matt/inventory
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Start migrating and fixing calculate scripts

Browse Source
This commit is contained in:
Matt
2025-03-26 01:19:44 -04:00
parent 108181c63d
commit 749907bd30
10 changed files with 569 additions and 463 deletions
Download Patch File Download Diff File Expand all files Collapse all files

59
inventory-server/scripts/metrics/brand-metrics.js
View File

@@ -32,12 +32,12 @@ async function calculateBrandMetrics(startTime, totalProducts, processedCount =
}
// Get order count that will be processed
const [orderCount] = await connection.query(`
const orderCount = await connection.query(`
SELECT COUNT(*) as count
FROM orders o
WHERE o.canceled = false
`);
processedOrders = orderCount[0].count;
processedOrders = parseInt(orderCount.rows[0].count);
outputProgress({
status: 'running',
@@ -98,14 +98,14 @@ async function calculateBrandMetrics(startTime, totalProducts, processedCount =
SUM(o.quantity * (o.price - COALESCE(o.discount, 0) - p.cost_price)) as period_margin,
COUNT(DISTINCT DATE(o.date)) as period_days,
CASE
WHEN o.date >= DATE_SUB(CURRENT_DATE, INTERVAL 3 MONTH) THEN 'current'
WHEN o.date BETWEEN DATE_SUB(CURRENT_DATE, INTERVAL 15 MONTH)
AND DATE_SUB(CURRENT_DATE, INTERVAL 12 MONTH) THEN 'previous'
WHEN o.date >= CURRENT_DATE - INTERVAL '3 months' THEN 'current'
WHEN o.date BETWEEN CURRENT_DATE - INTERVAL '15 months'
AND CURRENT_DATE - INTERVAL '12 months' THEN 'previous'
END as period_type
FROM filtered_products p
JOIN orders o ON p.pid = o.pid
WHERE o.canceled = false
AND o.date >= DATE_SUB(CURRENT_DATE, INTERVAL 15 MONTH)
AND o.date >= CURRENT_DATE - INTERVAL '15 months'
GROUP BY p.brand, period_type
),
brand_data AS (
@@ -165,15 +165,16 @@ async function calculateBrandMetrics(startTime, totalProducts, processedCount =
LEFT JOIN sales_periods sp ON bd.brand = sp.brand
GROUP BY bd.brand, bd.product_count, bd.active_products, bd.total_stock_units,
bd.total_stock_cost, bd.total_stock_retail, bd.total_revenue, bd.avg_margin
ON DUPLICATE KEY UPDATE
product_count = VALUES(product_count),
active_products = VALUES(active_products),
total_stock_units = VALUES(total_stock_units),
total_stock_cost = VALUES(total_stock_cost),
total_stock_retail = VALUES(total_stock_retail),
total_revenue = VALUES(total_revenue),
avg_margin = VALUES(avg_margin),
growth_rate = VALUES(growth_rate),
ON CONFLICT (brand) DO UPDATE
SET
product_count = EXCLUDED.product_count,
active_products = EXCLUDED.active_products,
total_stock_units = EXCLUDED.total_stock_units,
total_stock_cost = EXCLUDED.total_stock_cost,
total_stock_retail = EXCLUDED.total_stock_retail,
total_revenue = EXCLUDED.total_revenue,
avg_margin = EXCLUDED.avg_margin,
growth_rate = EXCLUDED.growth_rate,
last_calculated_at = CURRENT_TIMESTAMP
`);
@@ -230,8 +231,8 @@ async function calculateBrandMetrics(startTime, totalProducts, processedCount =
monthly_metrics AS (
SELECT
p.brand,
YEAR(o.date) as year,
MONTH(o.date) as month,
EXTRACT(YEAR FROM o.date) as year,
EXTRACT(MONTH FROM o.date) as month,
COUNT(DISTINCT p.valid_pid) as product_count,
COUNT(DISTINCT p.active_pid) as active_products,
SUM(p.valid_stock) as total_stock_units,
@@ -255,19 +256,20 @@ async function calculateBrandMetrics(startTime, totalProducts, processedCount =
END as avg_margin
FROM filtered_products p
LEFT JOIN orders o ON p.pid = o.pid AND o.canceled = false
WHERE o.date >= DATE_SUB(CURRENT_DATE, INTERVAL 12 MONTH)
GROUP BY p.brand, YEAR(o.date), MONTH(o.date)
WHERE o.date >= CURRENT_DATE - INTERVAL '12 months'
GROUP BY p.brand, EXTRACT(YEAR FROM o.date), EXTRACT(MONTH FROM o.date)
)
SELECT *
FROM monthly_metrics
ON DUPLICATE KEY UPDATE
product_count = VALUES(product_count),
active_products = VALUES(active_products),
total_stock_units = VALUES(total_stock_units),
total_stock_cost = VALUES(total_stock_cost),
total_stock_retail = VALUES(total_stock_retail),
total_revenue = VALUES(total_revenue),
avg_margin = VALUES(avg_margin)
ON CONFLICT (brand, year, month) DO UPDATE
SET
product_count = EXCLUDED.product_count,
active_products = EXCLUDED.active_products,
total_stock_units = EXCLUDED.total_stock_units,
total_stock_cost = EXCLUDED.total_stock_cost,
total_stock_retail = EXCLUDED.total_stock_retail,
total_revenue = EXCLUDED.total_revenue,
avg_margin = EXCLUDED.avg_margin
`);
processedCount = Math.floor(totalProducts * 0.99);
@@ -294,7 +296,8 @@ async function calculateBrandMetrics(startTime, totalProducts, processedCount =
await connection.query(`
INSERT INTO calculate_status (module_name, last_calculation_timestamp)
VALUES ('brand_metrics', NOW())
ON DUPLICATE KEY UPDATE last_calculation_timestamp = NOW()
ON CONFLICT (module_name) DO UPDATE
SET last_calculation_timestamp = NOW()
`);
return {

