Inventory Reorder Calculator

Estimate ecommerce reorder timing and quantity using demand, lead time, and safety stock assumptions so teams can set reorder points and reduce stockout risk with less guesswork.

Estimate when to reorder and how much to buy before stock risk turns into lost revenue or excess inventory.

This skill goes beyond plugging numbers into a formula. It applies a structured inventory-planning workflow — demand analysis, lead-time modeling, safety stock calibration, and cash-vs-stockout tradeoff framing — to produce reorder recommendations operators can actually act on.

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Quick Reference

Decision	Key Signal	Strong	Acceptable	Weak
Demand estimation	Historical vs assumed	Uses actual sales data + trend/seasonality	Reasonable assumption documented	Made-up round number
Safety stock	Risk calibration	Service-level-based (z-score × σ)	Days-of-cover heuristic	No safety stock or arbitrary buffer
Lead time	Supplier reliability	Avg + variability modeled	Single estimate documented	Ignored or assumed instant
Reorder point	Formula clarity	ROP = LT demand + safety stock, shown	Calculated but not explained	Just a number with no breakdown
Order quantity	Constraint-aware	Accounts for MOQ, carton multiples, cash	Basic EOQ or demand × days	Arbitrary round number
Risk framing	Actionable tradeoffs	Stockout cost vs carrying cost quantified	Risks named qualitatively	No risk discussion

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Solves

Most ecommerce teams get reorder planning wrong not because they lack data, but because:

• Gut-feel ordering — buying "about the same as last time" without modeling demand changes
• Ignoring lead-time variability — treating supplier lead time as fixed when it fluctuates 20–50%
• No safety stock logic — either zero buffer (stockouts) or massive buffer (cash drag)
• Formula without context — calculating ROP without explaining what drives it or when it breaks
• Missing constraints — ignoring MOQs, carton multiples, storage limits, or cash flow
• No risk framing — presenting a single number without showing the stockout vs overstock tradeoff
• Static calculations — one-time number with no guidance on when to recalculate

Goal: Produce a reorder recommendation that an ops lead, buyer, or founder can act on today — with the math shown, assumptions visible, and risks framed.

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Use when

• You need a practical reorder point for a SKU or product group
• Demand is growing, volatile, or seasonal
• Lead time is long or unreliable
• You want to reduce stockouts without overbuying cash-intensive inventory
• A team needs to explain reorder logic to a buyer, founder, or ops lead
• You're setting up initial reorder rules for a new product or supplier
• Transitioning from gut-feel ordering to data-informed replenishment

Do not use when

• You need a full supply-chain planning system or ERP implementation
• Historical demand is too weak to support even rough assumptions
• Supplier constraints are unknown and nobody can estimate them
• The task is warehouse slotting or operations design rather than reorder planning
• You need multi-echelon or multi-warehouse optimization

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Inputs

Gather these inputs — mark any gaps explicitly:

Demand data:

• Average daily or weekly unit sales (last 30/60/90 days)
• Demand trend direction (growing / stable / declining)
• Demand variability (standard deviation of daily sales, or coefficient of variation)
• Known seasonality, promotions, or launches upcoming
• Historical stockout periods (to adjust demand estimates)

Supply data:

• Supplier average lead time (order-to-receipt, in days)
• Lead-time variability (best case / worst case / std dev)
• Minimum order quantity (MOQ)
• Carton multiples or packaging constraints
• Supplier reliability notes (late shipment frequency, quality issues)

Inventory data:

• Current on-hand stock (units)
• Current in-transit stock (units, ETA)
• Storage capacity constraints
• Current inventory carrying cost (% of COGS per year, or $/unit/month)

Business context:

• Target service level (e.g., 95%, 98%, 99%)
• Stockout cost estimate (lost margin + customer impact)
• Cash flow constraints or budget limits
• Review cycle / reorder cadence (daily / weekly / monthly)
• Product lifecycle stage (launch / growth / mature / clearance)

See references/safety-stock-guide.md for service level and z-score tables. See references/demand-analysis-guide.md for demand estimation methods.

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Workflow

1. Analyze demand pattern

Before calculating anything, understand the demand signal:

Average daily demand: [X] units/day
Demand std deviation: [σd] units/day
Trend: [growing / stable / declining at Y% per period]
Seasonality: [none / seasonal with peak in Z months]
Data quality: [strong (90+ days) / moderate (30–90 days) / weak (<30 days)]

If demand data is weak, flag this prominently — the entire calculation depends on this input.

