Stores with several warehouses or shops often set their inventory buffer as a flat rule — ‟hold back five everywhere.” In reality, though, protecting a distribution center that ships dozens of orders a day with the same five units as a retail location that moves a few items a week is a stretch. You easily end up too thin in one place and too tight in the other.
So this time we go one step beyond applying a single uniform buffer to everything, and look at how to set a separate safety stock per location. In Shopify, inventory is tracked per location, and the available quantity for the same product can differ from one location to the next. That is exactly why varying the buffer per location feels natural.
Why Vary the Buffer by Location
A Shopify location is a place that stocks, fulfills, and sells inventory. Inventory is recorded per location as states (available, committed, on hand, and so on), and even for the same SKU the available quantity at warehouse A moves independently of store B. In other words, the thing a buffer applies to is already split per location.
A single store-wide buffer cannot absorb that difference. A high-throughput warehouse with real oversell risk wants a thick buffer, but applying that same cushion to rarely moving store stock leaves perfectly sellable inventory asleep — present, yet not offered. Splitting the buffer per location lets you tune how thick the protection is to each location’s circumstances.
Deciding the Buffer by Throughput and Lead Time
When you set a per-location buffer, the clearest criteria are shipping throughput and replenishment lead time. The more a location ships per day, the higher the risk that the last unit sells twice during the few minutes of sync lag. And the longer it takes to restock after a stockout, the heavier the impact of running dry — so you give that location a thicker, safer buffer.
- High-throughput location: thicker buffer (for example, 5 to 10 units)
- Low-throughput location: thinner buffer (for example, 1 to 2 units)
- Long replenishment lead time: thicker, to avoid stockouts
- Low physical-count accuracy: thicker, to absorb discrepancies
Use Different Baselines for Store and Warehouse Locations
Store locations and warehouse locations move inventory differently in the first place. A store sells face to face and stock drops on the spot, so the online figure can lag for a moment. A warehouse, meanwhile, handles a concentrated stream of online orders, so sync lag translates almost directly into overselling. Rather than applying one buffer baseline to both, decide the thickness for different reasons — a cushion for in-person sales at the store, and a cushion for sync lag at the warehouse. It simply makes more sense.
Designing Per-Location Buffer Columns in the Sheet
If Google Sheets is your inventory source of truth, the basic move for per-location buffers is to give each one its own columns. Keep one SKU per row, and for each location place an actual-stock column and a buffer column side by side. That way you can see at a glance how much you are protecting at each location.
- 01Column A: SKU
- 02Column B: warehouse A actual stock Column C: warehouse A buffer
- 03Column D: store B actual stock Column E: store B buffer
- 04Column F: warehouse A published quantity (Column B minus Column C, by formula)
- 05Column G: store B published quantity (Column D minus Column E, by formula)
With buffer columns split per location like this, an adjustment such as ‟thicken only the warehouse, leave the store as is” is done by touching just the relevant columns. To vary a value per product, you edit the cells on that product’s row. And when you want to change the buffer baseline across all locations at once, point each buffer column at a parameter on a separate sheet so you can update everything in one place.
Computing the Buffer-Adjusted Published Quantity
Each location’s published quantity is simply ‟actual stock minus that location’s buffer.” Watch out for cases where the result goes negative: if actual stock is 2 and the buffer is 5, you get minus 3. Send that to Shopify as is and you will get an error or an unexpected value. In your sheet, use a formula that floors at zero, like ‟MAX(0, actual − buffer).”
Once the published-quantity columns are computed, all that remains is to write each one as the on-hand quantity for its location. Sync Master supports multiple locations, so it can write published quantities like your columns F and G to the matching Shopify locations respectively. Its connection test lets you confirm the column-to-location mapping before any real sync, so you can check that each buffer-adjusted value lands at the right location before a single write happens.
Reviewing Buffers So They Do Not Become Dead Stock
Per-location buffers are handy, but leave them untouched and you eventually grow a location that sits at zero in public while stock is right there. That is missed revenue from an over-thick buffer. Once a month, review per location whether the buffer is too aggressive. Lower the buffer where a location has spent a long stretch published at zero, and raise it where stock oversold through the buffer.
Splitting safety stock per location may look like extra work at first, with more columns to manage. But the upside — not chaining a fast-shipping warehouse and a slowly moving store to the same baseline — is significant. Start by splitting buffer columns for just two or three key locations, and tune the values as you operate. That small first step is the shortcut to reducing both stockouts and overselling.