123
inventory-server/scripts/metrics/category-metrics.js
View File

@@ -32,12 +32,12 @@ async function calculateCategoryMetrics(startTime, totalProducts, processedCount
}
// Get order count that will be processed
const [orderCount] = await connection.query(`
const orderCount = await connection.query(`
SELECT COUNT(*) as count
FROM orders o
WHERE o.canceled = false
`);
processedOrders = orderCount[0].count;
processedOrders = parseInt(orderCount.rows[0].count);
outputProgress({
status: 'running',
@@ -76,12 +76,13 @@ async function calculateCategoryMetrics(startTime, totalProducts, processedCount
LEFT JOIN product_categories pc ON c.cat_id = pc.cat_id
LEFT JOIN products p ON pc.pid = p.pid
GROUP BY c.cat_id, c.status
ON DUPLICATE KEY UPDATE
product_count = VALUES(product_count),
active_products = VALUES(active_products),
total_value = VALUES(total_value),
status = VALUES(status),
last_calculated_at = VALUES(last_calculated_at)
ON CONFLICT (category_id) DO UPDATE
SET
product_count = EXCLUDED.product_count,
active_products = EXCLUDED.active_products,
total_value = EXCLUDED.total_value,
status = EXCLUDED.status,
last_calculated_at = EXCLUDED.last_calculated_at
`);
processedCount = Math.floor(totalProducts * 0.90);
@@ -127,17 +128,13 @@ async function calculateCategoryMetrics(startTime, totalProducts, processedCount
(tc.category_id IS NULL AND tc.vendor = p.vendor) OR
(tc.category_id IS NULL AND tc.vendor IS NULL)
WHERE o.canceled = false
AND o.date >= DATE_SUB(CURRENT_DATE, INTERVAL COALESCE(tc.calculation_period_days, 30) DAY)
AND o.date >= CURRENT_DATE - (COALESCE(tc.calculation_period_days, 30) || ' days')::INTERVAL
GROUP BY pc.cat_id
)
UPDATE category_metrics cm
JOIN category_sales cs ON cm.category_id = cs.cat_id
LEFT JOIN turnover_config tc ON
(tc.category_id = cm.category_id AND tc.vendor IS NULL) OR
(tc.category_id IS NULL AND tc.vendor IS NULL)
SET
cm.avg_margin = COALESCE(cs.total_margin * 100.0 / NULLIF(cs.total_sales, 0), 0),
cm.turnover_rate = CASE
avg_margin = COALESCE(cs.total_margin * 100.0 / NULLIF(cs.total_sales, 0), 0),
turnover_rate = CASE
WHEN cs.avg_stock > 0 AND cs.active_days > 0
THEN LEAST(
(cs.units_sold / cs.avg_stock) * (365.0 / cs.active_days),
@@ -145,7 +142,12 @@ async function calculateCategoryMetrics(startTime, totalProducts, processedCount
)
ELSE 0
END,
cm.last_calculated_at = NOW()
last_calculated_at = NOW()
FROM category_sales cs
LEFT JOIN turnover_config tc ON
(tc.category_id = cm.category_id AND tc.vendor IS NULL) OR
(tc.category_id IS NULL AND tc.vendor IS NULL)
WHERE cm.category_id = cs.cat_id
`);
processedCount = Math.floor(totalProducts * 0.95);
@@ -184,9 +186,9 @@ async function calculateCategoryMetrics(startTime, totalProducts, processedCount
FROM product_categories pc
JOIN products p ON pc.pid = p.pid
JOIN orders o ON p.pid = o.pid
LEFT JOIN sales_seasonality ss ON MONTH(o.date) = ss.month
LEFT JOIN sales_seasonality ss ON EXTRACT(MONTH FROM o.date) = ss.month
WHERE o.canceled = false
AND o.date >= DATE_SUB(CURRENT_DATE, INTERVAL 3 MONTH)
AND o.date >= CURRENT_DATE - INTERVAL '3 months'
GROUP BY pc.cat_id
),
previous_period AS (
@@ -198,26 +200,26 @@ async function calculateCategoryMetrics(startTime, totalProducts, processedCount
FROM product_categories pc
JOIN products p ON pc.pid = p.pid
JOIN orders o ON p.pid = o.pid
LEFT JOIN sales_seasonality ss ON MONTH(o.date) = ss.month
LEFT JOIN sales_seasonality ss ON EXTRACT(MONTH FROM o.date) = ss.month
WHERE o.canceled = false
AND o.date BETWEEN DATE_SUB(CURRENT_DATE, INTERVAL 15 MONTH)
AND DATE_SUB(CURRENT_DATE, INTERVAL 12 MONTH)
AND o.date BETWEEN CURRENT_DATE - INTERVAL '15 months'
AND CURRENT_DATE - INTERVAL '12 months'
GROUP BY pc.cat_id
),
trend_data AS (
SELECT
pc.cat_id,
MONTH(o.date) as month,
EXTRACT(MONTH FROM o.date) as month,
SUM(o.quantity * (o.price - COALESCE(o.discount, 0)) /
(1 + COALESCE(ss.seasonality_factor, 0))) as revenue,
COUNT(DISTINCT DATE(o.date)) as days_in_month
FROM product_categories pc
JOIN products p ON pc.pid = p.pid
JOIN orders o ON p.pid = o.pid
LEFT JOIN sales_seasonality ss ON MONTH(o.date) = ss.month
LEFT JOIN sales_seasonality ss ON EXTRACT(MONTH FROM o.date) = ss.month
WHERE o.canceled = false
AND o.date >= DATE_SUB(CURRENT_DATE, INTERVAL 15 MONTH)
GROUP BY pc.cat_id, MONTH(o.date)
AND o.date >= CURRENT_DATE - INTERVAL '15 months'
GROUP BY pc.cat_id, EXTRACT(MONTH FROM o.date)
),
trend_stats AS (
SELECT
@@ -261,16 +263,12 @@ async function calculateCategoryMetrics(startTime, totalProducts, processedCount
JOIN products p ON pc.pid = p.pid
JOIN orders o ON p.pid = o.pid
WHERE o.canceled = false
AND o.date >= DATE_SUB(CURRENT_DATE, INTERVAL 3 MONTH)
AND o.date >= CURRENT_DATE - INTERVAL '3 months'
GROUP BY pc.cat_id
)
UPDATE category_metrics cm
LEFT JOIN current_period cp ON cm.category_id = cp.cat_id
LEFT JOIN previous_period pp ON cm.category_id = pp.cat_id
LEFT JOIN trend_analysis ta ON cm.category_id = ta.cat_id
LEFT JOIN margin_calc mc ON cm.category_id = mc.cat_id
SET
cm.growth_rate = CASE
growth_rate = CASE
WHEN pp.revenue = 0 AND COALESCE(cp.revenue, 0) > 0 THEN 100.0
WHEN pp.revenue = 0 OR cp.revenue IS NULL THEN 0.0
WHEN ta.trend_slope IS NOT NULL THEN
@@ -291,9 +289,13 @@ async function calculateCategoryMetrics(startTime, totalProducts, processedCount
)
)
END,
cm.avg_margin = COALESCE(mc.avg_margin, cm.avg_margin),
cm.last_calculated_at = NOW()
WHERE cp.cat_id IS NOT NULL OR pp.cat_id IS NOT NULL
avg_margin = COALESCE(mc.avg_margin, cm.avg_margin),
last_calculated_at = NOW()
FROM current_period cp
FULL OUTER JOIN previous_period pp ON cm.category_id = pp.cat_id
LEFT JOIN trend_analysis ta ON cm.category_id = ta.cat_id
LEFT JOIN margin_calc mc ON cm.category_id = mc.cat_id
WHERE cm.category_id = cp.cat_id OR cm.category_id = pp.cat_id
`);
processedCount = Math.floor(totalProducts * 0.97);
@@ -335,8 +337,8 @@ async function calculateCategoryMetrics(startTime, totalProducts, processedCount
)
SELECT
pc.cat_id,
YEAR(o.date) as year,
MONTH(o.date) as month,
EXTRACT(YEAR FROM o.date) as year,
EXTRACT(MONTH FROM o.date) as month,
COUNT(DISTINCT p.pid) as product_count,
COUNT(DISTINCT CASE WHEN p.visible = true THEN p.pid END) as active_products,
SUM(p.stock_quantity * p.cost_price) as total_value,
@@ -364,15 +366,16 @@ async function calculateCategoryMetrics(startTime, totalProducts, processedCount
JOIN products p ON pc.pid = p.pid
JOIN orders o ON p.pid = o.pid
WHERE o.canceled = false
AND o.date >= DATE_SUB(CURRENT_DATE, INTERVAL 12 MONTH)
GROUP BY pc.cat_id, YEAR(o.date), MONTH(o.date)
ON DUPLICATE KEY UPDATE
product_count = VALUES(product_count),
active_products = VALUES(active_products),
total_value = VALUES(total_value),
total_revenue = VALUES(total_revenue),
avg_margin = VALUES(avg_margin),
turnover_rate = VALUES(turnover_rate)
AND o.date >= CURRENT_DATE - INTERVAL '12 months'
GROUP BY pc.cat_id, EXTRACT(YEAR FROM o.date), EXTRACT(MONTH FROM o.date)
ON CONFLICT (category_id, year, month) DO UPDATE
SET
product_count = EXCLUDED.product_count,
active_products = EXCLUDED.active_products,
total_value = EXCLUDED.total_value,
total_revenue = EXCLUDED.total_revenue,
avg_margin = EXCLUDED.avg_margin,
turnover_rate = EXCLUDED.turnover_rate
`);
processedCount = Math.floor(totalProducts * 0.99);
@@ -414,20 +417,20 @@ async function calculateCategoryMetrics(startTime, totalProducts, processedCount
)
WITH date_ranges AS (
SELECT
DATE_SUB(CURRENT_DATE, INTERVAL 30 DAY) as period_start,
CURRENT_DATE - INTERVAL '30 days' as period_start,
CURRENT_DATE as period_end
UNION ALL
SELECT
DATE_SUB(CURRENT_DATE, INTERVAL 90 DAY),
DATE_SUB(CURRENT_DATE, INTERVAL 31 DAY)
CURRENT_DATE - INTERVAL '90 days',
CURRENT_DATE - INTERVAL '31 days'
UNION ALL
SELECT
DATE_SUB(CURRENT_DATE, INTERVAL 180 DAY),
DATE_SUB(CURRENT_DATE, INTERVAL 91 DAY)
CURRENT_DATE - INTERVAL '180 days',
CURRENT_DATE - INTERVAL '91 days'
UNION ALL
SELECT
DATE_SUB(CURRENT_DATE, INTERVAL 365 DAY),
DATE_SUB(CURRENT_DATE, INTERVAL 181 DAY)
CURRENT_DATE - INTERVAL '365 days',
CURRENT_DATE - INTERVAL '181 days'
),
sales_data AS (
SELECT
@@ -466,12 +469,13 @@ async function calculateCategoryMetrics(startTime, totalProducts, processedCount
END as avg_price,
NOW() as last_calculated_at
FROM sales_data
ON DUPLICATE KEY UPDATE
avg_daily_sales = VALUES(avg_daily_sales),
total_sold = VALUES(total_sold),
num_products = VALUES(num_products),
avg_price = VALUES(avg_price),
last_calculated_at = VALUES(last_calculated_at)
ON CONFLICT (category_id, brand, period_start, period_end) DO UPDATE
SET
avg_daily_sales = EXCLUDED.avg_daily_sales,
total_sold = EXCLUDED.total_sold,
num_products = EXCLUDED.num_products,
avg_price = EXCLUDED.avg_price,
last_calculated_at = EXCLUDED.last_calculated_at
`);
processedCount = Math.floor(totalProducts * 1.0);
@@ -498,7 +502,8 @@ async function calculateCategoryMetrics(startTime, totalProducts, processedCount
await connection.query(`
INSERT INTO calculate_status (module_name, last_calculation_timestamp)
VALUES ('category_metrics', NOW())
ON DUPLICATE KEY UPDATE last_calculation_timestamp = NOW()
ON CONFLICT (module_name) DO UPDATE
SET last_calculation_timestamp = NOW()
`);
return {

