--- sourceDocument: Australia Employee Service Management sourceDocumentLink: https://www.servicenow.com/docs/r/employee-service-management Release : - australia ft:locale : - en-US ft:publication_title : - Australia Employee Service Management ft:clusterId : - emplsm bundleId : - emplsm workflow : - Employee --- # Estimated time to resolve HR cases # Estimated time to resolve HR cases {#ariaid-title1} * Release version: Australia * * Updated March 12, 2026 * * ![](https://www.servicenow.com/docs/portal-asset/ico-clock) 2 minutes to read Summarize ![AI sparkle icon](https://servicenow.com/docs/portal-asset/ai-sparkle-icon) Summarized using AI This content was generated using new OpenAI-powered functionality. Results are provided on an as is basis and are not guaranteed to be accurate or complete. ## Summary of Estimated time to resolve HR cases Estimated Time to Resolve a Case (ETTR) leverages machine learning to predict how long it will take to resolve HR cases by analyzing similar closed cases. This prediction enhances transparency for employees, helps agents prioritize work, and provides managers with valuable insights into SLA management and case resolution times. Show full answer Show less ## Key Features * **Machine Learning Prediction:** Uses attributes like case description, category, and priority to estimate resolution time. * **Configurable Regression Solution:** Customers can use an existing ETTR solution or adjust settings such as fields, filters, and training frequency for their needs. * **Integration with HR Scoped Apps:** Requires installation of the Human Resources Scoped App: Core and Predictive Intelligence for Contextual Search plugins. * **Auto-Training Model:** The predictive model is automatically trained when system properties and plugins are correctly configured. * **Visibility Across Interfaces:** ETTR predictions are visible to employees (via Employee Portal and Now Mobile), agents (Agent Workspace), and admins (Case Configuration and Platform views). * **System Properties and Business Rules:** Allows customization of ETTR display settings and confidence levels to align with organizational requirements. * **ETTR Metrics:** Includes minimum, maximum, and point estimate resolution times in days, plus actual resolution time data for continuous accuracy. ## Practical Application and Benefits * **For Employees:** Provides clear expectations on case resolution timelines, improving their experience and trust. * **For Agents:** Supports workload prioritization and performance measurement based on predicted resolution times. * **For Managers:** Offers actionable data to monitor SLA compliance and enhance case management strategies. * **Data Accuracy Maintenance:** Running the "Populate Actual Resolution Time" scheduled job after upgrades ensures historical case data is accurate for predictions. ## Implementation Notes * Creation of new regression solutions for ETTR is deprecated since the Washington DC release; however, existing solutions can still be trained and edited. * Ensure relevant plugins are active and system properties like `glide.platformml.autotraining.enabled` are set to enable auto training of the predictive model. * Configure business rules and system properties to customize how ETTR values are shown to different user roles. Determine the Estimated Time to Resolve a Case (ETTR) for a case by analyzing similar closed cases in the past for better visibility and transparency. Note: Support for creating new regression solutions was deprecated in the Washington DC release. You can train and edit any existing solutions, but you can't initiate new ones. The following information is provided for legacy context. For more information see [Create and train a regression solution](https://www.servicenow.com/docs/access?context=create-regression-solution&version=australia&pubname=australia-intelligent-experiences&ft:locale=en-US). Machine learning predicts the estimated time to resolve a case (ETTR) based on attributes of a case such as its short description, category, priority, and so on. For more information about configuring the machine learning regression solution definition for ETTR, see [HR PIWB template: Recommend estimated time to resolve](https://www.servicenow.com/docs/DzhgNAwhzDGMPkkBDn5RKQ "Train your solution by using historical data to predict numeric outputs based on the historic data. Configure the solution definition to predict the estimated time to resolve a HR case.") To make predictions, you can use the existing ETTR solution definition or change some of the default settings such as the fields, a filter, and the training frequency. By analyzing the resolution time for similar closed cases in the past, the ETTR capability offers the following benefits: * Gives employees better transparency and set expectations on resolution time for their cases. * Helps agents in prioritization of work and measure performance. * Provides data that can help managers with insights into SLA management and estimated time to resolve for cases. {#train-model-ettr__ul_hjv_z5k_hpb} ## ETTR for HR Cases {#train-model-ettr__section_sm5_l14_jnb} On configuring Estimated HR Case Resolution Time (ml_sn_sn_hr_core_global_hr_case_resolution_time) and training the predictive model, you can see the HR Case Resolution Time option. See the Estimated time to resolve value from: * Employee portal view and Now Mobile view * Agent workspace * Platform view with a list of cases * Case configuration view for admins {#train-model-ettr__ul_rxs_22x_3pb}For more information, see [Viewing ETTR predictions](https://www.servicenow.com/docs/sJk0DwiRxAxXcXBINJ38~Q "View the examples of ETTR views across the journey of a case which indicates the estimated time to resolve based on the historical data.") ## ETTR predictive model {#train-model-ettr__section_tm5_l14_jnb} The Estimated HR Case Resolution Time (ml_sn_sn_hr_core_global_hr_case_resolution_time) is configured and the predictive model is auto trained when all the following conditions are met: * The Human Resources Scoped App: Core (com.sn_hr_core) plugin is installed. * The Predictive Intelligence for Contextual Search (com.snc.contextual_search_ml) plugin is installed. * The glide.platform_ml.auto_training.enabled system property is set to true. {#train-model-ettr__ul_um5_l14_jnb} Business rules and system properties Verify the following system properties and business rules: * Use or configure the following system properties and corresponding values to display the ETTR values per your requirement: * ml_sn_sn_hr_core.COE_ETTR_display_agent * ml_sn_sn_hr_core.COE_ETTR_display_employee * ml_sn_sn_hr_core.estimated_resolution_time_confidence_level {#train-model-ettr__ul_ypd_2gz_1qb} * Use or configure the Predict Estimated Resolution Time business rule per your business needs. {#train-model-ettr__ul_xct_hhb_bqb} ## ETTR model {#train-model-ettr__section_szk_ndg_ypb} Regression solutions such as ETTR enable you to predict a point estimate and prediction interval. This capability informs the agent, employee, and service owners on the estimated resolution time for a case based on the time taken to resolve similar issues historically. * Actual resolution time: Stores the actual time of resolution of a particular case. This field stores the resolution time from opening to closing of the case. Note: When you upgrade, run this Populate Actual Resolution Time schedule job once for populating the actual resolution time for existing HR cases. For all upcoming cases, the business rule Populate Actual Resolution Time automatically generates the resolution time. * Max ETTR: Indicates the maximum estimated resolution time in days for case completion. Estimate is based on the time taken for resolving similar HR cases. * Min ETTR: Indicates the minimum estimated resolution time in days for case completion. Estimate is based on the time taken for resolving similar HR cases. * ETTR in days: Stores the point estimated resolution time in days for case completion. {#train-model-ettr__ul_ls5_m2g_ypb} **Related concepts** * [Viewing ETTR predictions](https://www.servicenow.com/docs/sJk0DwiRxAxXcXBINJ38~Q "View the examples of ETTR views across the journey of a case which indicates the estimated time to resolve based on the historical data.")