I recommend selecting more fields in scenarios and decide about final selection in the model, based on data distribution statistics. Moreover, under Input Field Selection, you should select the fields to be included in the training which may contribute to decide the outcome of Target field. If there are no work center views in the list, it means that there is either no report created for the “Production Orders” data source or none of the reports have the work center view assigned. Next, from the list of work center views, select the Work Center View ID. Then you must select the field that you want to predict in the Target Field section,” Production Order Status” in this case. For this case select “Production Orders/Lot: Dates and Quantities” You should now select the data source that you want to use for your scenario in the Data Source field. Name your scenario and make sure it starts with the letter Z, e.g., Z_PO_CANCELLATION, otherwise the tool will throw out an error as By Design has this rule for all user defined scenarios. This will open a new window where you can enter the details for your new scenario. Once you’re in the Scenario work center view, click the “New” button to create a new scenario. This is where you will find all the existing scenarios and be able to create new or edit existing ones To create a new scenario, navigate to the Machine Learning Cockpit work center and select the Scenario work center view. So, let’s get started with steps to create a new Scenario in Machine Learning Cockpit. By following these steps, you’ll have a comprehensive understanding of how to implement the solution end-to-end. This will give you a visual representation of your data, making it easy to understand and work with. This will allow you to see your model in action, making predictions based on your specific data.įinally, we’ll look at how to view your predictions in the Production Order UI. Once you have your model, we’ll see how to create and execute a prediction run. We’ll walk through the steps to create and train your own model, so you can get accurate predictions. Next, we’ll dive into the model building and training process. Now to give you an overview, firstly we’ll cover the creation of a scenario, which is a set of data and parameters that are used to create and train a machine learning model. In this blog, we’ll go through all the steps to make predictions with the help of Production Orders data source where predictions can be consumed in the General tab of Production Orders document. You must have the authorization to access the following work center/ work center views: It’s as if you have your very own crystal ball, guiding you through the intricacies of your business processes.Īfter reading this blog post, make sure to check the links at the end to get your hands on my other blogs related to Machine Learning Cockpit use cases. With this innovative solution at your fingertips, you gain the power to proactively address high-risk production orders, averting disruptions, reducing costs, and enhancing operational efficiency. Say goodbye to uncertainties and welcome to Machine Learning Cockpit. But here’s the real enchantment: these predictions materialize right before your eyes, seamlessly integrated within the Production Order document, empowering you to take swift and decisive action. Just as a crystal ball offers tantalizing glimpses into what lies ahead, the Machine Learning Cockpit taps into the power of historical data to predict the likelihood of production order cancellations. Brace yourself for a thrilling journey where foresight meets innovation! Now, get ready to be captivated by the extraordinary Machine Learning Cockpit in SAP Business ByDesign! This revolutionary application harnesses cutting-edge technologies to unveil the hidden risks of production order cancellations, empowering you to steer clear of any potential disasters. Picture this: a magical crystal ball that grants you the power to peer into the future and anticipate potential production order cancellations. In the world of production planning, where uncertainties hang in the air and risks lurk around every corner.
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