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Matt
2025-06-19 15:49:31 -04:00
parent 8606a90e34
commit 2e3e81a02b
17 changed files with 741 additions and 613 deletions
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1
inventory-server/scripts/calculate-metrics-new.js
View File

@@ -437,7 +437,6 @@ async function executeSqlStep(config, progress) {
try {
// Try executing exactly as individual scripts do
console.log('Executing SQL with simple query method...');
const result = await connection.query(sqlQuery);
// Try to extract row count from result

27
inventory-server/scripts/metrics-new/calculate_brand_metrics.sql
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@@ -42,6 +42,20 @@ BEGIN
JOIN public.products p ON pm.pid = p.pid
GROUP BY brand_group
),
PreviousPeriodBrandMetrics AS (
-- Get previous period metrics for growth calculation
SELECT
COALESCE(p.brand, 'Unbranded') AS brand_group,
SUM(CASE WHEN dps.snapshot_date >= CURRENT_DATE - INTERVAL '59 days'
AND dps.snapshot_date < CURRENT_DATE - INTERVAL '29 days'
THEN dps.units_sold ELSE 0 END) AS sales_prev_30d,
SUM(CASE WHEN dps.snapshot_date >= CURRENT_DATE - INTERVAL '59 days'
AND dps.snapshot_date < CURRENT_DATE - INTERVAL '29 days'
THEN dps.net_revenue ELSE 0 END) AS revenue_prev_30d
FROM public.daily_product_snapshots dps
JOIN public.products p ON dps.pid = p.pid
GROUP BY brand_group
),
AllBrands AS (
-- Ensure all brands from products table are included, mapping NULL/empty to 'Unbranded'
SELECT DISTINCT COALESCE(brand, 'Unbranded') as brand_group
@@ -53,7 +67,8 @@ BEGIN
current_stock_units, current_stock_cost, current_stock_retail,
sales_7d, revenue_7d, sales_30d, revenue_30d, profit_30d, cogs_30d,
sales_365d, revenue_365d, lifetime_sales, lifetime_revenue,
avg_margin_30d
avg_margin_30d,
sales_growth_30d_vs_prev, revenue_growth_30d_vs_prev
)
SELECT
b.brand_group,
@@ -78,9 +93,13 @@ BEGIN
-- This is mathematically equivalent to profit/revenue but more explicit
((COALESCE(ba.revenue_30d, 0) - COALESCE(ba.cogs_30d, 0)) / COALESCE(ba.revenue_30d, 1)) * 100.0
ELSE NULL -- No margin for low/no revenue brands
END
END,
-- Growth metrics
std_numeric(safe_divide((ba.sales_30d - ppbm.sales_prev_30d) * 100.0, ppbm.sales_prev_30d), 2),
std_numeric(safe_divide((ba.revenue_30d - ppbm.revenue_prev_30d) * 100.0, ppbm.revenue_prev_30d), 2)
FROM AllBrands b
LEFT JOIN BrandAggregates ba ON b.brand_group = ba.brand_group
LEFT JOIN PreviousPeriodBrandMetrics ppbm ON b.brand_group = ppbm.brand_group
ON CONFLICT (brand_name) DO UPDATE SET
last_calculated = EXCLUDED.last_calculated,
@@ -95,7 +114,9 @@ BEGIN
profit_30d = EXCLUDED.profit_30d, cogs_30d = EXCLUDED.cogs_30d,
sales_365d = EXCLUDED.sales_365d, revenue_365d = EXCLUDED.revenue_365d,
lifetime_sales = EXCLUDED.lifetime_sales, lifetime_revenue = EXCLUDED.lifetime_revenue,
avg_margin_30d = EXCLUDED.avg_margin_30d
avg_margin_30d = EXCLUDED.avg_margin_30d,
sales_growth_30d_vs_prev = EXCLUDED.sales_growth_30d_vs_prev,
revenue_growth_30d_vs_prev = EXCLUDED.revenue_growth_30d_vs_prev
WHERE -- Only update if at least one value has changed
brand_metrics.product_count IS DISTINCT FROM EXCLUDED.product_count OR
brand_metrics.active_product_count IS DISTINCT FROM EXCLUDED.active_product_count OR

328
inventory-server/scripts/metrics-new/calculate_category_metrics.sql
View File

@@ -1,5 +1,5 @@
-- Description: Calculates and updates aggregated metrics per category.
-- Dependencies: product_metrics, products, categories, product_categories, calculate_status table.
-- Description: Calculates and updates aggregated metrics per category with hierarchy rollups.
-- Dependencies: product_metrics, products, categories, product_categories, category_hierarchy, calculate_status table.
-- Frequency: Daily (after product_metrics update).
DO $$
@@ -9,55 +9,21 @@ DECLARE
_min_revenue NUMERIC := 50.00; -- Minimum revenue threshold for margin calculation
BEGIN
RAISE NOTICE 'Running % calculation...', _module_name;
-- Refresh the category hierarchy materialized view first
REFRESH MATERIALIZED VIEW CONCURRENTLY category_hierarchy;
WITH
-- Identify the hierarchy depth for each category
CategoryDepth AS (
WITH RECURSIVE CategoryTree AS (
-- Base case: Start with categories without parents (root categories)
SELECT cat_id, name, parent_id, 0 AS depth
FROM public.categories
WHERE parent_id IS NULL
UNION ALL
-- Recursive step: Add child categories with incremented depth
SELECT c.cat_id, c.name, c.parent_id, ct.depth + 1
FROM public.categories c
JOIN CategoryTree ct ON c.parent_id = ct.cat_id
)
SELECT cat_id, depth
FROM CategoryTree
),
-- For each product, find the most specific (deepest) category it belongs to
ProductDeepestCategory AS (
SELECT
pc.pid,
pc.cat_id
FROM public.product_categories pc
JOIN CategoryDepth cd ON pc.cat_id = cd.cat_id
-- This is the key part: for each product, select only the category with maximum depth
WHERE (pc.pid, cd.depth) IN (
SELECT pc2.pid, MAX(cd2.depth)
FROM public.product_categories pc2
JOIN CategoryDepth cd2 ON pc2.cat_id = cd2.cat_id
GROUP BY pc2.pid
)
),
-- Calculate metrics only at the most specific category level for each product
-- These are the direct metrics (only products directly in this category)
DirectCategoryMetrics AS (
-- First calculate direct metrics (products directly in each category)
WITH DirectCategoryMetrics AS (
SELECT
pdc.cat_id,
-- Counts
pc.cat_id,
COUNT(DISTINCT pm.pid) AS product_count,
COUNT(DISTINCT CASE WHEN pm.is_visible THEN pm.pid END) AS active_product_count,
COUNT(DISTINCT CASE WHEN pm.is_replenishable THEN pm.pid END) AS replenishable_product_count,
-- Current Stock
SUM(pm.current_stock) AS current_stock_units,
SUM(pm.current_stock_cost) AS current_stock_cost,
SUM(pm.current_stock_retail) AS current_stock_retail,
-- Rolling Periods - Only include products with actual sales in each period
-- Sales metrics with proper filtering
SUM(CASE WHEN pm.sales_7d > 0 THEN pm.sales_7d ELSE 0 END) AS sales_7d,
