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Add new schemas and scripts for calculate

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Matt
2025-03-29 17:08:30 -04:00
parent f4854423ab
commit 54cc4be1e3
6 changed files with 901 additions and 228 deletions
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142
inventory-server/scripts/metrics new/update_daily_snapshots.sql Normal file
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-- Description: Calculates and updates daily aggregated product data for the current day.
-- Uses UPSERT (INSERT ON CONFLICT UPDATE) for idempotency.
-- Dependencies: Core import tables (products, orders, purchase_orders), calculate_status table.
-- Frequency: Hourly (Run ~5-10 minutes after hourly data import completes).
DO $$
DECLARE
_module_name VARCHAR := 'daily_snapshots';
_start_time TIMESTAMPTZ := clock_timestamp(); -- Time execution started
_last_calc_time TIMESTAMPTZ;
_target_date DATE := CURRENT_DATE; -- Always recalculate today for simplicity with hourly runs
BEGIN
-- Get the timestamp before the last successful run of this module
SELECT last_calculation_timestamp INTO _last_calc_time
FROM public.calculate_status
WHERE module_name = _module_name;
RAISE NOTICE 'Running % for date %. Start Time: %', _module_name, _target_date, _start_time;
-- Use CTEs to aggregate data for the target date
WITH SalesData AS (
SELECT
p.pid,
p.sku,
-- Aggregate Sales (Quantity > 0, Status not Canceled/Returned)
COALESCE(SUM(CASE WHEN o.quantity > 0 AND COALESCE(o.status, 'pending') NOT IN ('canceled', 'returned') THEN o.quantity ELSE 0 END), 0) AS units_sold,
COALESCE(SUM(CASE WHEN o.quantity > 0 AND COALESCE(o.status, 'pending') NOT IN ('canceled', 'returned') THEN o.price * o.quantity ELSE 0 END), 0.00) AS gross_revenue_unadjusted, -- Before discount
COALESCE(SUM(CASE WHEN o.quantity > 0 AND COALESCE(o.status, 'pending') NOT IN ('canceled', 'returned') THEN o.discount ELSE 0 END), 0.00) AS discounts,
COALESCE(SUM(CASE WHEN o.quantity > 0 AND COALESCE(o.status, 'pending') NOT IN ('canceled', 'returned') THEN COALESCE(o.costeach, p.landing_cost_price, p.cost_price) * o.quantity ELSE 0 END), 0.00) AS cogs,
COALESCE(SUM(CASE WHEN o.quantity > 0 AND COALESCE(o.status, 'pending') NOT IN ('canceled', 'returned') THEN p.regular_price * o.quantity ELSE 0 END), 0.00) AS gross_regular_revenue, -- Use current regular price for simplicity here
-- Aggregate Returns (Quantity < 0 or Status = Returned)
COALESCE(SUM(CASE WHEN o.quantity < 0 OR COALESCE(o.status, 'pending') = 'returned' THEN ABS(o.quantity) ELSE 0 END), 0) AS units_returned,
COALESCE(SUM(CASE WHEN o.quantity < 0 OR COALESCE(o.status, 'pending') = 'returned' THEN o.price * ABS(o.quantity) ELSE 0 END), 0.00) AS returns_revenue
FROM public.products p -- Start from products to include those with no orders today
LEFT JOIN public.orders o
ON p.pid = o.pid
AND o.date >= _target_date -- Filter orders for the target date
AND o.date < _target_date + INTERVAL '1 day'
GROUP BY p.pid, p.sku
),
ReceivingData AS (
SELECT
po.pid,
COALESCE(SUM((rh.item->>'qty')::numeric), 0) AS units_received,
COALESCE(SUM((rh.item->>'qty')::numeric * COALESCE((rh.item->>'cost')::numeric, po.cost_price)), 0.00) AS cost_received
FROM public.purchase_orders po
CROSS JOIN LATERAL jsonb_array_elements(po.receiving_history) AS rh(item)
WHERE (rh.item->>'received_at')::date = _target_date -- Filter receipts for the target date
GROUP BY po.pid
),
CurrentStock AS (
-- Select current stock values directly from products table
SELECT
pid,
stock_quantity,
COALESCE(landing_cost_price, cost_price, 0.00) as effective_cost_price,
COALESCE(price, 0.00) as current_price,
COALESCE(regular_price, 0.00) as current_regular_price
FROM public.products
)
-- Upsert into the daily snapshots table
INSERT INTO public.daily_product_snapshots (
snapshot_date,
pid,
sku,
eod_stock_quantity,
eod_stock_cost,
eod_stock_retail,
eod_stock_gross,
stockout_flag,
units_sold,
units_returned,
gross_revenue,
discounts,
returns_revenue,
net_revenue,
cogs,
gross_regular_revenue,
profit,
units_received,
cost_received,
calculation_timestamp
)
SELECT
_target_date AS snapshot_date,
p.pid,
p.sku,
-- Inventory Metrics (Using CurrentStock)
cs.stock_quantity AS eod_stock_quantity,
cs.stock_quantity * cs.effective_cost_price AS eod_stock_cost,
cs.stock_quantity * cs.current_price AS eod_stock_retail,
cs.stock_quantity * cs.current_regular_price AS eod_stock_gross,
(cs.stock_quantity <= 0) AS stockout_flag,
-- Sales Metrics (From SalesData)
COALESCE(sd.units_sold, 0),
COALESCE(sd.units_returned, 0),
COALESCE(sd.gross_revenue_unadjusted, 0.00),
COALESCE(sd.discounts, 0.00),
COALESCE(sd.returns_revenue, 0.00),
COALESCE(sd.gross_revenue_unadjusted, 0.00) - COALESCE(sd.discounts, 0.00) AS net_revenue,
COALESCE(sd.cogs, 0.00),
COALESCE(sd.gross_regular_revenue, 0.00),
(COALESCE(sd.gross_revenue_unadjusted, 0.00) - COALESCE(sd.discounts, 0.00)) - COALESCE(sd.cogs, 0.00) AS profit, -- Basic profit: Net Revenue - COGS
-- Receiving Metrics (From ReceivingData)
COALESCE(rd.units_received, 0),
COALESCE(rd.cost_received, 0.00),
_start_time -- Timestamp of this calculation run
FROM public.products p
LEFT JOIN CurrentStock cs ON p.pid = cs.pid
