Decay rate is the daily fraction of inventory lost to spoilage, shrinkage, or other non-sales consumption. Formula: I(t) = I₀ × (1−d)^t, with category examples.
Jainul Vaghasia/Published /Updated /5 min read
Use the definition
Turn procurement terms into an operating system.
This reference page should help you understand the concept first. When the term affects purchasing execution, LineNow connects it to live POs, supplier replies, receiving, and accounting handoff.
The decay rate is the daily fraction of inventory that becomes unusable due to spoilage, shrinkage, theft, breakage, or any other loss not captured by sales. It explains why perishable and shrink-prone inventory often disappears faster than sales data alone can explain.
The formula
Inventory under decay follows exponential decay:
I(t) = I₀ × (1 − d)^t
where:
I₀ is starting inventory
d is the daily decay rate (e.g. 0.02 = 2%)
t is days elapsed
For a 5% daily decay rate, after 7 days, only (1−0.05)^7 ≈ 70% of the original inventory remains usable.
Typical decay rates by category
Category
Daily decay
Notes
Berries, leafy greens, fresh herbs
5–15%
Days-to-spoil < 7
Stone fruit, soft fruit
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3–8%
Days-to-spoil 7–14
Hard produce (potatoes, onions, citrus)
0.5–2%
Days-to-spoil > 30
Fresh proteins (fish, ground beef)
10–20%
Highly perishable
Dairy, eggs
2–5%
Sealed, cold-stored
Dry goods (flour, rice, beans)
0–0.1%
Effectively zero, except for pests
Apparel, accessories (retail)
0.01–0.05%
Mostly shrinkage / theft
Bar inventory (over-pour, breakage)
0.5–2%
Highly venue-dependent
How to pick a starting decay rate
If you do not have enough cycle-count history yet, use a conservative starting point and replace it with measured data as soon as possible:
Item type
Starting point
What to watch
Fresh berries, leafy greens
5–10% daily
Waste log, delivery quality, storage temp
Fresh proteins
8–15% daily
Expiry, trim loss, temperature control
Dairy and prepared refrigerated items
2–5% daily
Open-date discipline and prep batches
Dry goods
0–0.1% daily
Pests, broken bags, miscounts
Retail apparel/accessories
0.01–0.05% daily
Theft, damage, markdown history
Bar bottles
0.5–2% daily where over-pour exists
Pour cost, breakage, comp tracking
The starting number should be treated as a hypothesis. After two or three count cycles, the actual variance between expected and counted inventory should begin replacing the default.
How LineNow estimates decay automatically
You do not need to start by setting every decay rate by hand. LineNow estimates them from cycle data:
At each manual count, the system records the difference between expected (I + receipts − sales) and actual inventory.
The cumulative loss percentage f = min(Δ/S, 0.9) is computed for the cycle.
The implied daily rate is d = 1 − (1−f)^(1/L) where L is cycle length in days.
The recommended decay rate is the median of the last 3 cycles. As more cycles accumulate, the estimate stabilizes.
You can override this with a manual entry, or use the recommendation. High decay rates — above 5% — are a flag that something else is going on (recipe miscalculation, inventory leakage, theft).
Why decay matters for PAR and reorder math
If decay is non-zero and you ignore it, your PAR level is too low. Some inventory will be lost before it can be sold, so you need to order more aggressively to maintain the same effective coverage.
The base demand integral with decay is:
baseDemand = (s/d) × (s^(−T) − 1) × c
where s = 1 − d, T = order frequency days, c = consumption rate
For a 7-day cycle with 5% daily decay and 18 lbs/day consumption, baseDemand ≈ 147 lbs vs 126 lbs without decay handling — a 17% upward adjustment to PAR. You can test how consumption and buffer inputs change your own replenishment target with the free PAR level calculator.
Decay rate governs how much to order; FIFO, FEFO, and LIFO picking policies govern which physical lot to use or ship first once inventory is on hand. For perishables, the two work together: decay rate sets the replenishment urgency, and FEFO picking (first-expiry-first-out) ensures the lot with the shortest remaining shelf life exits inventory before it contributes to that decay loss.