47
inventory-server/scripts/metrics/financial-metrics.js
View File

@@ -32,13 +32,13 @@ async function calculateFinancialMetrics(startTime, totalProducts, processedCoun
}
// Get order count that will be processed
const [orderCount] = await connection.query(`
const orderCount = await connection.query(`
SELECT COUNT(*) as count
FROM orders o
WHERE o.canceled = false
AND DATE(o.date) >= DATE_SUB(CURDATE(), INTERVAL 12 MONTH)
AND DATE(o.date) >= CURRENT_DATE - INTERVAL '12 months'
`);
processedOrders = orderCount[0].count;
processedOrders = parseInt(orderCount.rows[0].count);
outputProgress({
status: 'running',
@@ -67,27 +67,28 @@ async function calculateFinancialMetrics(startTime, totalProducts, processedCoun
SUM(o.quantity * (o.price - p.cost_price)) as gross_profit,
MIN(o.date) as first_sale_date,
MAX(o.date) as last_sale_date,
DATEDIFF(MAX(o.date), MIN(o.date)) + 1 as calculation_period_days,
EXTRACT(DAY FROM (MAX(o.date) - MIN(o.date))) + 1 as calculation_period_days,
COUNT(DISTINCT DATE(o.date)) as active_days
FROM products p
LEFT JOIN orders o ON p.pid = o.pid
WHERE o.canceled = false
AND DATE(o.date) >= DATE_SUB(CURDATE(), INTERVAL 12 MONTH)
GROUP BY p.pid
AND DATE(o.date) >= CURRENT_DATE - INTERVAL '12 months'
GROUP BY p.pid, p.cost_price, p.stock_quantity
)
UPDATE product_metrics pm
JOIN product_financials pf ON pm.pid = pf.pid
SET
pm.inventory_value = COALESCE(pf.inventory_value, 0),
pm.total_revenue = COALESCE(pf.total_revenue, 0),
pm.cost_of_goods_sold = COALESCE(pf.cost_of_goods_sold, 0),
pm.gross_profit = COALESCE(pf.gross_profit, 0),
pm.gmroi = CASE
inventory_value = COALESCE(pf.inventory_value, 0),
total_revenue = COALESCE(pf.total_revenue, 0),
cost_of_goods_sold = COALESCE(pf.cost_of_goods_sold, 0),
gross_profit = COALESCE(pf.gross_profit, 0),
gmroi = CASE
WHEN COALESCE(pf.inventory_value, 0) > 0 AND pf.active_days > 0 THEN
(COALESCE(pf.gross_profit, 0) * (365.0 / pf.active_days)) / COALESCE(pf.inventory_value, 0)
ELSE 0
END,
pm.last_calculated_at = CURRENT_TIMESTAMP
last_calculated_at = CURRENT_TIMESTAMP
FROM product_financials pf
WHERE pm.pid = pf.pid
`);
processedCount = Math.floor(totalProducts * 0.65);
@@ -119,8 +120,8 @@ async function calculateFinancialMetrics(startTime, totalProducts, processedCoun
WITH monthly_financials AS (
SELECT
p.pid,
YEAR(o.date) as year,
MONTH(o.date) as month,
EXTRACT(YEAR FROM o.date) as year,
EXTRACT(MONTH FROM o.date) as month,
p.cost_price * p.stock_quantity as inventory_value,
SUM(o.quantity * (o.price - p.cost_price)) as gross_profit,
COUNT(DISTINCT DATE(o.date)) as active_days,
@@ -129,19 +130,20 @@ async function calculateFinancialMetrics(startTime, totalProducts, processedCoun
FROM products p
LEFT JOIN orders o ON p.pid = o.pid
WHERE o.canceled = false
GROUP BY p.pid, YEAR(o.date), MONTH(o.date)
GROUP BY p.pid, EXTRACT(YEAR FROM o.date), EXTRACT(MONTH FROM o.date), p.cost_price, p.stock_quantity
)
UPDATE product_time_aggregates pta
JOIN monthly_financials mf ON pta.pid = mf.pid
AND pta.year = mf.year
AND pta.month = mf.month
SET
pta.inventory_value = COALESCE(mf.inventory_value, 0),
pta.gmroi = CASE
inventory_value = COALESCE(mf.inventory_value, 0),
gmroi = CASE
WHEN COALESCE(mf.inventory_value, 0) > 0 AND mf.active_days > 0 THEN
(COALESCE(mf.gross_profit, 0) * (365.0 / mf.active_days)) / COALESCE(mf.inventory_value, 0)
ELSE 0
END
FROM monthly_financials mf
WHERE pta.pid = mf.pid
AND pta.year = mf.year
AND pta.month = mf.month
`);
processedCount = Math.floor(totalProducts * 0.70);
@@ -168,7 +170,8 @@ async function calculateFinancialMetrics(startTime, totalProducts, processedCoun
await connection.query(`
INSERT INTO calculate_status (module_name, last_calculation_timestamp)
VALUES ('financial_metrics', NOW())
ON DUPLICATE KEY UPDATE last_calculation_timestamp = NOW()
ON CONFLICT (module_name) DO UPDATE
SET last_calculation_timestamp = NOW()
`);
return {

