Guía de referencia para informes del panel de administración

[Discourse]

This is a reference guide for describing how the Admin Dashboard Reports function, the data they’re displaying, the corresponding Data Explorer SQL queries, and where to find the Ruby code for each report.

Required user level: Staff

Discourse contains several built-in admin dashboard Reports that can be useful for exploring stats about a community. To access these reports, you can visit discourse.example.com/admin/dashboard/reports on your site ( or click the Reports link at the top of the dashboard). Note that only staff users will have access to these reports.

Data from all users on a site is included in these reports (including staff activity like visiting admin pages). The only condition that is put on users in the reports is that they are ‘real’ users, which is used to exclude the system user from the reports.

Plugins can also add reports to the dashboard with add_report(name, &block).

Ruby models for most reports are located at: discourse/app/models/concerns/reports/. Some reports also reference: discourse/app/models/report.rb

The dashboard-sql topics contain all of the corresponding SQL queries that can be used to generate reports identical to the Admin Dashboard Reports. These queries can be used within the Data Explorer plugin and for Running Data Explorer queries with the Discourse API

Accepted solutions

Displays daily tally of posts marked as solutions.

Ruby code: discourse-solved/plugin.rb at main · discourse/discourse-solved · GitHub

SQL Query: Dashboard Report - Accepted Solutions

Admin Logins

List of admin login times with locations.

Ruby Code: discourse/app/models/concerns/reports/staff_logins.rb

SQL Query: Dashboard Report - Admin Logins

Anonymous

Number of new pageviews by visitors not logged in to an account.

Ruby Code: discourse/app/models/concerns/reports/consolidated_page_views.rb

SQL Query: Dashboard Report - Anonymous

Bookmarks

Number of new topics and posts bookmarked.

Ruby Code: discourse/app/models/concerns/reports/bookmarks.rb

SQL Query: Dashboard Report - Bookmarks

Consolidated API Requests

API usage statistics by date, tracking both regular API requests and user API requests.

Ruby Code: discourse/app/models/concerns/reports/consolidated_api_requests.rb at main · discourse/discourse · GitHub

SQL Query: Dashboard Report - Consolidated API Requests

Consolidated Pageviews

Pageviews for logged in users, anonymous users and crawlers.

Ruby Code: discourse/app/models/concerns/reports/consolidated_page_views.rb

SQL Query: Dashboard Report - Consolidated Pageviews

Consolidated Pageviews with Browser Detection (Experimental)

Pageviews for logged in users, anonymous users, known crawlers and other. This experimental report ensures logged-in/anon requests are coming from real browsers before counting them.

Ruby Code: discourse/app/models/concerns/reports/consolidated_page_views_browser_detection.rb

SQL Query: Dashboard Report - Consolidated Pageviews with Browser Detection

DAU/MAU

Number of members that logged in in the last day divided by number of members that logged in in the last month – returns a % which indicates community ‘stickiness’. Aim for >20%.

Ruby Code: discourse/app/models/concerns/reports/dau_by_mau.rb

SQL Query: Dashboard Report - DAU/MAU

Daily Engaged Users

Number of users that have liked or posted in the last day.

Ruby Code: discourse/app/models/concerns/reports/daily_engaged_users.rb

SQL Query: Dashboard Report - Daily Engaged Users

Emails Sent

Number of new emails sent.

Ruby Code: discourse/app/models/concerns/reports/emails.rb

SQL Query: Dashboard Report - Emails Sent

Flags

Number of new flags.

Ruby Code: discourse/app/models/concerns/reports/flags.rb

SQL Query: Dashboard Report - Flags

Flags Status

List of flags’ statuses including type of flag, poster, flagger, and time to resolution.

Ruby Code: discourse/app/models/concerns/reports/flags_status.rb

SQL Query: Dashboard Report - Flags Status

Likes

Number of new likes.

Ruby Code: discourse/app/models/concerns/reports/likes.rb

SQL Query: Dashboard Report - Likes

Logged In

Number of new pageviews from logged in users.

Ruby Code: discourse/app/controllers/admin/reports_controller.rb#L5

SQL Query: Dashboard Report - Logged In

Moderator Activity

List of moderator activity including flags reviewed, reading time, topics created, posts created, personal messages created, and revisions.

SQL Query: Dashboard Report - Moderator Activity

Moderator Warning

Number of warnings sent by personal messages from moderators.

