pf_app/pf_spec.md

Pivot Forecast — Application Spec

Overview

A web application for building named forecast scenarios against any PostgreSQL table. The core workflow is: load known historical actuals as a baseline, shift those dates forward by a specified interval into the forecast period to establish a no-change starting point, then apply incremental adjustments (scale, recode, clone) to build the plan. An admin configures a source table, generates a baseline, and opens it for users to make adjustments. Users interact with a pivot table to select slices of data and apply forecast operations. All changes are incremental (append-only), fully audited, and reversible.

---

Tech Stack

• Backend: Node.js / Express
• Database: PostgreSQL — isolated pf schema, installs into any existing DB
• Frontend: Vanilla JS + AG Grid (pivot mode)
• Pattern: Follows fc_webapp (shell) + pivot_forecast (operations)

---

Database Schema: pf

Everything lives in the pf schema. Install via sequential SQL scripts.

pf.source

Registered source tables available for forecasting.

CREATE TABLE pf.source (
    id          serial PRIMARY KEY,
    schema      text NOT NULL,
    tname       text NOT NULL,
    label       text,                   -- friendly display name
    status      text DEFAULT 'active',  -- active | archived
    created_at  timestamptz DEFAULT now(),
    created_by  text,
    UNIQUE (schema, tname)
);

pf.col_meta

Column configuration for each registered source table. Determines how the app treats each column.

CREATE TABLE pf.col_meta (
    id          serial PRIMARY KEY,
    source_id   integer REFERENCES pf.source(id),
    cname       text NOT NULL,          -- column name in source table
    label       text,                   -- friendly display name
    role        text NOT NULL,          -- 'dimension' | 'value' | 'units' | 'date' | 'ignore'
    is_key      boolean DEFAULT false,  -- true = part of natural key (used in WHERE slice)
    opos        integer,                -- ordinal position (for ordering)
    UNIQUE (source_id, cname)
);

Roles:

• dimension — categorical field (customer, part, channel, rep, geography, etc.) — appears as pivot rows/cols, used in WHERE filters for operations
• value — the money/revenue field to scale
• units — the quantity field to scale
• date — the primary date field; used for baseline/reference date range and stored in the forecast table
• filter — columns available as filter conditions in the Baseline Workbench (e.g. order status, ship date, open flag); used in baseline WHERE clauses but not stored in the forecast table
• ignore — exclude from forecast table entirely

pf.version

Named forecast scenarios. One forecast table (pf.fc_{tname}_{version_id}) is created per version.

CREATE TABLE pf.version (
    id              serial PRIMARY KEY,
    source_id       integer REFERENCES pf.source(id),
    name            text NOT NULL,
    description     text,
    status          text DEFAULT 'open',        -- open | closed
    exclude_iters   jsonb DEFAULT '["reference"]', -- iter values excluded from all operations
    created_at      timestamptz DEFAULT now(),
    created_by      text,
    closed_at       timestamptz,
    closed_by       text,
    UNIQUE (source_id, name)
);

exclude_iters: jsonb array of iter values that are excluded from operation WHERE clauses. Defaults to ["reference"]. Reference rows are still returned by get_data (visible in pivot) but are never touched by scale/recode/clone. Additional iters can be added to lock them from further adjustment.

Forecast table naming: pf.fc_{tname}_{version_id} — e.g., pf.fc_sales_3. One table per version, physically isolated. Contains both operational rows and reference rows.

Creating a version → CREATE TABLE pf.fc_{tname}_{version_id} (...) Deleting a version → DROP TABLE pf.fc_{tname}_{version_id} + delete from pf.version + delete from pf.log

pf.log

Audit log. Every write operation gets one entry here.

CREATE TABLE pf.log (
    id          bigserial PRIMARY KEY,
    version_id  integer REFERENCES pf.version(id),
    pf_user     text NOT NULL,
    stamp       timestamptz DEFAULT now(),
    operation   text NOT NULL,  -- 'baseline' | 'reference' | 'scale' | 'recode' | 'clone'
    slice       jsonb,          -- the WHERE conditions that defined the selection
    params      jsonb,          -- operation parameters (increments, new values, scale factor, etc.)
    note        text            -- user-provided comment
);

pf.fc_{tname}_{version_id} (dynamic, one per version)

Created when a version is created. Mirrors source table dimension/value/units/date columns plus forecast metadata. Contains both operational rows (iter = 'baseline' | 'scale' | 'recode' | 'clone') and reference rows (iter = 'reference').