304
inventory-server/scripts/metrics/product-metrics.js
View File

@@ -22,8 +22,8 @@ async function calculateProductMetrics(startTime, totalProducts, processedCount
// Get total product count if not provided
if (!totalProducts) {
const [productCount] = await connection.query('SELECT COUNT(*) as count FROM products');
totalProducts = productCount[0].count;
const productCount = await connection.query('SELECT COUNT(*) as count FROM products');
totalProducts = parseInt(productCount.rows[0].count);
}
if (isCancelled) {
@@ -52,19 +52,20 @@ async function calculateProductMetrics(startTime, totalProducts, processedCount
// First ensure all products have a metrics record
await connection.query(`
INSERT IGNORE INTO product_metrics (pid, last_calculated_at)
INSERT INTO product_metrics (pid, last_calculated_at)
SELECT pid, NOW()
FROM products
ON CONFLICT (pid) DO NOTHING
`);
// Get threshold settings once
const [thresholds] = await connection.query(`
const thresholds = await connection.query(`
SELECT critical_days, reorder_days, overstock_days, low_stock_threshold
FROM stock_thresholds
WHERE category_id IS NULL AND vendor IS NULL
LIMIT 1
`);
const defaultThresholds = thresholds[0];
const defaultThresholds = thresholds.rows[0];
// Calculate base product metrics
if (!SKIP_PRODUCT_BASE_METRICS) {
@@ -85,16 +86,43 @@ async function calculateProductMetrics(startTime, totalProducts, processedCount
});
// Get order count that will be processed
const [orderCount] = await connection.query(`
const orderCount = await connection.query(`
SELECT COUNT(*) as count
FROM orders o
WHERE o.canceled = false
`);
processedOrders = orderCount[0].count;
processedOrders = parseInt(orderCount.rows[0].count);
// Clear temporary tables
await connection.query('TRUNCATE TABLE temp_sales_metrics');
await connection.query('TRUNCATE TABLE temp_purchase_metrics');
await connection.query('DROP TABLE IF EXISTS temp_sales_metrics');
await connection.query('DROP TABLE IF EXISTS temp_purchase_metrics');
// Create temp_sales_metrics
await connection.query(`
CREATE TEMPORARY TABLE temp_sales_metrics (
pid BIGINT NOT NULL,
daily_sales_avg DECIMAL(10,3),
weekly_sales_avg DECIMAL(10,3),
monthly_sales_avg DECIMAL(10,3),
total_revenue DECIMAL(10,3),
avg_margin_percent DECIMAL(10,3),
first_sale_date DATE,
last_sale_date DATE,
PRIMARY KEY (pid)
)
`);
// Create temp_purchase_metrics
await connection.query(`
CREATE TEMPORARY TABLE temp_purchase_metrics (
pid BIGINT NOT NULL,
avg_lead_time_days DOUBLE PRECISION,
last_purchase_date DATE,
first_received_date DATE,
last_received_date DATE,
PRIMARY KEY (pid)
)
`);
// Populate temp_sales_metrics with base stats and sales averages
await connection.query(`
@@ -115,7 +143,7 @@ async function calculateProductMetrics(startTime, totalProducts, processedCount
FROM products p
LEFT JOIN orders o ON p.pid = o.pid
AND o.canceled = false
AND o.date >= DATE_SUB(CURDATE(), INTERVAL 90 DAY)
AND o.date >= CURRENT_DATE - INTERVAL '90 days'
GROUP BY p.pid
`);
@@ -124,14 +152,20 @@ async function calculateProductMetrics(startTime, totalProducts, processedCount
INSERT INTO temp_purchase_metrics
SELECT
p.pid,
AVG(DATEDIFF(po.received_date, po.date)) as avg_lead_time_days,
AVG(
CASE
WHEN po.received_date IS NOT NULL AND po.date IS NOT NULL
THEN EXTRACT(EPOCH FROM (po.received_date::timestamp with time zone - po.date::timestamp with time zone)) / 86400.0
ELSE NULL
END
) as avg_lead_time_days,
MAX(po.date) as last_purchase_date,
MIN(po.received_date) as first_received_date,
MAX(po.received_date) as last_received_date
FROM products p
LEFT JOIN purchase_orders po ON p.pid = po.pid
AND po.received_date IS NOT NULL
AND po.date >= DATE_SUB(CURDATE(), INTERVAL 365 DAY)
AND po.date >= CURRENT_DATE - INTERVAL '365 days'
GROUP BY p.pid
`);
@@ -140,73 +174,73 @@ async function calculateProductMetrics(startTime, totalProducts, processedCount
while (true) {
if (isCancelled) break;
const [batch] = await connection.query(
'SELECT pid FROM products WHERE pid > ? ORDER BY pid LIMIT ?',
const batch = await connection.query(
'SELECT pid FROM products WHERE pid > $1 ORDER BY pid LIMIT $2',
[lastPid, BATCH_SIZE]
);
if (batch.length === 0) break;
if (batch.rows.length === 0) break;
await connection.query(`
UPDATE product_metrics pm
JOIN products p ON pm.pid = p.pid
LEFT JOIN temp_sales_metrics sm ON pm.pid = sm.pid
LEFT JOIN temp_purchase_metrics lm ON pm.pid = lm.pid
SET
pm.inventory_value = p.stock_quantity * NULLIF(p.cost_price, 0),
pm.daily_sales_avg = COALESCE(sm.daily_sales_avg, 0),
pm.weekly_sales_avg = COALESCE(sm.weekly_sales_avg, 0),
pm.monthly_sales_avg = COALESCE(sm.monthly_sales_avg, 0),
pm.total_revenue = COALESCE(sm.total_revenue, 0),
pm.avg_margin_percent = COALESCE(sm.avg_margin_percent, 0),
pm.first_sale_date = sm.first_sale_date,
pm.last_sale_date = sm.last_sale_date,
pm.avg_lead_time_days = COALESCE(lm.avg_lead_time_days, 30),
pm.days_of_inventory = CASE
inventory_value = p.stock_quantity * NULLIF(p.cost_price, 0),
daily_sales_avg = COALESCE(sm.daily_sales_avg, 0),
weekly_sales_avg = COALESCE(sm.weekly_sales_avg, 0),
monthly_sales_avg = COALESCE(sm.monthly_sales_avg, 0),
total_revenue = COALESCE(sm.total_revenue, 0),
avg_margin_percent = COALESCE(sm.avg_margin_percent, 0),
first_sale_date = sm.first_sale_date,
last_sale_date = sm.last_sale_date,
avg_lead_time_days = COALESCE(lm.avg_lead_time_days, 30),
days_of_inventory = CASE
WHEN COALESCE(sm.daily_sales_avg, 0) > 0
THEN FLOOR(p.stock_quantity / NULLIF(sm.daily_sales_avg, 0))
ELSE NULL
END,
pm.weeks_of_inventory = CASE
weeks_of_inventory = CASE
WHEN COALESCE(sm.weekly_sales_avg, 0) > 0
THEN FLOOR(p.stock_quantity / NULLIF(sm.weekly_sales_avg, 0))
ELSE NULL
END,
pm.stock_status = CASE
stock_status = CASE
WHEN p.stock_quantity <= 0 THEN 'Out of Stock'
WHEN COALESCE(sm.daily_sales_avg, 0) = 0 AND p.stock_quantity <= ? THEN 'Low Stock'
WHEN COALESCE(sm.daily_sales_avg, 0) = 0 AND p.stock_quantity <= $1 THEN 'Low Stock'
WHEN COALESCE(sm.daily_sales_avg, 0) = 0 THEN 'In Stock'
WHEN p.stock_quantity / NULLIF(sm.daily_sales_avg, 0) <= ? THEN 'Critical'
WHEN p.stock_quantity / NULLIF(sm.daily_sales_avg, 0) <= ? THEN 'Reorder'
WHEN p.stock_quantity / NULLIF(sm.daily_sales_avg, 0) > ? THEN 'Overstocked'
WHEN p.stock_quantity / NULLIF(sm.daily_sales_avg, 0) <= $2 THEN 'Critical'
WHEN p.stock_quantity / NULLIF(sm.daily_sales_avg, 0) <= $3 THEN 'Reorder'
WHEN p.stock_quantity / NULLIF(sm.daily_sales_avg, 0) > $4 THEN 'Overstocked'
ELSE 'Healthy'
END,
pm.safety_stock = CASE
safety_stock = CASE
WHEN COALESCE(sm.daily_sales_avg, 0) > 0 THEN
CEIL(sm.daily_sales_avg * SQRT(COALESCE(lm.avg_lead_time_days, 30)) * 1.96)
ELSE ?
CEIL(sm.daily_sales_avg * SQRT(ABS(COALESCE(lm.avg_lead_time_days, 30))) * 1.96)
ELSE $5
END,
pm.reorder_point = CASE
reorder_point = CASE
WHEN COALESCE(sm.daily_sales_avg, 0) > 0 THEN
CEIL(sm.daily_sales_avg * COALESCE(lm.avg_lead_time_days, 30)) +
CEIL(sm.daily_sales_avg * SQRT(COALESCE(lm.avg_lead_time_days, 30)) * 1.96)
ELSE ?
CEIL(sm.daily_sales_avg * SQRT(ABS(COALESCE(lm.avg_lead_time_days, 30))) * 1.96)
ELSE $6
END,
pm.reorder_qty = CASE
WHEN COALESCE(sm.daily_sales_avg, 0) > 0 AND NULLIF(p.cost_price, 0) IS NOT NULL THEN
reorder_qty = CASE
WHEN COALESCE(sm.daily_sales_avg, 0) > 0 AND NULLIF(p.cost_price, 0) IS NOT NULL AND NULLIF(p.cost_price, 0) > 0 THEN
GREATEST(
CEIL(SQRT((2 * (sm.daily_sales_avg * 365) * 25) / (NULLIF(p.cost_price, 0) * 0.25))),
?
CEIL(SQRT(ABS((2 * (sm.daily_sales_avg * 365) * 25) / (NULLIF(p.cost_price, 0) * 0.25)))),
$7
)
ELSE ?
ELSE $8
END,
pm.overstocked_amt = CASE
WHEN p.stock_quantity / NULLIF(sm.daily_sales_avg, 0) > ?
THEN GREATEST(0, p.stock_quantity - CEIL(sm.daily_sales_avg * ?))
overstocked_amt = CASE
WHEN p.stock_quantity / NULLIF(sm.daily_sales_avg, 0) > $9
THEN GREATEST(0, p.stock_quantity - CEIL(sm.daily_sales_avg * $10))
ELSE 0
END,
pm.last_calculated_at = NOW()
WHERE p.pid IN (${batch.map(() => '?').join(',')})
last_calculated_at = NOW()
FROM products p
LEFT JOIN temp_sales_metrics sm ON p.pid = sm.pid
LEFT JOIN temp_purchase_metrics lm ON p.pid = lm.pid
WHERE p.pid = ANY($11::bigint[])
`,
[
defaultThresholds.low_stock_threshold,
@@ -219,12 +253,12 @@ async function calculateProductMetrics(startTime, totalProducts, processedCount
defaultThresholds.low_stock_threshold,
defaultThresholds.overstock_days,
defaultThresholds.overstock_days,
...batch.map(row => row.pid)
batch.rows.map(row => row.pid)
]
);
lastPid = batch[batch.length - 1].pid;
processedCount += batch.length;
lastPid = batch.rows[batch.rows.length - 1].pid;
processedCount += batch.rows.length;
outputProgress({
status: 'running',
@@ -248,26 +282,31 @@ async function calculateProductMetrics(startTime, totalProducts, processedCount
while (true) {
if (isCancelled) break;
const [batch] = await connection.query(
'SELECT pid FROM products WHERE pid > ? ORDER BY pid LIMIT ?',
const batch = await connection.query(
'SELECT pid FROM products WHERE pid > $1 ORDER BY pid LIMIT $2',
