Enhance supply chain network design with sophisticated inventory optimization

The complexity involved in simultaneously optimizing a supply chain network and the inventory within that network has traditionally proven to be a sufficient barrier to completing such exercises. With improvements in tools and methodologies, this no longer needs to be the case. And under certain conditions, the benefits can more than justify the commitment in time and resources.

Most of us in the supply chain field are familiar with traditional approaches to modeling inventory in supply chain network optimization exercises. These approaches (square root law, power curves and turns analyses, among others) are often justifiable but underpinned by potentially limiting assumptions, including that a company’s turns or target fill rate will be held constant. They also approximate how inventory will react in response to a change in the number of stocking locations. And they help isolate and identify cost and service changes generated directly by changes to the network configuration.

There are often good reasons to use traditional approaches, because they:

• Provide reasonable, defensible estimates of working capital and warehouse space requirements
• Are straight-forward to model
• Often represent the right trade-off between effort and value generation

But what if your company is considering a more fundamental challenge to the status quo? Potential changes to your supply chain that could materially impact working capital and space requirements? For example, you may want to:

• Reshape or refine your supply chain fulfillment model
• Consider moving from a multi- to a single-echelon network (or vice versa)
• Reevaluate on-shelf product availability
• Build an ecommerce solution that offers guaranteed service response times
• Understand what a redesigned network and inventory position looks like to challenge traditional thinking about what’s “fixed” and what’s “up for grabs”

In such cases, the traditional approach to supply chain network design may not go far enough to consider the breadth of operational and financial implications. NTT DATA has developed a solution that combines supply chain network optimization with multi-echelon inventory optimization at the SKU and location levels.

Combining these two competencies can be difficult for a few key reasons:

• Increased complexity. Combining the two optimization exercises requires an iterative approach, which brings a degree of complexity that doesn’t exist when you conduct separate optimizations.
• Lack of enabling tools. There simply hasn’t been a leading optimization platform that simultaneously optimizes supply chain networks and inventory. As a result, you need one model for supply chain network design and another for inventory optimization.
• Lack of required skill mix. Most practitioners specialize in either supply chain network optimization or inventory optimization. Rarely will an individual with deep expertise bridge both competencies. Without this, it’s difficult to combine two distinct workstreams into a holistic and integrated solution.

Applying our approach has helped many clients achieve significant organizational objectives, including:

• Re-aligning the supply chain network and inventory planning with the mission of the business
• Achieving the service promise (regarding product availability and fulfillment lead times) with the lowest possible working capital
• Reducing operating expense through efficient product flows, less expediting and “right-sizing” facilities based on a rigorous assessment of storage requirements
• Providing an SKU-level inventory deployment strategy for the go-forward supply chain. Doing so helps companies achieve desired service outcomes and simplifies benefit realization

So, while it’s complex, it’s achievable and well worth it in situations where “getting inventory right” is critical. We’re firm believers that the role of good supply chain network design is about helping you move your firm beyond operating silos, constraints, skill gaps and questionable information. And toward rigorously tested, practical solutions that can be planned for and realized.

— By Chris Keller