Ruby Code: discourse/app/models/concerns/reports/moderator_warning_private_messages.rb

SQL Query: Dashboard Report - Moderator Warnings

New Contributors

Number of users who made their first post during this period.

Ruby Code: discourse/app/models/concerns/reports/new_contributors.rb

SQL Query: Dashboard Report - New Contributors

Notify Moderators

Number of times moderators have been privately notified by a flag.

Ruby Code: discourse/app/models/concerns/reports/notify_moderators_private_messages.rb

SQL Query: Dashboard Report - Notify Moderators

Notify User

Number of times users have been privately notified by a flag.

Ruby Code: discourse/app/models/concerns/reports/notify_user_private_messages.rb

SQL Query: Dashboard Report - Notify User

Overall Sentiment

Number of posts classified either positive or negative with the “Sentiment” AI, over a specified period.

Ruby Code: discourse-ai/lib/sentiment/entry_point.rb at main · discourse/discourse-ai · GitHub

SQL Query: Dashboard Report - Overall Sentiment

Pageviews

Number of new pageviews from all visitors. Same as the total for Consolidated Pageviews.

Discourse uses the follow query to determine total pageviews:

SQL Query: Dashboard Report - Consolidated Pageviews

Post Edits

Number of new post edits.

Ruby Code: discourse/app/models/concerns/reports/post_edits.rb

SQL Query: Dashboard Report - Post Edits

Posts

New posts created during the selected time period

Ruby Code: discourse/app/models/concerns/reports/posts.rb

SQL Query: Dashboard Report - Posts

Post Emotion

Number of posts classified by AI with one of following emotions: Sadness, Surprise, Fear, Anger, Joy, Disgust - group by poster trust level, over a specified period.

Ruby Code: discourse-ai/lib/sentiment/entry_point.rb at main · discourse/discourse-ai · GitHub

SQL Query: Dashboard Report - Post Emotion

Reactions

List most recent reactions.

Ruby code: discourse-reactions/plugin.rb at main · discourse/discourse-reactions · GitHub

SQL Query: Dashboard Report - Reactions

Signups

New account registrations for this period.

Ruby Code: discourse/app/models/concerns/reports/signups.rb

SQL Query: Dashboard Report - Signups

Site Traffic

Ruby Code: discourse/app/models/concerns/reports/site_traffic.rb

SQL Query: Dashboard Report - Site Traffic

Suspicious Logins

Details of new logins that differ suspiciously from previous logins.

Ruby Code: discourse/app/models/concerns/reports/suspicious_logins.rb

SQL Query: Dashboard Report - Suspicious Logins

System

Number of personal messages sent automatically by the System.

Ruby Code: discourse/app/models/concerns/reports/system_private_messages.rb

SQL Query: Dashboard Report - System

Time to first response

Average time (in hours) of the first response to new topics.

Ruby Code: discourse/app/models/concerns/reports/time_to_first_response.rb + discourse/discourse/blob/main/app/models/topic.rb#L1799-L1844

SQL Query: Dashboard Report - Time to First Response

Top Ignored / Muted Users

Users who have been muted and/or ignored by many other users.

Ruby Code: discourse/app/models/concerns/reports/top_ignored_users.rb

SQL Query: Dashboard Report - Top Ignored / Muted Users

Top Referred Topics

Topics that have received the most clicks from external sources.

Ruby Code: discourse/app/models/concerns/reports/top_referred_topics.rb

SQL Query: Dashboard Report - Top Referred Topics

Top Referrers

Users listed by number of clicks on links they have shared.

Ruby Code: discourse/app/models/concerns/reports/top_referrers.rb

SQL Query: Dashboard Report - Top Referrers

Top Traffic Sources

External sources that have linked to this site the most.

Ruby Code: discourse/app/models/concerns/reports/top_traffic_sources.rb

SQL Query: Dashboard Report - Top Traffic Sources

Top Uploads

List all uploads by extension, filesize and author.

Ruby Code: discourse/app/models/concerns/reports/top_uploads.rb

SQL Query: Dashboard Report - Top Uploads

Top Users by likes received

Top 10 users who have received likes.

Ruby Code: discourse/app/models/concerns/reports/top_users_by_likes_received.rb

SQL Query: Dashboard Report - Top Users by Likes Received

Top Users by likes received from a user with a lower trust level

Top 10 users in a higher trust level being liked by people in a lower trust level.