LEFT JOIN SalesData sd ON p.pid = sd.pid
LEFT JOIN ReceivingData rd ON p.pid = rd.pid
WHERE p.pid IS NOT NULL -- Ensure we only insert for existing products
ON CONFLICT (snapshot_date, pid) DO UPDATE SET
sku = EXCLUDED.sku,
eod_stock_quantity = EXCLUDED.eod_stock_quantity,
eod_stock_cost = EXCLUDED.eod_stock_cost,
eod_stock_retail = EXCLUDED.eod_stock_retail,
eod_stock_gross = EXCLUDED.eod_stock_gross,
stockout_flag = EXCLUDED.stockout_flag,
units_sold = EXCLUDED.units_sold,
units_returned = EXCLUDED.units_returned,
gross_revenue = EXCLUDED.gross_revenue,
discounts = EXCLUDED.discounts,
returns_revenue = EXCLUDED.returns_revenue,
net_revenue = EXCLUDED.net_revenue,
cogs = EXCLUDED.cogs,
gross_regular_revenue = EXCLUDED.gross_regular_revenue,
profit = EXCLUDED.profit,
units_received = EXCLUDED.units_received,
cost_received = EXCLUDED.cost_received,
calculation_timestamp = EXCLUDED.calculation_timestamp; -- Use the timestamp from this run
-- Update the status table with the timestamp from the START of this run
UPDATE public.calculate_status
SET last_calculation_timestamp = _start_time
WHERE module_name = _module_name;
RAISE NOTICE 'Finished % for date %. Duration: %', _module_name, _target_date, clock_timestamp() - _start_time;
END $$;

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inventory-server/scripts/metrics new/update_periodic_metrics.sql Normal file
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-- Description: Calculates metrics that don't need hourly updates, like ABC class
-- and average lead time.
-- Dependencies: product_metrics, purchase_orders, settings_global, calculate_status.
-- Frequency: Daily or Weekly (e.g., run via cron job overnight).
DO $$
DECLARE
_module_name VARCHAR := 'periodic_metrics';
_start_time TIMESTAMPTZ := clock_timestamp();
_last_calc_time TIMESTAMPTZ;
_abc_basis VARCHAR;
_abc_period INT;
_threshold_a NUMERIC;
_threshold_b NUMERIC;
BEGIN
-- Get the timestamp before the last successful run of this module
SELECT last_calculation_timestamp INTO _last_calc_time
FROM public.calculate_status
WHERE module_name = _module_name;
RAISE NOTICE 'Running % module. Start Time: %', _module_name, _start_time;
-- 1. Calculate Average Lead Time
RAISE NOTICE 'Calculating Average Lead Time...';
WITH LeadTimes AS (
SELECT
pid,
AVG(GREATEST(1, DATE_PART('day', last_received_date - date))) AS avg_days -- Use GREATEST(1,...) to avoid 0 or negative days
FROM public.purchase_orders
WHERE status = 'received' -- Or potentially 'full_received' if using that status
AND last_received_date IS NOT NULL
AND date IS NOT NULL
AND last_received_date >= date -- Ensure received date is not before order date
GROUP BY pid
)
UPDATE public.product_metrics pm
SET avg_lead_time_days = lt.avg_days::int
FROM LeadTimes lt
WHERE pm.pid = lt.pid
AND pm.avg_lead_time_days IS DISTINCT FROM lt.avg_days::int; -- Only update if changed
RAISE NOTICE 'Finished Average Lead Time calculation.';
-- 2. Calculate ABC Classification
RAISE NOTICE 'Calculating ABC Classification...';
-- Get ABC settings
SELECT setting_value INTO _abc_basis FROM public.settings_global WHERE setting_key = 'abc_calculation_basis' LIMIT 1;
SELECT setting_value::numeric INTO _threshold_a FROM public.settings_global WHERE setting_key = 'abc_revenue_threshold_a' LIMIT 1;
SELECT setting_value::numeric INTO _threshold_b FROM public.settings_global WHERE setting_key = 'abc_revenue_threshold_b' LIMIT 1;
_abc_basis := COALESCE(_abc_basis, 'revenue_30d'); -- Default basis
_threshold_a := COALESCE(_threshold_a, 0.80);
_threshold_b := COALESCE(_threshold_b, 0.95);
RAISE NOTICE 'Using ABC Basis: %, Threshold A: %, Threshold B: %', _abc_basis, _threshold_a, _threshold_b;
WITH RankedProducts AS (
SELECT
pid,
-- Dynamically select the metric based on setting
CASE _abc_basis
WHEN 'sales_30d' THEN COALESCE(sales_30d, 0)
WHEN 'lifetime_revenue' THEN COALESCE(lifetime_revenue, 0)::numeric -- Cast needed if different type
ELSE COALESCE(revenue_30d, 0) -- Default to revenue_30d
END AS metric_value
FROM public.product_metrics
WHERE is_replenishable = TRUE -- Typically only classify replenishable items
),
Cumulative AS (
SELECT
pid,
metric_value,
SUM(metric_value) OVER (ORDER BY metric_value DESC NULLS LAST ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) as cumulative_metric,
SUM(metric_value) OVER () as total_metric
FROM RankedProducts
WHERE metric_value > 0 -- Exclude items with no contribution
)
UPDATE public.product_metrics pm
SET abc_class =
CASE
WHEN c.cumulative_metric / NULLIF(c.total_metric, 0) <= _threshold_a THEN 'A'
WHEN c.cumulative_metric / NULLIF(c.total_metric, 0) <= _threshold_b THEN 'B'
ELSE 'C'
END
FROM Cumulative c
WHERE pm.pid = c.pid
AND pm.abc_class IS DISTINCT FROM ( -- Only update if changed
CASE
WHEN c.cumulative_metric / NULLIF(c.total_metric, 0) <= _threshold_a THEN 'A'
WHEN c.cumulative_metric / NULLIF(c.total_metric, 0) <= _threshold_b THEN 'B'
ELSE 'C'
END);
-- Set non-contributing or non-replenishable to 'C' or NULL if preferred
UPDATE public.product_metrics
SET abc_class = 'C' -- Or NULL
WHERE abc_class IS NULL AND is_replenishable = TRUE; -- Catch those with 0 metric value
UPDATE public.product_metrics
SET abc_class = NULL -- Or 'N/A'?