Decay rate vs safety stock
Decay rate and safety stock solve different problems:
Concept
Protects against
What happens if you overuse it
Decay rate
Expected loss before the item is used
You normalize waste instead of fixing it
Safety stock
Demand and lead-time variability
You tie up cash and increase waste on perishables
For perishables, increasing safety stock without modeling decay can make the problem worse. You buffer against stockouts by buying more, but the extra inventory may spoil before it is used. A good reorder model separates "we need more because demand is volatile" from "we need more because some stock will become unusable."
The operational question is: does the item disappear because customers bought it, because the supplier shipped late, or because it decayed? The procurement response is different in each case.
Decay rate vs shrinkage vs spoilage
Operators often use these words interchangeably, but they are useful in different ways:
Term
What it means
Procurement use
Decay rate
Daily percentage loss from usable inventory
Adjusts PAR, reorder point, and order quantity
Spoilage
Product that becomes unusable before sale
Identifies shelf-life and rotation problems
Shrinkage
Inventory loss from theft, breakage, or error
Identifies control and count problems
Waste
Broad operating loss, often including prep trim
Helps restaurants tune recipes and prep volume
Decay rate is the term that belongs in replenishment math. Spoilage and shrinkage explain why the decay exists. A cafe with berries spoiling after five days has a different problem than a bar losing bottles to over-pour or breakage, but both show up as inventory that disappears faster than sales explain.
How to audit decay in a small business
Use a short audit before trusting the number:
Pick 10 high-risk items: fresh produce, proteins, flowers, cannabis lots, fragile retail goods, or bar inventory.
Record starting quantity, receipts, POS sales, transfers, and ending count for one cycle.
The point is not to build a perfect academic model on day one. The point is to stop treating perishable and shrink-prone items like shelf-stable goods. Once a category has a measured decay rate, the buying plan can order enough to cover real usable demand instead of idealized demand.
Decay-rate mistakes that create bad purchase orders
The common mistakes:
Using recipe usage as the only demand signal. Recipe usage tells you what should have been consumed. Counts reveal what actually disappeared.
Ignoring receiving quality. A case of produce arriving half-ripe has a higher effective decay rate than a fresh case.
Treating all suppliers the same. Two suppliers can sell the same item with different shelf life on arrival.
Using one decay rate per category forever. Berries in July and berries in December behave differently.
Not separating waste from theft or counting error. A high decay rate can be a storage problem, a process problem, or a control problem. The math surfaces the gap; operators still need to interpret it.
Over-correcting with bigger orders. If a high decay rate comes from poor rotation, bigger orders add more waste.
Decay-aware procurement is not a license to overbuy perishables. It is a way to make the tradeoff explicit: what stockout risk are we accepting, and what waste risk are we creating?
When decay should change the order
Decay should affect a purchase order when:
the item regularly spoils before the next order cycle
the supplier sells in packs larger than weekly demand
the item is required for high-margin recipes or bundles
the item has a regulatory expiration or FEFO requirement
the revenue lost from stockout is greater than the waste risk
That last point is important. A zero-waste order is not always the best order. A restaurant may accept some herb waste if the alternative is running out of a high-margin dish on Friday night. Decay-aware procurement makes that tradeoff explicit.
How LineNow uses decay-rate inputs
LineNow has inventory metadata for manual and recommended decay rates, plus cycle data that can support decay estimates. In the buying workflow, decay rate is an input to perishable PAR and replenishment math, not a generic AI claim. The system can show estimated daily loss, apply decay-aware order math, and let operators override the rate when their domain knowledge beats the current data.
The strongest use case is not academic forecasting. It is owner-operator control:
a cafe can stop treating milk, berries, flour, and cups as if they age the same way
a restaurant can separate recipe demand from waste
a florist can order stems with shelf life in mind
a regulated retailer can pair expiry-aware inventory with FEFO discipline
a bar can distinguish sales from over-pour, breakage, and shrink
Decay rate is the bridge between inventory math and the physical reality of goods that age, break, leak, spoil, or disappear.
Ready to stop ordering perishables as if they were shelf-stable? Book a demo to start your 90-day free trial.