Ruby Code: discourse/app/models/concerns/reports/top_users_by_likes_received_from_inferior_trust_level.rb

SQL Query: Dashboard Report - Top Users by Likes Received from a User with a Lower Trust Level

Top Users by likes received from a variety of people

Top 10 users who have had likes from a wide range of people.

Ruby Code: discourse/app/models/concerns/reports/top_users_by_likes_received_from_a_variety_of_people.rb

SQL Query: Dashboard Report - Top Users by Likes Received From a Variety of People

Topics

New topics created during this period.

Ruby Code: discourse/app/models/concerns/reports/topics.rb

SQL Query: Dashboard Report - Topics

Topics with no response

Number of new topics created that did not receive a response.

Ruby Code: discourse/app/models/concerns/reports/topics_with_no_response.rb

SQL Query: Dashboard Report - Topics with No Response

Topic View Stats

Ruby Code: discourse/app/models/concerns/reports/topic_view_stats.rb

SQL Query: Dashboard Report - Topic View Stats

Trending Search Terms

Most popular search terms with their click-through rates.

Ruby Code: discourse/app/models/concerns/reports/trending_search.rb

SQL Query: Dashboard Report - Trending Search Terms

Trust Level growth

Number of users who increased their Trust Level during this period.

The Trust Level Growth report is pulling data from the user_histories table in the Discourse database. Specifically, this report is counting the number of times a user_histories.action is recorded for an increase in a user trust level.

Ruby Code: discourse/app/models/concerns/reports/trust_level_growth.rb

SQL Query: Dashboard Report - Trust Level Growth

Unaccepted policies

This dashboard report identifies topics with policies that have not been accepted by certain users.

Ruby code: discourse-policy/plugin.rb at main · discourse/discourse-policy · GitHub

SQL Query: Dashboard Report - Unaccepted Policies

User Flagging Ratio

List of users ordered by ratio of staff response to their flags (disagreed to agreed).

Ruby Code: discourse/app/models/concerns/reports/user_flagging_ratio.rb

SQL Query: Dashboard Report - User Flagging Ratio

User notes

List most recent user notes.

Ruby code: discourse-user-notes/plugin.rb at main · discourse/discourse-user-notes · GitHub

SQL Query: Dashboard Report - User Notes

User Profile Views

Total new views of user profiles.

Ruby Code: discourse/app/models/concerns/reports/profile_views.rb

SQL Query: Dashboard Report - User Profile Views

User Visits

The total number of logged-in user visits in the forum for the selected time period (today, yesterday, last 7 days, etc).

A User Visit is counted anytime a unique logged in user visits the site, up to once per day. For example, if a user visited a site every day within a week, Discourse would count that as 7 user visits.

Ruby Code: discourse/app/models/concerns/reports/visits.rb

SQL Query: Dashboard Report - User Visits

User Visits (mobile)

Number of unique logged-in users who visited using a mobile device.

Ruby Code: discourse/app/models/concerns/reports/mobile_visits.rb

SQL Query: Dashboard Report - User Visits

User-to-User (excluding replies)

Number of newly initiated personal messages.

Ruby Code: discourse/app/models/concerns/reports/user_to_user_private_messages.rb

SQL Query: Dashboard Report - User-to-User

User-to-User (with replies)

Number of all new personal messages and responses.

Ruby Code: discourse/app/models/concerns/reports/user_to_user_private_messages_with_replies.rb

SQL Query: Dashboard Report - User-to-User

Users per Trust Level

Number of users grouped by trust level.

Ruby Code: discourse/app/models/concerns/reports/users_by_trust_level.rb

SQL Query: Dashboard Report - Users Per Trust Level

Users per Type

Number of users grouped by admin, moderator, suspended, and silenced.

Ruby Code: discourse/app/models/concerns/reports/users_by_type.rb

SQL Query: Dashboard Report - Users Per Type

Web Crawler Pageviews

Total pageviews from web crawlers over time.

Ruby Code: discourse/app/models/report.rb

SQL Query: Dashboard Report - Web Crawler Pageviews

Web Crawler User Agents

List of web crawler user agents, sorted by pageviews.

Ruby Code: discourse/app/models/concerns/reports/web_crawlers.rb

SQL Query: Dashboard Report - Web Crawler User Agents

Last edited by @SaraDev 2025-10-15T20:49:23Z

Last checked by @SaraDev 2025-01-28T21:42:37Z

Check document

Perform check on document:

[pfaffman (Jay Pfaffman)]

@SaraDev

Discourse contiene varios informes integrados en el Panel de administración que pueden ser útiles para explorar estadísticas sobre una comunidad. Para acceder a estos informes, puedes visitar /admin/dashboard/reports en tu sitio.