-- Example: source table "sales", version id 3 → pf.fc_sales_3
CREATE TABLE pf.fc_sales_3 (
    id          bigserial PRIMARY KEY,

    -- mirrored from source (role = dimension | value | units | date only):
    customer    text,
    channel     text,
    part        text,
    geography   text,
    order_date  date,
    units       numeric,
    value       numeric,

    -- forecast metadata:
    iter        text,       -- 'baseline' | 'reference' | 'scale' | 'recode' | 'clone'
    logid       bigint REFERENCES pf.log(id),
    pf_user     text,
    created_at  timestamptz DEFAULT now()
);

Note: no version_id column on the forecast table — it's implied by the table itself.

pf.sql

Generated SQL stored per source and operation. Built once when col_meta is finalized, fetched at request time.

CREATE TABLE pf.sql (
    id           serial PRIMARY KEY,
    source_id    integer REFERENCES pf.source(id),
    operation    text NOT NULL,  -- 'baseline' | 'reference' | 'scale' | 'recode' | 'clone' | 'get_data' | 'undo'
    sql          text NOT NULL,
    generated_at timestamptz DEFAULT now(),
    UNIQUE (source_id, operation)
);

Column names are baked in at generation time. Runtime substitution tokens:

Token	Resolved from
{{fc_table}}	pf.fc_{tname}_{version_id} — derived at request time
{{where_clause}}	built from slice JSON by build_where() in JS
{{exclude_clause}}	built from version.exclude_iters — e.g. AND iter NOT IN ('reference')
{{logid}}	newly inserted pf.log id
{{pf_user}}	from request body
{{date_from}} / {{date_to}}	baseline/reference date range (source period)
{{date_offset}}	PostgreSQL interval string to shift dates into the forecast period — e.g. 1 year, 6 months, 2 years 3 months (baseline only; empty string = no shift)
{{value_incr}} / {{units_incr}}	scale operation increments
{{pct}}	scale mode: absolute or percentage
{{set_clause}}	recode/clone dimension overrides
{{scale_factor}}	clone multiplier

Request-time flow:

1. Fetch SQL from pf.sql for source_id + operation
2. Fetch version.exclude_iters, build {{exclude_clause}}
3. Build {{where_clause}} from slice JSON via build_where()
4. Substitute all tokens
5. Execute — single round trip

WHERE clause safety: build_where() validates every key in the slice against col_meta (only role = 'dimension' columns are permitted). Values are sanitized (escaped single quotes). No parameterization — consistent with existing projects, debuggable in Postgres logs.

---

Setup / Install Scripts

setup_sql/
  01_schema.sql   -- CREATE SCHEMA pf; create all metadata tables (source, col_meta, version, log, sql)

Source registration, col_meta configuration, SQL generation, version creation, and forecast table DDL all happen via API.

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API Routes

DB Browser

Method	Route	Description
GET	/api/tables	List all tables in the DB with row counts
GET	/api/tables/:schema/:tname/preview	Preview columns + sample rows

Source Management

Method	Route	Description
GET	/api/sources	List registered sources
POST	/api/sources	Register a source table
GET	/api/sources/:id/cols	Get col_meta for a source
PUT	/api/sources/:id/cols	Save col_meta configuration
POST	/api/sources/:id/generate-sql	Generate/regenerate all operation SQL into pf.sql
GET	/api/sources/:id/sql	View generated SQL for a source (inspection/debug)
DELETE	/api/sources/:id	Deregister a source (does not affect existing forecast tables)

Forecast Versions

Method	Route	Description
GET	/api/sources/:id/versions	List versions for a source
POST	/api/sources/:id/versions	Create a new version (CREATE TABLE for forecast table)
PUT	/api/versions/:id	Update version (name, description, exclude_iters)
POST	/api/versions/:id/close	Close a version (blocks further edits)
POST	/api/versions/:id/reopen	Reopen a closed version
DELETE	/api/versions/:id	Delete a version (DROP TABLE + delete log entries)

Baseline & Reference Data

Method	Route	Description
POST	/api/versions/:id/baseline	Load one baseline segment (additive — does not clear existing baseline rows)
DELETE	/api/versions/:id/baseline	Clear all baseline rows and baseline log entries for this version
POST	/api/versions/:id/reference	Load reference rows from source table for a date range (additive)

Baseline load request body:

