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Fourth model

Staffing accuracy

Fourth's productivity view: of the hours worked each day, how many were at the right staffing level, and how many under or over the requirement.

See the SQLAll Fourth models
Grain
date x location
Built from
  1. 1
    Raw tables
  2. 1
    Staging
  3. 1
    Core
  4. 1
    Analytics
Reads from Fourth
labour_daily_summary
Ships as
Weld and dbt SQL, open source on GitHub
core.fourth.staffing_accuracy
1-- core.fourth.staffing_accuracy
2-- Fourth's productivity view: of the hours worked each day, how many were at the
3-- right staffing level, and how many under or over the requirement.
4-- Grain: date x location.
5--
6-- Shares are computed per row over correctly + under + over staffed hours.
7-- Re-aggregate by summing the hour columns and dividing, not by averaging shares.
8
9SELECT
10    date,
11    location_id,
12    location_name,
13    correctly_staffed_hours,
14    understaffed_hours,
15    overstaffed_hours,
16    passive_hours,
17    SAFE_DIVIDE(correctly_staffed_hours, correctly_staffed_hours + understaffed_hours + overstaffed_hours) AS correctly_staffed_share,
18    SAFE_DIVIDE(understaffed_hours,      correctly_staffed_hours + understaffed_hours + overstaffed_hours) AS understaffed_share,
19    SAFE_DIVIDE(overstaffed_hours,       correctly_staffed_hours + understaffed_hours + overstaffed_hours) AS overstaffed_share
20FROM {{staging.fourth.labour_daily_summary}}

This model and the 2 it depends on. Deploy them together; refs like {{staging.…}} resolve to the matching folders in Weld, and the dbt view uses source() and ref().

Notes

How it counts. And where it stops.

Shares are computed per row over correctly + under + over staffed hours. Re-aggregate by summing the hour columns and dividing, not by averaging shares.

Raw tables it reads

  • raw.fourth.labour_daily_summary

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