See references/demand-analysis-guide.md for methods to handle trend, seasonality, and sparse data.

2. Model lead time

Supplier lead time is rarely constant. Model both average and variability:

Average lead time: [LT] days
Lead time std deviation: [σLT] days
Best case: [X] days
Worst case: [Y] days
Data source: [supplier quote / historical POs / assumption]

Rule: If lead time is based on a supplier quote alone (not historical data), add 20–30% buffer. Suppliers are optimistic.

3. Calculate safety stock

Safety stock bridges the gap between average expectations and real-world variability:

Method 1: Service-level approach (preferred when data exists)

SS = z × √(LT × σd² + d² × σLT²)

Where:
z = service level z-score (1.65 for 95%, 1.96 for 97.5%, 2.33 for 99%)
LT = average lead time in days
σd = standard deviation of daily demand
d = average daily demand
σLT = standard deviation of lead time in days

Method 2: Days-of-cover heuristic (when data is limited)

SS = average daily demand × safety days

Where safety days = typically 5–14 days depending on:
- Lead time length (longer LT → more safety days)
- Demand variability (higher variability → more safety days)
- Stockout cost (higher cost → more safety days)

See references/safety-stock-guide.md for z-score tables and method selection guidance.

4. Calculate reorder point

ROP = (average daily demand × average lead time) + safety stock
ROP = (d × LT) + SS

Interpret the result: "When on-hand inventory drops to [ROP] units, place a new order."

If in-transit stock exists, use effective inventory position:

Inventory position = on-hand + in-transit - backorders
Trigger reorder when: inventory position ≤ ROP

5. Determine reorder quantity

Basic approach:

Reorder quantity = average daily demand × days of coverage target

Constraint-adjusted approach:

Raw quantity = demand × coverage days
Adjusted for MOQ: max(raw quantity, MOQ)
Adjusted for carton multiple: round up to nearest carton multiple
Adjusted for cash: min(adjusted quantity, budget ÷ unit cost)
Adjusted for storage: min(adjusted quantity, available storage)

EOQ approach (when holding and ordering costs are known):

EOQ = √(2 × annual demand × order cost / holding cost per unit per year)

See references/output-template.md for the complete output format.

6. Frame the risk tradeoffs

Every reorder decision involves tradeoffs. Make them visible:

Scenario	Stockout Risk	Cash Tied Up	Coverage
Conservative (ROP + 20%)	Very low	High	[X] days
Recommended (ROP)	Low	Moderate	[Y] days
Aggressive (ROP - 20%)	Moderate	Low	[Z] days

Quantify when possible:

• "Stockout of [X] days costs ~$[Y] in lost margin"
• "Extra [X] units ties up $[Y] in cash for [Z] weeks"

7. Quality-check the recommendation

Before delivering, verify with assets/reorder-checklist.md:

• Is the demand estimate based on data (not just a guess)?
• Is lead-time variability accounted for?
• Is safety stock calibrated to a service level or risk tolerance?
• Does the reorder quantity respect MOQ and packaging constraints?
• Are cash flow implications visible?
• Are assumptions explicitly stated?
• Is there guidance on when to recalculate?

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Output

Return a structured package (see references/output-template.md):

1. Assumptions table

   • Every input value with source and confidence level

2. Demand and lead-time model

   • Demand stats, trend, variability
   • Lead time stats and variability

3. Reorder point calculation

   • Safety stock with method shown
   • ROP with formula and plain-English interpretation

4. Reorder quantity recommendation

   • Raw quantity and constraint-adjusted quantity
   • Cash impact estimate

5. Risk scenario table

   • Conservative / recommended / aggressive options
   • Stockout risk and cash tradeoff for each

6. Action items and review triggers

   • When to place the next order
   • When to recalculate (demand shift, supplier change, etc.)
   • Sensitivity warnings

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Quality bar

Strong output should:

• Show the math AND explain it in plain English
• Separate reorder point (when to order) from reorder quantity (how much)
• Account for demand variability, not just averages
• Account for lead-time variability, not just supplier quotes
• Respect real-world constraints (MOQ, cash, storage)
• Frame the stockout vs overstock tradeoff explicitly
• Flag fragile assumptions that could change the recommendation

What "better" looks like

Better output helps the operator act with confidence:

• Knows when to reorder (and understands why that number, not another)
• Knows roughly how much to buy (and sees the tradeoff in buying more or less)
• Sees the cash vs stockout tradeoff in concrete terms
• Understands where lead-time risk changes the answer
• Can explain the decision to a buyer, founder, or ops lead
• Has clear triggers for when to recalculate