{
  "date_from":    "2024-01-01",
  "date_to":      "2024-12-31",
  "date_col":     "order_date",
  "date_offset":  "1 year",
  "filters": [
    { "col": "order_status", "op": "IN",      "values": ["OPEN", "PENDING"] },
    { "col": "ship_date",    "op": "BETWEEN", "values": ["2025-04-01", "2025-05-31"] }
  ],
  "pf_user":      "admin",
  "note":         "Open orders regardless of order date",
  "replay":       false
}

• date_from / date_to / date_col — removed; period selection is expressed as a filter condition in the filters array like any other condition
• date_offset — PostgreSQL interval string applied to the primary role = 'date' column when inserting (not to filter columns). Examples: "1 year", "6 months", "2 years 3 months". Defaults to "0 days".
• filters — zero or more additional filter conditions. Each has:
  • col — must be role = 'filter' or role = 'date' in col_meta
  • op — one of =, !=, IN, NOT IN, BETWEEN, IS NULL, IS NOT NULL
  • values — array of strings; two elements for BETWEEN, multiple for IN/NOT IN, omitted for IS NULL/IS NOT NULL
• Baseline loads are additive — existing iter = 'baseline' rows are not touched. Each load is its own log entry and is independently undoable.

replay controls behavior when incremental rows exist (applies to Clear + reload, not individual segments):

• replay: false (default) — after clearing, re-load baseline segments, leave incremental rows untouched
• replay: true — after clearing, re-load baseline, then re-execute each incremental log entry in chronological order

v1 note: replay: true returns 501 Not Implemented until the replay engine is built.

Clear baseline (DELETE /api/versions/:id/baseline) — deletes all rows where iter = 'baseline' and all operation = 'baseline' log entries. Irreversible (no undo). Returns { rows_deleted, log_entries_deleted }.

Reference request body: same shape as baseline load without replay. Reference dates land verbatim (no offset). Additive — multiple reference loads stack independently, each undoable by logid.

Forecast Data

Method	Route	Description
GET	/api/versions/:id/data	Return all rows for this version (all iters including reference)

Returns flat array. AG Grid pivot runs client-side on this data.

Forecast Operations

All operations share a common request envelope:

{
  "pf_user": "paul.trowbridge",
  "note":    "optional comment",
  "slice": {
    "channel":   "WHS",
    "geography": "WEST"
  }
}

slice keys must be role = 'dimension' columns per col_meta. Stored in pf.log as the implicit link to affected rows.

Scale

POST /api/versions/:id/scale

{
  "pf_user":    "paul.trowbridge",
  "note":       "10% volume lift Q3 West",
  "slice":      { "channel": "WHS", "geography": "WEST" },
  "value_incr": null,
  "units_incr": 5000,
  "pct":        false
}

• value_incr / units_incr — absolute amounts to add (positive or negative). Either can be null.
• pct: true — treat as percentage of current slice total instead of absolute
• Excludes exclude_iters rows from the source selection
• Distributes increment proportionally across rows in the slice
• Inserts rows tagged iter = 'scale'

Recode

POST /api/versions/:id/recode

{
  "pf_user": "paul.trowbridge",
  "note":    "Part discontinued, replaced by new SKU",
  "slice":   { "part": "OLD-SKU-001" },
  "set":     { "part": "NEW-SKU-002" }
}

• set — one or more dimension fields to replace (can swap multiple at once)
• Inserts negative rows to zero out the original slice
• Inserts positive rows with replaced dimension values
• Both sets of rows share the same logid — undone together
• Inserts rows tagged iter = 'recode'

Clone

POST /api/versions/:id/clone

{
  "pf_user": "paul.trowbridge",
  "note":    "New customer win, similar profile to existing",
  "slice":   { "customer": "EXISTING CO", "channel": "DIR" },
  "set":     { "customer": "NEW CO" },
  "scale":   0.75
}

• set — dimension values to override on cloned rows
• scale — optional multiplier on value/units (default 1.0)
• Does not offset original slice
• Inserts rows tagged iter = 'clone'

Audit & Undo

Method	Route	Description
GET	/api/versions/:id/log	List all log entries for a version, newest first
DELETE	/api/log/:logid	Undo: delete all forecast rows with this logid, then delete log entry

---

Frontend (Web UI)

Navigation (sidebar)

1. Sources — browse DB tables, register sources, configure col_meta, generate SQL
2. Versions — list forecast versions per source, create/close/reopen/delete
3. Baseline — baseline workbench for the selected version
4. Forecast — main working view (pivot + operation panel)
5. Log — change history with undo