SUM(CASE WHEN pm.revenue_7d > 0 THEN pm.revenue_7d ELSE 0 END) AS revenue_7d,
SUM(CASE WHEN pm.sales_30d > 0 THEN pm.sales_30d ELSE 0 END) AS sales_30d,
@@ -67,179 +33,141 @@ BEGIN
SUM(CASE WHEN pm.sales_365d > 0 THEN pm.sales_365d ELSE 0 END) AS sales_365d,
SUM(CASE WHEN pm.revenue_365d > 0 THEN pm.revenue_365d ELSE 0 END) AS revenue_365d,
SUM(CASE WHEN pm.lifetime_sales > 0 THEN pm.lifetime_sales ELSE 0 END) AS lifetime_sales,
SUM(CASE WHEN pm.lifetime_revenue > 0 THEN pm.lifetime_revenue ELSE 0 END) AS lifetime_revenue,
-- Data for KPIs - Only average stock for products with stock
SUM(CASE WHEN pm.avg_stock_units_30d > 0 THEN pm.avg_stock_units_30d ELSE 0 END) AS total_avg_stock_units_30d
FROM public.product_metrics pm
JOIN ProductDeepestCategory pdc ON pm.pid = pdc.pid
GROUP BY pdc.cat_id
SUM(CASE WHEN pm.lifetime_revenue > 0 THEN pm.lifetime_revenue ELSE 0 END) AS lifetime_revenue
FROM public.product_categories pc
JOIN public.product_metrics pm ON pc.pid = pm.pid
GROUP BY pc.cat_id
),
-- Build a category lookup table for parent relationships
CategoryHierarchyPaths AS (
WITH RECURSIVE ParentPaths AS (
-- Base case: All categories with their immediate parents
SELECT
cat_id,
cat_id as leaf_id, -- Every category is its own leaf initially
ARRAY[cat_id] as path
FROM public.categories
UNION ALL
-- Recursive step: Walk up the parent chain
SELECT
c.parent_id as cat_id,
pp.leaf_id, -- Keep the original leaf_id
c.parent_id || pp.path as path
FROM ParentPaths pp
JOIN public.categories c ON pp.cat_id = c.cat_id
WHERE c.parent_id IS NOT NULL -- Stop at root categories
)
-- Select distinct paths to avoid duplication
SELECT DISTINCT cat_id, leaf_id
FROM ParentPaths
),
-- Aggregate metrics from leaf categories to their ancestors without duplication
-- These are the rolled-up metrics (including all child categories)
RollupMetrics AS (
-- Calculate rolled-up metrics (including all descendant categories)
RolledUpMetrics AS (
SELECT
chp.cat_id,
-- For each parent category, count distinct products to avoid duplication
COUNT(DISTINCT dcm.cat_id) AS child_categories_count,
SUM(dcm.product_count) AS rollup_product_count,
SUM(dcm.active_product_count) AS rollup_active_product_count,
SUM(dcm.replenishable_product_count) AS rollup_replenishable_product_count,
SUM(dcm.current_stock_units) AS rollup_current_stock_units,
SUM(dcm.current_stock_cost) AS rollup_current_stock_cost,
SUM(dcm.current_stock_retail) AS rollup_current_stock_retail,
SUM(dcm.sales_7d) AS rollup_sales_7d,
SUM(dcm.revenue_7d) AS rollup_revenue_7d,
SUM(dcm.sales_30d) AS rollup_sales_30d,
SUM(dcm.revenue_30d) AS rollup_revenue_30d,
SUM(dcm.cogs_30d) AS rollup_cogs_30d,
SUM(dcm.profit_30d) AS rollup_profit_30d,
SUM(dcm.sales_365d) AS rollup_sales_365d,
SUM(dcm.revenue_365d) AS rollup_revenue_365d,
SUM(dcm.lifetime_sales) AS rollup_lifetime_sales,
SUM(dcm.lifetime_revenue) AS rollup_lifetime_revenue,
SUM(dcm.total_avg_stock_units_30d) AS rollup_total_avg_stock_units_30d
FROM CategoryHierarchyPaths chp
JOIN DirectCategoryMetrics dcm ON chp.leaf_id = dcm.cat_id
GROUP BY chp.cat_id
ch.cat_id,
-- Sum metrics from this category and all its descendants
SUM(dcm.product_count) AS product_count,
SUM(dcm.active_product_count) AS active_product_count,
SUM(dcm.replenishable_product_count) AS replenishable_product_count,
SUM(dcm.current_stock_units) AS current_stock_units,
SUM(dcm.current_stock_cost) AS current_stock_cost,
SUM(dcm.current_stock_retail) AS current_stock_retail,
SUM(dcm.sales_7d) AS sales_7d,
SUM(dcm.revenue_7d) AS revenue_7d,
SUM(dcm.sales_30d) AS sales_30d,
SUM(dcm.revenue_30d) AS revenue_30d,
SUM(dcm.cogs_30d) AS cogs_30d,
SUM(dcm.profit_30d) AS profit_30d,
SUM(dcm.sales_365d) AS sales_365d,
SUM(dcm.revenue_365d) AS revenue_365d,
SUM(dcm.lifetime_sales) AS lifetime_sales,
SUM(dcm.lifetime_revenue) AS lifetime_revenue
FROM category_hierarchy ch
LEFT JOIN DirectCategoryMetrics dcm ON
dcm.cat_id = ch.cat_id OR
dcm.cat_id = ANY(SELECT cat_id FROM category_hierarchy WHERE ch.cat_id = ANY(ancestor_ids))
GROUP BY ch.cat_id
),
-- Combine direct and rollup metrics
CombinedMetrics AS (
PreviousPeriodCategoryMetrics AS (
-- Get previous period metrics for growth calculation
SELECT
pc.cat_id,
SUM(CASE WHEN dps.snapshot_date >= CURRENT_DATE - INTERVAL '59 days'
AND dps.snapshot_date < CURRENT_DATE - INTERVAL '29 days'
THEN dps.units_sold ELSE 0 END) AS sales_prev_30d,
SUM(CASE WHEN dps.snapshot_date >= CURRENT_DATE - INTERVAL '59 days'
AND dps.snapshot_date < CURRENT_DATE - INTERVAL '29 days'
THEN dps.net_revenue ELSE 0 END) AS revenue_prev_30d
FROM public.daily_product_snapshots dps
JOIN public.product_categories pc ON dps.pid = pc.pid
GROUP BY pc.cat_id
),
RolledUpPreviousPeriod AS (
-- Calculate rolled-up previous period metrics
SELECT
ch.cat_id,
SUM(ppcm.sales_prev_30d) AS sales_prev_30d,
SUM(ppcm.revenue_prev_30d) AS revenue_prev_30d
FROM category_hierarchy ch
LEFT JOIN PreviousPeriodCategoryMetrics ppcm ON
ppcm.cat_id = ch.cat_id OR
ppcm.cat_id = ANY(SELECT cat_id FROM category_hierarchy WHERE ch.cat_id = ANY(ancestor_ids))
GROUP BY ch.cat_id
),
AllCategories AS (
-- Ensure all categories are included
SELECT
c.cat_id,
c.name,
c.type,
c.parent_id,
-- Direct metrics (just this category)
COALESCE(dcm.product_count, 0) AS direct_product_count,
COALESCE(dcm.active_product_count, 0) AS direct_active_product_count,
COALESCE(dcm.replenishable_product_count, 0) AS direct_replenishable_product_count,
COALESCE(dcm.current_stock_units, 0) AS direct_current_stock_units,
COALESCE(dcm.current_stock_cost, 0) AS direct_current_stock_cost,
COALESCE(dcm.current_stock_retail, 0) AS direct_current_stock_retail,
COALESCE(dcm.sales_7d, 0) AS direct_sales_7d,
COALESCE(dcm.revenue_7d, 0) AS direct_revenue_7d,
COALESCE(dcm.sales_30d, 0) AS direct_sales_30d,
COALESCE(dcm.revenue_30d, 0) AS direct_revenue_30d,
COALESCE(dcm.cogs_30d, 0) AS direct_cogs_30d,