WHERE is_replenishable = FALSE AND abc_class IS NOT NULL; -- Unclassify non-replenishable items
RAISE NOTICE 'Finished ABC Classification calculation.';
-- Add other periodic calculations here if needed (e.g., recalculating first/last dates)
-- Update the status table with the timestamp from the START of this run
UPDATE public.calculate_status
SET last_calculation_timestamp = _start_time
WHERE module_name = _module_name;
RAISE NOTICE 'Finished % module. Duration: %', _module_name, clock_timestamp() - _start_time;
END $$;

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inventory-server/scripts/metrics new/update_product_metrics.sql Normal file
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-- Description: Calculates and updates the main product_metrics table based on current data
-- and aggregated daily snapshots. Uses UPSERT for idempotency.
-- Dependencies: Core import tables, daily_product_snapshots, configuration tables, calculate_status.
-- Frequency: Hourly (Run AFTER update_daily_snapshots.sql completes).
DO $$
DECLARE
_module_name VARCHAR := 'product_metrics';
_start_time TIMESTAMPTZ := clock_timestamp();
_last_calc_time TIMESTAMPTZ;
_current_date DATE := CURRENT_DATE;
BEGIN
-- Get the timestamp before the last successful run of this module
SELECT last_calculation_timestamp INTO _last_calc_time
FROM public.calculate_status
WHERE module_name = _module_name;
RAISE NOTICE 'Running % module. Start Time: %', _module_name, _start_time;
-- Use CTEs to gather all necessary information
WITH CurrentInfo AS (
SELECT
p.pid,
p.sku,
p.title,
p.brand,
p.vendor,
COALESCE(p.image_175, p.image) as image_url,
p.visible as is_visible,
p.replenishable as is_replenishable,
COALESCE(p.price, 0.00) as current_price,
COALESCE(p.regular_price, 0.00) as current_regular_price,
COALESCE(p.cost_price, 0.00) as current_cost_price,
COALESCE(p.landing_cost_price, p.cost_price, 0.00) as current_effective_cost, -- Use landing if available, else cost
p.stock_quantity as current_stock,
p.created_at,
p.first_received,
p.date_last_sold,
p.moq,
p.uom -- Assuming UOM logic is handled elsewhere or simple (e.g., 1=each)
FROM public.products p
),
OnOrderInfo AS (
SELECT
pid,
COALESCE(SUM(ordered - received), 0) AS on_order_qty,
COALESCE(SUM((ordered - received) * cost_price), 0.00) AS on_order_cost,
MIN(expected_date) AS earliest_expected_date
FROM public.purchase_orders
WHERE status IN ('open', 'partially_received', 'ordered', 'preordered', 'receiving_started', 'electronically_sent', 'electronically_ready_send') -- Adjust based on your status workflow representing active POs not fully received
AND (ordered - received) > 0
GROUP BY pid
),
HistoricalDates AS (
-- Note: Calculating these MIN/MAX values hourly can be slow on large tables.
-- Consider calculating periodically or storing on products if import can populate them.