Sources View

• Left: DB table browser (like fc_webapp) — all tables with row counts, preview on click
• Right: Registered sources list — click to open col_meta editor
• Col_meta editor: AG Grid editable table — set role per column, toggle is_key, set label
• "Generate SQL" button — triggers generate-sql route, shows confirmation
• Must generate SQL before versions can be created against this source

Versions View

• List of versions for selected source — name, status (open/closed), created date, row count
• Create version form — name, description, exclude_iters (defaults to ["reference"])
• Per-version actions: open forecast, load baseline, load reference, close, reopen, delete

Load Baseline modal:

• Source date range (date_from / date_to) — the actuals period to pull from
• Date offset (years + months spinners) — how far forward to project the dates
• Before/after preview: left side shows source months, right side shows where they land after the offset
• Note field
• On submit: shows row count; grid reloads

Load Reference modal:

• Source date range only — no offset
• Month chip preview of the period being loaded
• Note field

Baseline Workbench

A dedicated view for constructing the baseline for the selected version. The baseline is built from one or more segments — each segment is an independent query against the source table that appends rows to iter = 'baseline'. Segments are additive; clearing is explicit.

Layout:

┌─────────────────────────────────────────────────────────────┐
│  Baseline — [Version name]              [Clear Baseline]     │
├─────────────────────────────────────────────────────────────┤
│  Segments loaded (from log):                                 │
│  ┌──────┬────────────────┬──────────┬───────┬──────────┐    │
│  │  ID  │  Description   │  Rows    │  By   │  [Undo]  │    │
│  └──────┴────────────────┴──────────┴───────┴──────────┘    │
├─────────────────────────────────────────────────────────────┤
│  Add Segment                                                 │
│                                                              │
│  Description  [_______________________________________]      │
│                                                              │
│  Date range   [date_from] to [date_to]  on [date col ▾]     │
│  Date offset  [0] years  [0] months                         │
│                                                              │
│  Additional filters:                                         │
│  [ + Add filter ]                                            │
│  ┌──────────────────┬──────────┬──────────────┬───────┐     │
│  │  Column          │  Op      │  Value(s)    │  [ x ]│     │
│  └──────────────────┴──────────┴──────────────┴───────┘     │
│                                                              │
│  Preview: [projected month chips]                            │
│                                                              │
│  Note  [___________]          [Load Segment]                 │
└─────────────────────────────────────────────────────────────┘

Segments list — shows all operation = 'baseline' log entries for this version, newest first. Each has an Undo button. Undo removes only that segment's rows (by logid), leaving other segments intact.

Clear Baseline — deletes ALL iter = 'baseline' rows and all operation = 'baseline' log entries for this version. Prompts for confirmation. Used when starting over from scratch.

Add Segment form:

• Description — free text label stored as the log note, shown in the segments list
• Date offset — years + months spinners; shifts the primary role = 'date' column forward on insert
• Filters — one or more filter conditions that define what rows to pull. There is no separate "date range" section — period selection is just a filter like any other:
  • Column — any role = 'date' or role = 'filter' column
  • Operator — =, !=, IN, NOT IN, BETWEEN, IS NULL, IS NOT NULL
  • Value(s) — for BETWEEN: two date/text inputs; for IN/NOT IN: comma-separated list; for =/!=: single input; omitted for IS NULL/IS NOT NULL
  • At least one filter is required to load a segment
• Timeline preview — rendered when any filter condition is a BETWEEN or = on a role = 'date' column. Shows a horizontal bar (number-line style) for the source period and, if offset > 0, a second bar below for the projected period. Each bar shows start date on the left, end date on the right, duration in the centre. The two bars share the same visual width so the shift is immediately apparent. For non-date filters (e.g. season IN (...)) no timeline is shown.
• Note — optional free text
• Load Segment — submits; appends rows, does not clear existing baseline rows

Example — three-segment baseline:

#	Description	Filters	Offset
1	All orders taken 6/1/25–3/31/26	order_date BETWEEN 2025-06-01 AND 2026-03-31	0
2	All open/unshipped orders	status IN (OPEN, PENDING)	0
3	Prior year book-and-ship 4/1/25–5/31/25	order_date BETWEEN 2025-04-01 AND 2025-05-31, ship_date BETWEEN 2025-04-01 AND 2025-05-31	0

Note: segment 2 has no date filter — any filter combination is valid as long as at least one filter is present.

