--- sourceDocument: Yokohama Employee Service Management sourceDocumentLink: https://www.servicenow.com/docs/r/yokohama/employee-service-management Release : - yokohama ft:locale : - en-US ft:publication_title : - Yokohama 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: Yokohama * * Updated January 30, 2025 * * ![](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) uses machine learning to predict how long it will take to resolve HR cases by analyzing similar closed cases. This capability enhances transparency for employees, helps agents prioritize work, and provides managers with insights for SLA management and case resolution forecasting. Show full answer Show less Note: Creating new regression solutions is deprecated as of the Washington DC release, but existing solutions can still be trained and edited. ## Key Features * **Machine Learning Prediction:** ETTR predicts resolution time based on case attributes such as short description, category, and priority. * **Configurable Solution:** Customers can use an existing ETTR solution or adjust settings like fields, filters, and training frequency to tailor predictions. * **Multiple Views of ETTR:** ETTR values are visible in the Employee Portal, Now Mobile, Agent Workspace, Platform case lists, and admin case configuration views. * **Preconfigured Predictive Model:** The HR Case Resolution Time model (mlsnsnhrcoreglobalhrcaseresolutiontime) is auto-trained when key HR and Predictive Intelligence plugins are installed and the auto-training system property is enabled. * **System Properties and Business Rules:** Several system properties control ETTR display for agents and employees, confidence levels, and business rules can be customized to fit organizational needs. * **ETTR Metrics:** Includes actual resolution time, minimum and maximum ETTR in days, and a point estimate for resolution time, all based on historical data from similar cases. ## Practical Application for ServiceNow Customers By enabling ETTR for HR cases, organizations can provide employees with clear expectations about case resolution times, improve agent workload management, and gain actionable data for SLA adherence and performance analysis. To implement, ensure the required plugins and system properties are configured, run the "Populate Actual Resolution Time" job after upgrades to backfill historical cases, and customize the regression solution as needed. ETTR predictions will then be available across multiple user interfaces for comprehensive visibility. 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=yokohama&pubname=yokohama-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/SNcmeHidAErXLzlmzZZqqA "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/UM6Hndgv~BaOphe0uhfqMw "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/UM6Hndgv~BaOphe0uhfqMw "View the examples of ETTR views across the journey of a case which indicates the estimated time to resolve based on the historical data.")