SELECT
p.pid,
MIN(o.date)::date AS date_first_sold,
MAX(o.date)::date AS max_order_date, -- Use MAX for potential recalc of date_last_sold
MIN(rh.first_receipt_date) AS date_first_received_calc,
MAX(rh.last_receipt_date) AS date_last_received_calc
FROM public.products p
LEFT JOIN public.orders o ON p.pid = o.pid AND o.quantity > 0 AND o.status NOT IN ('canceled', 'returned')
LEFT JOIN (
SELECT
po.pid,
MIN((rh.item->>'received_at')::date) as first_receipt_date,
MAX((rh.item->>'received_at')::date) as last_receipt_date
FROM public.purchase_orders po
CROSS JOIN LATERAL jsonb_array_elements(po.receiving_history) AS rh(item)
WHERE jsonb_typeof(po.receiving_history) = 'array' AND jsonb_array_length(po.receiving_history) > 0
GROUP BY po.pid
) rh ON p.pid = rh.pid
GROUP BY p.pid
),
SnapshotAggregates AS (
SELECT
pid,
-- Rolling periods (ensure dates are inclusive/exclusive as needed)
SUM(CASE WHEN snapshot_date >= _current_date - INTERVAL '6 days' THEN units_sold ELSE 0 END) AS sales_7d,
SUM(CASE WHEN snapshot_date >= _current_date - INTERVAL '6 days' THEN net_revenue ELSE 0 END) AS revenue_7d,
SUM(CASE WHEN snapshot_date >= _current_date - INTERVAL '13 days' THEN units_sold ELSE 0 END) AS sales_14d,
SUM(CASE WHEN snapshot_date >= _current_date - INTERVAL '13 days' THEN net_revenue ELSE 0 END) AS revenue_14d,
SUM(CASE WHEN snapshot_date >= _current_date - INTERVAL '29 days' THEN units_sold ELSE 0 END) AS sales_30d,
SUM(CASE WHEN snapshot_date >= _current_date - INTERVAL '29 days' THEN net_revenue ELSE 0 END) AS revenue_30d,
SUM(CASE WHEN snapshot_date >= _current_date - INTERVAL '29 days' THEN cogs ELSE 0 END) AS cogs_30d,
SUM(CASE WHEN snapshot_date >= _current_date - INTERVAL '29 days' THEN profit ELSE 0 END) AS profit_30d,
SUM(CASE WHEN snapshot_date >= _current_date - INTERVAL '29 days' THEN units_returned ELSE 0 END) AS returns_units_30d,
SUM(CASE WHEN snapshot_date >= _current_date - INTERVAL '29 days' THEN returns_revenue ELSE 0 END) AS returns_revenue_30d,
SUM(CASE WHEN snapshot_date >= _current_date - INTERVAL '29 days' THEN discounts ELSE 0 END) AS discounts_30d,
SUM(CASE WHEN snapshot_date >= _current_date - INTERVAL '29 days' THEN gross_revenue ELSE 0 END) AS gross_revenue_30d,
SUM(CASE WHEN snapshot_date >= _current_date - INTERVAL '29 days' THEN gross_regular_revenue ELSE 0 END) AS gross_regular_revenue_30d,
SUM(CASE WHEN snapshot_date >= _current_date - INTERVAL '29 days' AND stockout_flag THEN 1 ELSE 0 END) AS stockout_days_30d,
SUM(CASE WHEN snapshot_date >= _current_date - INTERVAL '364 days' THEN units_sold ELSE 0 END) AS sales_365d,
SUM(CASE WHEN snapshot_date >= _current_date - INTERVAL '364 days' THEN net_revenue ELSE 0 END) AS revenue_365d,
SUM(CASE WHEN snapshot_date >= _current_date - INTERVAL '29 days' THEN units_received ELSE 0 END) AS received_qty_30d,
SUM(CASE WHEN snapshot_date >= _current_date - INTERVAL '29 days' THEN cost_received ELSE 0 END) AS received_cost_30d,
-- Averages (check for NULLIF 0 days in period if filtering dates)
AVG(CASE WHEN snapshot_date >= _current_date - INTERVAL '29 days' THEN eod_stock_quantity END) AS avg_stock_units_30d,
AVG(CASE WHEN snapshot_date >= _current_date - INTERVAL '29 days' THEN eod_stock_cost END) AS avg_stock_cost_30d,
AVG(CASE WHEN snapshot_date >= _current_date - INTERVAL '29 days' THEN eod_stock_retail END) AS avg_stock_retail_30d,
AVG(CASE WHEN snapshot_date >= _current_date - INTERVAL '29 days' THEN eod_stock_gross END) AS avg_stock_gross_30d,
-- Lifetime
SUM(units_sold) AS lifetime_sales,
SUM(net_revenue) AS lifetime_revenue,
-- Yesterday
SUM(CASE WHEN snapshot_date = _current_date - INTERVAL '1 day' THEN units_sold ELSE 0 END) as yesterday_sales
FROM public.daily_product_snapshots
WHERE snapshot_date <= _current_date -- Include today's snapshot
AND snapshot_date >= _current_date - INTERVAL '365 days' -- Limit history scan slightly
GROUP BY pid
),
FirstPeriodMetrics AS (
SELECT
pid,
date_first_sold,
SUM(CASE WHEN snapshot_date BETWEEN date_first_sold AND date_first_sold + INTERVAL '6 days' THEN units_sold ELSE 0 END) AS first_7_days_sales,
SUM(CASE WHEN snapshot_date BETWEEN date_first_sold AND date_first_sold + INTERVAL '6 days' THEN net_revenue ELSE 0 END) AS first_7_days_revenue,
SUM(CASE WHEN snapshot_date BETWEEN date_first_sold AND date_first_sold + INTERVAL '29 days' THEN units_sold ELSE 0 END) AS first_30_days_sales,
