### Joannes Vermorel

We have published an introductory tutorial that provides the formula used to compute the reorder point based on the forecasted demand, the demand uncertainty, the lead time and a couple of other factors.

This *classical* safety stock calculation comes with a couple of key assumptions about the demand. We have already posted about how to handle varying lead time. Then, there is another implicit assumption in the classical formula: **buyers are assumed to be independent**.

Recently, we have been approached by **a company that frequently sell items in bulk to classrooms**. Although most of the sales are single-item order, from time to time, comes a 20-30 items order for a whole classroom. The graph below illustrate the resulting sales patterns of intermittent bulk purchase.

*Dislaimer: numbers made up and some results are widly approximated for the sake of simplicity*.

Over those 12 months, we can see that we have 2 patterns:

- ongoing single-unit orders which account for 13 orders per months on average.
- intermittent bulk sales which account for +30 orders on average.

*The average monthly sales is at 23 orders per month, but if we remove the bulk order factor, then the average purchase drop to 13 orders per month.*

Now, what is the *right* safety stock in this situation? If we consider the classical safety stock formula with typical settings, then we are going to have a reorder point established at roughly 30 items: the 23 orders per month average, plus the safety stock itself covering the demand uncertainty. Bulk purchases at 30 items are very likely to be missed short of a hand-few items.

Yet, **the classical safety stock calculation is less than optimal**: here, we end-up storing about twice as much items than we need to to address the

*individual*purchase, and yet, the safety stock is not high enough to cover big bulk purchases.

In order to address bulk purchases, we **need to refine our safety stock formula** to take this pattern into account. For sake of simplicity, we are going to model the bulk purchase pattern as a *single factor* later reintegrated in the safety stock formula.

In order to reflect the *bulkiness* of the sales, we could consider the ** largest purchase** for each item being sold. Yet, this value is not robust in the statistical sense, as a single super-large historical purchase can completely skew the results.

Instead, we should rather consider a quantile of the bulk order quantity distribution as illustrated by the threshold *Q* in the illustration here above where all orders have been ordered from the smallest quantity to the largest one.

In this safety stock analysis, there is a **natural fit for the quantile value to adopted for Q**: it should be equal to the **service level** - as defined for the classical safety stock formula - that is to say the desired probability of not having a shortage.

Let call `y``Q`

the bulk quantity associated to probability *Q* (in this illustration here above, we have `y``Q`

= 30). Technically, `y``Q`

is the *inverse cumulative distribution* of sales function taken at the quantile *Q*. The reorder point calculation becomes:

```
R = D + MAX(σL * cdf(P); y``Q``)
```

where `σL * cdf(P)`

happens to be the *safety stock* as computed based on the demand uncertainty.

Computing `y``Q`

in Excel is a bit tedious because there is not equivalent of the PERCENTILE function for inverse cumulative distribution. We have to resort either to an histogram scheme or to a VBA macro.

The *ICMD* User Defined Function for Excel, pasted below, performs the `y``Q`

calculation, assuming the sales orders are listed in an Excel range and **sorted in increasing order**.

’ Inverse cumulative distribution

Function ICMD(r As Range, q As Double)

’ Computing the total

Dim s As Double

For Each c In r

s = s + c.Value

Next

’ Finding the threshold

Dim a As Double

For Each d In r

a = a + d.Value

If a >= (q * s) Then

ICMD = d.Value

Exit For

End If

Next

End Function

Based on this refined formula applied to the sample data, we obtain a reorder point R = 13 (demand forecast) + 30 (bulk quantity) = 43 which is sufficient to address the bulk purchase with a high probability while keep the inventory as low as possible.

Got business-specific constraints too? Don’t hesitate to let us know. We can adjust Salescast to better fit your business.

## Reader Comments (1)

I have a similar issue in my trade. Very interesting article.

`8 years ago | Martin`