Optimising Ratio Packs - Part 1
While ratio packs prove very useful in apportioning stock across fringe elements of a range of SKU’s (eg. very small and very large sizes) in a cost effective manner, the do however present a clear and well known problem which is that whenever any sales demand occurs which isn’t in alignment with the ratio pack contribution (which is always), both excess volumes and out of stocks will occur assuming no other actions take place to move product between locations.
Let’s explore the concept of single fixed ratios against multiple ratios and the benefits it can realise. This is by no means the only option available however it is one of the first that can be attempted.
Let’s imagine the following scenario, where we have a product which comes in case quantities of 19 units across 7 sizes and where the units per size in each case closely reflects the sales contribution of each size.
In this scenario the total sales volume for the season is 21,383 units.
Although the units per size couldn’t be more aligned to sales contribution, we can already see that even if all stores sell in the same size contribution, there will be imbalances at the total level given there is some variance which will drive out an imbalance.
Of course, all stores don’t sell in the same size contributions and will look something similar to this graphic which represents the total sales by size and store for the season.
In all of these cases, the variance between the ratio pack and the sales contribution will be larger still and will therefore require more packs to be purchased in order to maintain the same level of availability.
In this case, to achieve 100% availability for the season (just for the purpose of the exercise), 1,440 packs of the previously mentioned ratio will be required which totals 27,360 units which is 28% higher than the units intended to be sold.
Now if we were to move away from 1 fixed ratio and source 3 ratios, each with the bias towards a certain section of the size curve, we should expect to see a reduction in the packs required to meet the same level of availability.
This is indeed the case. By applying Linear Programming we can simulate this scenario which results in only requiring 1,288 packs, or 24,472 units which is now 14% above the planned sales which is half the amount of markdowns required.
The question does remain though, “How many of each pack should be purchased?”.
By applying the same modelling, in this scenario with these pack ratios the answer is
732 of Pack 1
91 of Pack 2
465 of Pack 3.
As you can see, providing you have the systems in place to manage some of the complexities that come with handling multiple ratio curves simultaneously, the benefits can be well worth the exercise.