than treating each stocking location as an island.

MEIO is a far more sophisticated method of performing the deployment. However, there is an adjustment for those who are primarily used to using DRP for deployment.

The Problem: Preparing for Inventory Optimization

Inventory optimization projects tend to take a long time and to be a significant expense. As with most complex implementations, the actual effective usage of inventory optimization software will significantly lag after whatever the initial project plan predicts. For companies that are interested in inventory optimization, we have a simpler solution that should be tested first before investing in inventory optimization software.

A major part of getting supply planning right is setting these parameters. Testing of the extracted parameters of ERP and external supply planning systems clearly shows that these values are poorly maintained. The result is far worse planning results than could be obtained otherwise.

Being Part of the Solution: Our Evolution of Thinking on Maintaining Inventory Parameters

Maintaining inventory parameters like rounding values and lot size in systems comes with a number of negatives that tend not to be discussed. One issue is that when using ERP systems, inventory parameters are typically managed on a “one by one” basis. This leads to individual planners entering values without any consideration for how inventory parameters are set across the supply network. After years we have given up managing safety stock or other inventory parameters in we now calculate inventory parameters in our application, the Brightwork Explorer, and then upload the data into the ERP system. See our link below. We have developed a SaaS application that sets the inventory parameters that allow for simulations to be created very quickly. These parameters can then be easily exported, and it allows for far more control over the parameters. In our testing, the approach, which is within the Brightwork Explorer, is one of the most effective methods for managing planning in any system. This approach is laid out in the book How to Repair Your MRP System.

In our testing, the approach, which is within the Brightwork Explorer, is one of the most effective methods for managing planning in SAP applications.

See our link below.

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