[lastPid, BATCH_SIZE]
);
if (batch.length === 0) break;
if (batch.rows.length === 0) break;
await connection.query(`
UPDATE product_metrics pm
JOIN (
SET
forecast_accuracy = GREATEST(0, 100 - LEAST(fa.avg_forecast_error, 100)),
forecast_bias = GREATEST(-100, LEAST(fa.avg_forecast_bias, 100)),
last_forecast_date = fa.last_forecast_date,
last_calculated_at = NOW()
FROM (
SELECT
sf.pid,
AVG(CASE
WHEN o.quantity > 0
THEN ABS(sf.forecast_units - o.quantity) / o.quantity * 100
THEN ABS(sf.forecast_quantity - o.quantity) / o.quantity * 100
ELSE 100
END) as avg_forecast_error,
AVG(CASE
WHEN o.quantity > 0
THEN (sf.forecast_units - o.quantity) / o.quantity * 100
THEN (sf.forecast_quantity - o.quantity) / o.quantity * 100
ELSE 0
END) as avg_forecast_bias,
MAX(sf.forecast_date) as last_forecast_date
@@ -275,19 +314,14 @@ async function calculateProductMetrics(startTime, totalProducts, processedCount
JOIN orders o ON sf.pid = o.pid
AND DATE(o.date) = sf.forecast_date
WHERE o.canceled = false
AND sf.forecast_date >= DATE_SUB(CURRENT_DATE, INTERVAL 90 DAY)
AND sf.pid IN (?)
AND sf.forecast_date >= CURRENT_DATE - INTERVAL '90 days'
AND sf.pid = ANY($1::bigint[])
GROUP BY sf.pid
) fa ON pm.pid = fa.pid
SET
pm.forecast_accuracy = GREATEST(0, 100 - LEAST(fa.avg_forecast_error, 100)),
pm.forecast_bias = GREATEST(-100, LEAST(fa.avg_forecast_bias, 100)),
pm.last_forecast_date = fa.last_forecast_date,
pm.last_calculated_at = NOW()
WHERE pm.pid IN (?)
`, [batch.map(row => row.pid), batch.map(row => row.pid)]);
) fa
WHERE pm.pid = fa.pid
`, [batch.rows.map(row => row.pid)]);
lastPid = batch[batch.length - 1].pid;
lastPid = batch.rows[batch.rows.length - 1].pid;
}
}
@@ -326,8 +360,8 @@ async function calculateProductMetrics(startTime, totalProducts, processedCount
)
SELECT
p.pid,
YEAR(o.date) as year,
MONTH(o.date) as month,
EXTRACT(YEAR FROM o.date) as year,
EXTRACT(MONTH FROM o.date) as month,
SUM(o.quantity) as total_quantity_sold,
SUM(o.quantity * o.price) as total_revenue,
SUM(o.quantity * p.cost_price) as total_cost,
@@ -346,17 +380,18 @@ async function calculateProductMetrics(startTime, totalProducts, processedCount
END as gmroi
FROM products p
LEFT JOIN orders o ON p.pid = o.pid AND o.canceled = false
WHERE o.date >= DATE_SUB(CURRENT_DATE, INTERVAL 12 MONTH)
GROUP BY p.pid, YEAR(o.date), MONTH(o.date)
ON DUPLICATE KEY UPDATE
total_quantity_sold = VALUES(total_quantity_sold),
total_revenue = VALUES(total_revenue),
total_cost = VALUES(total_cost),
order_count = VALUES(order_count),
avg_price = VALUES(avg_price),
profit_margin = VALUES(profit_margin),
inventory_value = VALUES(inventory_value),
gmroi = VALUES(gmroi)
WHERE o.date >= CURRENT_DATE - INTERVAL '12 months'
GROUP BY p.pid, EXTRACT(YEAR FROM o.date), EXTRACT(MONTH FROM o.date)
ON CONFLICT (pid, year, month) DO UPDATE
SET
total_quantity_sold = EXCLUDED.total_quantity_sold,
total_revenue = EXCLUDED.total_revenue,
total_cost = EXCLUDED.total_cost,
order_count = EXCLUDED.order_count,
avg_price = EXCLUDED.avg_price,
profit_margin = EXCLUDED.profit_margin,
inventory_value = EXCLUDED.inventory_value,
gmroi = EXCLUDED.gmroi
`);
processedCount = Math.floor(totalProducts * 0.6);
@@ -418,11 +453,11 @@ async function calculateProductMetrics(startTime, totalProducts, processedCount
success
};
const [abcConfig] = await connection.query('SELECT a_threshold, b_threshold FROM abc_classification_config WHERE id = 1');
const abcThresholds = abcConfig[0] || { a_threshold: 20, b_threshold: 50 };
const abcConfig = await connection.query('SELECT a_threshold, b_threshold FROM abc_classification_config WHERE id = 1');
const abcThresholds = abcConfig.rows[0] || { a_threshold: 20, b_threshold: 50 };
// First, create and populate the rankings table with an index
await connection.query('DROP TEMPORARY TABLE IF EXISTS temp_revenue_ranks');
await connection.query('DROP TABLE IF EXISTS temp_revenue_ranks');
await connection.query(`
CREATE TEMPORARY TABLE temp_revenue_ranks (
pid BIGINT NOT NULL,
@@ -431,12 +466,12 @@ async function calculateProductMetrics(startTime, totalProducts, processedCount
dense_rank_num INT,
percentile DECIMAL(5,2),
total_count INT,
PRIMARY KEY (pid),
INDEX (rank_num),
INDEX (dense_rank_num),
INDEX (percentile)
) ENGINE=MEMORY
PRIMARY KEY (pid)
)
`);
await connection.query('CREATE INDEX ON temp_revenue_ranks (rank_num)');
await connection.query('CREATE INDEX ON temp_revenue_ranks (dense_rank_num)');
await connection.query('CREATE INDEX ON temp_revenue_ranks (percentile)');
// Calculate rankings with proper tie handling
await connection.query(`
@@ -463,8 +498,8 @@ async function calculateProductMetrics(startTime, totalProducts, processedCount
`);
// Get total count for percentage calculation
const [rankingCount] = await connection.query('SELECT MAX(rank_num) as total_count FROM temp_revenue_ranks');
const totalCount = rankingCount[0].total_count || 1;
const rankingCount = await connection.query('SELECT MAX(rank_num) as total_count FROM temp_revenue_ranks');
const totalCount = parseInt(rankingCount.rows[0].total_count) || 1;
const max_rank = totalCount;
// Process updates in batches
@@ -480,7 +515,7 @@ async function calculateProductMetrics(startTime, totalProducts, processedCount
};
// Get a batch of PIDs that need updating
const [pids] = await connection.query(`
const pids = await connection.query(`
SELECT pm.pid
FROM product_metrics pm
LEFT JOIN temp_revenue_ranks tr ON pm.pid = tr.pid
@@ -488,33 +523,46 @@ async function calculateProductMetrics(startTime, totalProducts, processedCount
OR pm.abc_class !=
CASE
WHEN tr.pid IS NULL THEN 'C'
WHEN tr.percentile <= ? THEN 'A'
WHEN tr.percentile <= ? THEN 'B'
WHEN tr.percentile <= $1 THEN 'A'
WHEN tr.percentile <= $2 THEN 'B'
ELSE 'C'
END
LIMIT ?
LIMIT $3
`, [abcThresholds.a_threshold, abcThresholds.b_threshold, batchSize]);
if (pids.length === 0) break;
if (pids.rows.length === 0) break;
const pidValues = pids.rows.map(row => row.pid);
await connection.query(`
UPDATE product_metrics pm
LEFT JOIN temp_revenue_ranks tr ON pm.pid = tr.pid
SET pm.abc_class =
SET abc_class =
CASE
WHEN tr.pid IS NULL THEN 'C'
WHEN tr.percentile <= ? THEN 'A'
WHEN tr.percentile <= ? THEN 'B'
WHEN tr.percentile <= $1 THEN 'A'
WHEN tr.percentile <= $2 THEN 'B'
ELSE 'C'
END,
pm.last_calculated_at = NOW()
WHERE pm.pid IN (?)
`, [abcThresholds.a_threshold, abcThresholds.b_threshold, pids.map(row => row.pid)]);
last_calculated_at = NOW()
FROM (SELECT pid, percentile FROM temp_revenue_ranks) tr
WHERE pm.pid = tr.pid AND pm.pid = ANY($3::bigint[])
OR (pm.pid = ANY($3::bigint[]) AND tr.pid IS NULL)
`, [abcThresholds.a_threshold, abcThresholds.b_threshold, pidValues]);
// Now update turnover rate with proper handling of zero inventory periods
await connection.query(`
UPDATE product_metrics pm
JOIN (
SET
turnover_rate = CASE
WHEN sales.avg_nonzero_stock > 0 AND sales.active_days > 0
THEN LEAST(
(sales.total_sold / sales.avg_nonzero_stock) * (365.0 / sales.active_days),
999.99
)
ELSE 0
END,
last_calculated_at = NOW()
FROM (
SELECT
o.pid,
SUM(o.quantity) as total_sold,
@@ -526,22 +574,33 @@ async function calculateProductMetrics(startTime, totalProducts, processedCount
FROM orders o
JOIN products p ON o.pid = p.pid
WHERE o.canceled = false
AND o.date >= DATE_SUB(CURRENT_DATE, INTERVAL 90 DAY)
AND o.pid IN (?)
AND o.date >= CURRENT_DATE - INTERVAL '90 days'
AND o.pid = ANY($1::bigint[])
GROUP BY o.pid
) sales ON pm.pid = sales.pid
SET
pm.turnover_rate = CASE
WHEN sales.avg_nonzero_stock > 0 AND sales.active_days > 0
THEN LEAST(
(sales.total_sold / sales.avg_nonzero_stock) * (365.0 / sales.active_days),
999.99
)
ELSE 0
END,
pm.last_calculated_at = NOW()
WHERE pm.pid IN (?)
`, [pids.map(row => row.pid), pids.map(row => row.pid)]);
) sales
WHERE pm.pid = sales.pid
`, [pidValues]);
abcProcessedCount += pids.rows.length;
// Calculate progress proportionally to batch size
processedCount = Math.floor(totalProducts * (0.60 + (abcProcessedCount / totalProducts) * 0.2));
outputProgress({
status: 'running',
operation: 'ABC classification progress',
current: processedCount,
total: totalProducts,
elapsed: formatElapsedTime(startTime),
remaining: estimateRemaining(startTime, processedCount, totalProducts),
rate: calculateRate(startTime, processedCount),
percentage: ((processedCount / totalProducts) * 100).toFixed(1),
timing: {
start_time: new Date(startTime).toISOString(),
end_time: new Date().toISOString(),
elapsed_seconds: Math.round((Date.now() - startTime) / 1000)
}
});
}
// If we get here, everything completed successfully
@@ -551,7 +610,8 @@ async function calculateProductMetrics(startTime, totalProducts, processedCount
await connection.query(`
INSERT INTO calculate_status (module_name, last_calculation_timestamp)
VALUES ('product_metrics', NOW())
ON DUPLICATE KEY UPDATE last_calculation_timestamp = NOW()
ON CONFLICT (module_name) DO UPDATE
SET last_calculation_timestamp = NOW()
`);
return {