Forecast View

Layout:

┌──────────────────────────────────────────────────────────┐
│  [Source: sales]  [Version: FY2024 v1 — open]  [Refresh] │
├────────────────────────┬─────────────────────────────────┤
│                        │                                  │
│  Pivot Grid            │  Operation Panel                 │
│  (AG Grid pivot mode)  │  (active when slice selected)   │
│                        │                                  │
│                        │  Slice:                          │
│                        │    channel = WHS                 │
│                        │    geography = WEST              │
│                        │                                  │
│                        │  [ Scale ] [ Recode ] [ Clone ]  │
│                        │                                  │
│                        │  ... operation form ...          │
│                        │                                  │
│                        │  [ Submit ]                      │
│                        │                                  │
└────────────────────────┴─────────────────────────────────┘

Interaction flow:

1. Select cells in pivot — selected dimension values populate Operation Panel as slice
2. Pick operation tab, fill in parameters
3. Submit → POST to API → response shows rows affected
4. Grid refreshes (re-fetch get_data)

Reference rows shown in pivot (for context) but visually distinguished (e.g., muted color). Operations never affect them.

Log View

AG Grid list of log entries — user, timestamp, operation, slice, note, rows affected. "Undo" button per row → DELETE /api/log/:logid → grid and pivot refresh.

---

Forecast SQL Patterns

Column names baked in at generation time. Tokens substituted at request time.

Baseline Load (one segment)

WITH ilog AS (
    INSERT INTO pf.log (version_id, pf_user, operation, slice, params, note)
    VALUES ({{version_id}}, '{{pf_user}}', 'baseline', NULL, '{{params}}'::jsonb, '{{note}}')
    RETURNING id
)
INSERT INTO {{fc_table}} (
    {dimension_cols}, {value_col}, {units_col}, {date_col},
    iter, logid, pf_user, created_at
)
SELECT
    {dimension_cols}, {value_col}, {units_col},
    ({date_col} + '{{date_offset}}'::interval)::date,
    'baseline', (SELECT id FROM ilog), '{{pf_user}}', now()
FROM
    {schema}.{tname}
WHERE
    {{date_range_clause}}
    {{filter_clause}}

Baseline loads are additive — no DELETE before INSERT. Each segment appends independently.

Token details:

• {{date_offset}} — PostgreSQL interval string (e.g. 1 year); defaults to 0 days; applied only to the primary role = 'date' column on insert
• {{date_range_clause}} — built from date_from/date_to/date_col by the route; omitted entirely (replaced with TRUE) if no dates provided
• {{filter_clause}} — zero or more AND conditions built from the filters array; each validated against col_meta (column must be role = 'filter' or role = 'date'); operators: =, !=, IN, NOT IN, BETWEEN, IS NULL, IS NOT NULL

Both clauses are built at request time (not baked into stored SQL) since they vary per segment load.

Clear Baseline

Two queries, run in a transaction:

DELETE FROM {{fc_table}} WHERE iter = 'baseline';
DELETE FROM pf.log WHERE version_id = {{version_id}} AND operation = 'baseline';

Reference Load

WITH ilog AS (
    INSERT INTO pf.log (version_id, pf_user, operation, slice, params, note)
    VALUES ({{version_id}}, '{{pf_user}}', 'reference', NULL, '{{params}}'::jsonb, '{{note}}')
    RETURNING id
)
INSERT INTO {{fc_table}} (
    {dimension_cols}, {value_col}, {units_col}, {date_col},
    iter, logid, pf_user, created_at
)
SELECT
    {dimension_cols}, {value_col}, {units_col}, {date_col},
    'reference', (SELECT id FROM ilog), '{{pf_user}}', now()
FROM
    {schema}.{tname}
WHERE
    {date_col} BETWEEN '{{date_from}}' AND '{{date_to}}'

No date offset — reference rows land at their original dates for prior-period comparison.

