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Add new filter options and metrics to product filters and pages; enhance SQL schema for financial calculations

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This commit is contained in:
Matt
2025-03-27 16:27:13 -04:00
parent 8b8845b423
commit 957c7b5eb1
17 changed files with 2216 additions and 482 deletions
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143
inventory-server/scripts/metrics/financial-metrics.js
View File

@@ -56,36 +56,94 @@ async function calculateFinancialMetrics(startTime, totalProducts, processedCoun
}
});
// Calculate financial metrics with optimized query
// First, calculate beginning inventory values (12 months ago)
await connection.query(`
CREATE TEMPORARY TABLE IF NOT EXISTS temp_beginning_inventory AS
WITH beginning_inventory_calc AS (
SELECT
p.pid,
p.stock_quantity as current_quantity,
COALESCE(SUM(o.quantity), 0) as sold_quantity,
COALESCE(SUM(po.received), 0) as received_quantity,
GREATEST(0, (p.stock_quantity + COALESCE(SUM(o.quantity), 0) - COALESCE(SUM(po.received), 0))) as beginning_quantity,
p.cost_price
FROM
products p
LEFT JOIN
orders o ON p.pid = o.pid
AND o.canceled = false
AND o.date >= CURRENT_DATE - INTERVAL '12 months'::interval
LEFT JOIN
purchase_orders po ON p.pid = po.pid
AND po.received_date IS NOT NULL
AND po.received_date >= CURRENT_DATE - INTERVAL '12 months'::interval
GROUP BY
p.pid, p.stock_quantity, p.cost_price
)
SELECT
pid,
beginning_quantity,
beginning_quantity * cost_price as beginning_value,
current_quantity * cost_price as current_value,
((beginning_quantity * cost_price) + (current_quantity * cost_price)) / 2 as average_inventory_value
FROM
beginning_inventory_calc
`);
processedCount = Math.floor(totalProducts * 0.60);
outputProgress({
status: 'running',
operation: 'Beginning inventory values calculated, computing financial metrics',
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)
}
});
// Calculate financial metrics with optimized query and standard formulas
await connection.query(`
WITH product_financials AS (
SELECT
p.pid,
p.cost_price * p.stock_quantity as inventory_value,
SUM(o.quantity * o.price) as total_revenue,
SUM(o.quantity * p.cost_price) as cost_of_goods_sold,
SUM(o.quantity * (o.price - p.cost_price)) as gross_profit,
COALESCE(bi.average_inventory_value, p.cost_price * p.stock_quantity) as avg_inventory_value,
p.cost_price * p.stock_quantity as current_inventory_value,
SUM(o.quantity * (o.price - COALESCE(o.discount, 0))) as total_revenue,
SUM(o.quantity * COALESCE(o.costeach, 0)) as cost_of_goods_sold,
SUM(o.quantity * (o.price - COALESCE(o.discount, 0) - COALESCE(o.costeach, 0))) as gross_profit,
MIN(o.date) as first_sale_date,
MAX(o.date) as last_sale_date,
EXTRACT(DAY FROM (MAX(o.date)::timestamp with time zone - MIN(o.date)::timestamp with time zone)) + 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
LEFT JOIN temp_beginning_inventory bi ON p.pid = bi.pid
WHERE o.canceled = false
AND DATE(o.date) >= CURRENT_DATE - INTERVAL '12 months'
GROUP BY p.pid, p.cost_price, p.stock_quantity
AND DATE(o.date) >= CURRENT_DATE - INTERVAL '12 months'::interval
GROUP BY p.pid, p.cost_price, p.stock_quantity, bi.average_inventory_value
)
UPDATE product_metrics pm
SET
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)
inventory_value = COALESCE(pf.current_inventory_value, 0)::decimal(10,3),
total_revenue = COALESCE(pf.total_revenue, 0)::decimal(10,3),
cost_of_goods_sold = COALESCE(pf.cost_of_goods_sold, 0)::decimal(10,3),
gross_profit = COALESCE(pf.gross_profit, 0)::decimal(10,3),
turnover_rate = CASE
WHEN COALESCE(pf.avg_inventory_value, 0) > 0 THEN
COALESCE(pf.cost_of_goods_sold, 0) / NULLIF(pf.avg_inventory_value, 0)
ELSE 0
END,
END::decimal(12,3),
gmroi = CASE
WHEN COALESCE(pf.avg_inventory_value, 0) > 0 THEN
COALESCE(pf.gross_profit, 0) / NULLIF(pf.avg_inventory_value, 0)
