For brands that know how much stock is left, but not when to reorder

"Inventory management? We check quantities in EZAdmin."

Fair enough. That’s what most brands do. They check on-hand quantities in an integrated solution like EZAdmin or Sabangnet, and place a purchase order when a product looks noticeably low.

But this approach has one structural flaw: it tells you "how much is left," but not "when, and how much, to reorder." So the same things keep happening:

• A bestseller suddenly sells out → you rush a purchase order, but lose sales during the lead time it takes for stock to arrive

• Meanwhile, slow sellers pile up by the hundreds in the warehouse, and nobody realizes it’s a problem

• When the CEO asks, "Why is our inventory so large?", nobody can answer at the SKU level

Drawing on a case where EZAdmin inventory data was connected to Laplace and analyzed, this article introduces three analyses that tackle stockouts and overstock at the same time — safety stock, reorder point (ROP) and ABC analysis. None of them require building a new system; they run entirely on the inbound/outbound data already sitting in EZAdmin.

First, a quick self-check for your brand

If you answer "I don’t know" to even one of these questions, keep reading.

1. Do you know how many products will sell out within the lead time if you don’t reorder now?

2. Does each SKU have a safety stock baseline? If so, is that number a gut feeling or a calculation?

3. What share of your total inventory value is in products that haven’t shipped a single unit in the last 30 days?

4. Does the inventory split between your top 20% of products by shipments and the rest match their share of sales?

These questions are hard to answer not because the data is missing. EZAdmin already holds the full inbound/outbound history for every SKU. The problem is that the data exists only for "looking up," and is never processed for "deciding."

Analysis 1. Safety stock — drawing your stockout defense line in numbers

The concept

Safety stock is the minimum inventory you need to hold to absorb swings in demand and supplier delays. It’s the defense line that says, "As long as we always have at least this much on hand, a sudden rush of orders won’t sell us out."

The key is to set that line not by gut feel, but by calculating it from the volatility of your shipment history.

Safety stock = Z × σ (standard deviation of daily shipments) × √lead time

The formula may look intimidating, but the logic is simple: the more erratic a product’s shipments (the larger σ) and the longer you wait after ordering (the longer the lead time), the bigger the buffer you should keep.

What matters is that each of the three variables is a different kind of input.

σ is calculated from the data for you, and lead time just needs to be confirmed and entered. The real question is Z. There is no single right Z value; it should differ from brand to brand. We cover how to set it later in this article.

What it looks like in real data

Laplace’s safety stock chart shows the inventory buffer (on-hand stock − safety stock) for each SKU. On the actual analysis screen of one D2C brand:

SKU A: buffer -307  ← 307 units short of safety stock
SKU B: buffer -230
SKU C: buffer -176
SKU D: buffer -93

Products with a negative buffer lined the top of the chart. These SKUs had already dropped below the safety stock line — the slightest spike in demand would send them straight to a stockout. And, tellingly, every one of the top entries was a best-selling core product. They sell fast, so they run out fast, and purchasing can’t keep up with the burn rate — a textbook pattern.

How to read the chart

• No bar / short bar → on-hand stock ≈ safety stock; consider replenishing

• Long bar → ample buffer; you’re safe

• Negative → already below safety stock; reorder immediately

Actions you can take right away

• Add the list of negative-buffer SKUs to your buyer’s weekly routine. Just opening this chart every Monday automatically surfaces your list of "fires to put out."

• Flag SKUs with an unusually large σ. High shipment volatility means demand swings with promotions and exposure, making these candidates for syncing your purchase schedule with the marketing calendar.

• Manage lead times as measured values per supplier. Base them not on "about a week" but on actual order-date → receipt-date records from recent orders, so your safety stock reflects reality.

Analysis 2. Reorder point (ROP) — the answer to "when do we order?"

The concept

If safety stock answers "how much to keep on hand," the reorder point (ROP) is the threshold for "how low on-hand stock can fall before we hit the order button."