COALESCE(dcm.profit_30d, 0) AS direct_profit_30d,
COALESCE(dcm.sales_365d, 0) AS direct_sales_365d,
COALESCE(dcm.revenue_365d, 0) AS direct_revenue_365d,
COALESCE(dcm.lifetime_sales, 0) AS direct_lifetime_sales,
COALESCE(dcm.lifetime_revenue, 0) AS direct_lifetime_revenue,
COALESCE(dcm.total_avg_stock_units_30d, 0) AS direct_avg_stock_units_30d,
-- Rolled up metrics (this category + all children)
COALESCE(rm.rollup_product_count, 0) AS product_count,
COALESCE(rm.rollup_active_product_count, 0) AS active_product_count,
COALESCE(rm.rollup_replenishable_product_count, 0) AS replenishable_product_count,
COALESCE(rm.rollup_current_stock_units, 0) AS current_stock_units,
COALESCE(rm.rollup_current_stock_cost, 0) AS current_stock_cost,
COALESCE(rm.rollup_current_stock_retail, 0) AS current_stock_retail,
COALESCE(rm.rollup_sales_7d, 0) AS sales_7d,
COALESCE(rm.rollup_revenue_7d, 0) AS revenue_7d,
COALESCE(rm.rollup_sales_30d, 0) AS sales_30d,
COALESCE(rm.rollup_revenue_30d, 0) AS revenue_30d,
COALESCE(rm.rollup_cogs_30d, 0) AS cogs_30d,
COALESCE(rm.rollup_profit_30d, 0) AS profit_30d,
COALESCE(rm.rollup_sales_365d, 0) AS sales_365d,
COALESCE(rm.rollup_revenue_365d, 0) AS revenue_365d,
COALESCE(rm.rollup_lifetime_sales, 0) AS lifetime_sales,
COALESCE(rm.rollup_lifetime_revenue, 0) AS lifetime_revenue,
COALESCE(rm.rollup_total_avg_stock_units_30d, 0) AS total_avg_stock_units_30d
c.parent_id
FROM public.categories c
LEFT JOIN DirectCategoryMetrics dcm ON c.cat_id = dcm.cat_id
LEFT JOIN RollupMetrics rm ON c.cat_id = rm.cat_id
WHERE c.status = 'active'
)
INSERT INTO public.category_metrics (
category_id, category_name, category_type, parent_id, last_calculated,
-- Store all direct and rolled up metrics
-- Rolled-up metrics
product_count, active_product_count, replenishable_product_count,
current_stock_units, current_stock_cost, current_stock_retail,
sales_7d, revenue_7d, sales_30d, revenue_30d, profit_30d, cogs_30d,
sales_365d, revenue_365d, lifetime_sales, lifetime_revenue,
-- Also store direct metrics with direct_ prefix
-- Direct metrics
direct_product_count, direct_active_product_count, direct_replenishable_product_count,
direct_current_stock_units, direct_stock_cost, direct_stock_retail,
direct_sales_7d, direct_revenue_7d, direct_sales_30d, direct_revenue_30d,
direct_sales_7d, direct_revenue_7d, direct_sales_30d, direct_revenue_30d,
direct_profit_30d, direct_cogs_30d, direct_sales_365d, direct_revenue_365d,
direct_lifetime_sales, direct_lifetime_revenue,
-- KPIs
avg_margin_30d, stock_turn_30d
avg_margin_30d,
sales_growth_30d_vs_prev, revenue_growth_30d_vs_prev
)
SELECT
cm.cat_id,
cm.name,
cm.type,
cm.parent_id,
ac.cat_id,
ac.name,
ac.type,
ac.parent_id,
_start_time,
-- Rolled-up metrics (total including children)
cm.product_count,
cm.active_product_count,
cm.replenishable_product_count,
cm.current_stock_units,
cm.current_stock_cost,
cm.current_stock_retail,
cm.sales_7d, cm.revenue_7d,
cm.sales_30d, cm.revenue_30d, cm.profit_30d, cm.cogs_30d,
cm.sales_365d, cm.revenue_365d,
cm.lifetime_sales, cm.lifetime_revenue,
-- Direct metrics (just this category)
cm.direct_product_count,
cm.direct_active_product_count,
cm.direct_replenishable_product_count,
cm.direct_current_stock_units,
cm.direct_current_stock_cost,
cm.direct_current_stock_retail,
cm.direct_sales_7d, cm.direct_revenue_7d,
cm.direct_sales_30d, cm.direct_revenue_30d, cm.direct_profit_30d, cm.direct_cogs_30d,
cm.direct_sales_365d, cm.direct_revenue_365d,
cm.direct_lifetime_sales, cm.direct_lifetime_revenue,
-- Rolled-up metrics (includes descendants)
COALESCE(rum.product_count, 0),
COALESCE(rum.active_product_count, 0),
COALESCE(rum.replenishable_product_count, 0),
COALESCE(rum.current_stock_units, 0),
COALESCE(rum.current_stock_cost, 0.00),
COALESCE(rum.current_stock_retail, 0.00),
COALESCE(rum.sales_7d, 0), COALESCE(rum.revenue_7d, 0.00),
COALESCE(rum.sales_30d, 0), COALESCE(rum.revenue_30d, 0.00),
COALESCE(rum.profit_30d, 0.00), COALESCE(rum.cogs_30d, 0.00),
COALESCE(rum.sales_365d, 0), COALESCE(rum.revenue_365d, 0.00),
COALESCE(rum.lifetime_sales, 0), COALESCE(rum.lifetime_revenue, 0.00),
-- Direct metrics (only this category)
COALESCE(dcm.product_count, 0),
COALESCE(dcm.active_product_count, 0),
COALESCE(dcm.replenishable_product_count, 0),
COALESCE(dcm.current_stock_units, 0),
COALESCE(dcm.current_stock_cost, 0.00),
COALESCE(dcm.current_stock_retail, 0.00),
COALESCE(dcm.sales_7d, 0), COALESCE(dcm.revenue_7d, 0.00),
COALESCE(dcm.sales_30d, 0), COALESCE(dcm.revenue_30d, 0.00),
COALESCE(dcm.profit_30d, 0.00), COALESCE(dcm.cogs_30d, 0.00),
COALESCE(dcm.sales_365d, 0), COALESCE(dcm.revenue_365d, 0.00),
COALESCE(dcm.lifetime_sales, 0), COALESCE(dcm.lifetime_revenue, 0.00),
-- KPIs - Calculate margin only for categories with significant revenue
CASE
WHEN cm.revenue_30d >= _min_revenue THEN
((cm.revenue_30d - cm.cogs_30d) / cm.revenue_30d) * 100.0
ELSE NULL -- No margin for low/no revenue categories
WHEN COALESCE(rum.revenue_30d, 0) >= _min_revenue THEN
((COALESCE(rum.revenue_30d, 0) - COALESCE(rum.cogs_30d, 0)) / COALESCE(rum.revenue_30d, 1)) * 100.0
ELSE NULL
END,
-- Stock Turn calculation
CASE
WHEN cm.total_avg_stock_units_30d > 0 THEN
cm.sales_30d / cm.total_avg_stock_units_30d
ELSE NULL -- No stock turn if no average stock
END
FROM CombinedMetrics cm
-- Growth metrics for rolled-up values
std_numeric(safe_divide((rum.sales_30d - rupp.sales_prev_30d) * 100.0, rupp.sales_prev_30d), 2),
std_numeric(safe_divide((rum.revenue_30d - rupp.revenue_prev_30d) * 100.0, rupp.revenue_prev_30d), 2)
FROM AllCategories ac
LEFT JOIN DirectCategoryMetrics dcm ON ac.cat_id = dcm.cat_id
LEFT JOIN RolledUpMetrics rum ON ac.cat_id = rum.cat_id
LEFT JOIN RolledUpPreviousPeriod rupp ON ac.cat_id = rupp.cat_id
ON CONFLICT (category_id) DO UPDATE SET
last_calculated = EXCLUDED.last_calculated,
category_name = EXCLUDED.category_name,
category_type = EXCLUDED.category_type,
parent_id = EXCLUDED.parent_id,
