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Service Levels in Inventory Optimization

When discussing safety stocks, one cannot get away from asking the fundamental question, to what service level is safety stock designed? Alternately, there are those of us who love the metrics on days-of-forward-coverage based on “what typically works.” And the more technical of us will quickly explain Excel’s NORMSINV function to create a mathematical translation to the service-level expectation to calculate safety stock. Regardless, the implicit questions are how do you figure out the “right” service levels and how do you develop comfort with inventory levels and forward coverage?
This in itself can be an interesting discussion in that the supply chain is multi-dimensional. In this post, I will cover three common dimensions for determining the right inventory service strategy:

• Service-Level Qualification (the what)

• Quantifying Service Levels (the how)

• Quantitatively Measuring the Impact of Service Levels (the how much)

Service levels in inventory planning are utilized to define stock availability expectation while we cater to (or buffer for) demand and supply variability. The best way to relate to a service level is as a probability of filling all item demand, a probability of filling an order completely, or a probability of filling a percentage of all item demand (and yes, the service-level expectation is different than fill rate).

However, what is even further fundamental to this discussion is how service level is qualified? Are we defining it as line fill or unit fill? Those of us who have served as a retailer’s supply source have encountered discussions where line fill is a more omnipresent requirement contractually and a significant consideration in fulfillment and inventory planning. The common reason for this is retailers want their order lines to be filled fully with minimal back-orders and “shorting” of an order line (e.g., a retailer orders ten lines, and we filled nine lines fully of varying order sizes). However, we are still typically thinking in terms of unit fill (e.g., a customer ordered 100 units, and I filled 99 units). How we define this can have an impact on the overall inventory position as we try and appreciate the trade-off between line fill versus unit fill.

Quantification of service levels (i.e., what should we set the service level at now that we have figured out if we need line fill or unit fill) is an interesting topic. Should it be based on the risk of a stock-out? How does one define the cost of a stock-out? Should one blindly follow the competition and match the service guarantee they provide? Or, should one use the quantification through service loss calculations?

Furthermore, one could assume that every SKU in the portfolio has the same service level. But in today’s times of segmenting the supply chain, this is not necessarily the case. Every exercise we take in this space is a learning in itself, and the key enabler is typically achieved through a deep-end portfolio review.
The third dimension is just as interesting. As an example, let’s say I service a handful of retailers and several wholesale distributors. Given that my supply chain is established, I know the “what,” and I know the “how,” so I have defined how I want to measure service levels and I know what the service expectations are (let’s say 96%). A retailer I service has asked for a 99% service expectation. How can I define the impact on my inventory holding and stock position of this request? Also, how can I now assure differential service to the retailer for the same set of SKUs? Do I create a separate inventory pile, or can I solve this through combinatorial optimization methods commonplace in inventory planning tools for the last several years but not considered relevant?

As more supply chain executives are getting a seat at board tables and commercial discussions, these considerations are becoming more relevant and discussed more often. Shifts in the inventory optimization space are continuing to define frontiers in a more prevalent manner, and it is exciting to see how processes and technology are both pivoting in response to these needs.
 
—-Aman Sapra, St. Onge Company
 
 

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