Machine Learning Cockpit – What’s New in 2302

SAP Delivered Scenario

Maybe you like an idea of using machine learning for making predictions in SAP Business ByDesign And you heard of Machine Learning Cockpit, released in 2211 but you are not sure how to start?

In SAP Business ByDesign release 2302 we provide out-of-the-box one scenario that you can inspect and try it out directly. Objective of the scenario is to predict if Opportunity will end up Won or Lost.

You can find the scenario in Scenario work center view after you select view filter All SAP Delivered Scenarios. Name of the scenario is CRM_OPP_SUCCESS.

For detail instructions how to use the scenario please refer to Online Help > Use Cases > Opportunity – Success Prediction (SAP Delivered Scenario).


Use Cases

Machine Learning Cockpit for SAP Business ByDesign allows to you design you own predictive solutions end-to-end without ever leaving SAP Business ByDesign UI.

In case, you are searching for inspiration of what exactly could be predicted and how to set it all up make sure to check out new repository of ML use cases in Online Help > Use Cases.

As of release 2302 we provide detailed, step-by-step instructions how to implement following solutions:

  • Opportunity – Success Prediction (SAP Delivered Scenario)
  • Sales Quote Item – Success Prediction
  • Purchase Order Item – Prediction of Incomplete Delivery
  • Purchase Request Item – Supplier Proposal
  • Customer – ABC Classification Proposal
  • Outbound Delivery – Cancelation Prediction

Feel free to try them out.

We are working on more use cases, so stay tuned.


UX Improvements

Feature functionality is never complete if the feature is hard to use. Feature functionality is never complete if it is hard to understand what-is-what in UI, and what exactly I am expected to do in any specific step. In Machine Learning Cockpit team we never take UX lighthearted.

In 2302 version we have made two improvements which I would like to explain bit bit more in details

Scenario Field Selection

Selection of fields in Scenario requires that you need to decide and manually select those fields that will be used in scenario Models. Specifically, you need to flag a Target Field.

Before 2302 – field selection was error-prone process because field names and field selection checkboxes were on different parts of the screen. It was easy to make a mistake and select wrong field, or to select regular field as Target Field. It was also hard to tell what field was selected as Target Field, if any.

Starting 2302 – the Target Field is selectable from new selection-box and its value is always visible. Field selection checkboxes are positioned next to field names, bringing clarity what fields are selected and what not.

Model Training Results

Model Training Results provides quantitative indicators of Model quality

Before 2302 – there were four different Model Evaluation Indicators displayed and it was not clear which one was the most important one. Secondly, the Field Contributions list was unsorted, showing 5 rows only. To see and understand how important individual fields were for model training you needed to expand the list and sort it manually.

Starting 2302 – we show Model Accuracy only, as dominant indicator of model quality. Experts, who demand more detailed indicators will find them organized in a table – Model Evaluation Indicators per Class. The Field Contributions lists was enlarged and sorted by Contribution ranking by default.

These were not the only UX improvements in 2302. Several other UX issues were fixed, which are maybe more subtle to notice. However, these two are the most visible ones and I believe that you  will like them.

Feel free to share your experience and impression either, here, in comments. Or drop me an email at: Dalibor Knis . Thank you.



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