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Examples

Example 1: Growing DTC skincare brand

Inputs:

• Product: Vitamin C Serum 30ml
• Average daily sales: 42 units/day (last 90 days, growing ~8%/month)
• Daily demand std dev: 12 units
• Unit cost: $8.50, selling price: $29.99
• Supplier lead time: 21 days avg (std dev: 4 days)
• MOQ: 500 units, carton multiple: 50
• Current stock: 890 units, none in transit
• Target service level: 95%
• Review cadence: weekly

Output excerpt:

DEMAND MODEL
Avg daily demand: 42 units → adjusted for growth: 46 units/day (8%/mo trend)
Demand σ: 12 units/day
Data quality: Strong (90 days, consistent)

SAFETY STOCK (Service-level method, z=1.65 for 95%)
SS = 1.65 × √(21 × 12² + 46² × 4²)
SS = 1.65 × √(3024 + 33856)
SS = 1.65 × 192
SS = 317 units → round to 320

REORDER POINT
ROP = (46 × 21) + 320 = 966 + 320 = 1,286 units

⚠️ Current stock (890) is BELOW reorder point. Order now.

REORDER QUANTITY
Target coverage: 30 days post-receipt
Raw qty: 46 × 30 = 1,380 units
Adjusted for MOQ: 1,380 (above 500 MOQ ✓)
Adjusted for carton: 1,400 (nearest 50 multiple)
Cash required: 1,400 × $8.50 = $11,900

RISK SCENARIOS
| Scenario | Order Qty | Stockout Risk | Cash | Coverage |
|---|---|---|---|---|
| Conservative | 1,700 | <2% | $14,450 | 37 days |
| Recommended | 1,400 | ~5% | $11,900 | 30 days |
| Aggressive | 1,100 | ~12% | $9,350 | 24 days |

Example 2: Seasonal product with unreliable supplier

Inputs:

• Product: Insulated water bottle
• Average daily sales: 18 units/day (but seasonal: 30/day in summer, 8/day in winter)
• Current month: April (ramping up)
• Supplier lead time: 35 days avg, range: 28–50 days
• MOQ: 200, unit cost: $6.20
• Current stock: 520, 300 in transit (ETA 2 weeks)

Output excerpt:

DEMAND MODEL
Current avg: 18 units/day
Seasonal forecast (next 60 days): ramping to ~25 units/day
Using forward estimate: 25 units/day
Demand σ: 7 units/day (higher variability due to seasonal transition)

⚠️ LEAD TIME WARNING
Avg LT: 35 days, but range is 28–50 days (σLT ≈ 6 days)
This supplier has high variability — safety stock must account for this.

SAFETY STOCK (z=1.65 for 95%)
SS = 1.65 × √(35 × 49 + 625 × 36) = 1.65 × √(1715 + 22500) = 1.65 × 156 = 257 units

REORDER POINT
ROP = (25 × 35) + 257 = 875 + 257 = 1,132 units

INVENTORY POSITION
On-hand: 520 + in-transit: 300 = 820
820 < 1,132 → ⚠️ Below ROP. Order immediately.

Days until stockout (no reorder): 520 ÷ 25 = 20.8 days
In-transit arrives in ~14 days → post-arrival: (520 - 350) + 300 = 470 units
470 ÷ 25 = 18.8 more days → ~33 days total before stockout

ACTION: Order now. Lead time of 35 days means new stock arrives just as
current + in-transit runs out. Any delay = stockout during peak season.

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Common mistakes

1. Using averages without variability — "We sell 20/day" ignores that some days are 8 and others are 35
2. Trusting supplier lead times — Quoted lead times are best-case; actual delivery is often 20–50% longer
3. Forgetting in-transit inventory — Reordering when stock is low but 1,000 units are already shipping
4. Ignoring MOQ and carton constraints — Calculating a perfect 347-unit order when MOQ is 500
5. No cash flow context — Recommending a $50K order to a business with $30K available
6. Static one-time calculation — Giving a number without saying when it should be recalculated
7. Safety stock = gut feel — Using "2 weeks of safety stock" without connecting it to demand variability
8. Not adjusting for trend — Using historical averages for a product that's growing 15%/month

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Resources

• references/output-template.md — Complete structured output template
• references/safety-stock-guide.md — Service levels, z-scores, and safety stock methods
• references/demand-analysis-guide.md — Demand estimation, trend adjustment, and seasonality handling
• assets/reorder-checklist.md — Pre-delivery quality checklist