No veo un enlace a esto en /admin. ¿No estoy leyendo bien? Parece que esto debería ser más fácil de encontrar. Creo que sabía que estos informes estaban aquí, pero busqué y no pude encontrarlos.

Aunque me tomó solo unos minutos encontrarlo, podría ser bueno agregar algo como

Los complementos pueden agregar informes al panel con add_report(name, &block).

[maiki]

@pfaffman

No veo un enlace a esto en /admin.

@pfaffman

Parecería que esto debería ser más fácil de encontrar. Creo que sabía que estos informes estaban aquí, pero busqué y no pude encontrarlos.

Sí, podría ser bueno en un mensaje privado al personal para mencionarlos cuando se crea un sitio por primera vez.

[pfaffman (Jay Pfaffman)]

Lo siento. Seguro pensé que lo había visto en alguna parte antes.

Simplemente no puedes hacer que la gente lea cosas. . . . ¿Excepto que podría leer el código fuente para averiguar cómo hacerlo en un plugin?

Pero tal vez actualizar lo anterior a

@SaraDev

. Para acceder a estos informes, puedes visitar /admin/dashboard/reports en tu sitio o hacer clic en el enlace informes en el panel.

Creo que eso es lo que realmente me desconcertó. (Pero no, no tengo excusa.)

[maiki]

@pfaffman

Pero quizás actualiza lo anterior a

¡Hice que el tema fuera una wiki, adelante!

[hello-smile6 (9pfs)]

@SaraDev

Apunta a >30%.

Eso no coincide con la interfaz de usuario (la interfaz de usuario usa 20%), ¿cuál debería ser?

[JammyDodger]

Buena observación. Se actualizó recientemente al 20%. Haré el cambio en el OP.

Is there a difference in target retention rate between a support-first forum and discussion-first forum?

Here’s a PR with that change: Longer term, I think a more nuanced approach could be powerful. I’d love for communities to be able to set their own goals (with some guidance like what you shared above about why they might choose one vs. the other) and use the dashboard or other related features to help them track their results vs. what they are aiming for.

[Yotam_Hagay]

@SaraDev

Principales fuentes de tráfico

Fuentes externas que más han enlazado a este sitio.

Código Ruby: discourse/app/models/concerns/reports/top_traffic_sources.rb

Hola @SaraDev ¿Se puede obtener la salida de este informe con una consulta SQL? ¿Puedes compartirla?
Gracias

[SaraDev (Sara Devlaeminck)]

@Yotam_Hagay

Hola @SaraDev ¿Se puede obtener el resultado de este informe con una consulta SQL? ¿Puedes compartirla?
Gracias

Sí, puedes usar el siguiente informe SQL para las principales fuentes de tráfico:

-- [params]
-- date :start_date = 01/05/2023
-- date :end_date = 03/06/2023

WITH count_links AS (

SELECT COUNT(*) AS clicks,
       ind.name AS domain
FROM incoming_links il
  INNER JOIN posts p ON p.deleted_at ISNULL AND p.id = il.post_id
  INNER JOIN topics t ON t.deleted_at ISNULL AND t.id = p.topic_id
  INNER JOIN incoming_referers ir ON ir.id = il.incoming_referer_id
  INNER JOIN incoming_domains ind ON ind.id = ir.incoming_domain_id
WHERE t.archetype = 'regular'
  AND il.created_at::date BETWEEN :start_date AND :end_date
GROUP BY ind.name
ORDER BY clicks DESC
),

count_topics AS (

SELECT COUNT(DISTINCT p.topic_id) AS topics,
       ind.name AS domain
FROM incoming_links il
INNER JOIN posts p ON p.deleted_at ISNULL AND p.id = il.post_id
INNER JOIN topics t ON t.deleted_at ISNULL AND t.id = p.topic_id
INNER JOIN incoming_referers ir ON ir.id = il.incoming_referer_id
INNER JOIN incoming_domains ind ON ind.id = ir.incoming_domain_id
WHERE t.archetype = 'regular'
  AND il.created_at > (CURRENT_TIMESTAMP - INTERVAL '30 DAYS')
GROUP BY ind.name
)

SELECT cl.domain,
       cl.clicks AS "Clicks",
       ct.topics AS "Topics"
FROM count_links cl
JOIN count_topics ct ON cl.domain = ct.domain
LIMIT 10

Con esta consulta, ten en cuenta que los parámetros de fecha aceptan fechas en el formato día/mes/año.

