The maximum number of days offset an item can receive. This ensures that safety stock doesn't get too big if an item is heavily under-forecasted.

A common misconception is that one could limit the amount of safety stock being calculated by merely setting limits for the offset days. However, the Risk % also plays a huge role in calculating the safety stock days. These values are not dependent on each other.

When using these parameters, it is important to determine whether the business has a bias towards over or under forecasting and whether accumulating excess stock or stocking out will be more detrimental.

Take note that changing these global settings may mask granular risk calculations for certain items where it would have been beneficial to explore and correct the cause of the high risk percentage.

Refer to this article for a detailed explanation of Forecast Risk and Offset and this article for a detailed explanation of how the Recommended order quantity is calculated.

Let’s look at Maximum (%), Minimum (%), Maximum offset and Minimum offset collectively.

What exactly is Risk offset days? In simplest terms, it’s the difference between the “planning” value and the “actual” value that needs to be added or subtracted from the safety stock days value in order to get things a little more accurate (for accurate ordering).

So let’s suppose we have an item with a monthly forecast of 100 units. We’ll call this our “planned” forecast or future forecast. Let’s assume this item has a replenishment cycle (RC) of 15 days, calculated safety stock (SS) of 15 days and a lead time of 30 days.

We know that safety stock, replenishment cycle and lead time are all converted from days to units by looking at the forecast. In this example, our Order-Up-To level will be 60 days (RC + SS + LT) and translate to 200 units (FC of 100 units every 30 days).

The app captures historical forecasts and compares those to the actual historical sales that occurred.

Suppose that historically, we’ve forecasted 100 units a month and sold 200 units. Historically, we under-forecasted, sold more than we had forecasted and ordered, and we ran a risk of stocking out. If this was the case historically, then the app will assume we’re bound to under-forecast again and compensate for our low forecast by adding additional stock to the initial calculated safety stock. This is known as a positive days offset.

Suppose that historically, we’ve forecasted 100 units a month and sold 50 units. Historically, we over-forecasted, sold less than we had forecasted and ordered, and we ran a risk of ending up with excess stock. If this was the case historically, then the app will assume we’re bound to over-forecast again and compensate for our high forecast by subtracting stock from the initial calculated safety stock. This is known as a negative days offset.

What exactly is the risk percentage? This indicated the degree of variability in the data. Simply put, how “all-over-the-place” are the data points the app has to work with?

Suppose historically we forecasted 100 units every month and sold 200 units every month. It’s easy for the app to plan using these data points. It’s clear that you under-forecast by 100 units a month, so it will ensure to take this into consideration for future forecasts when calculating the offset.

However, suppose historically we forecasted 100 units every month and sold 110 in some months and 290 in other months. This still means that on AVERAGE, you are under-forecasting 100 units a month, but this large range of data points make it hard to plan for. Are you under-forecasting by 10 units or 190 units for next month? The “risk” of applying a less-than-optimal offset is high and thus this item will have a high risk percentage.

A common misconception is that a large risk percentage results in large risk offset days. This is not necessarily the case as these two values are calculated (mostly) independently.

And how does all this affect the calculation of safety stock?

We know that Safety stock is calculated using the following 5 factor:

These factors mostly impact each other in order to calculate the required safety stock, except for risk offset days, which gets dealt with after.

It’s therefore possible to have a large risk percentage (data points are all over the place), but a low risk offset (the average variance between historical forecasts and actual sales are low as some may be positive and some negative).

It’s also possible to have a low risk percentage (all the data points indicate a historical forecast of 200 units) and a large risk offset (the future forecast is 100 units).

Limiting the Minimum risk percentage and Maximum risk percentage as well as the Minimum risk offset days and Maximum risk offset days is therefore crucial when the goal is to limit safety stock due to the cash flow impact it might have, and therefore the capital tied up in days worth of safety stock. However, if not stocking out is the goal, then it may be preferred to not have cut-offs in place for these parameters.

Take note that changing these global settings may mask granular risk calculations for certain items where it would have been beneficial to explore and correct the cause of the high risk percentage.

Remember: You can set cut-offs for safety stock in the Configuration settings too.