Safety Birgðir & Reorder Point Reiknivél (Demand and Afgreiðslutími)

Estimate safety birgðir and reorder points from demand and afgreiðslutími variability.

Supports min/max estimation, low/medium/high þjónusta levels, and period-based calculations for seasonal demand.

Inntaks, share URLs, and on-device storage stay inside this browser and are not sent to a server.
Start even if you do not know standard deviations
See the assumptions and calculation skráic clearly
Yfirferð busy and slow seasons by period
Reuse niðurstaðas quickly with copy and share URL

How to use

  1. Choose the demand average and how you want to enter variability.
  2. Enter the afgreiðslutími average and variability, then choose a þjónusta level.
  3. Turn on seasonality if needed and enter multiple periods with separate demand values.
  4. Copy the niðurstaða or save it as a shareable URL for the next reorder-point yfirferð.

Sample

Standard case

Inntak

Average demand 120/day, demand 80 to 160, average afgreiðslutími 7 days, afgreiðslutími 5 to 10 days, þjónusta level 95%

Úttak

Reorder point 1,103, safety birgðir 263, average demand × afgreiðslutími 840

With seasonality

Inntak

Peak season 180 (140 to 240), normal season 120 (80 to 160), slow season 70 (50 to 95), average afgreiðslutími 7 days, afgreiðslutími 5 to 10 days

Úttak

The peak season is highlighted as the highest reorder-point period, and each period shows its own safety birgðir and reorder point.

Quick safety birgðir and reorder point calculation with afgreiðslutími variability

Yfirferð safety birgðir and reorder point on the same skjár.

The verkfæri assumes demand variability and afgreiðslutími variability are independent and uses a normal approximation.

Higher þjónusta levels increase both safety birgðir and reorder point.

The more afgreiðslutími varies, the more safety birgðir tends to matter.

What to enter when variability is unknown

Starting with a min/max estimate is usually good enough.

If you have historical data, enter the average and standard deviation directly.

If you are unsure, start with Medium (95%) as the þjónusta level.

The rough low / medium / high option is useful for an initial samanburður.

Glossary

Safety birgðir

Extra birgðir kept to absorb demand or afgreiðslutími variability.

Reorder point

The birgðir level where you should place the next order.

Þjónusta level

Here it means the approximate probability of not birgðiring out during afgreiðslutími when you reorder at the reorder point.

Lead time variability

How much ordering-to-móttaka time varies in days or weeks.

How the calculation works

  • σDL = sqrt((μL × σD²) + (μD² × σL²))
  • Safety birgðir SS = z × σDL
  • Reorder point ROP = (μD × μL) + SS
  • Min/max estimate: σ ≈ (max - min) ÷ 4 (assuming about 95% coverage)

Algengar spurningar

I do not know the standard deviation.

You can estimate it from min and max values. The verkfæri treats that range as roughly 95% of the usual spread and estimates the standard deviation automatically. That is often practical enough to start with.

What does þjónusta level mean here?

Here it is the approximate probability of not birgðiring out during afgreiðslutími when you reorder at the reorder point. Higher targets usually increase safety birgðir.

My item has seasonal demand.

You can calculate multiple periods separately. Enter average demand and variability for busy, normal, and slow periods, then bera saman each period's safety birgðir and reorder point.

I have the reorder point. Will this verkfæri also tell me order quantity?

No. This verkfæri estimates when to reorder. Order quantity still depends on MOQ, lot size, storage capacity, cost, and ordering policy.

Is this the same as fill rate?

No. The þjónusta level in this verkfæri is an approximation of cycle þjónusta level. It does not calculate fill rate directly.

Notice

  • These niðurstaðas are only a áætlunning guide. You still need to consider MOQ, order multiples, storage space, fjárhagsáætlun, and obsolescence risk.
  • The verkfæri assumes demand variability and afgreiðslutími variability are independent and uses a normal approximation.
  • For intermittent demand or frequent supply disruptions, a different model may fit your operation better.