122
inventory-server/scripts/metrics/sales-forecasts.js
View File

@@ -32,13 +32,13 @@ async function calculateSalesForecasts(startTime, totalProducts, processedCount
}
// Get order count that will be processed
const [orderCount] = await connection.query(`
const orderCount = await connection.query(`
SELECT COUNT(*) as count
FROM orders o
WHERE o.canceled = false
AND o.date >= DATE_SUB(CURRENT_DATE, INTERVAL 90 DAY)
AND o.date >= CURRENT_DATE - INTERVAL '90 days'
`);
processedOrders = orderCount[0].count;
processedOrders = parseInt(orderCount.rows[0].count);
outputProgress({
status: 'running',
@@ -69,15 +69,15 @@ async function calculateSalesForecasts(startTime, totalProducts, processedCount
await connection.query(`
INSERT INTO temp_forecast_dates
SELECT
DATE_ADD(CURRENT_DATE, INTERVAL n DAY) as forecast_date,
DAYOFWEEK(DATE_ADD(CURRENT_DATE, INTERVAL n DAY)) as day_of_week,
MONTH(DATE_ADD(CURRENT_DATE, INTERVAL n DAY)) as month
CURRENT_DATE + (n || ' days')::INTERVAL as forecast_date,
EXTRACT(DOW FROM CURRENT_DATE + (n || ' days')::INTERVAL) + 1 as day_of_week,
EXTRACT(MONTH FROM CURRENT_DATE + (n || ' days')::INTERVAL) as month
FROM (
SELECT a.N + b.N * 10 as n
SELECT a.n + b.n * 10 as n
FROM
(SELECT 0 as N UNION SELECT 1 UNION SELECT 2 UNION SELECT 3 UNION SELECT 4 UNION
(SELECT 0 as n UNION SELECT 1 UNION SELECT 2 UNION SELECT 3 UNION SELECT 4 UNION
SELECT 5 UNION SELECT 6 UNION SELECT 7 UNION SELECT 8 UNION SELECT 9) a,
(SELECT 0 as N UNION SELECT 1 UNION SELECT 2) b
(SELECT 0 as n UNION SELECT 1 UNION SELECT 2) b
ORDER BY n
LIMIT 31
) numbers
@@ -109,17 +109,17 @@ async function calculateSalesForecasts(startTime, totalProducts, processedCount
// Create temporary table for daily sales stats
await connection.query(`
CREATE TEMPORARY TABLE IF NOT EXISTS temp_daily_sales AS
CREATE TEMPORARY TABLE temp_daily_sales AS
SELECT
o.pid,
DAYOFWEEK(o.date) as day_of_week,
EXTRACT(DOW FROM o.date) + 1 as day_of_week,
SUM(o.quantity) as daily_quantity,
SUM(o.price * o.quantity) as daily_revenue,
COUNT(DISTINCT DATE(o.date)) as day_count
FROM orders o
WHERE o.canceled = false
AND o.date >= DATE_SUB(CURRENT_DATE, INTERVAL 90 DAY)
GROUP BY o.pid, DAYOFWEEK(o.date)
AND o.date >= CURRENT_DATE - INTERVAL '90 days'
GROUP BY o.pid, EXTRACT(DOW FROM o.date) + 1
`);
processedCount = Math.floor(totalProducts * 0.94);
@@ -148,7 +148,7 @@ async function calculateSalesForecasts(startTime, totalProducts, processedCount
// Create temporary table for product stats
await connection.query(`
CREATE TEMPORARY TABLE IF NOT EXISTS temp_product_stats AS
CREATE TEMPORARY TABLE temp_product_stats AS
SELECT
pid,
AVG(daily_revenue) as overall_avg_revenue,
@@ -186,10 +186,9 @@ async function calculateSalesForecasts(startTime, totalProducts, processedCount
INSERT INTO sales_forecasts (
pid,
forecast_date,
forecast_units,
forecast_revenue,
forecast_quantity,
confidence_level,
last_calculated_at
created_at
)
WITH daily_stats AS (
SELECT
@@ -223,29 +222,9 @@ async function calculateSalesForecasts(startTime, totalProducts, processedCount
WHEN ds.std_daily_qty / NULLIF(ds.avg_daily_qty, 0) > 1.0 THEN 0.9
WHEN ds.std_daily_qty / NULLIF(ds.avg_daily_qty, 0) > 0.5 THEN 0.95
ELSE 1.0
END,
2
END
)
) as forecast_units,
GREATEST(0,
ROUND(
COALESCE(
CASE
WHEN ds.data_points >= 4 THEN ds.avg_daily_revenue
ELSE ps.overall_avg_revenue
END *
(1 + COALESCE(sf.seasonality_factor, 0)) *
CASE
WHEN ds.std_daily_revenue / NULLIF(ds.avg_daily_revenue, 0) > 1.5 THEN 0.85
WHEN ds.std_daily_revenue / NULLIF(ds.avg_daily_revenue, 0) > 1.0 THEN 0.9
WHEN ds.std_daily_revenue / NULLIF(ds.avg_daily_revenue, 0) > 0.5 THEN 0.95
ELSE 1.0
END,
0
),
2
)
) as forecast_revenue,
) as forecast_quantity,
CASE
WHEN ds.total_days >= 60 AND ds.daily_variance_ratio < 0.5 THEN 90
WHEN ds.total_days >= 60 THEN 85
@@ -255,17 +234,18 @@ async function calculateSalesForecasts(startTime, totalProducts, processedCount
WHEN ds.total_days >= 14 THEN 65
ELSE 60
END as confidence_level,
NOW() as last_calculated_at
NOW() as created_at
FROM daily_stats ds
JOIN temp_product_stats ps ON ds.pid = ps.pid
CROSS JOIN temp_forecast_dates fd
LEFT JOIN sales_seasonality sf ON fd.month = sf.month
GROUP BY ds.pid, fd.forecast_date, ps.overall_avg_revenue, sf.seasonality_factor
ON DUPLICATE KEY UPDATE
forecast_units = VALUES(forecast_units),
forecast_revenue = VALUES(forecast_revenue),
confidence_level = VALUES(confidence_level),
last_calculated_at = NOW()
GROUP BY ds.pid, fd.forecast_date, ps.overall_avg_revenue, sf.seasonality_factor,
ds.avg_daily_qty, ds.std_daily_qty, ds.avg_daily_qty, ds.total_days, ds.daily_variance_ratio
ON CONFLICT (pid, forecast_date) DO UPDATE
SET
forecast_quantity = EXCLUDED.forecast_quantity,
confidence_level = EXCLUDED.confidence_level,
created_at = NOW()
`);
processedCount = Math.floor(totalProducts * 0.98);
@@ -294,22 +274,22 @@ async function calculateSalesForecasts(startTime, totalProducts, processedCount
// Create temporary table for category stats
await connection.query(`
CREATE TEMPORARY TABLE IF NOT EXISTS temp_category_sales AS
CREATE TEMPORARY TABLE temp_category_sales AS
SELECT
pc.cat_id,
DAYOFWEEK(o.date) as day_of_week,
EXTRACT(DOW FROM o.date) + 1 as day_of_week,
SUM(o.quantity) as daily_quantity,
SUM(o.price * o.quantity) as daily_revenue,
COUNT(DISTINCT DATE(o.date)) as day_count
FROM orders o
JOIN product_categories pc ON o.pid = pc.pid
WHERE o.canceled = false
AND o.date >= DATE_SUB(CURRENT_DATE, INTERVAL 90 DAY)
GROUP BY pc.cat_id, DAYOFWEEK(o.date)
AND o.date >= CURRENT_DATE - INTERVAL '90 days'
GROUP BY pc.cat_id, EXTRACT(DOW FROM o.date) + 1
`);
await connection.query(`
CREATE TEMPORARY TABLE IF NOT EXISTS temp_category_stats AS
CREATE TEMPORARY TABLE temp_category_stats AS
SELECT
cat_id,
AVG(daily_revenue) as overall_avg_revenue,
@@ -350,10 +330,10 @@ async function calculateSalesForecasts(startTime, totalProducts, processedCount
forecast_units,
forecast_revenue,
confidence_level,
last_calculated_at
created_at
)
SELECT
cs.cat_id as category_id,
cs.cat_id::bigint as category_id,
fd.forecast_date,
GREATEST(0,
AVG(cs.daily_quantity) *
@@ -366,7 +346,7 @@ async function calculateSalesForecasts(startTime, totalProducts, processedCount
ELSE ct.overall_avg_revenue
END *
(1 + COALESCE(sf.seasonality_factor, 0)) *
(0.95 + (RAND() * 0.1)),
(0.95 + (random() * 0.1)),
0
)
) as forecast_revenue,
@@ -376,27 +356,34 @@ async function calculateSalesForecasts(startTime, totalProducts, processedCount
WHEN ct.total_days >= 14 THEN 70
ELSE 60
END as confidence_level,
NOW() as last_calculated_at
NOW() as created_at
FROM temp_category_sales cs
JOIN temp_category_stats ct ON cs.cat_id = ct.cat_id
CROSS JOIN temp_forecast_dates fd
LEFT JOIN sales_seasonality sf ON fd.month = sf.month
GROUP BY cs.cat_id, fd.forecast_date, ct.overall_avg_revenue, ct.total_days, sf.seasonality_factor
GROUP BY
cs.cat_id,
fd.forecast_date,
ct.overall_avg_revenue,
ct.total_days,
sf.seasonality_factor,
sf.month
HAVING AVG(cs.daily_quantity) > 0
ON DUPLICATE KEY UPDATE
forecast_units = VALUES(forecast_units),
forecast_revenue = VALUES(forecast_revenue),
confidence_level = VALUES(confidence_level),
last_calculated_at = NOW()
ON CONFLICT (category_id, forecast_date) DO UPDATE
SET
forecast_units = EXCLUDED.forecast_units,
forecast_revenue = EXCLUDED.forecast_revenue,
confidence_level = EXCLUDED.confidence_level,
created_at = NOW()
`);
// Clean up temporary tables
await connection.query(`
DROP TEMPORARY TABLE IF EXISTS temp_forecast_dates;
DROP TEMPORARY TABLE IF EXISTS temp_daily_sales;
DROP TEMPORARY TABLE IF EXISTS temp_product_stats;
DROP TEMPORARY TABLE IF EXISTS temp_category_sales;
DROP TEMPORARY TABLE IF EXISTS temp_category_stats;
DROP TABLE IF EXISTS temp_forecast_dates;
DROP TABLE IF EXISTS temp_daily_sales;
DROP TABLE IF EXISTS temp_product_stats;
DROP TABLE IF EXISTS temp_category_sales;
DROP TABLE IF EXISTS temp_category_stats;
`);
processedCount = Math.floor(totalProducts * 1.0);
@@ -423,7 +410,8 @@ async function calculateSalesForecasts(startTime, totalProducts, processedCount
await connection.query(`
INSERT INTO calculate_status (module_name, last_calculation_timestamp)
VALUES ('sales_forecasts', NOW())
ON DUPLICATE KEY UPDATE last_calculation_timestamp = NOW()
ON CONFLICT (module_name) DO UPDATE
SET last_calculation_timestamp = NOW()
`);
return {