ELSE 0
END::decimal(10,3),
last_calculated_at = CURRENT_TIMESTAMP
FROM product_financials pf
WHERE pm.pid = pf.pid
@@ -115,53 +173,8 @@ async function calculateFinancialMetrics(startTime, totalProducts, processedCoun
success
};
// Update time-based aggregates with optimized query
await connection.query(`
WITH monthly_financials AS (
SELECT
p.pid,
EXTRACT(YEAR FROM o.date::timestamp with time zone) as year,
EXTRACT(MONTH FROM o.date::timestamp with time zone) 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,
MIN(o.date) as period_start,
MAX(o.date) as period_end
FROM products p
LEFT JOIN orders o ON p.pid = o.pid
WHERE o.canceled = false
GROUP BY p.pid, EXTRACT(YEAR FROM o.date::timestamp with time zone), EXTRACT(MONTH FROM o.date::timestamp with time zone), p.cost_price, p.stock_quantity
)
UPDATE product_time_aggregates pta
SET
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);
outputProgress({
status: 'running',
operation: 'Time-based aggregates updated',
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)
}
});
// Clean up temporary tables
await connection.query('DROP TABLE IF EXISTS temp_beginning_inventory');
// If we get here, everything completed successfully
success = true;
@@ -187,6 +200,12 @@ async function calculateFinancialMetrics(startTime, totalProducts, processedCoun
throw error;
} finally {
if (connection) {
try {
// Make sure temporary tables are always cleaned up
await connection.query('DROP TABLE IF EXISTS temp_beginning_inventory');
} catch (err) {
console.error('Error cleaning up temp tables:', err);
}
connection.release();
}
}

286
inventory-server/scripts/metrics/product-metrics.js
View File

@@ -66,8 +66,36 @@ async function calculateProductMetrics(startTime, totalProducts, processedCount
WHERE category_id IS NULL AND vendor IS NULL
LIMIT 1
`);
// Check if threshold data was returned
if (!thresholds.rows || thresholds.rows.length === 0) {
console.warn('No default thresholds found in the database. Using explicit type casting in the query.');
}
const defaultThresholds = thresholds.rows[0];
// Get financial calculation configuration parameters
const financialConfig = await connection.query(`
SELECT
order_cost,
holding_rate,
service_level_z_score,
min_reorder_qty,
default_reorder_qty,
default_safety_stock
FROM financial_calc_config
WHERE id = 1
LIMIT 1
`);
const finConfig = financialConfig.rows[0] || {
order_cost: 25.00,
holding_rate: 0.25,
service_level_z_score: 1.96,
min_reorder_qty: 1,
default_reorder_qty: 5,
default_safety_stock: 5
};
// Calculate base product metrics
if (!SKIP_PRODUCT_BASE_METRICS) {
outputProgress({
@@ -109,6 +137,7 @@ async function calculateProductMetrics(startTime, totalProducts, processedCount
avg_margin_percent DECIMAL(10,3),
first_sale_date DATE,
last_sale_date DATE,
stddev_daily_sales DECIMAL(10,3),
PRIMARY KEY (pid)
)
`);
@@ -117,10 +146,11 @@ async function calculateProductMetrics(startTime, totalProducts, processedCount
await connection.query(`
CREATE TEMPORARY TABLE temp_purchase_metrics (
pid BIGINT NOT NULL,
avg_lead_time_days DOUBLE PRECISION,
avg_lead_time_days DECIMAL(10,2),
last_purchase_date DATE,
first_received_date DATE,
last_received_date DATE,
stddev_lead_time_days DECIMAL(10,2),
PRIMARY KEY (pid)
)
`);
@@ -140,11 +170,22 @@ async function calculateProductMetrics(startTime, totalProducts, processedCount
ELSE 0
END as avg_margin_percent,
MIN(o.date) as first_sale_date,
MAX(o.date) as last_sale_date
MAX(o.date) as last_sale_date,
COALESCE(STDDEV_SAMP(daily_qty.quantity), 0) as stddev_daily_sales
FROM products p
LEFT JOIN orders o ON p.pid = o.pid
AND o.canceled = false
AND o.date >= CURRENT_DATE - INTERVAL '90 days'
LEFT JOIN (
SELECT
pid,
DATE(date) as sale_date,
SUM(quantity) as quantity
FROM orders
WHERE canceled = false
AND date >= CURRENT_DATE - INTERVAL '90 days'
GROUP BY pid, DATE(date)
