Inventory Reorder Calculator

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:

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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:

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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)
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Method 2: Days-of-cover heuristic (when data is limited)
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See references/safety-stock-guide.md for z-score tables and method selection guidance.

4. Calculate reorder point

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

If in-transit stock exists, use effective inventory position:
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5. Determine reorder quantity

Basic approach:
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Constraint-adjusted approach:
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EOQ approach (when holding and ordering costs are known):
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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. 1. Assumptions table

- Every input value with source and confidence level

1. 2. Demand and lead-time model

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

1. 3. Reorder point calculation

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

1. 4. Reorder quantity recommendation

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

1. 5. Risk scenario table

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

1. 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:

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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:

CODEBLOCK10

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Common mistakes

1. 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
• INLINECODE8 — Service levels, z-scores, and safety stock methods
• INLINECODE9 — Demand estimation, trend adjustment, and seasonality handling
• INLINECODE10 — Pre-delivery quality checklist