Production engineers face significant challenges when setting up and managing production processes. For example, tight timelines to start production and the need to resolve errors quickly to minimize downtime and costly delays. SAP DM addresses these issues with two AI assistants. In the Production Process Designer, an AI-powered script-task generator cuts the time spent writing and debugging code by automatically producing executable script steps from your requirements. For production process incidents, an AI-driven issue analysis tool rapidly analyzes error logs, identify root causes, and generates actionable instructions, shortening time-to-resolution, and accelerating time-to-value.
Generate Script Task
In the Production Process Designer, script tasks enable you to integrate user-specific logic directly into your production process workflows. You can generate AI-based JavaScript code based on your natural language descriptions of your requirements. This allows you to create script tasks more efficiently and less error-prone, without requiring any coding knowledge.
Now, we want to use the AI-based script task generating feature in our business user story.
Business Requirement:
Using the Welding Operation POD (created in the lesson on creating a customized POD) for plants in countries where Fahrenheit is the standard unit of measurement.
Business User Story:
Consider the Welding Operation POD from the Creating a Customized POD lesson, where we designed a POD for production operators at the welding work center to support the customization of bike frames with customer-specific engravings. As part of this setup, we configured a dialog that allows operators to view the welding temperature before starting their work.
Instead of using fixed values, we leveraged the set point group functionality in combination with a production process in SAP DM. This ensures that any updates to the set point group are automatically reflected in the dialog, eliminating the need for manual updates and reducing the risk of errors.
Currently, the temperature values are maintained in degrees Celsius. However, when deploying the same POD in regions where Fahrenheit is the standard unit of measurement, an additional calculation is required.
Rather than creating and maintaining a separate set point group for Fahrenheit values, it is more efficient to integrate a script task into the production process. This script task can dynamically convert the welding temperature parameters into Fahrenheit and provide them as additional output data alongside the values in degrees Celsius.
This approach offers clear advantages: It avoids the need to maintain two set point groups, reduces the risk of inconsistencies (for example, when one set point group is not updated), and saves time by requiring updates in only a single source of truth.
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Production Process Issue Analysis
You can use the Monitor Production Processes app to monitor and track the execution status of all production processes. The application allows you to respond to errors and efficiently identify the root causes of execution failures or high defect rates.
To further enhance this capability, you can leverage an AI assistant that analyses failed production processes, detects the underlying causes of issues, and generates resolution instructions. The AI feature presents its results in natural language, ensuring accessibility and comprehensibility.
Now we will utilize the AI assistant within our previous business case to analyse issues in the production process.
Business User Story:
Assume the script had been created manually instead of using AI-based generation. When running the POD, an error occurs and no temperature values are displayed. Since the error message is difficult to interpret, the AI assistant can be used to analyze the failed production process and suggest possible causes and solutions.