.svg)
.svg)
.webp)
The problem we want to tackle in this installment of Shred the Spreadsheet is one of those issues that no matter how far you zoom into it, you see more and more detail. This problem represents a cannabis-retail-buying-kaleidoscope of a spreadsheet.
The spreadsheet challenge in question? Defining “demand ceilings” across subcategories of products so you can budget and allocate inventory from vendors accurately and effectively.
And we’re gonna shred it … or at least a good bit of it.
The Problem: Defining True Product Level Demand
Imagine for a moment that you’re a buyer at a multi-location retail store. You’re trying to budget for what you’re going to spend across your inventory to restock in certain categories.
Are you looking at a total number to spend across a category like vapes? How do you define the product mix between .5g 510 carts, all-in-ones, or proprietary pods like STIIIZY or Pax?
Are you reupping by brand instead? How are you determining how much of any given SKU that brand offers to reorder?
What you need to define in any case is a demand ceiling, which is a number that represents the total amount of demand a given subcategory or SKU is capable of pulling.
No matter what you do, the number of people that want a given thing is pretty absolute, influenced outside of your control. The best thing you can do is correctly purchase inventory based on that demand ceiling number.
Demand is a little fickle, however. You can very incorrectly assume the wrong amount of demand if you’re not categorizing your inventory based on how people are actually buying, and how daily inventory levels influence what is selling.
Go Many Layers Deep
Consider each category as a layer cake, with a bunch of uneven layers representing subcategories, the thickness of each layer determined by consumer demand. Let’s imagine a vape cake. Doesn’t sound very tasty.
Your vape cake is layered based on consumer purchasing preferences, not based on your default subcategories. How are people buying vapes, generally?
- 510 carts: people that buy 510s typically already have their own batteries and are just refilling the cart. They rarely buy AIOs or proprietary.
- .5g and 1g carts are not that different for these buyers, but could serve as a useful deeper category depending on your inventory levels or pricing
- Same goes for rosin vapes vs solvent-based vapes
- AIOs: people buying all-in-ones don’t care to keep a battery, or are traveling and need something convenient and “disposable” (the tree-huggers in us hate that word)
- Proprietary carts: these should be drilled down by brand, as proprietary brand batteries and form factors are going to have different levels of demand
Within these three layers are a few thinner layers, either by weight or brand. But you wouldn’t need to drill down that far to already experience better and more accurate ordering. You’ll be well on your way to solving this challenge just keeping it at the three.
Consider this type of analysis for all your categories. For instance, the preroll category alone isn’t layered enough. You want to understand your 1g prerolls, your infused 1g prerolls, your preroll packs, your dogwalkers, etc.
Each layer has its own demand ceiling, calculated by the daily sell-through of units under that layer.
Most Retailers’ Solution: Pivot Table After Pivot Table
As you probably can imagine, this is not easily accomplished via spreadsheets. It’s more like a tesseract of spreadsheets is required to solve for this.
To vastly oversimplify it, you need to create groups of products in your spreadsheet that fit the layer you want to analyze via pivot tables. Let’s say 1g prerolls.
Group those 1g prerolls into their own table, and include the units/day sold as a column. Sort by it. Determine the total amount of SKUs to reorder based on the units/day sold data, and cut off the ones that sell less than whatever you feel is too little, say 2 units/day as an example.
This is creating the ceiling and floor. The floor omits SKUs you don’t need to stock because no one is really buying them. The ceiling is the total units/day sold on all other SKUs above the floor. It’s likely you’ll easily identify at least 20% of your SKUs that can be omitted because they’re living in your basement.
Now you can total up the costs of those SKUs you want to reorder by the amount based on your demand ceiling and restocking intervals, and then set your budget for 1g prerolls.
Do that for every layer you can identify in your inventory.
Note: IT WILL STILL BE WRONG. Some of these SKUs will only be in stock for a few days of the period you're analyzing. Some SKUs replace each other - this one sells for 3 days and then this other one sells for 5 days. You have to track the in and out of stocks live and adjust the run rate or else totalling the individual run rates will be off by 30% or more.
Once you’ve amassed all that information, roll it up into your store buying budget. Do that for every store.
You start to see how even just going that 1 layer deeper into your categories can take a TON of spreadsheet jockeying, yeah?
Our Solution: Demand Groups
In Happy Buyers, we make this super easy by creating Demand Groups. This is a feature that allows buyers to lump together any given set of SKUs, products or brands to help them analyze the demand of that particular group of things. It then proactively notifies buyers when groups need to be reordered and generates a recommended order for each store automatically.
Our buyers create as many of these as are useful to them. When you click into any one of them, you immediately see the “Demand Ceiling” we referenced above. This number tells you the total amount of units sold for that group. No pivot tables or spreadsheet tesseracts required.
The Demand Ceiling number is provided throughout the platform for every Store, Category, Brand, and Demand Group. With our new Inventory Health metric, you'll have smart run rates at each of those levels as you drill down into your inventory.
We're not just replacing the pivot table, we're doing things you can’t do with pivot tables that use POS data:
- Tracking live inventory movement so the demand ceiling is our "smart run rate" and is more accurate than what can be pulled from the POS into a pivot table
- It's not just click-in and view data. Happy Buyers tells you when you're low on stock in a group and builds a suggested order for you for every store.
Not only does this feature represent countless hours of savings for buyers, it also unlocks this expert-level strategy for smaller organizations that have never considered buying in this way. It instructs them on high-level purchasing behavior and actively prevents them from overstocking when dollars are most critical.
We provided this feature because we truly are on a mission to reduce the amount of spreadsheet work retail cannabis buyers have to do just to do a great job.
Whether you’re using Happy Buyers or Excel to accomplish this, it’s a challenge you must take on. Start thinking about your inventory in more dimensions, with more layers. It’s hard work that takes time, but it’s worth it in the end if it keeps you from overcommitting anywhere from $15k - $150k / mo in overstocked inventory.
Want to shred the spreadsheets with us?
Click here to see Happy Buyers first-hand.
.png)
.png)
.png)