last_calculated = EXCLUDED.last_calculated,
-- ROLLED-UP METRICS (includes this category + all descendants)
-- Rolled-up metrics
product_count = EXCLUDED.product_count,
active_product_count = EXCLUDED.active_product_count,
replenishable_product_count = EXCLUDED.replenishable_product_count,
@@ -251,8 +179,7 @@ BEGIN
profit_30d = EXCLUDED.profit_30d, cogs_30d = EXCLUDED.cogs_30d,
sales_365d = EXCLUDED.sales_365d, revenue_365d = EXCLUDED.revenue_365d,
lifetime_sales = EXCLUDED.lifetime_sales, lifetime_revenue = EXCLUDED.lifetime_revenue,
-- DIRECT METRICS (only products directly in this category)
-- Direct metrics
direct_product_count = EXCLUDED.direct_product_count,
direct_active_product_count = EXCLUDED.direct_active_product_count,
direct_replenishable_product_count = EXCLUDED.direct_replenishable_product_count,
@@ -264,10 +191,9 @@ BEGIN
direct_profit_30d = EXCLUDED.direct_profit_30d, direct_cogs_30d = EXCLUDED.direct_cogs_30d,
direct_sales_365d = EXCLUDED.direct_sales_365d, direct_revenue_365d = EXCLUDED.direct_revenue_365d,
direct_lifetime_sales = EXCLUDED.direct_lifetime_sales, direct_lifetime_revenue = EXCLUDED.direct_lifetime_revenue,
-- Calculated KPIs
avg_margin_30d = EXCLUDED.avg_margin_30d,
stock_turn_30d = EXCLUDED.stock_turn_30d
sales_growth_30d_vs_prev = EXCLUDED.sales_growth_30d_vs_prev,
revenue_growth_30d_vs_prev = EXCLUDED.revenue_growth_30d_vs_prev
WHERE -- Only update if at least one value has changed
category_metrics.product_count IS DISTINCT FROM EXCLUDED.product_count OR
category_metrics.active_product_count IS DISTINCT FROM EXCLUDED.active_product_count OR
@@ -291,19 +217,23 @@ WITH update_stats AS (
SELECT
COUNT(*) as total_categories,
COUNT(*) FILTER (WHERE last_calculated >= NOW() - INTERVAL '5 minutes') as rows_processed,
COUNT(*) FILTER (WHERE category_type = 11) as main_categories, -- 11 = category
COUNT(*) FILTER (WHERE category_type = 12) as subcategories, -- 12 = subcategory
SUM(product_count) as total_products,
SUM(active_product_count) as total_active_products,
SUM(current_stock_units) as total_stock_units
COUNT(*) FILTER (WHERE category_type = 10) as sections,
COUNT(*) FILTER (WHERE category_type = 11) as categories,
COUNT(*) FILTER (WHERE category_type = 12) as subcategories,
SUM(product_count) as total_products_rolled,
SUM(direct_product_count) as total_products_direct,
SUM(sales_30d) as total_sales_30d,
SUM(revenue_30d) as total_revenue_30d
FROM public.category_metrics
)
SELECT
rows_processed,
total_categories,
main_categories,
sections,
categories,
subcategories,
total_products::int,
total_active_products::int,
total_stock_units::int
total_products_rolled::int,
total_products_direct::int,
total_sales_30d::int,
ROUND(total_revenue_30d, 2) as total_revenue_30d
FROM update_stats;

28
inventory-server/scripts/metrics-new/calculate_vendor_metrics.sql
View File

@@ -44,6 +44,21 @@ BEGIN
WHERE p.vendor IS NOT NULL AND p.vendor <> ''
GROUP BY p.vendor
),
PreviousPeriodVendorMetrics AS (
-- Get previous period metrics for growth calculation
SELECT
p.vendor,
SUM(CASE WHEN dps.snapshot_date >= CURRENT_DATE - INTERVAL '59 days'
AND dps.snapshot_date < CURRENT_DATE - INTERVAL '29 days'
THEN dps.units_sold ELSE 0 END) AS sales_prev_30d,
SUM(CASE WHEN dps.snapshot_date >= CURRENT_DATE - INTERVAL '59 days'
AND dps.snapshot_date < CURRENT_DATE - INTERVAL '29 days'
THEN dps.net_revenue ELSE 0 END) AS revenue_prev_30d
FROM public.daily_product_snapshots dps
JOIN public.products p ON dps.pid = p.pid
WHERE p.vendor IS NOT NULL AND p.vendor <> ''
GROUP BY p.vendor
),
VendorPOAggregates AS (
-- Aggregate PO related stats including lead time calculated from POs to receivings
SELECT
@@ -78,7 +93,8 @@ BEGIN
po_count_365d, avg_lead_time_days,
sales_7d, revenue_7d, sales_30d, revenue_30d, profit_30d, cogs_30d,
sales_365d, revenue_365d, lifetime_sales, lifetime_revenue,
avg_margin_30d
avg_margin_30d,
sales_growth_30d_vs_prev, revenue_growth_30d_vs_prev
)
SELECT
v.vendor,
@@ -102,10 +118,14 @@ BEGIN
COALESCE(vpa.sales_365d, 0), COALESCE(vpa.revenue_365d, 0.00),
COALESCE(vpa.lifetime_sales, 0), COALESCE(vpa.lifetime_revenue, 0.00),
-- KPIs
(vpa.profit_30d / NULLIF(vpa.revenue_30d, 0)) * 100.0
(vpa.profit_30d / NULLIF(vpa.revenue_30d, 0)) * 100.0,
-- Growth metrics
std_numeric(safe_divide((vpa.sales_30d - ppvm.sales_prev_30d) * 100.0, ppvm.sales_prev_30d), 2),
std_numeric(safe_divide((vpa.revenue_30d - ppvm.revenue_prev_30d) * 100.0, ppvm.revenue_prev_30d), 2)
FROM AllVendors v
LEFT JOIN VendorProductAggregates vpa ON v.vendor = vpa.vendor
LEFT JOIN VendorPOAggregates vpoa ON v.vendor = vpoa.vendor
LEFT JOIN PreviousPeriodVendorMetrics ppvm ON v.vendor = ppvm.vendor
ON CONFLICT (vendor_name) DO UPDATE SET
last_calculated = EXCLUDED.last_calculated,
@@ -124,7 +144,9 @@ BEGIN
profit_30d = EXCLUDED.profit_30d, cogs_30d = EXCLUDED.cogs_30d,
sales_365d = EXCLUDED.sales_365d, revenue_365d = EXCLUDED.revenue_365d,
lifetime_sales = EXCLUDED.lifetime_sales, lifetime_revenue = EXCLUDED.lifetime_revenue,
avg_margin_30d = EXCLUDED.avg_margin_30d
avg_margin_30d = EXCLUDED.avg_margin_30d,
sales_growth_30d_vs_prev = EXCLUDED.sales_growth_30d_vs_prev,
revenue_growth_30d_vs_prev = EXCLUDED.revenue_growth_30d_vs_prev
WHERE -- Only update if at least one value has changed
vendor_metrics.product_count IS DISTINCT FROM EXCLUDED.product_count OR
vendor_metrics.active_product_count IS DISTINCT FROM EXCLUDED.active_product_count OR

9
inventory-server/scripts/metrics-new/update_daily_snapshots.sql
View File

@@ -86,7 +86,14 @@ BEGIN
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
COALESCE(
o.costeach, -- First use order-specific cost if available
get_weighted_avg_cost(p.pid, o.date::date), -- Then use weighted average cost
p.landing_cost_price, -- Fallback to landing cost
p.cost_price -- Final fallback to current cost
) * 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)

573
inventory-server/scripts/metrics-new/update_product_metrics.sql