) daily_qty ON p.pid = daily_qty.pid
GROUP BY p.pid
`);
@@ -163,7 +204,14 @@ async function calculateProductMetrics(startTime, totalProducts, processedCount
) 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
MAX(po.received_date) as last_received_date,
STDDEV_SAMP(
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 stddev_lead_time_days
FROM products p
LEFT JOIN purchase_orders po ON p.pid = po.pid
AND po.received_date IS NOT NULL
@@ -184,7 +232,8 @@ async function calculateProductMetrics(startTime, totalProducts, processedCount
30.0 as avg_lead_time_days,
NULL as last_purchase_date,
NULL as first_received_date,
NULL as last_received_date
NULL as last_received_date,
0.0 as stddev_lead_time_days
FROM products p
LEFT JOIN temp_purchase_metrics tpm ON p.pid = tpm.pid
WHERE tpm.pid IS NULL
@@ -208,6 +257,17 @@ async function calculateProductMetrics(startTime, totalProducts, processedCount
if (batch.rows.length === 0) break;
// Process the entire batch in a single efficient query
const lowStockThreshold = parseInt(defaultThresholds?.low_stock_threshold) || 5;
const criticalDays = parseInt(defaultThresholds?.critical_days) || 7;
const reorderDays = parseInt(defaultThresholds?.reorder_days) || 14;
const overstockDays = parseInt(defaultThresholds?.overstock_days) || 90;
const serviceLevel = parseFloat(finConfig?.service_level_z_score) || 1.96;
const defaultSafetyStock = parseInt(finConfig?.default_safety_stock) || 5;
const defaultReorderQty = parseInt(finConfig?.default_reorder_qty) || 5;
const orderCost = parseFloat(finConfig?.order_cost) || 25.00;
const holdingRate = parseFloat(finConfig?.holding_rate) || 0.25;
const minReorderQty = parseInt(finConfig?.min_reorder_qty) || 1;
await connection.query(`
UPDATE product_metrics pm
SET
@@ -219,7 +279,7 @@ async function calculateProductMetrics(startTime, totalProducts, processedCount
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),
avg_lead_time_days = COALESCE(lm.avg_lead_time_days, 30.0),
days_of_inventory = CASE
WHEN COALESCE(sm.daily_sales_avg, 0) > 0
THEN FLOOR(p.stock_quantity / NULLIF(sm.daily_sales_avg, 0))
@@ -232,57 +292,61 @@ async function calculateProductMetrics(startTime, totalProducts, processedCount
END,
stock_status = CASE
WHEN p.stock_quantity <= 0 THEN 'Out of 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 AND p.stock_quantity <= ${lowStockThreshold} THEN 'Low Stock'
WHEN COALESCE(sm.daily_sales_avg, 0) = 0 THEN 'In Stock'
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'
WHEN p.stock_quantity / NULLIF(sm.daily_sales_avg, 0) <= ${criticalDays} THEN 'Critical'
WHEN p.stock_quantity / NULLIF(sm.daily_sales_avg, 0) <= ${reorderDays} THEN 'Reorder'
WHEN p.stock_quantity / NULLIF(sm.daily_sales_avg, 0) > ${overstockDays} THEN 'Overstocked'
ELSE 'Healthy'
END,
safety_stock = CASE
WHEN COALESCE(sm.daily_sales_avg, 0) > 0 THEN
CEIL(sm.daily_sales_avg * SQRT(ABS(COALESCE(lm.avg_lead_time_days, 30))) * 1.96)
ELSE $5
WHEN COALESCE(sm.daily_sales_avg, 0) > 0 AND COALESCE(lm.avg_lead_time_days, 0) > 0 THEN
CEIL(
${serviceLevel} * SQRT(
GREATEST(0, COALESCE(lm.avg_lead_time_days, 0)) * POWER(COALESCE(sm.stddev_daily_sales, 0), 2) +
POWER(COALESCE(sm.daily_sales_avg, 0), 2) * POWER(COALESCE(lm.stddev_lead_time_days, 0), 2)
)
)
ELSE ${defaultSafetyStock}
END,
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(ABS(COALESCE(lm.avg_lead_time_days, 30))) * 1.96)
ELSE $6
CEIL(sm.daily_sales_avg * GREATEST(0, COALESCE(lm.avg_lead_time_days, 30.0))) +
(CASE
WHEN COALESCE(sm.daily_sales_avg, 0) > 0 AND COALESCE(lm.avg_lead_time_days, 0) > 0 THEN
CEIL(
${serviceLevel} * SQRT(
GREATEST(0, COALESCE(lm.avg_lead_time_days, 0)) * POWER(COALESCE(sm.stddev_daily_sales, 0), 2) +
POWER(COALESCE(sm.daily_sales_avg, 0), 2) * POWER(COALESCE(lm.stddev_lead_time_days, 0), 2)
)
)
ELSE ${defaultSafetyStock}
END)
ELSE ${lowStockThreshold}
END,
