--- title: "FEM: Outlier Detection Guide for Brands" description: "FEM: Outlier Detection Guide for Brands" --- [Skip to content](https://support.worldly.io/hc/en-us/articles/39937252665499-fem-outlier-detection-guide-for-brands#main-content) English Show submenu for translations [Submit a request](https://support.worldly.io/kb-tickets/new?hsLang=en) [![Worldly logo](https://support.worldly.io/hs-fs/hubfs/Worldly%20Media%20Kit/Worldly%20Logos/Worldly_LogoTM_Red.png?width=250&height=61&name=Worldly_LogoTM_Red.png)](https://www.worldly.io/) Open main navigation Close main navigation - English Show submenu for translations - [Submit a request](https://support.worldly.io/kb-tickets/new) How can we help? - There are no suggestions because the search field is empty. 1. [Help Center](https://support.worldly.io/?hsLang=en) 2. [Assessments](https://support.worldly.io/assessments?hsLang=en) 3. [Facility Environmental Module (FEM)](https://support.worldly.io/assessments?hsLang=en#facility-environmental-module-fem) # FEM: Outlier Detection Guide for Brands **Table of Contents** - [Insights Hub Outliers](https://support.worldly.io/hc/en-us/articles/39937252665499-fem-outlier-detection-guide-for-brands#h_01K2FW81CWC003B6VJPJPCRE3T) - [What kinds of automated data quality checks are available?](https://support.worldly.io/hc/en-us/articles/39937252665499-fem-outlier-detection-guide-for-brands#h_01K2FX2NV7FYWD4FQP2WVENPX5) - [Higg FEM Outlier Detection Methodology](https://support.worldly.io/hc/en-us/articles/39937252665499-fem-outlier-detection-guide-for-brands#h_01K2FXAZE6WNAXGWVYW3191GVX) - [What can I expect with Outlier Detection?](https://support.worldly.io/hc/en-us/articles/39937252665499-fem-outlier-detection-guide-for-brands#h_01K2FX2NVJ87PCJV31XXZFFF0X) - [What is considered an Outlier?](https://support.worldly.io/hc/en-us/articles/39937252665499-fem-outlier-detection-guide-for-brands#h_01JMDKN72AM0SQ4T9F77P8KMAN) - [FDM to FEM Outlier Detection](https://support.worldly.io/hc/en-us/articles/39937252665499-fem-outlier-detection-guide-for-brands#h_01K3RYXMDA75K3FM6E4SSESCWD) - [FAQ](https://support.worldly.io/hc/en-us/articles/39937252665499-fem-outlier-detection-guide-for-brands#h_01JTS3HGWYB2V9PZW2RW7JQJR4)   High-quality data is the most valuable component of the Higg Index and the Worldly platform. In order to maintain high-quality data, it is important to identify and correct any outliers.  An **Outlier **is a data point or calculation that differs significantly from our historical observations and expectations.   ## **Insights Hub Outliers** In the left column of Insights Hub, you can see how many outliers were excluded from the Insights Hub Dashboard. Next to the red X, you'll see the total number of outliers excluded, along with an "i" icon. ![55018147697435.png](https://support.worldly.io/hs-fs/hubfs/Knowledge%20Base%20Import/55018147697435.png?width=2930&height=966&name=55018147697435.png) Click on the icon to access the **Data Worth A Second Look **window.   There are outliers detected in 4 assessments, which are automatically excluded from the Insights Hub dashboard. Click on the checkbox to **Include Outliers** in the Insights Hub Dashboard. This will update the dashboard to include the 4 assessments with detected outliers. ![55018141277595.png](https://support.worldly.io/hs-fs/hubfs/Knowledge%20Base%20Import/55018141277595.png?width=920&height=784&name=55018141277595.png)   Click **Outliers CSV** to download a CSV file of the assessments with detected outliers. ![55018147700891.png](https://support.worldly.io/hs-fs/hubfs/Knowledge%20Base%20Import/55018147700891.png?width=920&height=774&name=55018147700891.png)   The table in the file includes information about the assessments, including Worldly ID, Facility, and Assessment ID. The other columns in the table are used to check for outliers, including: - Worldly ID - Facility - Assessment ID - total\_energy\_mj - is\_outlier\_total\_energy\_mj - total\_ghg\_kgco2e - is\_outlier\_total\_ghg\_kgco2e - total\_finalProductAssembly\_mj - is\_outlier\_total\_finalProductAssembly\_mj - finalProductAssembly\_pcs - finalProductAssembly\_mj\_intensity - is\_outlier\_finalProductAssembly\_mj\_intensity - total\_finishedProductProcessing\_mj - is\_outlier\_total\_fin\_proc\_mj - finishedProductProcessing\_pcs - finishedProductProcessing\_mj\_intensity - is\_outlier\_finishedProductProcessing\_mj\_intensity - total\_hardComponentTrimProduction\_mj - is\_outlier\_trim\_prod\_mj - hardComponentTrimProduction\_kg - hardComponentTrimProduction\_mj\_intensity - is\_outlier\_hardComponentTrimProduction\_mj\_intensity - total\_materialProduction\_mj - is\_outlier\_total\_materialProduction\_mj - materialProduction\_kg - materialProduction\_mj\_intensity - is\_outlier\_materialProduction\_mj\_intensity - total\_printingProductDyeingAndLaundering\_mj - is\_outlier\_print\_dye\_mj - printingDyeingLaundering\_kg - printingDyeingLaundering\_mj\_intensity - is\_outlier\_printingDyeingLaundering\_mj\_intensity - total\_rawMaterialProcessing\_mj - is\_outlier\_total\_rawMaterialProcessing\_mj - rawMaterialProcessing\_kg - rawMaterialProcessing\_mj\_intensity - is\_outlier\_rawMaterialProcessing\_mj\_intensity - total\_rawMaterialCollection\_mj - is\_outlier\_total\_rawMaterialCollection\_mj - rawMaterialCollection\_kg - rawMaterialCollection\_mj\_intensity - is\_outlier\_rawMaterialCollection\_mj\_intensity - domestic\_total\_mj - is\_outlier\_domestic\_total\_mj ### Outlier Checks and What They Show The outlier report shows which checks were flagged for each assessment. You can use this information to diagnose outlier issues and find solutions. ### How Outlier Checks Work The system tracks outliers for each facility type and assessment combination. If you report multiple facility types, the system checks the data for each facility type separately. This approach catches outliers more effectively because it compares similar facility types to one another. ### What Happens When an Outlier Is Found When the system identifies an outlier for a facility type, the entire facility is removed from your current results. To include flagged facilities in your dashboard, you can toggle the option to include outliers in the outlier modal. ### The New Outlier CSV The updated outlier report includes new columns with true or false values. These values indicate whether each check was flagged as an outlier. Example of outlier detection columns ![55021262060059.png](https://support.worldly.io/hs-fs/hubfs/Knowledge%20Base%20Import/55021262060059.png?width=2748&height=502&name=55021262060059.png) ## **What kinds of automated data quality checks are available?** The Worldly platform automatically runs the following data checks during the facility self-assessment process.   **Check** **Type** **Description** **FEM2023** **FEM2024** **FEM2025** **Finished Product Assembly Quantity** Validation Annual Quantity for Finished Product Assembler must be whole number Yes Yes Yes **Biomass Source Mix** Validation The percentage of all biomass sustainably sourced with certification = 100 Yes Yes Yes **Steam Source Mix** Validation The percentage of all steam sources = 100 Yes Yes Yes **Steam Vapor Check** Validation The entered values for temperature and pressure would not create steam. Yes Yes Yes **District Heating Temperature Check** Validation District heating water exit temperature greater than or equal to entrance temperature. Yes Yes Yes **Vehicle Energy Sources** Validation Vehicle energy sources selected but no company owned or controlled vehicles. Yes Yes Yes **Low Total Energy** Validation Total production or combined energy usage reported must be greater than the average American annual household use of electricity. Energy total \< 38574 MJ IF the facility reports tracking all of its energy sources. No New in 2024 Yes **High Total Energy** Validation The total reported energy for the facility is more than 10,000,000,000 MJ. No New in 2024 Yes **High Total LNG** Validation The total reported LNG for the facility is greater than 1,000,000,000 MJ No New in 2024 Yes **Reported rainwater use and harvesting answers do not match** Validation The total reported amount of rainwater used does not match reported values for maximum rainwater harvesting capacity, and the facility responded that they utilized the maximum roof/ground area that is feasible for rainwater harvesting at the facility. Yes Yes Yes **Low Total Water Usage** Validation The total reported water use for a facility must be greater than 0 liters. The FEM will not accept zero or a negative value. No No New in 2025 **High Total Water Usage** Validation The total reported water use for a facility must must not exceed 1e+11 (billion) liters. No No New in 2025 **High Diesel Percentage** Outlier Detection The amount of energy from Diesel is more than 90% of