I find this question comes up a lot as the lines of understanding start to blur the two offerings.
At the heart of this confusion is the network. Any time you want to optimize across an enterprise, whether it is inventory or product flows, you need to represent a logical representation of the supply chain. This creates a structure that math can be applied to. In this way the network structure is the ‘bones’ of the supply chain around which the optimization can be performed.
You have all seen supply chain network representations. They have nodes, each connected to another by arcs. These nodes could be geographic locations, machines, processes, capacity, or more. Arcs can be process flows, transportation lanes, modes of transportation, product flows, or bill of material links. It all depends on what problem you have designed the network to solve.
If you look at the history of applying network models to practical supply chain applications you will see quite different approaches depending on what problems they were originally designed to address. You could say it really depends on ‘what neighborhood they grew up in’. Some solutions were designed to optimize transportation problems. Some were designed to optimize manufacturing operations. A few will look at both distribution and storage needs.
The way these networks are logically represented colors the level of usefulness they bring to other sets of problems. Models designed for transportation problems typically struggle with manufacturing problems and vice versa.
This brings us full circle to what the difference is between multi-echelon inventory optimization (MEIO) and network design. Network design optimizes the network and uses inventory policy as a constraint. MEIO optimizes inventory policy (how much and where) to meet service and cost goals using uncertainty within the network constraints.
- Network design solutions were created to solve high-level, aggregate problems. Inventory optimization applications solve a real-world detailed inventory policy and deployment problem.
- Inventory optimization is a non-linear problem that requires different approaches than the mixed integer / linear programming used in commercial network design applications.
- Inventory optimization requires the network to be modeled at the SKU / location level detail. General network design tools use aggregated inventory to solve the problem.
- Inventory optimization is a time-phased problem. Your inventory plan has to be in the same cadence as the rest of your planning process. General network design tools aggregate the time horizon into one or a handful of time buckets.
- MEIO does not merely use the network model to propagate inventory constraints. MEIO uses the network constraints plus the uncertainty of supply and demand to optimize the inventory levels and policies.
- MEIO focuses on the inventory problem, not the transportation or manufacturing problem. Network design tools do not have the same focus on inventory as the strategic driver in their design and application.
- At best a general network approach is going to allow you to define an inventory policy at each node and use that as a constraint for solving the network. This is the tail wagging the dog if you want to optimize inventory policy.
The confusion stems from the fact that both approaches model the supply chain network. In fact any optimization that looks at more than one node in your supply chain is going to have some sort of network representation.
It is how these network representations are designed and applied that makes the difference. Having a network model does not make you automatically able to optimize inventory in that network. The devil truly is in the detail.
If you are looking for broad, aggregate swags on the inventory positions in your supply chain you may be able to get there with a general network design tool. However, if you want to optimize how inventory strategically drives your business, you need to lean on multi-echelon inventory optimization.