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(ABS((2 * (sm.daily_sales_avg * 365) * 25) / (NULLIF(p.cost_price, 0) * 0.25)))),
$7
CEIL(SQRT(
(2 * (sm.daily_sales_avg * 365) * ${orderCost}) /
NULLIF(p.cost_price * ${holdingRate}, 0)
)),
${minReorderQty}
)
ELSE $8
ELSE ${defaultReorderQty}
END,
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))
WHEN p.stock_quantity / NULLIF(sm.daily_sales_avg, 0) > ${overstockDays}
THEN GREATEST(0, p.stock_quantity - CEIL(sm.daily_sales_avg * ${overstockDays}))
ELSE 0
END,
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[])
WHERE p.pid = ANY($1::BIGINT[])
AND pm.pid = p.pid
`,
[
defaultThresholds.low_stock_threshold,
defaultThresholds.critical_days,
defaultThresholds.reorder_days,
defaultThresholds.overstock_days,
defaultThresholds.low_stock_threshold,
defaultThresholds.low_stock_threshold,
defaultThresholds.low_stock_threshold,
defaultThresholds.low_stock_threshold,
defaultThresholds.overstock_days,
defaultThresholds.overstock_days,
batch.rows.map(row => row.pid)
]);
`, [batch.rows.map(row => row.pid)]);
lastPid = batch.rows[batch.rows.length - 1].pid;
processedCount += batch.rows.length;
@@ -311,25 +375,22 @@ async function calculateProductMetrics(startTime, totalProducts, processedCount
}
// Calculate forecast accuracy and bias in batches
lastPid = 0;
let forecastPid = 0;
while (true) {
if (isCancelled) break;
const batch = await connection.query(
const forecastBatch = await connection.query(
'SELECT pid FROM products WHERE pid > $1 ORDER BY pid LIMIT $2',
[lastPid, BATCH_SIZE]
[forecastPid, BATCH_SIZE]
);
if (batch.rows.length === 0) break;
if (forecastBatch.rows.length === 0) break;
const forecastPidArray = forecastBatch.rows.map(row => row.pid);
// Use array_to_string to convert the array to a string of comma-separated values
await connection.query(`
UPDATE product_metrics pm
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 (
WITH forecast_metrics AS (
SELECT
sf.pid,
AVG(CASE
@@ -348,13 +409,20 @@ async function calculateProductMetrics(startTime, totalProducts, processedCount
AND DATE(o.date) = sf.forecast_date
WHERE o.canceled = false
AND sf.forecast_date >= CURRENT_DATE - INTERVAL '90 days'
AND sf.pid = ANY($1::bigint[])
AND sf.pid = ANY('{${forecastPidArray.join(',')}}'::BIGINT[])
GROUP BY sf.pid
) fa
WHERE pm.pid = fa.pid
`, [batch.rows.map(row => row.pid)]);
)
UPDATE product_metrics pm
SET
forecast_accuracy = GREATEST(0, 100 - LEAST(fm.avg_forecast_error, 100)),
forecast_bias = GREATEST(-100, LEAST(fm.avg_forecast_bias, 100)),
last_forecast_date = fm.last_forecast_date,
last_calculated_at = NOW()
FROM forecast_metrics fm
WHERE pm.pid = fm.pid
`);
lastPid = batch.rows[batch.rows.length - 1].pid;
forecastPid = forecastBatch.rows[forecastBatch.rows.length - 1].pid;
}
// Calculate product time aggregates
@@ -375,61 +443,12 @@ async function calculateProductMetrics(startTime, totalProducts, processedCount
}
});
// Calculate time-based aggregates
await connection.query(`
INSERT INTO product_time_aggregates (
pid,
year,
month,
total_quantity_sold,
total_revenue,
total_cost,
order_count,
avg_price,
profit_margin,
inventory_value,
gmroi
)
SELECT
p.pid,
EXTRACT(YEAR FROM o.date::timestamp with time zone) as year,
EXTRACT(MONTH FROM o.date::timestamp with time zone) as month,
SUM(o.quantity) as total_quantity_sold,
SUM(o.price * o.quantity) as total_revenue,
SUM(p.cost_price * o.quantity) as total_cost,
COUNT(DISTINCT o.order_number) as order_count,
AVG(o.price) as avg_price,
CASE
WHEN SUM(o.quantity * o.price) > 0
THEN ((SUM(o.quantity * o.price) - SUM(o.quantity * p.cost_price)) / SUM(o.quantity * o.price)) * 100
ELSE 0
END as profit_margin,
p.cost_price * p.stock_quantity as inventory_value,
CASE
WHEN p.cost_price * p.stock_quantity > 0
THEN (SUM(o.quantity * (o.price - p.cost_price))) / (p.cost_price * p.stock_quantity)
ELSE 0
END as gmroi
FROM products p
