Demand planning process usually starts with some type of products segmentation, for example ABC, XYZ and FMR or time-series analysis. In SAP Integrated Business Planning for Supply Chain there are two different apps to cover this requirement:
In this blog post, we will focus on Manage Forecast Automation Profiles app and as it’s mentioned above, time-series analysis can be executed at any planning level and the results can be also opened in IBP excel add-in at any level if you configure system using virtual master data types. Please read the blog post to check how to create virtual master data type in SAP Integrated Business Planning for Supply Chain https://blogs.sap.com/2020/09/15/master-data-types-in-sap-ibp/
For example, you can generate your time-series analysis profile at Location-Brand level because you would like to check data pattern of your actual sales at this level:
After that, you can generate virtual master data type to assign results of time-series analysis to more detailed level, for example, Location-Product. In this case, you will see the same pattern for all Products within one Brand.
Sometimes it’s really necessary to execute time-series analysis at more than just one planning level at the same time to identify data patterns for all different situations. For example, you can have upward trend at Location-Brand level, but for some products within one brand it can have downward trend thus if you execute your forecast at Product level you should be careful with time-series results from Brand level. This app can easily help you to understand your data from different point of view, which can bring you an opportunity to decide at which level you should make your forecast to have a better result but it will not automatically select it for you.
Inside of this app, you can also set your own thresholds for identification of seasonality, trend, intermittency etc., as well as set some other settings to identify patterns more accurate and stable.
Time-series analysis patterns as an output from this app will be taken into account by forecast model to exclude some unnecessary forecast models for different planning combinations if you activate shown below feature in the forecast model. For example, Croston algorithm is not relevant for continuous sales, thus it will not be used for the combinations with such a result of time-series analysis.
It will also be taken into account by outlier correction algorithms soon to help you to clean your sales and not to lose your seasonal peaks and trends.
You can save the results in the attribute of master data specially predefined with all necessary root attributes. For example, if you run time-series analysis at Location-Brand level, master data must have Location and Brand as a root attributes and some additional attributes to save data patterns. If you do not select it in the app, you will still have an opportunity to check results but only in the log of the application job in WebUI of SAP Integrated Business Planning for Supply Chain.
As you can see, time-series analysis is a very useful tool released in SAP Integrated Business Planning for Supply Chain which can help you to decrease your efforts for a daily demand planning process tasks, make your forecast more accurate and increase your knowledge of your sales. In the near future it will become even more powerful and smarter.
- Manage Forecast Automation Profiles – helps to analyze time-series data patterns (Trend, Seasonality, Intermittency & Volatility, Lumpy, Continuous) on different levels
- Manage ABC/XYZ Segmentation rules – helps to make ABC/XYZ segments based on different levels, segmentation methods and thresholds
In this blog post, we will focus on Manage Forecast Automation Profiles app and as it’s mentioned above, time-series analysis can be executed at any planning level and the results can be also opened in IBP excel add-in at any level if you configure system using virtual master data types. Please read the blog post to check how to create virtual master data type in SAP Integrated Business Planning for Supply Chain https://blogs.sap.com/2020/09/15/master-data-types-in-sap-ibp/
For example, you can generate your time-series analysis profile at Location-Brand level because you would like to check data pattern of your actual sales at this level:
After that, you can generate virtual master data type to assign results of time-series analysis to more detailed level, for example, Location-Product. In this case, you will see the same pattern for all Products within one Brand.
Sometimes it’s really necessary to execute time-series analysis at more than just one planning level at the same time to identify data patterns for all different situations. For example, you can have upward trend at Location-Brand level, but for some products within one brand it can have downward trend thus if you execute your forecast at Product level you should be careful with time-series results from Brand level. This app can easily help you to understand your data from different point of view, which can bring you an opportunity to decide at which level you should make your forecast to have a better result but it will not automatically select it for you.
Inside of this app, you can also set your own thresholds for identification of seasonality, trend, intermittency etc., as well as set some other settings to identify patterns more accurate and stable.
Time-series analysis patterns as an output from this app will be taken into account by forecast model to exclude some unnecessary forecast models for different planning combinations if you activate shown below feature in the forecast model. For example, Croston algorithm is not relevant for continuous sales, thus it will not be used for the combinations with such a result of time-series analysis.
It will also be taken into account by outlier correction algorithms soon to help you to clean your sales and not to lose your seasonal peaks and trends.
You can save the results in the attribute of master data specially predefined with all necessary root attributes. For example, if you run time-series analysis at Location-Brand level, master data must have Location and Brand as a root attributes and some additional attributes to save data patterns. If you do not select it in the app, you will still have an opportunity to check results but only in the log of the application job in WebUI of SAP Integrated Business Planning for Supply Chain.
As you can see, time-series analysis is a very useful tool released in SAP Integrated Business Planning for Supply Chain which can help you to decrease your efforts for a daily demand planning process tasks, make your forecast more accurate and increase your knowledge of your sales. In the near future it will become even more powerful and smarter.
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