Enabling recommended problem codes for Work orders

You can use the AI configuration application to enable AI-recommended problem codes for Work orders. The problem code recommendation AI feature uses the pcc model template.

1. In Maximo Manage, in the AI configuration application, select the WOPROBLEMCODE configuration.
2. Review the Edit AI configuration dialog.
   1. Click Actions > Edit.
   2. In the Template version field, ensure that the latest version is selected. To view all versions, click the Lookup icon.
   3. In the Additional details for AI explained section, provide any information that is specific to your organization and relevant to your end users to help them understand the model and its output.
      An AI icon is located alongside your model's output. Your users can click the AI icon and then access any information that you specify in this section alongside other general model information that's provided by IBM. You can enable the AI configuration, view the AI icon and its content in context, and then edit this section later as needed.
   4. Click Save.
3. Optional: Set up arguments.
   1. Click Actions > Set arguments
   2. Change the default value for the score_threshold argument.
      A numerical score is used to measure how recommendable each problem code is for a given work order. Problem codes that have scores above the threshold are considered for recommendation.
      If you are setting up problem code recommendations for the first time, you might choose to set the threshold to a lower value. A lower value increases the likelihood of any output from the model, although it does typically decrease desirable output, especially if the training filter does not contain diverse or adequate amounts of data.
   3. Click Save.
4. Click Actions > Check data requirement.

   When you check the data requirements, the training data is reviewed to determine whether the data contains enough problem codes and work orders. If the data check fails, you must add or improve the quality of data in your training filter. For more information, see Preparing required data for problem code recommendations.

5. Click Actions > Activate

   Activating the AI configuration indicates that the AI configuration is prepared and the model is ready to be trained.

6. Optional: Change the frequency of the training process.

   Training is controlled on a crontask schedule. The AITRAINJOB crontask initiates training for all eligible AI configurations. By default, the crontask runs every five minutes.

   For more information, see Training and inferencing.

   If you want training to run sooner, you can edit the cron task schedule.

   1. In Maximo Manage, in the Cron Task Setup application, open the AITRAINJOB cron task.
   2. In the Cron Task Instances table, for the WOAI instance, in the Schedule field, change the value.
   3. Wait a few minutes before continuing to the next step.
7. In the AI configuration application, in the AI configuration for problem codes, click Actions > Train model

   Training begins when the AITRAINJOB crontask runs. Training can take a few hours.

   You can monitor training in the Model training log table or in the Model status dialog.

   The Model training log table is in the AI configuration that you created and contains step-by-step updates for the training process, but you must refresh the page to see updates. To refresh the page, click Refresh.

   To access the Model status dialog, in the AI configuration, click Actions > Check model status.

   The model accuracy score is a measure of how the model performs on the training data. The score represents the amount of values that the model recommended that it considers reasonable to be the correct or best value. The closer to 1, the more accurate the output likely is in context of the data on which the model was trained. If the model was trained on data that was not complete or diverse but the score threshold is low, the accuracy score might be high but the output is not accurate.

   If training fails, you can complete some troubleshooting steps. For more information, see Troubleshooting Maximo AI Service and AI features.

   For conceptual information about training, see Model training overview.