56
inventory-server/scripts/metrics/time-aggregates.js
View File

@@ -32,12 +32,12 @@ async function calculateTimeAggregates(startTime, totalProducts, processedCount
}
// Get order count that will be processed
const [orderCount] = await connection.query(`
const orderCount = await connection.query(`
SELECT COUNT(*) as count
FROM orders o
WHERE o.canceled = false
`);
processedOrders = orderCount[0].count;
processedOrders = parseInt(orderCount.rows[0].count);
outputProgress({
status: 'running',
@@ -75,8 +75,8 @@ async function calculateTimeAggregates(startTime, totalProducts, processedCount
WITH monthly_sales AS (
SELECT
o.pid,
YEAR(o.date) as year,
MONTH(o.date) as month,
EXTRACT(YEAR FROM o.date) as year,
EXTRACT(MONTH FROM o.date) as month,
SUM(o.quantity) as total_quantity_sold,
SUM((o.price - COALESCE(o.discount, 0)) * o.quantity) as total_revenue,
SUM(COALESCE(p.cost_price, 0) * o.quantity) as total_cost,
@@ -93,17 +93,17 @@ async function calculateTimeAggregates(startTime, totalProducts, processedCount
FROM orders o
JOIN products p ON o.pid = p.pid
WHERE o.canceled = false
GROUP BY o.pid, YEAR(o.date), MONTH(o.date)
GROUP BY o.pid, EXTRACT(YEAR FROM o.date), EXTRACT(MONTH FROM o.date), p.cost_price, p.stock_quantity
),
monthly_stock AS (
SELECT
pid,
YEAR(date) as year,
MONTH(date) as month,
EXTRACT(YEAR FROM date) as year,
EXTRACT(MONTH FROM date) as month,
SUM(received) as stock_received,
SUM(ordered) as stock_ordered
FROM purchase_orders
GROUP BY pid, YEAR(date), MONTH(date)
GROUP BY pid, EXTRACT(YEAR FROM date), EXTRACT(MONTH FROM date)
),
base_products AS (
SELECT
@@ -197,17 +197,18 @@ async function calculateTimeAggregates(startTime, totalProducts, processedCount
AND s.year = ms.year
AND s.month = ms.month
)
ON DUPLICATE KEY UPDATE
total_quantity_sold = VALUES(total_quantity_sold),
total_revenue = VALUES(total_revenue),
total_cost = VALUES(total_cost),
order_count = VALUES(order_count),
stock_received = VALUES(stock_received),
stock_ordered = VALUES(stock_ordered),
avg_price = VALUES(avg_price),
profit_margin = VALUES(profit_margin),
inventory_value = VALUES(inventory_value),
gmroi = VALUES(gmroi)
ON CONFLICT (pid, year, month) DO UPDATE
SET
total_quantity_sold = EXCLUDED.total_quantity_sold,
total_revenue = EXCLUDED.total_revenue,
total_cost = EXCLUDED.total_cost,
order_count = EXCLUDED.order_count,
stock_received = EXCLUDED.stock_received,
stock_ordered = EXCLUDED.stock_ordered,
avg_price = EXCLUDED.avg_price,
profit_margin = EXCLUDED.profit_margin,
inventory_value = EXCLUDED.inventory_value,
gmroi = EXCLUDED.gmroi
`);
processedCount = Math.floor(totalProducts * 0.60);
@@ -237,23 +238,23 @@ async function calculateTimeAggregates(startTime, totalProducts, processedCount
// Update with financial metrics
await connection.query(`
UPDATE product_time_aggregates pta
JOIN (
SET inventory_value = COALESCE(fin.inventory_value, 0)
FROM (
SELECT
p.pid,
YEAR(o.date) as year,
MONTH(o.date) as month,
EXTRACT(YEAR FROM o.date) as year,
EXTRACT(MONTH FROM o.date) as month,
p.cost_price * p.stock_quantity as inventory_value,
SUM(o.quantity * (o.price - p.cost_price)) as gross_profit,
COUNT(DISTINCT DATE(o.date)) as active_days
FROM products p
LEFT JOIN orders o ON p.pid = o.pid
WHERE o.canceled = false
GROUP BY p.pid, YEAR(o.date), MONTH(o.date)
) fin ON pta.pid = fin.pid
GROUP BY p.pid, EXTRACT(YEAR FROM o.date), EXTRACT(MONTH FROM o.date), p.cost_price, p.stock_quantity
) fin
WHERE pta.pid = fin.pid
AND pta.year = fin.year
AND pta.month = fin.month
SET
pta.inventory_value = COALESCE(fin.inventory_value, 0)
`);
processedCount = Math.floor(totalProducts * 0.65);
@@ -280,7 +281,8 @@ async function calculateTimeAggregates(startTime, totalProducts, processedCount
await connection.query(`
INSERT INTO calculate_status (module_name, last_calculation_timestamp)
VALUES ('time_aggregates', NOW())
ON DUPLICATE KEY UPDATE last_calculation_timestamp = NOW()
ON CONFLICT (module_name) DO UPDATE
SET last_calculation_timestamp = NOW()
`);
return {

38
inventory-server/scripts/metrics/utils/db.js
View File

@@ -1,4 +1,4 @@
const mysql = require('mysql2/promise');
const { Pool } = require('pg');
const path = require('path');
require('dotenv').config({ path: path.resolve(__dirname, '../../..', '.env') });
@@ -8,36 +8,24 @@ const dbConfig = {
user: process.env.DB_USER,
password: process.env.DB_PASSWORD,
database: process.env.DB_NAME,
waitForConnections: true,
connectionLimit: 10,
queueLimit: 0,
port: process.env.DB_PORT || 5432,
ssl: process.env.DB_SSL === 'true',
// Add performance optimizations
namedPlaceholders: true,
maxPreparedStatements: 256,
enableKeepAlive: true,
keepAliveInitialDelay: 0,
// Add memory optimizations
flags: [
'FOUND_ROWS',
'LONG_PASSWORD',
'PROTOCOL_41',
'TRANSACTIONS',
'SECURE_CONNECTION',
'MULTI_RESULTS',
'PS_MULTI_RESULTS',
'PLUGIN_AUTH',
'CONNECT_ATTRS',
'PLUGIN_AUTH_LENENC_CLIENT_DATA',
'SESSION_TRACK',
'MULTI_STATEMENTS'
]
max: 10, // connection pool max size
idleTimeoutMillis: 30000,
connectionTimeoutMillis: 60000
};
// Create a single pool instance to be reused
const pool = mysql.createPool(dbConfig);
const pool = new Pool(dbConfig);
// Add event handlers for pool
pool.on('error', (err, client) => {
console.error('Unexpected error on idle client', err);
});
async function getConnection() {
return await pool.getConnection();
return await pool.connect();
}
async function closePool() {