ROP = average daily shipments × lead time + safety stock

On-hand stock < ROP → reorder immediately

The logic goes like this: even after you place an order, it takes the full lead time for stock to arrive, and products keep selling in the meantime. So the last safe moment to order is when you have "what will sell during the lead time + safety stock" left. Any later, and you’ll run out before the delivery lands.

How is it different from safety stock analysis?

ROP is a more conservative threshold (one that sounds the alarm earlier) than safety stock. A product that still seems to have room under safety stock analysis may already be past its reorder point, because ROP also covers consumption during the lead time.

The case brand above showed exactly this gap. The same core SKU had a buffer of -307 against safety stock, but -395 against ROP. That means not just "we’re short," but "a stockout within the lead time is already locked in."

What stood out in particular was that gift and packaging-supply SKUs shipped alongside main products ranked high on the ROP shortfall list. Gift shipments track main-product sales, yet they rarely get the same purchasing attention, so they often run out quietly. And a gift stockout can block sales of the main product too.

Actions you can take right away

• Switch your reorder criterion from "on-hand stock" to "on-hand stock vs. ROP." Change the decision frame from "5,000 left, we’re fine" to "ROP is 6,200, so at 5,000 we’re already late."

• Always include gift and packaging-supply SKUs in ROP monitoring. They are a blind spot that’s easy to miss when your thinking centers on main products.

• Track the number of SKUs below ROP as a weekly metric. If that number is rising, it’s an early warning that your ordering cycle itself can’t keep pace with sales.

Analysis 3. ABC analysis — seeing where your inventory value is locked up

The concept

ABC analysis is a classic but powerful method that splits SKUs into three groups based on their contribution to shipments (or sales).

• Group A: the few core SKUs that drive most shipments (typically ~70-80% of cumulative shipments)

• Group B: SKUs with a mid-level contribution

• Group C: the long tail of many SKUs that contribute little to shipments

What it looks like in real data

The contrast uncovered in the case brand’s ABC table was dramatic.

Group A SKU: 33 shipped/mo | ending stock 23    | turnover 0.85
Group C SKU: 1 shipped/mo  | ending stock 975   | turnover 0.00
Group C SKU: 5 shipped/mo  | ending stock 1,029 | turnover 0.00
Group C SKU: 0 shipped/mo  | ending stock 368   | turnover 0

A best-selling Group A product had only 23 units left and was on the verge of selling out, while a Group C product that sold just one unit in a month had 975 units sitting in the warehouse. The stock isn’t where it needs to be, and it’s locked up where it isn’t needed.

This brand’s overall inventory turnover was 0.13 (last 30 days), meaning shipments equaled 13% of inventory. The culprits behind that low turnover were the Group C SKUs with zero shipments and hundreds of units piled up.

One caveat: in a situation like this, averages such as days of supply (DOS) become extremely distorted. The "near-infinite days to sell through" of non-moving SKUs drag the average up, so you only see the real picture when you break the numbers down by ABC group rather than looking at the overall average.

Actions you can take right away

• Differentiate ordering and safety stock policies by group. For Group A, prevent stockouts at all costs with a high service level; for Group C, stopping further orders is the default. Applying the same yardstick to every SKU is the root cause of overstock.

• Separate Group C SKUs with "zero shipments + large stock" as dead-stock candidates. Put them on the decision agenda: clear them through promotions, convert them into gifts, fold them into bundles, and ultimately write them off or dispose of them. Stock that only racks up warehouse fees and carrying costs quietly erodes your P&L every month.

• Cross-check Group A SKUs against the safety stock and ROP analyses. As in the case above, a SKU that is "Group A with a negative buffer" is the top-priority action item — because your biggest revenue contributor is also the one most at risk of selling out.

How should your brand set its Z value?

Earlier we said that of the three safety stock variables, σ is data, lead time is fact, and Z is policy. Setting your Z value is, in effect, setting your inventory strategy.

