Using Sawtooth model – constant time between purchase orders

Gal Merom

3 years ago

This post is the continuation of the How the Sawtooth model works. If you haven’t read it, read it before reading this post.

I mentioned in the previous post that the calculation becomes complicated if the lead time is longer than the time between orders. So I have been asked to write a post about it.

When I refer to purchase orders, I will use the abbreviation PO.

How does the Sawthoosth model with a constant time between PO work?

Let’s assume that we want to deal with new purchase orders once a week or once a month.

Why and who uses it?

Let’s look at an example:

Suppose that we buy bubble gums from our American supplier (and we are not located in the U.S.):

Lead time = 45 days

Average monthly consumption = 1,000 units

We can order 1,000 units once a month or 250 units every week.

Advantages of ordering 1,000 units once a month: The logistics of handling only one order (warehouse, custom, ext.) are four times less than handling four orders. Multiply it by 500 SKU and assume the logistics needed for 500 orders a month vs. 2,000 orders a month.

Advantages of ordering every week:

Less average stock – The amount of stock moves from 0+ to 250+ and not from 0+ to 1000+ (the plus sign represents the safety stock).

Advantages of less stock – less warehouse space needed, fewer (financial and insurance) inventory costs, less expired stock.

Faster and more flexible response to actual consumption of stock – When we build a PO for 30-45 days ahead, we have to assume the consumption for a long period of time. When we create a weekly PO, we can adjust it to actual consumption. If we have an increase in consumption, we increase the weekly PO. If we have a decrease in consumption, we can order less or skip a week. This method is usually much better for dealing with unstable consumption.

New product version – If we made a change to our product, such as putting more mint in every bubble gum, the consumption of mint might change, and we can respond to it better when working with weekly POs.

How does the model work?

We assume that there is something constant, such as the time between POs, which can be a month, a week or two, or even a day.
We use inventory days for the calculations, and then we change them back to values (as I explained in the previous post).

Reorder point – this is constant (week, month….)

Target max stock – lead time + safety stock

Current stock – current stock in inventory days

Open PO – The amount of stock we previously ordered and did not receive yet

Order quantity = Target max stock – Current stock – Open PO

Example:

(you can download the excel file at the end of the example)

We want to buy cocoa beans from our African supplier:

Lead time = 45 days

Safety stock = 7 days

Time between orders = 10 days (we will build a new PO every 10 days if necessary)

Open PO = 22 inventory days

Current stock = 15 days

Target max stock = 45 days

The last PO was on 1/1/2030

Solution:

When to place the next order:

Next order date = 1/1/2030 + 10 days = 11/1/2030

Amount to order = 52 -22 – 15 = 15 inventory days

So our next PO will be on 11/1/2030 and contain 15 inventory days.

Check out the excel example that I attach. You can find the model that gives you the amount you should put in your PO. You can have a report exported from your system and get the right amount to order every period. I also added an example of how it works. It is a log that shows how your inventory changes in time and how to find out how much to order every time. The green figures are the consumption I made up, but you can use the file with your data to see a what-if scenario since the rest of the cells are formulas that can fit your scenario.

Log explanation:

Building PO – That is the reason for this post. We calculate the amount per PO at any given time. Notice that we start at PO number 5 and that there are 4 previous POs.

Actual consumption – the actual stock consumption between the previous line and the following line

Arrival of PO – That is when the PO is received, and the inventory is updated.

My recommendation:

Use the excel simulator to play with the amount of consumed stock (green font). Change the numbers and see how the model corrects itself by changing the amount in the PO and how it affects the stock on hand. To make it easy I made a chart that is also updated in the excel file:

Let’s look at “open PO” and the “inventory” on a chart:

We can see how the stock stabilizes, although the consumption is not constant.
If we get zero or a negative value in the amount of stock to order, don’t send a PO and skip to next week. That way the model keeps adjusting itself to fit the actual consumption. Check the result of the model every X amount of days/weeks/months, and send a PO only when needed.
You can download the excel file by clicking on the excel icon:
Download an excel simulator

Summary

This model is not for everybody since it requires precision and many more POs. If you don’t need it, use the classic model.

On the other hand if you work in an environment of dynamic consumption and need to react very quickly and be LESS surprised, this is an excellent model to use.

The benefit of the model is its ability to balance the stock and the open PO. If you react every week instead of every month, you can avoid many shortages or surpluses of stock.

This model is the algorithm that the MRP models use in most ERP systems.

If you fill the field of maximal stock in the MRP system with the maximal stock, both the excel result and the MRP should be very similar.

If you have more questions, ask me.