SUM(CASE WHEN snapshot_date BETWEEN date_first_sold AND date_first_sold + INTERVAL '29 days' THEN net_revenue ELSE 0 END) AS first_30_days_revenue,
SUM(CASE WHEN snapshot_date BETWEEN date_first_sold AND date_first_sold + INTERVAL '59 days' THEN units_sold ELSE 0 END) AS first_60_days_sales,
SUM(CASE WHEN snapshot_date BETWEEN date_first_sold AND date_first_sold + INTERVAL '59 days' THEN net_revenue ELSE 0 END) AS first_60_days_revenue,
SUM(CASE WHEN snapshot_date BETWEEN date_first_sold AND date_first_sold + INTERVAL '89 days' THEN units_sold ELSE 0 END) AS first_90_days_sales,
SUM(CASE WHEN snapshot_date BETWEEN date_first_sold AND date_first_sold + INTERVAL '89 days' THEN net_revenue ELSE 0 END) AS first_90_days_revenue
FROM public.daily_product_snapshots ds
JOIN HistoricalDates hd USING(pid)
WHERE date_first_sold IS NOT NULL
AND snapshot_date >= date_first_sold
AND snapshot_date <= date_first_sold + INTERVAL '90 days' -- Limit scan range
GROUP BY pid, date_first_sold
),
Settings AS (
SELECT
p.pid,
COALESCE(sp.lead_time_days, sv.default_lead_time_days, (SELECT setting_value FROM settings_global WHERE setting_key = 'default_lead_time_days')::int, 14) AS effective_lead_time,
COALESCE(sp.days_of_stock, sv.default_days_of_stock, (SELECT setting_value FROM settings_global WHERE setting_key = 'default_days_of_stock')::int, 30) AS effective_days_of_stock,
COALESCE(sp.safety_stock, 0) AS effective_safety_stock, -- Assuming safety stock is units, not days from global for now
COALESCE(sp.exclude_from_forecast, FALSE) AS exclude_forecast
FROM public.products p
LEFT JOIN public.settings_product sp ON p.pid = sp.pid
LEFT JOIN public.settings_vendor sv ON p.vendor = sv.vendor
)
-- Final UPSERT into product_metrics
INSERT INTO public.product_metrics (
pid, last_calculated, sku, title, brand, vendor, image_url, is_visible, is_replenishable,
current_price, current_regular_price, current_cost_price, current_landing_cost_price,
current_stock, current_stock_cost, current_stock_retail, current_stock_gross,
on_order_qty, on_order_cost, on_order_retail, earliest_expected_date,
date_created, date_first_received, date_last_received, date_first_sold, date_last_sold, age_days,
sales_7d, revenue_7d, sales_14d, revenue_14d, sales_30d, revenue_30d, cogs_30d, profit_30d,
returns_units_30d, returns_revenue_30d, discounts_30d, gross_revenue_30d, gross_regular_revenue_30d,
stockout_days_30d, sales_365d, revenue_365d,
avg_stock_units_30d, avg_stock_cost_30d, avg_stock_retail_30d, avg_stock_gross_30d,
received_qty_30d, received_cost_30d,
lifetime_sales, lifetime_revenue,
first_7_days_sales, first_7_days_revenue, first_30_days_sales, first_30_days_revenue,
first_60_days_sales, first_60_days_revenue, first_90_days_sales, first_90_days_revenue,
asp_30d, acp_30d, avg_ros_30d, avg_sales_per_day_30d, avg_sales_per_month_30d,
margin_30d, markup_30d, gmroi_30d, stockturn_30d, return_rate_30d, discount_rate_30d,
stockout_rate_30d, markdown_30d, markdown_rate_30d, sell_through_30d,
-- avg_lead_time_days, -- Calculated periodically
-- abc_class, -- Calculated periodically
sales_velocity_daily, config_lead_time, config_days_of_stock, config_safety_stock,
planning_period_days, lead_time_forecast_units, days_of_stock_forecast_units,
planning_period_forecast_units, lead_time_closing_stock, days_of_stock_closing_stock,
replenishment_needed_raw, replenishment_units, replenishment_cost, replenishment_retail, replenishment_profit,
to_order_units, forecast_lost_sales_units, forecast_lost_revenue,
stock_cover_in_days, po_cover_in_days, sells_out_in_days, replenish_date,
overstocked_units, overstocked_cost, overstocked_retail, is_old_stock,
yesterday_sales
)
SELECT
ci.pid, _start_time, ci.sku, ci.title, ci.brand, ci.vendor, ci.image_url, ci.is_visible, ci.is_replenishable,
ci.current_price, ci.current_regular_price, ci.current_cost_price, ci.current_effective_cost,
ci.current_stock, ci.current_stock * ci.current_effective_cost, ci.current_stock * ci.current_price, ci.current_stock * ci.current_regular_price,
COALESCE(ooi.on_order_qty, 0), COALESCE(ooi.on_order_cost, 0.00), COALESCE(ooi.on_order_qty, 0) * ci.current_price, ooi.earliest_expected_date,