[Yotam_Hagay]

@SaraDev

Sí, puedes usar el siguiente informe SQL para las principales fuentes de tráfico:

Hola @SaraDev, gracias por compartir la consulta.
Una pregunta más general sobre este informe y la tabla incoming_links en realidad: solo representa el tráfico de las páginas de publicaciones y no el tráfico a todas las páginas del foro, ¿verdad?

Contexto: Estoy buscando analizar las tendencias del tráfico general del foro y esperaba obtener el tráfico general por fuentes del informe “Principales fuentes de tráfico”.
Pero mientras que el tráfico general es de ~ 272K en el último mes (usuarios y anónimos), los clics totales en el informe de fuentes de tráfico para el mismo período de tiempo son solo 59K.
Además, veo que usas inner join con las tablas topics y posts, lo que significa que si no hay un post_id adjunto al clic, no lo cuentas.

¿Puedes confirmar mi conclusión y quizás explicar un poco la lógica detrás de la tabla incoming_links?

[Yotam_Hagay]

@SaraDev

Publicaciones

Nuevas publicaciones creadas durante este período

Hola @SaraDev, ejecuté esta consulta y el resultado no coincide exactamente con el informe de Publicaciones en la pestaña general.
Por ejemplo, para el 30 de noviembre:
Consulta = 112 publicaciones
Informe = 120 publicaciones
¿Puedes comprobar la diferencia?
Gracias

[JammyDodger]

Solo para que sepas @Yotam_Hagay, aunque Sara es la autora original, la guía es responsabilidad de todos No es necesario una @mención en cada publicación.

[Yotam_Hagay]

Gracias @JammyDodger por la aclaración. ¿Hay alguien más a quien pueda etiquetar o recurrir para obtener una respuesta?

[Yotam_Hagay]

@SaraDev

Tiempo de respuesta inicial en SQL

--[params]
-- date :date_start
-- date :date_end

WITH first_reply AS (
    SELECT 
        p.topic_id, 
        MIN(post_number) post_number, 
        t.created_at
    FROM posts p
    INNER JOIN topics t ON (p.topic_id = t.id)
    WHERE p.deleted_at IS NULL
        AND p.post_number != 1
        AND p.post_type = 1
        AND p.user_id > 0
        AND t.user_id > 0
        AND t.deleted_at IS NULL
        AND t.archetype = 'regular'
        AND t.created_at::date BETWEEN :date_start AND :date_end
    GROUP BY p.topic_id, t.created_at
    ORDER BY 2 DESC
)

SELECT 
    p.topic_id, 
    fr.created_at::date dt_topic_created,
    (p.created_at - fr.created_at) response_time
FROM posts p
INNER JOIN first_reply fr 
    ON fr.topic_id = p.topic_id 
    AND fr.post_number = p.post_number
    AND p.created_at > fr.created_at
ORDER BY response_time

Los resultados de esta consulta son ligeramente diferentes al informe de Tiempo de respuesta inicial:
El 8 de noviembre, por ejemplo:
Consulta: 93 horas
Informe: 116 horas
¿Alguien puede aconsejarme?

[JammyDodger]

Creo que algunas de estas pueden llevar tiempo para investigar. Estoy echando un vistazo yo mismo para ver si puedo ver qué está pasando (aunque la brecha entre mis habilidades de SQL y mis habilidades de Ruby es bastante grande ).

Pero sigan enviando los hallazgos, ya que sería genial consolidar toda esta información.

[JammyDodger]

@Yotam_Hagay

Hola @SaraDev Ejecuté esta consulta y el resultado no coincide exactamente con el informe de publicaciones en la pestaña general.
Por ejemplo, para el 30 de noviembre:
Consulta = 112 publicaciones
Informe = 120 publicaciones
¿Puedes comprobar la diferencia?
Gracias

Para el de publicaciones, creo que el informe de existencias también está contando las publicaciones de temas, así como las de usuarios del sistema, pero también solo se dirige a aquellas con post_type 1 (es decir, no susurros, publicaciones de acciones pequeñas o acciones de moderador).

