Determining the impact of manufacturing and distribution network changes on inventory levels, and the storage space that inventory will require, is a key function of supply chain network design. It’s why most commercial network design software has functionality to examine this relationship. But, knowing exactly what you are optimizing though is essential to getting the right answer.
[Editor’s Note: Our goal with this post is to explain one common pitfall of inventory optimization, and help you avoid it. If your company is evaluating its supply and distribution networks in response to the novel coronavirus (COVID-19) pandemic, this becomes even more important to ensure that your network and inventory plans are in sync.]
You can only optimize one business problem at a time – be sure to pick the right one
Supply chain modelers and fellow math enthusiasts may see the obvious “chicken and the egg” paradox found in combined network and inventory optimization. Mathematically, you can only optimize one business problem at a time. In our case, you can either design the best network configuration based on projected inventory levels or determine the right amount of inventory to hold given your network parameters and desired service levels—not both, at least not at the same time, usually.
In most network modeling applications, this math problem is structured as a non-linear relationship (i.e., a power curve) between inventory levels and throughput (as illustrated in Figure 1). This technique supports the basic inventory principle that a distribution network with more inventory buffers – that is, nodes where you position inventory in the network to protect against fluctuating demand – will require more inventory than one with fewer buffers.
To complete this analysis, you would typically rely on historical data, key performance indicators (KPIs), or general rules of thumb. You might be able to guess, however, this traditional approach has some limitations and can lead to problems when operating your new network down the road.
The more changes you’d like to make to your network, the less you can depend on a conventional approach
Historical data and KPIs may be adequate data inputs when the network changes you’re considering aren’t too different from your current network. More specifically, if you are keeping the same numbers of buffer echelons and have comparable lead-times from the sources of supply, then you’re probably okay using this approach.
If, however, you are considering network changes that would result in changes to lead time, lead time variability, or variability in demand as depicted in Figure 2, then this conventional approach falls short. Similarly, if your current network has only one distribution center (or none at all), or you want to see what effect ramping up inventory fill rates will have on required inventory levels and storage space, then this approach will also be misleading.
When optimizing your network and inventory together, it’s crucial to correctly identify the types of optimization decisions involved, the order those decisions need to be made in, as well as the right level of detail for the model.
Use an optimization model to evaluate extensive network changes
If you are evaluating substantial changes to your supply chain network and want to simultaneously determine the right network configuration and inventory levels, the best approach is to use a single optimization model — one that includes supply chain constraints, capacities, and costs and acknowledges the impact of changes in lead-time, lead-time variability, and demand variability. Designing your model in a way that gives consideration to all these components will help you avoid the “chicken and the egg” sequential decision paradox.
Getting inventory projections just right when designing your supply chain network is critical. Inventory is one of the most effective, yet costly components in the entire supply chain. It can make your network more responsive to demand variability and less prone to shortages, but it also ties up significant working capital.
Chainalytics’ Supply Chain Design team has successfully employed an optimization-based approach for years. Our combination of top supply chain talent, proven methodologies, and proprietary market intelligence delivers actionable insights and measurable outcomes for organizations looking to maximize the value of their supply chain. Reach out to us if we can assist you in simultaneously optimizing your network and inventory.
Jeff Zoroya is a Principal in Chainalytics’ Supply Chain Design practice. Jeff leads data-driven engagements related to supply chain network design and strategic planning across all major industries, including Retail/Wholesale, Food & Beverage, Consumer Goods, Tech, and Industrial.
