Regardless of client size, category, or operational complexity, the first tangible deliverable in any supply chain engagement is the same: a master inventory positioning sheet. On-hand stock. Open purchase orders with expected arrival dates. Projected demand for the next 60 to 90 days. Days of supply per SKU.
This has been true for Realsy, for Zesty Greens, for Awesome Maps, and for every supply chain client in between. The complexity differs. The format adapts. The core question is always the same: what do you have, and when will you run out?
Why this deliverable, always
Most small and mid-size e-commerce brands have the data somewhere. It’s just not organized in a way that produces that answer. Inventory counts live in Cin7 or Shopify or a spreadsheet. Open POs live in an email thread or a Trello board or someone’s memory. Demand projections don’t exist formally. The owner has a feel for it. Days of supply per SKU has never been calculated.
The inventory positioning sheet takes all of that scattered data and produces one view: here is every SKU, here is how many you have, here is when you’ll run out given current demand, here is what’s incoming and when. For most clients, this is the first time they’ve seen their own data organized this way.
The reaction is almost always the same. They look at it and they find something they didn’t know.
What Realsy didn’t know
When we started the Realsy engagement, the first positioning sheet covered three snack pack variants (peanut butter, chocolate peanut butter, and almond butter) with Kroger retailer forecasts layered in for the next two months.
The sheet showed 1,310 units on hand with projected demand of 163 units in the near term. On its face, that’s significant cover. But when you break it out by variant and by channel, the picture was different. Certain variants had more stock than others, and the Kroger onboarding had specific variant demand that didn’t map neatly to the existing inventory position.
The almond butter dependency was the structural risk. Realsy’s production requires dates stuffed with nut butter, and almond butter comes from a separate supplier (Camel Foods) with its own lead time. If almond butter is delayed, the entire almond butter variant production run stops. Even if the dates are ready, even if the other suppliers are on schedule. That’s a single-supplier dependency that creates an outsized stockout risk for one product line.
That dependency was visible in the positioning sheet. It wasn’t visible in anyone’s head.
What the pattern looks like elsewhere
Across similar engagements with clients managing spare parts or component inventories, the positioning sheet surfaces the same category of problem: inventory that exists in the warehouse but isn’t connected to the demand picture.
Parts sitting in a 3PL that aren’t tracked in the demand planner. Components ordered as buffer stock that are now invisible because they’re not in the active PO cycle. The positioning sheet forces every SKU or component to be accounted for in the same view. When you do that, you find things.
The Realsy retailer onboarding case
When Realsy was onboarding to Kroger, the retailer required a 2-month demand projection per variant before the first order. This is standard for retail channel onboarding. Retailers want to see that the brand has modeled their demand before committing to shelf space, and they want confidence that the brand won’t be out of stock two weeks after launch.
We built that projection from the positioning sheet. Starting with current on-hand, layering in the open POs and their expected receipt dates, then modeling the Kroger-specific demand against the available supply timeline by variant. The projection showed where the cover was adequate and where a variant might be tight given Kroger’s expected order cadence.
That analysis informed both the production prioritization and the retailer conversation. It came directly from the positioning sheet work in the first onboarding session.
What the sheet actually reveals
The positioning sheet surfaces the same categories of problem across every engagement.
First: SKUs closer to stockout than the client thinks. The demand feels manageable until you calculate days of supply against the actual run rate and see a particular variant has 18 days of cover, not 60.
Second: open POs that have no expected receipt date. These are often orders that someone placed and hasn’t followed up on. They exist on paper, but nobody knows when the goods are arriving, which means the demand plan can’t rely on them.
Third: SKUs with more cover than necessary, tying up cash and generating long-term storage fees at FBA. Brands without a positioning sheet tend to over-order on products they’re comfortable with and under-order on the uncertain ones.
Fourth: dependencies that aren’t obvious until you see the full picture. The almond butter issue. A component that feeds three products. A supplier whose lead time makes certain SKUs structurally risky regardless of current inventory levels.
None of this is profound. It’s just the consequence of having all the relevant data in one place, organized to answer the question that actually matters. Build this first, every time. The rest of the engagement runs on it.