View File

@@ -171,6 +171,85 @@ BEGIN
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
),
LifetimeRevenue AS (
-- Calculate actual revenue from orders table
SELECT
o.pid,
SUM(o.price * o.quantity - COALESCE(o.discount, 0)) AS lifetime_revenue_from_orders,
SUM(o.quantity) AS lifetime_units_from_orders
FROM public.orders o
WHERE o.status NOT IN ('canceled', 'returned')
AND o.quantity > 0
GROUP BY o.pid
),
PreviousPeriodMetrics AS (
-- Calculate metrics for previous 30-day period for growth comparison
SELECT
pid,
SUM(CASE WHEN snapshot_date >= _current_date - INTERVAL '59 days'
AND snapshot_date < _current_date - INTERVAL '29 days'
THEN units_sold ELSE 0 END) AS sales_prev_30d,
SUM(CASE WHEN snapshot_date >= _current_date - INTERVAL '59 days'
AND snapshot_date < _current_date - INTERVAL '29 days'
THEN net_revenue ELSE 0 END) AS revenue_prev_30d,
-- Year-over-year comparison
SUM(CASE WHEN snapshot_date >= _current_date - INTERVAL '395 days'
AND snapshot_date < _current_date - INTERVAL '365 days'
THEN units_sold ELSE 0 END) AS sales_30d_last_year,
SUM(CASE WHEN snapshot_date >= _current_date - INTERVAL '395 days'
AND snapshot_date < _current_date - INTERVAL '365 days'
THEN net_revenue ELSE 0 END) AS revenue_30d_last_year
FROM public.daily_product_snapshots
GROUP BY pid
),
DemandVariability AS (
-- Calculate variance and standard deviation of daily sales
SELECT
pid,
COUNT(*) AS days_with_data,
AVG(units_sold) AS avg_daily_sales,
VARIANCE(units_sold) AS sales_variance,
STDDEV(units_sold) AS sales_std_dev,
-- Coefficient of variation
CASE
WHEN AVG(units_sold) > 0 THEN STDDEV(units_sold) / AVG(units_sold)
ELSE NULL
END AS sales_cv
FROM public.daily_product_snapshots
WHERE snapshot_date >= _current_date - INTERVAL '29 days'
AND snapshot_date <= _current_date
GROUP BY pid
),
ServiceLevels AS (
-- Calculate service level and fill rate metrics
SELECT
pid,
COUNT(*) FILTER (WHERE stockout_flag = true) AS stockout_incidents_30d,
COUNT(*) FILTER (WHERE stockout_flag = true AND units_sold > 0) AS lost_sales_incidents_30d,
-- Service level: percentage of days without stockouts
(1.0 - (COUNT(*) FILTER (WHERE stockout_flag = true)::NUMERIC / NULLIF(COUNT(*), 0))) * 100 AS service_level_30d,
-- Fill rate: units sold / (units sold + potential lost sales)
CASE
WHEN SUM(units_sold) > 0 THEN
(SUM(units_sold)::NUMERIC /
(SUM(units_sold) + SUM(CASE WHEN stockout_flag THEN units_sold * 0.2 ELSE 0 END))) * 100
ELSE NULL
END AS fill_rate_30d
FROM public.daily_product_snapshots
WHERE snapshot_date >= _current_date - INTERVAL '29 days'
AND snapshot_date <= _current_date
GROUP BY pid
),
SeasonalityAnalysis AS (
-- Simple seasonality detection
SELECT
p.pid,
sp.seasonal_pattern,
sp.seasonality_index,
sp.peak_season
FROM products p
CROSS JOIN LATERAL detect_seasonal_pattern(p.pid) sp
)
-- Final UPSERT into product_metrics
INSERT INTO public.product_metrics (
@@ -187,7 +266,7 @@ BEGIN
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,
lifetime_sales, lifetime_revenue, lifetime_revenue_quality,
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,
@@ -203,7 +282,13 @@ BEGIN
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,
status -- Add status field for calculated status
status, -- Add status field for calculated status
-- New fields
sales_growth_30d_vs_prev, revenue_growth_30d_vs_prev,
sales_growth_yoy, revenue_growth_yoy,
sales_variance_30d, sales_std_dev_30d, sales_cv_30d, demand_pattern,
fill_rate_30d, stockout_incidents_30d, service_level_30d, lost_sales_incidents_30d,
seasonality_index, seasonal_pattern, peak_season
)
SELECT
ci.pid, _start_time, ci.sku, ci.title, ci.brand, ci.vendor, ci.image_url, ci.is_visible, ci.is_replenishable,
@@ -227,27 +312,33 @@ BEGIN
sa.received_qty_30d, sa.received_cost_30d,
-- Use total_sold from products table as the source of truth for lifetime sales
-- This includes all historical data from the production database
ci.historical_total_sold AS lifetime_sales,
COALESCE(
-- Option 1: Use 30-day average price if available
CASE WHEN sa.sales_30d > 0 THEN
ci.historical_total_sold * (sa.revenue_30d / NULLIF(sa.sales_30d, 0))
ELSE NULL END,
-- Option 2: Try 365-day average price if available
CASE WHEN sa.sales_365d > 0 THEN
ci.historical_total_sold * (sa.revenue_365d / NULLIF(sa.sales_365d, 0))
ELSE NULL END,
-- Option 3: Use current price as a reasonable estimate
ci.historical_total_sold * ci.current_price,
-- Option 4: Use regular price if current price might be zero
ci.historical_total_sold * ci.current_regular_price,
-- Final fallback: Use accumulated revenue (this is less accurate for old products)
sa.total_net_revenue
) AS lifetime_revenue,
ci.historical_total_sold AS lifetime_sales,
-- Calculate lifetime revenue using actual historical prices where available
CASE
WHEN lr.lifetime_revenue_from_orders IS NOT NULL THEN
-- We have some order history - use it plus estimate for remaining
lr.lifetime_revenue_from_orders +
(GREATEST(0, ci.historical_total_sold - COALESCE(lr.lifetime_units_from_orders, 0)) *
COALESCE(
-- Use oldest known price from snapshots as proxy
(SELECT revenue_7d / NULLIF(sales_7d, 0)
FROM daily_product_snapshots
WHERE pid = ci.pid AND sales_7d > 0
ORDER BY snapshot_date ASC
LIMIT 1),
ci.current_price
))
ELSE
-- No order history - estimate using current price
ci.historical_total_sold * ci.current_price
END AS lifetime_revenue,
CASE
WHEN lr.lifetime_units_from_orders >= ci.historical_total_sold * 0.9 THEN 'exact'
WHEN lr.lifetime_units_from_orders >= ci.historical_total_sold * 0.5 THEN 'partial'
ELSE 'estimated'
END AS lifetime_revenue_quality,
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,
@@ -262,317 +353,59 @@ BEGIN
(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,
-- Fix sell-through rate: Industry standard is Units Sold / (Beginning Inventory + Units Received)
-- Approximating beginning inventory as current stock + units sold - units received