LEFT JOIN orders o ON p.pid = o.pid AND o.canceled = false
WHERE o.date >= CURRENT_DATE - INTERVAL '12 months'
GROUP BY p.pid, EXTRACT(YEAR FROM o.date::timestamp with time zone), EXTRACT(MONTH FROM o.date::timestamp with time zone)
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
`);
// Note: The time-aggregates calculation has been moved to time-aggregates.js
// This module will not duplicate that functionality
processedCount = Math.floor(totalProducts * 0.6);
outputProgress({
status: 'running',
operation: 'Product time aggregates calculated',
operation: 'Product time aggregates calculation delegated to time-aggregates module',
current: processedCount || 0,
total: totalProducts || 0,
elapsed: formatElapsedTime(startTime),
@@ -487,6 +506,10 @@ async function calculateProductMetrics(startTime, totalProducts, processedCount
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 };
// Extract values and ensure they are valid numbers
const aThreshold = parseFloat(abcThresholds.a_threshold) || 20;
const bThreshold = parseFloat(abcThresholds.b_threshold) || 50;
// First, create and populate the rankings table with an index
await connection.query('DROP TABLE IF EXISTS temp_revenue_ranks');
@@ -557,13 +580,13 @@ async function calculateProductMetrics(startTime, totalProducts, processedCount
OR pm.abc_class !=
CASE
WHEN tr.pid IS NULL THEN 'C'
WHEN tr.percentile <= $2 THEN 'A'
WHEN tr.percentile <= $3 THEN 'B'
WHEN tr.percentile <= ${aThreshold} THEN 'A'
WHEN tr.percentile <= ${bThreshold} THEN 'B'
ELSE 'C'
END)
ORDER BY pm.pid
LIMIT $4
`, [abcProcessedCount, abcThresholds.a_threshold, abcThresholds.b_threshold, batchSize]);
LIMIT $2
`, [abcProcessedCount, batchSize]);
if (pids.rows.length === 0) break;
@@ -574,15 +597,15 @@ async function calculateProductMetrics(startTime, totalProducts, processedCount
SET abc_class =
CASE
WHEN tr.pid IS NULL THEN 'C'
WHEN tr.percentile <= $1 THEN 'A'
WHEN tr.percentile <= $2 THEN 'B'
WHEN tr.percentile <= ${aThreshold} THEN 'A'
WHEN tr.percentile <= ${bThreshold} THEN 'B'
ELSE 'C'
END,
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]);
WHERE pm.pid = tr.pid AND pm.pid = ANY($1::BIGINT[])
OR (pm.pid = ANY($1::BIGINT[]) AND tr.pid IS NULL)
`, [pidValues]);
// Now update turnover rate with proper handling of zero inventory periods
await connection.query(`
@@ -610,7 +633,7 @@ async function calculateProductMetrics(startTime, totalProducts, processedCount
JOIN products p ON o.pid = p.pid
WHERE o.canceled = false
AND o.date >= CURRENT_DATE - INTERVAL '90 days'
AND o.pid = ANY($1::bigint[])
AND o.pid = ANY($1::BIGINT[])
GROUP BY o.pid
) sales
WHERE pm.pid = sales.pid
@@ -707,40 +730,7 @@ function calculateStockStatus(stock, config, daily_sales_avg, weekly_sales_avg,
return 'Healthy';
}
function calculateReorderQuantities(stock, stock_status, daily_sales_avg, avg_lead_time, config) {
// Calculate safety stock based on service level and lead time
const z_score = 1.96; // 95% service level
const lead_time = avg_lead_time || config.target_days;
const safety_stock = Math.ceil(daily_sales_avg * Math.sqrt(lead_time) * z_score);
// Calculate reorder point
const lead_time_demand = daily_sales_avg * lead_time;
const reorder_point = Math.ceil(lead_time_demand + safety_stock);
// Calculate reorder quantity using EOQ formula if we have the necessary data
let reorder_qty = 0;
if (daily_sales_avg > 0) {
const annual_demand = daily_sales_avg * 365;
const order_cost = 25; // Fixed cost per order
const holding_cost = config.cost_price * 0.25; // 25% of unit cost as annual holding cost
reorder_qty = Math.ceil(Math.sqrt((2 * annual_demand * order_cost) / holding_cost));
} else {
// If no sales data, use a basic calculation
reorder_qty = Math.max(safety_stock, config.low_stock_threshold);
}
// Calculate overstocked amount
const overstocked_amt = stock_status === 'Overstocked' ?