Scale

WITH ilog AS (
    INSERT INTO pf.log (version_id, pf_user, operation, slice, params, note)
    VALUES ({{version_id}}, '{{pf_user}}', 'scale', '{{slice}}'::jsonb, '{{params}}'::jsonb, '{{note}}')
    RETURNING id
)
,base AS (
    SELECT
        {dimension_cols}, {date_col},
        {value_col}, {units_col},
        sum({value_col}) OVER () AS total_value,
        sum({units_col}) OVER () AS total_units
    FROM {{fc_table}}
    WHERE {{where_clause}}
    {{exclude_clause}}
)
INSERT INTO {{fc_table}} (
    {dimension_cols}, {date_col}, {value_col}, {units_col},
    iter, logid, pf_user, created_at
)
SELECT
    {dimension_cols}, {date_col},
    round(({value_col} / NULLIF(total_value, 0)) * {{value_incr}}, 2),
    round(({units_col} / NULLIF(total_units, 0)) * {{units_incr}}, 5),
    'scale', (SELECT id FROM ilog), '{{pf_user}}', now()
FROM base

{{value_incr}} / {{units_incr}} are pre-computed in JS when pct: true (multiply slice total by pct).

Recode

WITH ilog AS (
    INSERT INTO pf.log (version_id, pf_user, operation, slice, params, note)
    VALUES ({{version_id}}, '{{pf_user}}', 'recode', '{{slice}}'::jsonb, '{{params}}'::jsonb, '{{note}}')
    RETURNING id
)
,src AS (
    SELECT {dimension_cols}, {date_col}, {value_col}, {units_col}
    FROM {{fc_table}}
    WHERE {{where_clause}}
    {{exclude_clause}}
)
,negatives AS (
    INSERT INTO {{fc_table}} ({dimension_cols}, {date_col}, {value_col}, {units_col}, iter, logid, pf_user, created_at)
    SELECT {dimension_cols}, {date_col}, -{value_col}, -{units_col}, 'recode', (SELECT id FROM ilog), '{{pf_user}}', now()
    FROM src
)
INSERT INTO {{fc_table}} ({dimension_cols}, {date_col}, {value_col}, {units_col}, iter, logid, pf_user, created_at)
SELECT {{set_clause}}, {date_col}, {value_col}, {units_col}, 'recode', (SELECT id FROM ilog), '{{pf_user}}', now()
FROM src

{{set_clause}} replaces the listed dimension columns with new values, passes others through unchanged.

Clone

WITH ilog AS (
    INSERT INTO pf.log (version_id, pf_user, operation, slice, params, note)
    VALUES ({{version_id}}, '{{pf_user}}', 'clone', '{{slice}}'::jsonb, '{{params}}'::jsonb, '{{note}}')
    RETURNING id
)
INSERT INTO {{fc_table}} ({dimension_cols}, {date_col}, {value_col}, {units_col}, iter, logid, pf_user, created_at)
SELECT
    {{set_clause}}, {date_col},
    round({value_col} * {{scale_factor}}, 2),
    round({units_col} * {{scale_factor}}, 5),
    'clone', (SELECT id FROM ilog), '{{pf_user}}', now()
FROM {{fc_table}}
WHERE {{where_clause}}
{{exclude_clause}}

Undo

DELETE FROM {{fc_table}} WHERE logid = {{logid}};
DELETE FROM pf.log WHERE id = {{logid}};

---

Admin Setup Flow (end-to-end)

1. Open Sources view → browse DB tables → register source table
2. Open col_meta editor → assign roles to columns (dimension, value, units, date, filter, ignore), mark is_key dimensions, set labels
3. Click Generate SQL → app writes operation SQL to pf.sql
4. Open Versions view → create a named version (sets exclude_iters, creates forecast table)
5. Open Baseline Workbench → build the baseline from one or more segments:
   • Each segment specifies a date range (on any date/filter column), date offset, and optional additional filter conditions
   • Add segments until the baseline is complete; each is independently undoable
   • Use "Clear Baseline" to start over if needed
6. Optionally load Reference → pick prior period date range → inserts iter = 'reference' rows at their original dates (for comparison in the pivot)
7. Open Forecast view → share with users

User Forecast Flow (end-to-end)

1. Open Forecast view → select version
2. Pivot loads — explore data, identify slice to adjust
3. Select cells → Operation Panel populates with slice
4. Choose operation → fill in parameters → Submit
5. Grid refreshes — adjustment visible immediately
6. Repeat as needed
7. Admin closes version when forecasting is complete

---

Open Questions / Future Scope

• Baseline replay — re-execute change log against a restated baseline (replay: true); v1 returns 501
• Approval workflow — user submits, admin approves before changes are visible to others (deferred)
• Territory filtering — restrict what a user can see/edit by dimension value (deferred)
• Export — download forecast as CSV or push results to a reporting table
• Version comparison — side-by-side view of two versions (facilitated by isolated tables via UNION)
• Multi-DB sources — currently assumes same DB; cross-DB would need connection config per source