Creo que el SQL se vería más así:

--[params]
-- date :start_date
-- date :end_date

SELECT 
    p.created_at::date AS "Día",
    COUNT(p.id) AS "Recuento"
FROM posts p
INNER JOIN topics t ON t.id = p.topic_id AND t.deleted_at ISNULL
WHERE p.created_at::date BETWEEN :start_date AND :end_date
    AND p.deleted_at ISNULL
    AND t.archetype = 'regular'
    AND p.post_type = 1
GROUP BY p.created_at::date
ORDER BY 1

¿Podrías ejecutar eso en tu sitio y ver si coincide?

[Yotam_Hagay]

Gracias Jammy, ¡lo revisaré!
Actualmente estoy trabajando en un análisis del tiempo de primera respuesta, así que agradecería que también pudieras echar un vistazo a este.

[JammyDodger]

Al mirar la versión SQL, creo que le falta AND p.user_id <> t.user_id para excluir las respuestas del OP. Si lo agrego, me da el tiempo preciso entre el OP y la primera respuesta de otra persona:

--[params]
-- date :date_start
-- date :date_end

WITH first_reply AS (
    SELECT 
        p.topic_id, 
        MIN(post_number) post_number, 
        t.created_at
    FROM posts p
    INNER JOIN topics t ON (p.topic_id = t.id)
    WHERE p.deleted_at IS NULL
        AND p.user_id <> t.user_id
        AND p.post_number != 1
        AND p.post_type = 1
        AND p.user_id > 0
        AND t.user_id > 0
        AND t.deleted_at IS NULL
        AND t.archetype = 'regular'
        AND t.created_at::date BETWEEN :date_start AND :date_end
    GROUP BY p.topic_id, t.created_at
    ORDER BY 2 DESC
)

SELECT 
    p.topic_id, 
    fr.created_at::date dt_topic_created,
    (p.created_at - fr.created_at) response_time
FROM posts p
INNER JOIN first_reply fr 
    ON fr.topic_id = p.topic_id 
    AND fr.post_number = p.post_number
    AND p.created_at > fr.created_at
ORDER BY response_time

También creo que el informe de stock está en decimal en lugar de horas y minutos como el SQL. Volveré a intentarlo para que coincida.

---

Solo una pequeña actualización para incluir el AVG para que sea más similar a la salida del informe de stock:

--[params]
-- date :date_start
-- date :date_end

WITH first_reply AS (
    SELECT 
        p.topic_id, 
        MIN(post_number) post_number, 
        t.created_at
    FROM posts p
    INNER JOIN topics t ON p.topic_id = t.id
    WHERE p.deleted_at IS NULL
        AND p.user_id <> t.user_id
        AND p.post_type = 1
        AND p.user_id > 0
        AND t.user_id > 0
        AND t.deleted_at IS NULL
        AND t.archetype = 'regular'
        AND t.created_at::date BETWEEN :date_start AND :date_end
    GROUP BY p.topic_id, t.created_at
)

SELECT 
    fr.created_at::date dt_topic_created,
    AVG(p.created_at - fr.created_at) response_time
FROM posts p
INNER JOIN first_reply fr 
    ON fr.topic_id = p.topic_id 
    AND fr.post_number = p.post_number
    AND p.created_at > fr.created_at
GROUP BY fr.created_at::date
ORDER BY response_time

Esto parece coincidir con el informe de stock siempre que se tenga en cuenta que uno está en decimal y el otro en HH:MM. Estoy seguro de que hay una manera de convertir el response_time de SQL a decimal, pero el HH:MM parece una forma más intuitiva de hacerlo. (Creo que también hay algunos criterios adicionales que pueden no ser necesarios, pero también podrían ser una protección contra circunstancias inusuales, así que finalmente he dejado esas partes tal como están hasta que pueda decirlo con seguridad )

¿Podrías ejecutar este y ver cómo coincide?

[Yotam_Hagay]

Sí, ahora suma los números que se muestran en el informe de existencias, ¡gracias!
Solo un comentario:
Encontré que la función AVG a continuación devuelve resultados faltantes en caso de que el tiempo sea > 24h (falta una sección de # de días, supongo).

AVG(p.created_at - fr.created_at)::time response_time

[JammyDodger]

Ah sí, convertir a time fue una mala elección. Si quitas ::time, volverá a la versión más precisa (aunque más difícil de leer).

También editaré la de arriba.