SAC & GCP: SAC Forecasting with Google Cloud Services
- Google Cloud Platform, SAP Analytics Cloud
- Forecasting, Google Cloud, sac, SAP Analytics Cloud
- 4 min reading time
Gary Lude
The introduction of the SAC Export & Import API opens up new possibilities to overcome the previous forecasting limitations of SAC. With the help of the Export API, the data from an SAC planning model can be easily transferred to the Google Cloud Platform. In the Google Cloud Platform, all services of the GCP are then available, including the option of implementing individual forecasting, for example, using R models.
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Google Cloud Services and SAC Forecasting at a glance
The SAP Analytics Cloud (SAC) natively provides forecasting functionalities for predicting planning data. However, these are often insufficient for individual needs or deliver unsuitable results and do not offer the possibility of including additional external data sources in the forecasts. Nevertheless, more and more companies want to automate their forecasting process as much as possible. This makes it possible to quickly and easily gain insights into the development of the coming periods in order to make the best possible strategic decisions.
With the introduction of the SAC Export & Import API, new possibilities arise to overcome the existing forecasting limitations of the SAC. With the help of the Export API, the data from an SAC planning model can be easily read into the Google Cloud Platform (GCP). In the Google Cloud Platform, all GCP services are then available. Among other things, the possibility to realize an individual forecasting e.g. with the help of R models, which covers practically all possible requirements and ideas. After the forecasting process is completed in the Google Cloud, the predicted values are written back into the original SAC planning model via Import API and can be manually revised or used directly for reporting.
How does forecasting work?
A web application is provided for the end user via GCP Cloud Run, which enables interaction with the forecasting process. Here, the forecast can be started, the current status can be queried and the predicted values can be imported into any target version of the SAC.
When the forecast is started, the data from the SAC model selected via dropdown is exported using a Google Cloud Function via SAC Export API and sent to Google Cloud Pub/Sub as suitable packages for the forecast. Each package starts an instance of Google Cloud Run, which includes both a processing layer with Python and an underlying forecast layer with R. Instead of forecasting using an R model, any other programming language would also be conceivable and possible.
Since the technical basis of Cloud Run is Docker Images, the use of various programming languages and environments is feasible.
Since a forecast can be quite complex and a run can take several minutes, the forecasts of the forecast run are first written by the respective Cloud Run instance to Google Cloud BigQuery, the data warehouse of the GCP. The forecast is created in the background, so the user does not have to remain logged into the online session in front of the computer. However, if interested, they can check the status of the current run at any time within the web application. The status is technically determined by comparing pending and already executed forecast packages within a Cloud Function.
Once the forecast is complete, the final predicted values can be imported into SAC. A Cloud Function is used again for this, which now uses the SAC Import API to write the data into the stored SAC planning model. Shortly afterwards, the forecast data is then available in SAC for further use.
What are the advantages of this solution?
Features of this approach
Creation of an individual forecast
Any forecasting scenario can be implemented with our solution, offering our customers high flexibility without restrictions. Together with the customer, we evaluate the optimal process and the necessary services to realize the customer's ideas. The customer can also decide how users can or should interact with the forecasting. There is also the possibility of fully automated forecasts, which are always executed automatically at a certain point in time. Our principle is: Let your ideas run wild and we will take care of the implementation into reality.
Customized web application
The individual needs of each company must be covered to enable easy integration into the working environment. Based on this, we have opted for an application that can be easily adapted to individual requirements. The focus is on interaction possibilities through forecasting, with no limits to further modifications. The use of Python Flask enables a high degree of flexibility, even for meeting complex demands.
Using the SAC Export & Import API
The natively available SAC Export & Import API offers an interface provided by SAP for communication with SAC planning models. We have integrated this technology into our solution to ensure a stable, secure, and functional export and import option. Furthermore, the interface is actively maintained and constantly developed by SAP.
What opportunities are you missing without this solution?
Are you currently struggling to implement your forecasting requirements in SAP Analytics Cloud? Can't find a way to implement your forecasts individually? If so, then you are missing the full potential of your data. This is exactly where our solution comes into play.
With Google Cloud Services as the foundation for your forecasting process, you can work with us to convert your business cases into a stable and scalable solution. Additionally, your analysts can interact with an intuitive user interface and individually control the forecasting as needed.
Without this customizable solution, you may not be able to implement your complex forecasting requirements as desired and will miss out on important insights into the future development of your business. We would be happy to support you in turning your ideas into reality.
Know more?
Would you like to delve deeper into this topic? Then we would be happy to talk to you personally about the possibilities of the Google Cloud Platform (GCP).