What Z means: service level

Z is a coefficient that statistically converts "the probability of covering a demand spike without a stockout (service level)."

A word of caution: service level and safety stock do not scale proportionally. Raising the service level by 4pp, from 95% to 99%, moves Z from 1.65 to 2.33 and increases safety stock by about 41%. The inventory cost of defending against the last few percent of stockouts grows exponentially, so "as high as possible" is not the answer.

The deciding factor: stockout cost vs. carrying cost

Ultimately, Z is a balancing act between the two.

When to raise Z (products with high stockout costs):

• High-margin products, where a single stockout means a large lost opportunity

• Products with many substitutes, where customers defect to competitors when you’re out of stock

• Products you’re running ads on — ad spend during a stockout simply evaporates

• Products whose marketplace ranking depends on uninterrupted sales

When you can lower Z (products with high carrying costs):

• Products with expiry dates or seasonality that get scrapped if they don’t sell

• High-unit-cost products that tie up a lot of capital in inventory

• Bulky products with heavy warehousing costs

• Products whose demand is already declining

Practical recommendation: differentiate by ABC group

Setting an individual Z value for every SKU is unrealistic. In practice, the most efficient approach is to apply different Z values by ABC group.

Then layer seasonality on top. Before periods with an expected demand surge, such as major holidays or Black Friday, raise Group A’s Z value by one notch, and restore it once the season ends. In Laplace, you can edit Z values and lead times directly in the Custom Formula tab, so these policy changes take effect without rebuilding any formulas.

If you need a starting point

If you don’t have a refined policy yet, start like this: set Z = 1.65 (95% service level) for all SKUs → run it for a month → raise Z for SKUs that stocked out and lower it for SKUs that consistently carry excess buffer. Z isn’t a constant you set once and forget; it’s a dial you keep tuning as you operate.

Bringing the three analyses together: the inventory operations matrix

Cross the three analyses, and the action for each SKU falls into place automatically.

The key is the bottom-right cell. A Group C product with plenty of stock is not "in a safe state" — it is "in a state where capital is locked up." The purpose of this matrix is to manage stockout risk (top left) and overstock risk (bottom right) on the same screen at the same time.

How to get started — EZAdmin × Laplace

The source data in this case is nothing special: it’s the SKU-level inbound/outbound history already accumulated in EZAdmin. Connect EZAdmin to Laplace, and this data is automatically collected and standardized, and the analyses above are assembled into a dashboard.

Step 1. Connect EZAdmin → inventory history is collected automatically (inbound, outbound and ending stock, at the SKU level)

Step 2. Check the core metrics on the inventory analysis dashboard — inbound/outbound trends, stock levels, inventory turnover and inventory value. If you see "inventory value rising while shipments stay flat," you definitely need the next steps.

Step 3. Find negative-buffer SKUs in the safety stock chart → secure this week’s reorder list. Adjust Z values and lead times to your brand’s standards in the Custom Formula tab (see the Z value guide above)

Step 4. Use the reorder point (ROP) analysis to also cover SKUs "set to sell out within the lead time" — check that gifts and packaging supplies are included

Step 5. Review inventory allocation by group in the ABC analysis table → extract Group C dead-stock candidates, cross-check Group A SKUs at risk of stockout, and apply differentiated Z values by group

On the dashboard, each analysis sits next to a text card explaining its formula and how to read the chart, so even a CEO or marketer — not just the inventory manager — can immediately see "what to do with this chart." This is the point where inventory data stops being the logistics team’s exclusive domain and becomes a management metric the whole company watches.

The goal of inventory management is neither "holding a lot of stock" nor "holding as little as possible." It’s plenty for the products that will sell, and the bare minimum for the ones that won’t — and getting that allocation right with data. The data you need for that judgment is already piling up in EZAdmin. Now it’s time to turn it from data you look up into data you decide with.

Connect EZAdmin to Laplace and check your brand’s safety stock, ROP and ABC analyses today.