(sa.sales_30d / NULLIF(
ci.current_stock + sa.sales_30d + sa.returns_units_30d - sa.received_qty_30d,
0
)) * 100 AS sell_through_30d,
-- Forecasting intermediate values
-- CRITICAL FIX: Use safer velocity calculation to prevent extreme values
-- Original problematic calculation: (sa.sales_30d / NULLIF(30.0 - sa.stockout_days_30d, 0))
-- Use available days (not stockout days) as denominator with a minimum safety value
(sa.sales_30d /
NULLIF(
GREATEST(
30.0 - sa.stockout_days_30d, -- Standard calculation
CASE
WHEN sa.sales_30d > 0 THEN 14.0 -- If we have sales, ensure at least 14 days denominator
ELSE 30.0 -- If no sales, use full period
END
),
0
)
) AS sales_velocity_daily,
-- Use the calculate_sales_velocity function instead of repetitive calculation
calculate_sales_velocity(sa.sales_30d::int, sa.stockout_days_30d::int) 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,
-- Apply the same fix to all derived calculations
(sa.sales_30d /
NULLIF(
GREATEST(
30.0 - sa.stockout_days_30d,
CASE WHEN sa.sales_30d > 0 THEN 14.0 ELSE 30.0 END
),
0
)
) * s.effective_lead_time AS lead_time_forecast_units,
calculate_sales_velocity(sa.sales_30d::int, sa.stockout_days_30d::int) * s.effective_lead_time AS lead_time_forecast_units,
(sa.sales_30d /
NULLIF(
GREATEST(
30.0 - sa.stockout_days_30d,
CASE WHEN sa.sales_30d > 0 THEN 14.0 ELSE 30.0 END
),
0
)
) * s.effective_days_of_stock AS days_of_stock_forecast_units,
calculate_sales_velocity(sa.sales_30d::int, sa.stockout_days_30d::int) * s.effective_days_of_stock AS days_of_stock_forecast_units,
(sa.sales_30d /
NULLIF(
GREATEST(
30.0 - sa.stockout_days_30d,
CASE WHEN sa.sales_30d > 0 THEN 14.0 ELSE 30.0 END
),
0
)
) * (s.effective_lead_time + s.effective_days_of_stock) AS planning_period_forecast_units,
calculate_sales_velocity(sa.sales_30d::int, sa.stockout_days_30d::int) * (s.effective_lead_time + s.effective_days_of_stock) AS planning_period_forecast_units,
(ci.current_stock + COALESCE(ooi.on_order_qty, 0) - ((sa.sales_30d /
NULLIF(
GREATEST(
30.0 - sa.stockout_days_30d,
CASE WHEN sa.sales_30d > 0 THEN 14.0 ELSE 30.0 END
),
0
)
) * s.effective_lead_time)) AS lead_time_closing_stock,
(ci.current_stock + COALESCE(ooi.on_order_qty, 0) - (calculate_sales_velocity(sa.sales_30d::int, sa.stockout_days_30d::int) * s.effective_lead_time)) AS lead_time_closing_stock,
((ci.current_stock + COALESCE(ooi.on_order_qty, 0) - ((sa.sales_30d /
NULLIF(
GREATEST(
30.0 - sa.stockout_days_30d,
CASE WHEN sa.sales_30d > 0 THEN 14.0 ELSE 30.0 END
),
0
)
) * s.effective_lead_time))) - ((sa.sales_30d /
NULLIF(
GREATEST(
30.0 - sa.stockout_days_30d,
CASE WHEN sa.sales_30d > 0 THEN 14.0 ELSE 30.0 END
),
0
)
) * s.effective_days_of_stock) AS days_of_stock_closing_stock,
((ci.current_stock + COALESCE(ooi.on_order_qty, 0) - (calculate_sales_velocity(sa.sales_30d::int, sa.stockout_days_30d::int) * s.effective_lead_time))) - (calculate_sales_velocity(sa.sales_30d::int, sa.stockout_days_30d::int) * s.effective_days_of_stock) AS days_of_stock_closing_stock,
(((sa.sales_30d /
NULLIF(
GREATEST(
30.0 - sa.stockout_days_30d,
CASE WHEN sa.sales_30d > 0 THEN 14.0 ELSE 30.0 END
),
0
)
) * s.effective_lead_time) + ((sa.sales_30d /
NULLIF(
GREATEST(
30.0 - sa.stockout_days_30d,
CASE WHEN sa.sales_30d > 0 THEN 14.0 ELSE 30.0 END
),
0
)
) * s.effective_days_of_stock)) + s.effective_safety_stock - ci.current_stock - COALESCE(ooi.on_order_qty, 0) AS replenishment_needed_raw,
((calculate_sales_velocity(sa.sales_30d::int, sa.stockout_days_30d::int) * s.effective_lead_time) + (calculate_sales_velocity(sa.sales_30d::int, sa.stockout_days_30d::int) * 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(
GREATEST(
30.0 - sa.stockout_days_30d,
CASE WHEN sa.sales_30d > 0 THEN 14.0 ELSE 30.0 END
),
0
)
) * s.effective_lead_time) + ((sa.sales_30d /
NULLIF(
GREATEST(
30.0 - sa.stockout_days_30d,
CASE WHEN sa.sales_30d > 0 THEN 14.0 ELSE 30.0 END
),
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(
GREATEST(
30.0 - sa.stockout_days_30d,
CASE WHEN sa.sales_30d > 0 THEN 14.0 ELSE 30.0 END
),
0
)
) * s.effective_lead_time) + ((sa.sales_30d /
NULLIF(
GREATEST(
30.0 - sa.stockout_days_30d,
CASE WHEN sa.sales_30d > 0 THEN 14.0 ELSE 30.0 END
),
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(
GREATEST(
30.0 - sa.stockout_days_30d,
CASE WHEN sa.sales_30d > 0 THEN 14.0 ELSE 30.0 END
),
0
)
) * s.effective_lead_time) + ((sa.sales_30d /
NULLIF(
GREATEST(
30.0 - sa.stockout_days_30d,
CASE WHEN sa.sales_30d > 0 THEN 14.0 ELSE 30.0 END
),
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(
GREATEST(
30.0 - sa.stockout_days_30d,
CASE WHEN sa.sales_30d > 0 THEN 14.0 ELSE 30.0 END
),
0
)
) * s.effective_lead_time) + ((sa.sales_30d /
NULLIF(
GREATEST(
30.0 - sa.stockout_days_30d,
CASE WHEN sa.sales_30d > 0 THEN 14.0 ELSE 30.0 END
),
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,
-- Final Forecasting / Replenishment Metrics
CEILING(GREATEST(0, (((calculate_sales_velocity(sa.sales_30d::int, sa.stockout_days_30d::int) * s.effective_lead_time) + (calculate_sales_velocity(sa.sales_30d::int, sa.stockout_days_30d::int) * 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, (((calculate_sales_velocity(sa.sales_30d::int, sa.stockout_days_30d::int) * s.effective_lead_time) + (calculate_sales_velocity(sa.sales_30d::int, sa.stockout_days_30d::int) * 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, (((calculate_sales_velocity(sa.sales_30d::int, sa.stockout_days_30d::int) * s.effective_lead_time) + (calculate_sales_velocity(sa.sales_30d::int, sa.stockout_days_30d::int) * 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, (((calculate_sales_velocity(sa.sales_30d::int, sa.stockout_days_30d::int) * s.effective_lead_time) + (calculate_sales_velocity(sa.sales_30d::int, sa.stockout_days_30d::int) * 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(
GREATEST(
30.0 - sa.stockout_days_30d,
CASE WHEN sa.sales_30d > 0 THEN 14.0 ELSE 30.0 END