the total reported energy. Yes Yes Yes **High LNG Percentage** Outlier Detection The amount of energy from LNG is more than 90% of the total reported energy. Yes Yes Yes **Low Purchased Electricity Percentage** Outlier Detection The amount of energy from Purchased Electricity is less than 50% of the total reported energy. Yes Removed Removed **Low Energy per Number of Employees** Outlier Detection The total reported energy per employee per working day is low. Yes Removed Removed **Low Water per Number of Employees** Outlier Detection The total reported water use per employee per working day is low. Yes Removed Removed **High Wastewater** Outlier Detection The total reported wastewater is high. Yes Yes Yes **Ignore high user-entered custom emissions factors** Calculation Users can enter any numerical values for custom emissions factors. In FEM 2023, values that exceed the range of expected normal emissions factors (between 0 and 1.6) for the fuel source will be ignored when GHG emissions are calculated. In FEM 2023, these user-entered values are not used in any calculations. Yes Yes Yes **High Energy Usage** Outlier Detection Individual Sources: The individual energy source (ex. coal) use is high in the context of energy totals. Totals: The sum of all reported energy sources in a facility type is high. No Yes Yes **High Water Usage** Outlier Detection Individual Sources: The individual water source (ex. rainwater) use is high in the context of energy totals.  Totals: The sum of all reported water sources in a facility type is high. No Yes Yes **High Normalized Energy Usage** Outlier Detection Totals: The normalized sum of all reported energy sources in a facility type is high. No Yes Yes **High Normalized Water Usage** Outlier Detection Totals: The sum of all reported water sources in a facility type is high. No Yes Yes **Significant Change in YOY Energy Usage** Outlier Detection The individual energy source use and total energy use significantly increased or decreased compared to FEM2023. No Yes Yes **Significant Change in YOY Water Usage** Outlier Detection The individual water source use and total water use significantly increased or decreased compared to FEM2023. No Yes Yes   ## **Higg FEM Outlier Detection Methodology** To learn more about outlier detection, read [Worldly's Higg FEM Outlier Detection Methodology](https://support.worldly.io/hc/en-us/articles/45149990287387-Outlier-Detection-in-FEM-2025?hsLang=en). **Summary:** A high-level overview of when outlier detection is triggered and what it means. **Definition of Data Outliers:** What is the difference between an erroneous outlier vs a true outlier? **Initial Data Set:** Cascale and Worldly used the complete set of FEM23 assessments to establish outlier thresholds for use with FEM24 assessments. **Approach to Identifying Outliers:** Learn how a standard statistical method, the Interquartile Range, is used to identify outliers. **Single Year-on-Year (YoY) Change: **Read about how the YoY comparison flags anomalously large values and what went into the development of this feature. **Total Energy/Water Outlier Thresholds:** View these two tables to understand the threshold values for Energy and Water outliers. ## **What can I expect with Outlier Detection?** Outlier detection was released on Feb. 19, 2025. If a facility’s Higg FEM assessment was posted before that date, the facility will need to wait until after this date to be able to take advantage of data checking.  In addition, as long as they have not begun verifying data with a verification body and it is before April 30, 2025, facilities can un-post their assessment to use data check to look for outliers in their responses before reposting their assessment. For example, if a facility posted their assessment on January 31, 2025, they will need to do the following between February 19 and April 30, 2025:  1. Un-post their assessment 2. Use outlier detection to check their assessment data 3. Repost their assessment Year-over-year anomaly detection will also be available in mid-March.   ## **What is considered an Outlier?