ci.created_at::date, COALESCE(ci.first_received::date, hd.date_first_received_calc), hd.date_last_received_calc, hd.date_first_sold, COALESCE(ci.date_last_sold, hd.max_order_date), DATE_PART('day', _current_date - LEAST(ci.created_at::date, hd.date_first_sold)),
sa.sales_7d, sa.revenue_7d, sa.sales_14d, sa.revenue_14d, sa.sales_30d, sa.revenue_30d, sa.cogs_30d, sa.profit_30d,
sa.returns_units_30d, sa.returns_revenue_30d, sa.discounts_30d, sa.gross_revenue_30d, sa.gross_regular_revenue_30d,
sa.stockout_days_30d, sa.sales_365d, sa.revenue_365d,
sa.avg_stock_units_30d, sa.avg_stock_cost_30d, sa.avg_stock_retail_30d, sa.avg_stock_gross_30d,
sa.received_qty_30d, sa.received_cost_30d,
sa.lifetime_sales, sa.lifetime_revenue,
fpm.first_7_days_sales, fpm.first_7_days_revenue, fpm.first_30_days_sales, fpm.first_30_days_revenue,
fpm.first_60_days_sales, fpm.first_60_days_revenue, fpm.first_90_days_sales, fpm.first_90_days_revenue,
-- Calculated KPIs
sa.revenue_30d / NULLIF(sa.sales_30d, 0) AS asp_30d,
sa.cogs_30d / NULLIF(sa.sales_30d, 0) AS acp_30d,
sa.profit_30d / NULLIF(sa.sales_30d, 0) AS avg_ros_30d,
sa.sales_30d / 30.0 AS avg_sales_per_day_30d,
sa.sales_30d AS avg_sales_per_month_30d, -- Using 30d sales as proxy for month
(sa.profit_30d / NULLIF(sa.revenue_30d, 0)) * 100 AS margin_30d,
(sa.profit_30d / NULLIF(sa.cogs_30d, 0)) * 100 AS markup_30d,
sa.profit_30d / NULLIF(sa.avg_stock_cost_30d, 0) AS gmroi_30d,
sa.sales_30d / NULLIF(sa.avg_stock_units_30d, 0) AS stockturn_30d,
(sa.returns_units_30d / NULLIF(sa.sales_30d + sa.returns_units_30d, 0)) * 100 AS return_rate_30d,
(sa.discounts_30d / NULLIF(sa.gross_revenue_30d, 0)) * 100 AS discount_rate_30d,
(sa.stockout_days_30d / 30.0) * 100 AS stockout_rate_30d,
sa.gross_regular_revenue_30d - sa.gross_revenue_30d AS markdown_30d,
((sa.gross_regular_revenue_30d - sa.gross_revenue_30d) / NULLIF(sa.gross_regular_revenue_30d, 0)) * 100 AS markdown_rate_30d,
(sa.sales_30d / NULLIF(ci.current_stock + sa.sales_30d, 0)) * 100 AS sell_through_30d,
-- Forecasting intermediate values
(sa.sales_30d / NULLIF(30.0 - sa.stockout_days_30d, 0)) AS sales_velocity_daily,
s.effective_lead_time AS config_lead_time,
s.effective_days_of_stock AS config_days_of_stock,
s.effective_safety_stock AS config_safety_stock,
(s.effective_lead_time + s.effective_days_of_stock) AS planning_period_days,
(sa.sales_30d / NULLIF(30.0 - sa.stockout_days_30d, 0)) * s.effective_lead_time AS lead_time_forecast_units,
(sa.sales_30d / NULLIF(30.0 - sa.stockout_days_30d, 0)) * s.effective_days_of_stock AS days_of_stock_forecast_units,
((sa.sales_30d / NULLIF(30.0 - sa.stockout_days_30d, 0)) * s.effective_lead_time) + ((sa.sales_30d / NULLIF(30.0 - sa.stockout_days_30d, 0)) * s.effective_days_of_stock) AS planning_period_forecast_units,
(ci.current_stock + COALESCE(ooi.on_order_qty, 0) - ((sa.sales_30d / NULLIF(30.0 - sa.stockout_days_30d, 0)) * s.effective_lead_time)) AS lead_time_closing_stock,
((ci.current_stock + COALESCE(ooi.on_order_qty, 0) - ((sa.sales_30d / NULLIF(30.0 - sa.stockout_days_30d, 0)) * s.effective_lead_time))) - ((sa.sales_30d / NULLIF(30.0 - sa.stockout_days_30d, 0)) * s.effective_days_of_stock) AS days_of_stock_closing_stock,
(((sa.sales_30d / NULLIF(30.0 - sa.stockout_days_30d, 0)) * s.effective_lead_time) + ((sa.sales_30d / NULLIF(30.0 - sa.stockout_days_30d, 0)) * s.effective_days_of_stock)) + s.effective_safety_stock - ci.current_stock - COALESCE(ooi.on_order_qty, 0) AS replenishment_needed_raw,
-- Final Forecasting / Replenishment Metrics (apply CEILING/GREATEST/etc.)
-- Note: These calculations are nested for clarity, can be simplified in prod
CEILING(GREATEST(0, ((((sa.sales_30d / NULLIF(30.0 - sa.stockout_days_30d, 0)) * s.effective_lead_time) + ((sa.sales_30d / NULLIF(30.0 - sa.stockout_days_30d, 0)) * s.effective_days_of_stock)) + s.effective_safety_stock - ci.current_stock - COALESCE(ooi.on_order_qty, 0))))::int AS replenishment_units,
(CEILING(GREATEST(0, ((((sa.sales_30d / NULLIF(30.0 - sa.stockout_days_30d, 0)) * s.effective_lead_time) + ((sa.sales_30d / NULLIF(30.0 - sa.stockout_days_30d, 0)) * s.effective_days_of_stock)) + s.effective_safety_stock - ci.current_stock - COALESCE(ooi.on_order_qty, 0))))::int) * ci.current_effective_cost AS replenishment_cost,
(CEILING(GREATEST(0, ((((sa.sales_30d / NULLIF(30.0 - sa.stockout_days_30d, 0)) * s.effective_lead_time) + ((sa.sales_30d / NULLIF(30.0 - sa.stockout_days_30d, 0)) * s.effective_days_of_stock)) + s.effective_safety_stock - ci.current_stock - COALESCE(ooi.on_order_qty, 0))))::int) * ci.current_price AS replenishment_retail,