),
0
)
) * s.effective_lead_time) + ((sa.sales_30d /
NULLIF(
GREATEST(
30.0 - sa.stockout_days_30d,
CASE WHEN sa.sales_30d > 0 THEN 14.0 ELSE 30.0 END
),
0
)
) * s.effective_days_of_stock)) + s.effective_safety_stock - ci.current_stock - COALESCE(ooi.on_order_qty, 0))))::int AS to_order_units,
-- To Order (Apply MOQ/UOM logic here if needed, otherwise equals replenishment)
CEILING(GREATEST(0, (((calculate_sales_velocity(sa.sales_30d::int, sa.stockout_days_30d::int) * s.effective_lead_time) + (calculate_sales_velocity(sa.sales_30d::int, sa.stockout_days_30d::int) * 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(
GREATEST(
30.0 - sa.stockout_days_30d,
CASE WHEN sa.sales_30d > 0 THEN 14.0 ELSE 30.0 END
),
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(
GREATEST(
30.0 - sa.stockout_days_30d,
CASE WHEN sa.sales_30d > 0 THEN 14.0 ELSE 30.0 END
),
0
)
) * s.effective_lead_time))) * ci.current_price AS forecast_lost_revenue,
GREATEST(0, - (ci.current_stock + COALESCE(ooi.on_order_qty, 0) - (calculate_sales_velocity(sa.sales_30d::int, sa.stockout_days_30d::int) * s.effective_lead_time))) AS forecast_lost_sales_units,
GREATEST(0, - (ci.current_stock + COALESCE(ooi.on_order_qty, 0) - (calculate_sales_velocity(sa.sales_30d::int, sa.stockout_days_30d::int) * s.effective_lead_time))) * ci.current_price AS forecast_lost_revenue,
ci.current_stock / NULLIF((sa.sales_30d /
NULLIF(
GREATEST(
30.0 - sa.stockout_days_30d,
CASE WHEN sa.sales_30d > 0 THEN 14.0 ELSE 30.0 END
),
0
)
), 0) AS stock_cover_in_days,
COALESCE(ooi.on_order_qty, 0) / NULLIF((sa.sales_30d /
NULLIF(
GREATEST(
30.0 - sa.stockout_days_30d,
CASE WHEN sa.sales_30d > 0 THEN 14.0 ELSE 30.0 END
),
0
)
), 0) AS po_cover_in_days,
(ci.current_stock + COALESCE(ooi.on_order_qty, 0)) / NULLIF((sa.sales_30d /
NULLIF(
GREATEST(
30.0 - sa.stockout_days_30d,
CASE WHEN sa.sales_30d > 0 THEN 14.0 ELSE 30.0 END
),
0
)
), 0) AS sells_out_in_days,
ci.current_stock / NULLIF(calculate_sales_velocity(sa.sales_30d::int, sa.stockout_days_30d::int), 0) AS stock_cover_in_days,
COALESCE(ooi.on_order_qty, 0) / NULLIF(calculate_sales_velocity(sa.sales_30d::int, sa.stockout_days_30d::int), 0) AS po_cover_in_days,
(ci.current_stock + COALESCE(ooi.on_order_qty, 0)) / NULLIF(calculate_sales_velocity(sa.sales_30d::int, sa.stockout_days_30d::int), 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(
GREATEST(
30.0 - sa.stockout_days_30d,
CASE WHEN sa.sales_30d > 0 THEN 14.0 ELSE 30.0 END
),
0
)
) > 0
THEN _current_date + FLOOR(GREATEST(0, ci.current_stock - s.effective_safety_stock) / (sa.sales_30d /
NULLIF(
GREATEST(
30.0 - sa.stockout_days_30d,
CASE WHEN sa.sales_30d > 0 THEN 14.0 ELSE 30.0 END
),
0
)
))::int - s.effective_lead_time
WHEN calculate_sales_velocity(sa.sales_30d::int, sa.stockout_days_30d::int) > 0
THEN _current_date + FLOOR(GREATEST(0, ci.current_stock - s.effective_safety_stock) / calculate_sales_velocity(sa.sales_30d::int, sa.stockout_days_30d::int))::int - s.effective_lead_time
ELSE NULL
END AS replenish_date,
GREATEST(0, ci.current_stock - s.effective_safety_stock - (((sa.sales_30d /
NULLIF(
GREATEST(
30.0 - sa.stockout_days_30d,
CASE WHEN sa.sales_30d > 0 THEN 14.0 ELSE 30.0 END
),
0
)
) * s.effective_lead_time) + ((sa.sales_30d /
NULLIF(
GREATEST(
30.0 - sa.stockout_days_30d,
CASE WHEN sa.sales_30d > 0 THEN 14.0 ELSE 30.0 END
),
0
)
) * s.effective_days_of_stock)))::int AS overstocked_units,
(GREATEST(0, ci.current_stock - s.effective_safety_stock - (((sa.sales_30d /
NULLIF(
GREATEST(
30.0 - sa.stockout_days_30d,
CASE WHEN sa.sales_30d > 0 THEN 14.0 ELSE 30.0 END
),
0
)
) * s.effective_lead_time) + ((sa.sales_30d /
NULLIF(
GREATEST(
30.0 - sa.stockout_days_30d,
CASE WHEN sa.sales_30d > 0 THEN 14.0 ELSE 30.0 END
),
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(
GREATEST(
30.0 - sa.stockout_days_30d,
CASE WHEN sa.sales_30d > 0 THEN 14.0 ELSE 30.0 END
),
0
)
) * s.effective_lead_time) + ((sa.sales_30d /
NULLIF(
GREATEST(
30.0 - sa.stockout_days_30d,
CASE WHEN sa.sales_30d > 0 THEN 14.0 ELSE 30.0 END
),
0
)
) * s.effective_days_of_stock)))) * ci.current_price AS overstocked_retail,
GREATEST(0, ci.current_stock - s.effective_safety_stock - ((calculate_sales_velocity(sa.sales_30d::int, sa.stockout_days_30d::int) * s.effective_lead_time) + (calculate_sales_velocity(sa.sales_30d::int, sa.stockout_days_30d::int) * s.effective_days_of_stock)))::int AS overstocked_units,
(GREATEST(0, ci.current_stock - s.effective_safety_stock - ((calculate_sales_velocity(sa.sales_30d::int, sa.stockout_days_30d::int) * s.effective_lead_time) + (calculate_sales_velocity(sa.sales_30d::int, sa.stockout_days_30d::int) * s.effective_days_of_stock)))) * ci.current_effective_cost AS overstocked_cost,
(GREATEST(0, ci.current_stock - s.effective_safety_stock - ((calculate_sales_velocity(sa.sales_30d::int, sa.stockout_days_30d::int) * s.effective_lead_time) + (calculate_sales_velocity(sa.sales_30d::int, sa.stockout_days_30d::int) * s.effective_days_of_stock)))) * ci.current_price AS overstocked_retail,
-- Old Stock Flag
(ci.created_at::date < _current_date - INTERVAL '60 day') AND
@@ -592,66 +425,18 @@ BEGIN
ELSE
CASE
-- Check for overstock first
WHEN GREATEST(0, ci.current_stock - s.effective_safety_stock - (((sa.sales_30d /
NULLIF(
GREATEST(
30.0 - sa.stockout_days_30d,
CASE WHEN sa.sales_30d > 0 THEN 14.0 ELSE 30.0 END
),
0
)
) * s.effective_lead_time) + ((sa.sales_30d /
NULLIF(
GREATEST(
30.0 - sa.stockout_days_30d,
CASE WHEN sa.sales_30d > 0 THEN 14.0 ELSE 30.0 END
),
0
)
) * s.effective_days_of_stock))) > 0 THEN 'Overstock'
WHEN GREATEST(0, ci.current_stock - s.effective_safety_stock - ((calculate_sales_velocity(sa.sales_30d::int, sa.stockout_days_30d::int) * s.effective_lead_time) + (calculate_sales_velocity(sa.sales_30d::int, sa.stockout_days_30d::int) * s.effective_days_of_stock))) > 0 THEN 'Overstock'
-- Check for Critical stock
WHEN ci.current_stock <= 0 OR