** - **Erroneous outliers** are data points that arise from errors during data collection, recording, calculation, or entry. They represent inaccuracies and do not reflect true underlying patterns in the data. For example, a typographical error that records a person’s age as 250 years instead of 25 would be an erroneous outlier, as it does not correspond to a realistic value. Similarly, recording an energy value in the wrong units without conversion (e.g., a value in megajoules, MJ, assigned units of kilowatt-hours, kWh) also leads to erroneous outliers. Such “bad data” must be identified and addressed – typically by removal or correction (e.g., via verification or replacement with imputed values) – to maintain the integrity of the analysis. - **True outliers** are legitimate data points that are statistically rare but not erroneous. They occur naturally in the data and reflect real phenomena or occurrences. True outliers often provide valuable information about unusual but valid behaviors or events. For example, an anomalous increase in facility energy use might correspond to increased cooling implemented during a heat wave. True outliers can reveal critical trends, risks, or opportunities and should not be dismissed without analysis.   ## **FDM to FEM Outlier Detection** Both FEM and FEM require outlier detection before posting. ![40493702275739.png](https://support.worldly.io/hubfs/Knowledge%20Base%20Import/40493702275739.png) Please note that although FDM data imported to FEM is not checked during the transfer, FEM runs outlier detection on the imported data before the assessment is posted.   ## **FAQ** A Higg FEM user has identified data that looks incorrect. What should they do? If a user or the Worldly team finds inaccurate data that has made it all the way through to final outputs from Higg FEM 2023, the user should report these issues directly to their Cascale member manager and Worldly Support. The process for correcting FEM responses after verification has been completed is outlined in more detail in the [FEM Verification Protocol](https://marketing-cdn.production.worldly.io/guides/femveri/Verification%20Protocol%20-%20FEMVP2024111.5%20_20251029_Final.pdf), section 4.10. It is critical that the facility be engaged and willing to participate in this process, and it must be initiated by the facility. Here is how to start the process, from the Protocol: \- 4.10.3 Currently, VRF Changes are only permitted for confirmed inaccuracies in Quantitative Data (e.g., energy or water use data, quantitative data related to GHG emissions calculations, wastewater volume, or waste quantities, etc.) \- 4.10.4 To initiate the VRF Change Request Process, the facility shall contact the VPM at [FEM@Sumerra.com](mailto:FEM@Sumerra.com) with their request and provide details of the inaccuracy, corrected value as well as the reason the facility did not identify the inaccuracy and use the VRE process before finalizing their module. To ensure data coming in through Higg FEM 2024 is high quality, customers should focus on: 1\. Guiding their facility partners to leverage all of the available resources and guidance available to them, including e-learning, question-level guidance, and translation/language selection. 2\. Using FDM to capture quantitative data throughout the year, both to detect data quality issues sooner and to streamline filling in of the Higg FEM at the end of the year. 3\. Double-checking quantitative data prior to submission to ASC, especially if after April 30 2025 when facilities are no longer able to un-post once submitted. 4\. Reviewing quantitative data again after verification is complete, prior to finalizing and posting to VRF. How do the anomaly detection checks appear within the assessment? **![39977927136283.png](https://support.worldly.io/hs-fs/hubfs/Knowledge%20Base%20Import/39977927136283.png?width=1562&height=647&name=39977927136283.png)** These checks that detect anomalies ask users to either update their values. They are able to post with these values if they review them and comment on why the value is accurate. Validations that fully prevent posting will appear in red underneath the value flagged.   What do I do with Outlier Data? We recommend following up with the relevant facilities to request them to confirm the data.   [![Worldly logo](https://support.worldly.io/hs-fs/hubfs/Worldly%20Media%20Kit/Worldly%20Logos/Worldly_LogoTM_Red.png?width=120&height=30&name=Worldly_LogoTM_Red.png "Worldly logo")](https://www.worldly.io) © 2026 Worldly. All Rights Reserved [Terms of use](https://worldly.io/terms-of-use/)  |  [DPA](https://worldly.io/data-privacy-addendum/)  |  [Privacy Policy](https://worldly.io/privacy-policy/)  |  [Cookie notice](https://worldly.io/cookie-notice/)