86
inventory-server/scripts/metrics/vendor-metrics.js
View File

@@ -33,7 +33,7 @@ async function calculateVendorMetrics(startTime, totalProducts, processedCount =
}
// Get counts of records that will be processed
const [[orderCount], [poCount]] = await Promise.all([
const [orderCountResult, poCountResult] = await Promise.all([
connection.query(`
SELECT COUNT(*) as count
FROM orders o
@@ -45,8 +45,8 @@ async function calculateVendorMetrics(startTime, totalProducts, processedCount =
WHERE po.status != 0
`)
]);
processedOrders = orderCount.count;
processedPurchaseOrders = poCount.count;
processedOrders = parseInt(orderCountResult.rows[0].count);
processedPurchaseOrders = parseInt(poCountResult.rows[0].count);
outputProgress({
status: 'running',
@@ -66,7 +66,7 @@ async function calculateVendorMetrics(startTime, totalProducts, processedCount =
// First ensure all vendors exist in vendor_details
await connection.query(`
INSERT IGNORE INTO vendor_details (vendor, status, created_at, updated_at)
INSERT INTO vendor_details (vendor, status, created_at, updated_at)
SELECT DISTINCT
vendor,
'active' as status,
@@ -74,6 +74,7 @@ async function calculateVendorMetrics(startTime, totalProducts, processedCount =
NOW() as updated_at
FROM products
WHERE vendor IS NOT NULL
ON CONFLICT (vendor) DO NOTHING
`);
processedCount = Math.floor(totalProducts * 0.8);
@@ -128,7 +129,7 @@ async function calculateVendorMetrics(startTime, totalProducts, processedCount =
FROM products p
JOIN orders o ON p.pid = o.pid
WHERE o.canceled = false
AND o.date >= DATE_SUB(CURRENT_DATE, INTERVAL 12 MONTH)
AND o.date >= CURRENT_DATE - INTERVAL '12 months'
GROUP BY p.vendor
),
vendor_po AS (
@@ -138,12 +139,15 @@ async function calculateVendorMetrics(startTime, totalProducts, processedCount =
COUNT(DISTINCT po.id) as total_orders,
AVG(CASE
WHEN po.receiving_status = 40
THEN DATEDIFF(po.received_date, po.date)
AND po.received_date IS NOT NULL
AND po.date IS NOT NULL
THEN EXTRACT(EPOCH FROM (po.received_date - po.date)) / 86400.0
ELSE NULL
END) as avg_lead_time_days,
SUM(po.ordered * po.po_cost_price) as total_purchase_value
FROM products p
JOIN purchase_orders po ON p.pid = po.pid
WHERE po.date >= DATE_SUB(CURRENT_DATE, INTERVAL 12 MONTH)
WHERE po.date >= CURRENT_DATE - INTERVAL '12 months'
GROUP BY p.vendor
),
vendor_products AS (
@@ -188,20 +192,21 @@ async function calculateVendorMetrics(startTime, totalProducts, processedCount =
LEFT JOIN vendor_po vp ON vs.vendor = vp.vendor
LEFT JOIN vendor_products vpr ON vs.vendor = vpr.vendor
WHERE vs.vendor IS NOT NULL
ON DUPLICATE KEY UPDATE
total_revenue = VALUES(total_revenue),
total_orders = VALUES(total_orders),
total_late_orders = VALUES(total_late_orders),
avg_lead_time_days = VALUES(avg_lead_time_days),
on_time_delivery_rate = VALUES(on_time_delivery_rate),
order_fill_rate = VALUES(order_fill_rate),
avg_order_value = VALUES(avg_order_value),
active_products = VALUES(active_products),
total_products = VALUES(total_products),
total_purchase_value = VALUES(total_purchase_value),
avg_margin_percent = VALUES(avg_margin_percent),
status = VALUES(status),
last_calculated_at = VALUES(last_calculated_at)
ON CONFLICT (vendor) DO UPDATE
SET
total_revenue = EXCLUDED.total_revenue,
total_orders = EXCLUDED.total_orders,
total_late_orders = EXCLUDED.total_late_orders,
avg_lead_time_days = EXCLUDED.avg_lead_time_days,
on_time_delivery_rate = EXCLUDED.on_time_delivery_rate,
order_fill_rate = EXCLUDED.order_fill_rate,
avg_order_value = EXCLUDED.avg_order_value,
active_products = EXCLUDED.active_products,
total_products = EXCLUDED.total_products,
total_purchase_value = EXCLUDED.total_purchase_value,
avg_margin_percent = EXCLUDED.avg_margin_percent,
status = EXCLUDED.status,
last_calculated_at = EXCLUDED.last_calculated_at
`);
processedCount = Math.floor(totalProducts * 0.9);
@@ -244,23 +249,23 @@ async function calculateVendorMetrics(startTime, totalProducts, processedCount =
WITH monthly_orders AS (
SELECT
p.vendor,
YEAR(o.date) as year,
MONTH(o.date) as month,
EXTRACT(YEAR FROM o.date) as year,
EXTRACT(MONTH FROM o.date) as month,
COUNT(DISTINCT o.id) as total_orders,
SUM(o.quantity * o.price) as total_revenue,
SUM(o.quantity * (o.price - p.cost_price)) as total_margin
FROM products p
JOIN orders o ON p.pid = o.pid
WHERE o.canceled = false
AND o.date >= DATE_SUB(CURRENT_DATE, INTERVAL 12 MONTH)
AND o.date >= CURRENT_DATE - INTERVAL '12 months'
AND p.vendor IS NOT NULL
GROUP BY p.vendor, YEAR(o.date), MONTH(o.date)
GROUP BY p.vendor, EXTRACT(YEAR FROM o.date), EXTRACT(MONTH FROM o.date)
),
monthly_po AS (
SELECT
p.vendor,
YEAR(po.date) as year,
MONTH(po.date) as month,
EXTRACT(YEAR FROM po.date) as year,
EXTRACT(MONTH FROM po.date) as month,
COUNT(DISTINCT po.id) as total_po,
COUNT(DISTINCT CASE
WHEN po.receiving_status = 40 AND po.received_date > po.expected_date
@@ -268,14 +273,17 @@ async function calculateVendorMetrics(startTime, totalProducts, processedCount =
END) as late_orders,
AVG(CASE
WHEN po.receiving_status = 40
THEN DATEDIFF(po.received_date, po.date)
AND po.received_date IS NOT NULL
AND po.date IS NOT NULL
THEN EXTRACT(EPOCH FROM (po.received_date - po.date)) / 86400.0
ELSE NULL
END) as avg_lead_time_days,
SUM(po.ordered * po.po_cost_price) as total_purchase_value
FROM products p
JOIN purchase_orders po ON p.pid = po.pid
WHERE po.date >= DATE_SUB(CURRENT_DATE, INTERVAL 12 MONTH)
WHERE po.date >= CURRENT_DATE - INTERVAL '12 months'
AND p.vendor IS NOT NULL
GROUP BY p.vendor, YEAR(po.date), MONTH(po.date)
GROUP BY p.vendor, EXTRACT(YEAR FROM po.date), EXTRACT(MONTH FROM po.date)
)
SELECT
mo.vendor,
@@ -311,13 +319,14 @@ async function calculateVendorMetrics(startTime, totalProducts, processedCount =
AND mp.year = mo.year
AND mp.month = mo.month
WHERE mo.vendor IS NULL
ON DUPLICATE KEY UPDATE
total_orders = VALUES(total_orders),
late_orders = VALUES(late_orders),
avg_lead_time_days = VALUES(avg_lead_time_days),
total_purchase_value = VALUES(total_purchase_value),
total_revenue = VALUES(total_revenue),
avg_margin_percent = VALUES(avg_margin_percent)
ON CONFLICT (vendor, year, month) DO UPDATE
SET
total_orders = EXCLUDED.total_orders,
late_orders = EXCLUDED.late_orders,
avg_lead_time_days = EXCLUDED.avg_lead_time_days,
total_purchase_value = EXCLUDED.total_purchase_value,
total_revenue = EXCLUDED.total_revenue,
avg_margin_percent = EXCLUDED.avg_margin_percent
`);
processedCount = Math.floor(totalProducts * 0.95);
@@ -344,7 +353,8 @@ async function calculateVendorMetrics(startTime, totalProducts, processedCount =
await connection.query(`
INSERT INTO calculate_status (module_name, last_calculation_timestamp)
VALUES ('vendor_metrics', NOW())
ON DUPLICATE KEY UPDATE last_calculation_timestamp = NOW()
ON CONFLICT (module_name) DO UPDATE
SET last_calculation_timestamp = NOW()
`);
return {