(CEILING(GREATEST(0, ((((sa.sales_30d / NULLIF(30.0 - sa.stockout_days_30d, 0)) * s.effective_lead_time) + ((sa.sales_30d / NULLIF(30.0 - sa.stockout_days_30d, 0)) * s.effective_days_of_stock)) + s.effective_safety_stock - ci.current_stock - COALESCE(ooi.on_order_qty, 0))))::int) * (ci.current_price - ci.current_effective_cost) AS replenishment_profit,
-- Placeholder for To Order (Apply MOQ/UOM logic here if needed, otherwise equals replenishment)
CEILING(GREATEST(0, ((((sa.sales_30d / NULLIF(30.0 - sa.stockout_days_30d, 0)) * s.effective_lead_time) + ((sa.sales_30d / NULLIF(30.0 - sa.stockout_days_30d, 0)) * s.effective_days_of_stock)) + s.effective_safety_stock - ci.current_stock - COALESCE(ooi.on_order_qty, 0))))::int AS to_order_units,
GREATEST(0, - (ci.current_stock + COALESCE(ooi.on_order_qty, 0) - ((sa.sales_30d / NULLIF(30.0 - sa.stockout_days_30d, 0)) * s.effective_lead_time))) AS forecast_lost_sales_units,
GREATEST(0, - (ci.current_stock + COALESCE(ooi.on_order_qty, 0) - ((sa.sales_30d / NULLIF(30.0 - sa.stockout_days_30d, 0)) * s.effective_lead_time))) * ci.current_price AS forecast_lost_revenue,
ci.current_stock / NULLIF((sa.sales_30d / NULLIF(30.0 - sa.stockout_days_30d, 0)), 0) AS stock_cover_in_days,
COALESCE(ooi.on_order_qty, 0) / NULLIF((sa.sales_30d / NULLIF(30.0 - sa.stockout_days_30d, 0)), 0) AS po_cover_in_days,
(ci.current_stock + COALESCE(ooi.on_order_qty, 0)) / NULLIF((sa.sales_30d / NULLIF(30.0 - sa.stockout_days_30d, 0)), 0) AS sells_out_in_days,
-- Replenish Date: Date when stock is projected to hit safety stock, minus lead time
CASE
WHEN (sa.sales_30d / NULLIF(30.0 - sa.stockout_days_30d, 0)) > 0
THEN _current_date + FLOOR(GREATEST(0, ci.current_stock - s.effective_safety_stock) / (sa.sales_30d / NULLIF(30.0 - sa.stockout_days_30d, 0)))::int - s.effective_lead_time
ELSE NULL
END AS replenish_date,
GREATEST(0, ci.current_stock - s.effective_safety_stock - (((sa.sales_30d / NULLIF(30.0 - sa.stockout_days_30d, 0)) * s.effective_lead_time) + ((sa.sales_30d / NULLIF(30.0 - sa.stockout_days_30d, 0)) * s.effective_days_of_stock)))::int AS overstocked_units,
(GREATEST(0, ci.current_stock - s.effective_safety_stock - (((sa.sales_30d / NULLIF(30.0 - sa.stockout_days_30d, 0)) * s.effective_lead_time) + ((sa.sales_30d / NULLIF(30.0 - sa.stockout_days_30d, 0)) * s.effective_days_of_stock)))) * ci.current_effective_cost AS overstocked_cost,
(GREATEST(0, ci.current_stock - s.effective_safety_stock - (((sa.sales_30d / NULLIF(30.0 - sa.stockout_days_30d, 0)) * s.effective_lead_time) + ((sa.sales_30d / NULLIF(30.0 - sa.stockout_days_30d, 0)) * s.effective_days_of_stock)))) * ci.current_price AS overstocked_retail,
-- Old Stock Flag
(ci.created_at::date < _current_date - INTERVAL '60 day') AND
(COALESCE(ci.date_last_sold, hd.max_order_date) IS NULL OR COALESCE(ci.date_last_sold, hd.max_order_date) < _current_date - INTERVAL '60 day') AND
(hd.date_last_received_calc IS NULL OR hd.date_last_received_calc < _current_date - INTERVAL '60 day') AND
COALESCE(ooi.on_order_qty, 0) = 0
AS is_old_stock,
sa.yesterday_sales
FROM CurrentInfo ci
LEFT JOIN OnOrderInfo ooi ON ci.pid = ooi.pid
LEFT JOIN HistoricalDates hd ON ci.pid = hd.pid
LEFT JOIN SnapshotAggregates sa ON ci.pid = sa.pid
LEFT JOIN FirstPeriodMetrics fpm ON ci.pid = fpm.pid
LEFT JOIN Settings s ON ci.pid = s.pid
WHERE s.exclude_forecast IS FALSE OR s.exclude_forecast IS NULL -- Exclude products explicitly marked
ON CONFLICT (pid) DO UPDATE SET
last_calculated = EXCLUDED.last_calculated,
sku = EXCLUDED.sku, title = EXCLUDED.title, brand = EXCLUDED.brand, vendor = EXCLUDED.vendor, image_url = EXCLUDED.image_url, is_visible = EXCLUDED.is_visible, is_replenishable = EXCLUDED.is_replenishable,
current_price = EXCLUDED.current_price, current_regular_price = EXCLUDED.current_regular_price, current_cost_price = EXCLUDED.current_cost_price, current_landing_cost_price = EXCLUDED.current_landing_cost_price,
current_stock = EXCLUDED.current_stock, current_stock_cost = EXCLUDED.current_stock_cost, current_stock_retail = EXCLUDED.current_stock_retail, current_stock_gross = EXCLUDED.current_stock_gross,
