How to Model Inventory Level Changes from Reduced Stock Locations

Executive Summary

  • How can one model the effect on inventory from a change in the stocked locations?
  • We cover two approaches.


We received this question, so we thought we would place the answer and the question into an article.

Question #1

Is there an industry standard way to think about the number of source points impact on Case Fill Rate? For example, we are considering reducing our stocking points for a volatile customer like Amazon, to just 2 DC’s (east & west coast) versus our normal 6 regional DC’s. Wondering if this consolidation of inventory and demand variability could account for some calcuable improvement in CFR?

Answer #1

There is no heuristic to answer this question as it depends upon the exact math.

However, this can be modeled. Two approaches come to mind.

Scenario 1: The Simpler Approach

  1. Step 1: Calculate the total stock and safety stock for the two or more locations you currently have.
  2. Step 2: Find a similar item with a very similar demand stocked at a few locations.

Then calculate the difference. You have to use real data because any assumption is going to change based upon the specific math. This is ok as a first cut, but it is not going to be very accurate.

Scenario 2: The Overall Supply Network Modeling Approach

  • Step 1: Take a sample of product locations to change the number of stocking locations.
  • Step 2: Calculate the as is stock and safety stock.
  • Step 3: Change (reduce) the locations for the second product location upload.
  • Step 4: Calculate the new stock and safety stock.

A Good Use for the Brightwork Explorer

When I do modeling like this, I have a tool that I use that makes this easy.

This allows me to upload scenarios, and this shows the total system inventory that is calculated. The safety stock calculation is a modification of the standard dynamic as the usual dynamic is not accurate and is not used. So this adjusted formula gives a realistic service level adjustable safety stock output.

This allows for modeling given service level assumptions. This is important because the service level assumptions have to be kept constant for the comparative analysis to have any meaning.