(ci.current_stock / NULLIF((sa.sales_30d /
NULLIF(
GREATEST(
30.0 - sa.stockout_days_30d,
CASE WHEN sa.sales_30d > 0 THEN 14.0 ELSE 30.0 END
),
0
)
), 0)) <= 0 THEN 'Critical'
(ci.current_stock / NULLIF(calculate_sales_velocity(sa.sales_30d::int, sa.stockout_days_30d::int), 0)) <= 0 THEN 'Critical'
WHEN (ci.current_stock / NULLIF((sa.sales_30d /
NULLIF(
GREATEST(
30.0 - sa.stockout_days_30d,
CASE WHEN sa.sales_30d > 0 THEN 14.0 ELSE 30.0 END
),
0
)
), 0)) < (COALESCE(s.effective_lead_time, 30) * 0.5) THEN 'Critical'
WHEN (ci.current_stock / NULLIF(calculate_sales_velocity(sa.sales_30d::int, sa.stockout_days_30d::int), 0)) < (COALESCE(s.effective_lead_time, 30) * 0.5) THEN 'Critical'
-- Check for reorder soon
WHEN ((ci.current_stock + COALESCE(ooi.on_order_qty, 0)) / NULLIF((sa.sales_30d /
NULLIF(
GREATEST(
30.0 - sa.stockout_days_30d,
CASE WHEN sa.sales_30d > 0 THEN 14.0 ELSE 30.0 END
),
0
)
), 0)) < (COALESCE(s.effective_lead_time, 30) + 7) THEN
WHEN ((ci.current_stock + COALESCE(ooi.on_order_qty, 0)) / NULLIF(calculate_sales_velocity(sa.sales_30d::int, sa.stockout_days_30d::int), 0)) < (COALESCE(s.effective_lead_time, 30) + 7) THEN
CASE
WHEN (ci.current_stock / NULLIF((sa.sales_30d /
NULLIF(
GREATEST(
30.0 - sa.stockout_days_30d,
CASE WHEN sa.sales_30d > 0 THEN 14.0 ELSE 30.0 END
),
0
)
), 0)) < (COALESCE(s.effective_lead_time, 30) * 0.5) THEN 'Critical'
WHEN (ci.current_stock / NULLIF(calculate_sales_velocity(sa.sales_30d::int, sa.stockout_days_30d::int), 0)) < (COALESCE(s.effective_lead_time, 30) * 0.5) THEN 'Critical'
ELSE 'Reorder Soon'
END
@@ -672,15 +457,7 @@ BEGIN
END) > 180 THEN 'At Risk'
-- Very high stock cover is at risk too
WHEN (ci.current_stock / NULLIF((sa.sales_30d /
NULLIF(
GREATEST(
30.0 - sa.stockout_days_30d,
CASE WHEN sa.sales_30d > 0 THEN 14.0 ELSE 30.0 END
),
0
)
), 0)) > 365 THEN 'At Risk'
WHEN (ci.current_stock / NULLIF(calculate_sales_velocity(sa.sales_30d::int, sa.stockout_days_30d::int), 0)) > 365 THEN 'At Risk'
-- New products (less than 30 days old)
WHEN (CASE
@@ -693,7 +470,30 @@ BEGIN
-- If none of the above, assume Healthy
ELSE 'Healthy'
END
END AS status
END AS status,
-- Growth Metrics (P3) - using safe_divide and std_numeric for consistency
std_numeric(safe_divide((sa.sales_30d - ppm.sales_prev_30d) * 100.0, ppm.sales_prev_30d), 2) AS sales_growth_30d_vs_prev,
std_numeric(safe_divide((sa.revenue_30d - ppm.revenue_prev_30d) * 100.0, ppm.revenue_prev_30d), 2) AS revenue_growth_30d_vs_prev,
std_numeric(safe_divide((sa.sales_30d - ppm.sales_30d_last_year) * 100.0, ppm.sales_30d_last_year), 2) AS sales_growth_yoy,
std_numeric(safe_divide((sa.revenue_30d - ppm.revenue_30d_last_year) * 100.0, ppm.revenue_30d_last_year), 2) AS revenue_growth_yoy,
-- Demand Variability (P3)
std_numeric(dv.sales_variance, 2) AS sales_variance_30d,
std_numeric(dv.sales_std_dev, 2) AS sales_std_dev_30d,
std_numeric(dv.sales_cv, 2) AS sales_cv_30d,
classify_demand_pattern(dv.avg_daily_sales, dv.sales_cv) AS demand_pattern,
-- Service Levels (P5)
std_numeric(COALESCE(sl.fill_rate_30d, 100), 2) AS fill_rate_30d,
COALESCE(sl.stockout_incidents_30d, 0)::int AS stockout_incidents_30d,
std_numeric(COALESCE(sl.service_level_30d, 100), 2) AS service_level_30d,
COALESCE(sl.lost_sales_incidents_30d, 0)::int AS lost_sales_incidents_30d,
-- Seasonality (P5)
std_numeric(season.seasonality_index, 2) AS seasonality_index,
COALESCE(season.seasonal_pattern, 'none') AS seasonal_pattern,
season.peak_season
FROM CurrentInfo ci
LEFT JOIN OnOrderInfo ooi ON ci.pid = ooi.pid
@@ -701,6 +501,11 @@ BEGIN
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
LEFT JOIN LifetimeRevenue lr ON ci.pid = lr.pid
LEFT JOIN PreviousPeriodMetrics ppm ON ci.pid = ppm.pid
LEFT JOIN DemandVariability dv ON ci.pid = dv.pid
LEFT JOIN ServiceLevels sl ON ci.pid = sl.pid
LEFT JOIN SeasonalityAnalysis season ON ci.pid = season.pid
WHERE s.exclude_forecast IS FALSE OR s.exclude_forecast IS NULL -- Exclude products explicitly marked
ON CONFLICT (pid) DO UPDATE SET
@@ -718,7 +523,7 @@ BEGIN
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,
lifetime_sales = EXCLUDED.lifetime_sales, lifetime_revenue = EXCLUDED.lifetime_revenue, lifetime_revenue_quality = EXCLUDED.lifetime_revenue_quality,
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,
@@ -734,7 +539,22 @@ BEGIN
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,
status = EXCLUDED.status
status = EXCLUDED.status,
sales_growth_30d_vs_prev = EXCLUDED.sales_growth_30d_vs_prev,
revenue_growth_30d_vs_prev = EXCLUDED.revenue_growth_30d_vs_prev,
sales_growth_yoy = EXCLUDED.sales_growth_yoy,
revenue_growth_yoy = EXCLUDED.revenue_growth_yoy,
sales_variance_30d = EXCLUDED.sales_variance_30d,
sales_std_dev_30d = EXCLUDED.sales_std_dev_30d,
sales_cv_30d = EXCLUDED.sales_cv_30d,
demand_pattern = EXCLUDED.demand_pattern,
fill_rate_30d = EXCLUDED.fill_rate_30d,
stockout_incidents_30d = EXCLUDED.stockout_incidents_30d,
service_level_30d = EXCLUDED.service_level_30d,
lost_sales_incidents_30d = EXCLUDED.lost_sales_incidents_30d,
seasonality_index = EXCLUDED.seasonality_index,
seasonal_pattern = EXCLUDED.seasonal_pattern,
peak_season = EXCLUDED.peak_season
WHERE -- Only update if at least one key metric has changed
product_metrics.current_stock IS DISTINCT FROM EXCLUDED.current_stock OR
product_metrics.current_price IS DISTINCT FROM EXCLUDED.current_price OR
@@ -750,7 +570,8 @@ BEGIN
-- Check a few other important fields that might change
product_metrics.date_last_sold IS DISTINCT FROM EXCLUDED.date_last_sold OR
product_metrics.earliest_expected_date IS DISTINCT FROM EXCLUDED.earliest_expected_date OR
product_metrics.lifetime_sales IS DISTINCT FROM EXCLUDED.lifetime_sales
product_metrics.lifetime_sales IS DISTINCT FROM EXCLUDED.lifetime_sales OR
product_metrics.lifetime_revenue_quality IS DISTINCT FROM EXCLUDED.lifetime_revenue_quality
;
-- Update the status table with the timestamp from the START of this run