on_order_qty = EXCLUDED.on_order_qty, on_order_cost = EXCLUDED.on_order_cost, on_order_retail = EXCLUDED.on_order_retail, earliest_expected_date = EXCLUDED.earliest_expected_date,
date_created = EXCLUDED.date_created, date_first_received = EXCLUDED.date_first_received, date_last_received = EXCLUDED.date_last_received, date_first_sold = EXCLUDED.date_first_sold, date_last_sold = EXCLUDED.date_last_sold, age_days = EXCLUDED.age_days,
sales_7d = EXCLUDED.sales_7d, revenue_7d = EXCLUDED.revenue_7d, sales_14d = EXCLUDED.sales_14d, revenue_14d = EXCLUDED.revenue_14d, sales_30d = EXCLUDED.sales_30d, revenue_30d = EXCLUDED.revenue_30d, cogs_30d = EXCLUDED.cogs_30d, profit_30d = EXCLUDED.profit_30d,
returns_units_30d = EXCLUDED.returns_units_30d, returns_revenue_30d = EXCLUDED.returns_revenue_30d, discounts_30d = EXCLUDED.discounts_30d, gross_revenue_30d = EXCLUDED.gross_revenue_30d, gross_regular_revenue_30d = EXCLUDED.gross_regular_revenue_30d,
stockout_days_30d = EXCLUDED.stockout_days_30d, sales_365d = EXCLUDED.sales_365d, revenue_365d = EXCLUDED.revenue_365d,
avg_stock_units_30d = EXCLUDED.avg_stock_units_30d, avg_stock_cost_30d = EXCLUDED.avg_stock_cost_30d, avg_stock_retail_30d = EXCLUDED.avg_stock_retail_30d, avg_stock_gross_30d = EXCLUDED.avg_stock_gross_30d,
received_qty_30d = EXCLUDED.received_qty_30d, received_cost_30d = EXCLUDED.received_cost_30d,
lifetime_sales = EXCLUDED.lifetime_sales, lifetime_revenue = EXCLUDED.lifetime_revenue,
first_7_days_sales = EXCLUDED.first_7_days_sales, first_7_days_revenue = EXCLUDED.first_7_days_revenue, first_30_days_sales = EXCLUDED.first_30_days_sales, first_30_days_revenue = EXCLUDED.first_30_days_revenue,
first_60_days_sales = EXCLUDED.first_60_days_sales, first_60_days_revenue = EXCLUDED.first_60_days_revenue, first_90_days_sales = EXCLUDED.first_90_days_sales, first_90_days_revenue = EXCLUDED.first_90_days_revenue,
asp_30d = EXCLUDED.asp_30d, acp_30d = EXCLUDED.acp_30d, avg_ros_30d = EXCLUDED.avg_ros_30d, avg_sales_per_day_30d = EXCLUDED.avg_sales_per_day_30d, avg_sales_per_month_30d = EXCLUDED.avg_sales_per_month_30d,
margin_30d = EXCLUDED.margin_30d, markup_30d = EXCLUDED.markup_30d, gmroi_30d = EXCLUDED.gmroi_30d, stockturn_30d = EXCLUDED.stockturn_30d, return_rate_30d = EXCLUDED.return_rate_30d, discount_rate_30d = EXCLUDED.discount_rate_30d,
stockout_rate_30d = EXCLUDED.stockout_rate_30d, markdown_30d = EXCLUDED.markdown_30d, markdown_rate_30d = EXCLUDED.markdown_rate_30d, sell_through_30d = EXCLUDED.sell_through_30d,
-- avg_lead_time_days = EXCLUDED.avg_lead_time_days, -- Updated Periodically
-- abc_class = EXCLUDED.abc_class, -- Updated Periodically
sales_velocity_daily = EXCLUDED.sales_velocity_daily, config_lead_time = EXCLUDED.config_lead_time, config_days_of_stock = EXCLUDED.config_days_of_stock, config_safety_stock = EXCLUDED.config_safety_stock,
planning_period_days = EXCLUDED.planning_period_days, lead_time_forecast_units = EXCLUDED.lead_time_forecast_units, days_of_stock_forecast_units = EXCLUDED.days_of_stock_forecast_units,
planning_period_forecast_units = EXCLUDED.planning_period_forecast_units, lead_time_closing_stock = EXCLUDED.lead_time_closing_stock, days_of_stock_closing_stock = EXCLUDED.days_of_stock_closing_stock,
replenishment_needed_raw = EXCLUDED.replenishment_needed_raw, replenishment_units = EXCLUDED.replenishment_units, replenishment_cost = EXCLUDED.replenishment_cost, replenishment_retail = EXcluded.replenishment_retail, replenishment_profit = EXCLUDED.replenishment_profit,
to_order_units = EXCLUDED.to_order_units, forecast_lost_sales_units = EXCLUDED.forecast_lost_sales_units, forecast_lost_revenue = EXCLUDED.forecast_lost_revenue,
stock_cover_in_days = EXCLUDED.stock_cover_in_days, po_cover_in_days = EXCLUDED.po_cover_in_days, sells_out_in_days = EXCLUDED.sells_out_in_days, replenish_date = EXCLUDED.replenish_date,
overstocked_units = EXCLUDED.overstocked_units, overstocked_cost = EXCLUDED.overstocked_cost, overstocked_retail = EXCLUDED.overstocked_retail, is_old_stock = EXCLUDED.is_old_stock,
yesterday_sales = EXCLUDED.yesterday_sales
;
-- Update the status table with the timestamp from the START of this run
UPDATE public.calculate_status
SET last_calculation_timestamp = _start_time
WHERE module_name = _module_name;
RAISE NOTICE 'Finished % module. Duration: %', _module_name, clock_timestamp() - _start_time;
END $$;