Best-Practice SOP Library — example input brief

Purpose: build a store best-practice submission, AI SOP drafting, human review and adoption tracking workspace.
This is a tutorial input brief, not a complete data export or production dataset.
Project: https://0dbd1884d22a4037b908ee0c656e902b.prod.enter.pro/

Input tables: Stores; Practices; SOP Drafts; Revisions; Reviews; Published SOPs; Adoption Plans; Store Feedback; Templates; Users.
Practice fields: practice_id, title, category, store, owner, owner_role, problem, context, steps, materials, time_required, impact_metrics, evidence_files, status.
Rules: every SOP draft stays linked to its source practice; AI-generated drafts require human approval and must label suggested durations and impacts as unmeasured; review decisions are Needs info, Pilot first, Approve & publish or Reject; only approved drafts enter the library; report coverage counts stores that submitted feedback, and stores without reports are unknown, not successful.
Workflow: store practice -> AI SOP draft (steps, checklist, training outline, risk notes) -> human review -> publish or pilot -> adoption plan -> store feedback -> revision.
Roles: Store Manager / Shift Lead, Operations / Library Manager, Regional Manager.

Observed example, 2026-10-12: "Lunch prep station readiness" submitted by Ava Torres (Austin Domain) on 09/16/2026. Problem: station supplies and prep priorities need a consistent check before lunch service. Steps: assign each prep station an owner from expected demand; prepare stations against approved par levels within existing food-safety procedures; record shortages and confirm the service lead has a replenishment plan. No measured outcomes or evidence were submitted. AI draft "Lunch Prep Station Readiness" (version 1, generated 09/17/2026) is In review; risk notes say the 15-minute duration is untested and recommend Pilot first with at least one week of shift data. Separately, the published SOP "Two-stage lunch prep board" v1.0 has an active adoption plan in 3 stores (Austin Domain, Boston Seaport, Chicago Loop) with 22% checklist completion and 0 of 3 store reports.
If additional demo data is needed, generate clearly labelled synthetic records with consistent IDs and dates.