Forecasting Periods in Demand Planner

Hi,

This isn’t possible using standard IFS functionality. 

When ‘Monthly’ Period Versions are selected, the ‘Generate Periods’ job will divide the year into monthly buckets starting 1st January and ending 31st December.

When ‘Weekly’ Period Versions are selected, the ‘Generate Periods’ job will essentially divide the year into weekly buckets starting 1st January and ending 31st December.  The difference with weekly periods is that each period will start on a Monday and end on a Sunday. 

This means that the first weekly period and last weekly period may have different ‘Length in Work Days’ values depending on which day of the week the 1st January and 31st December fall on within that year.

I hope that this helps.

Hello Richard,

Thank you very much. We thought of going for a customization.

What we are going to do through this  customization can be done manually today in IFS.

For an example 

1. We define the periods with User Defined and 52 Periods Per Year
2. Then adjusted the dates manually 
3. Some periods go across the years 
4. We made sure that there are no gaps and overlaps
5. Then we ran the validation and found no issue

In this customization we additionally make sure that the period length is the same for all the periods

Any advice will be really appreciated 

BR

Champika

Hello Richard,

Thank you very much for your advice.

BR

Champika

Hi all,

I would like to reopen this discussion because we are also using weekly periods and have encountered an issue around the 2026/2027 year transition.

As mentioned in the previous posts, week 53 is merged into either period 52 or period 1. This is not a problem when the total number of days in periods 52 and 1 remains 14 days. However, at the 2026/2027 transition, periods 2026-52 and 2027-01 together contain 21 days (11 + 10 days).

Since the system forecasts the same volume for each weekly period, the demand of one complete week is effectively lost during forecasting. As a result, the forecasted quantities for this period are significantly lower than expected.

This is a critical issue for our business. We manufacture finished products and purchase raw materials based on the forecast generated in the system. Customer orders are typically received only one day before shipment, which means we cannot rely on actual customer demand for production planning.

Consequently, this forecasting anomaly can lead to insufficient production volumes, raw material shortages, missed deliveries, and ultimately a direct financial impact on the business.

Although this situation occurs only once every 5 to 6 years, the impact can be substantial when it does occur.

I have attached a screenshot illustrating the issue.

Has anyone found a recommended solution, workaround, or best practice for handling this scenario?

Thank you.