stock - Math.ceil(daily_sales_avg * config.overstock_days) :
0;
return {
safety_stock,
reorder_point,
reorder_qty,
overstocked_amt
};
}
// Note: calculateReorderQuantities function has been removed as its logic has been incorporated
// in the main SQL query with configurable parameters
module.exports = calculateProductMetrics;

27
inventory-server/scripts/metrics/sales-forecasts.js
View File

@@ -216,13 +216,7 @@ async function calculateSalesForecasts(startTime, totalProducts, processedCount
GREATEST(0,
ROUND(
ds.avg_daily_qty *
(1 + COALESCE(sf.seasonality_factor, 0)) *
CASE
WHEN ds.std_daily_qty / NULLIF(ds.avg_daily_qty, 0) > 1.5 THEN 0.85
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
(1 + COALESCE(sf.seasonality_factor, 0))
)
) as forecast_quantity,
CASE
@@ -336,8 +330,8 @@ async function calculateSalesForecasts(startTime, totalProducts, processedCount
cs.cat_id::bigint as category_id,
fd.forecast_date,
GREATEST(0,
AVG(cs.daily_quantity) *
(1 + COALESCE(sf.seasonality_factor, 0))
ROUND(AVG(cs.daily_quantity) *
(1 + COALESCE(sf.seasonality_factor, 0)))
) as forecast_units,
GREATEST(0,
COALESCE(
@@ -345,8 +339,7 @@ async function calculateSalesForecasts(startTime, totalProducts, processedCount
WHEN SUM(cs.day_count) >= 4 THEN AVG(cs.daily_revenue)
ELSE ct.overall_avg_revenue
END *
(1 + COALESCE(sf.seasonality_factor, 0)) *
(0.95 + (random() * 0.1)),
(1 + COALESCE(sf.seasonality_factor, 0)),
0
)
) as forecast_revenue,
@@ -427,6 +420,18 @@ async function calculateSalesForecasts(startTime, totalProducts, processedCount
throw error;
} finally {
if (connection) {
try {
// Ensure temporary tables are cleaned up
await connection.query(`
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;
`);
} catch (err) {
console.error('Error cleaning up temporary tables:', err);
}
connection.release();
}
}

266
inventory-server/scripts/metrics/time-aggregates.js
View File

@@ -55,6 +55,93 @@ async function calculateTimeAggregates(startTime, totalProducts, processedCount
}
});
// Create a temporary table for end-of-month inventory values
await connection.query(`
CREATE TEMPORARY TABLE IF NOT EXISTS temp_monthly_inventory AS
WITH months AS (
-- Generate all year/month combinations for the last 12 months
SELECT
EXTRACT(YEAR FROM month_date)::INTEGER as year,
EXTRACT(MONTH FROM month_date)::INTEGER as month,
month_date as start_date,
(month_date + INTERVAL '1 month'::interval - INTERVAL '1 day'::interval)::DATE as end_date
FROM (
SELECT generate_series(
DATE_TRUNC('month', CURRENT_DATE - INTERVAL '12 months'::interval)::DATE,
DATE_TRUNC('month', CURRENT_DATE)::DATE,
INTERVAL '1 month'::interval
) as month_date
) dates
),
monthly_inventory_calc AS (
SELECT
p.pid,
m.year,
m.month,
m.end_date,
p.stock_quantity as current_quantity,
-- Calculate sold during period (before end_date)
COALESCE(SUM(
CASE
WHEN o.date <= m.end_date THEN o.quantity
ELSE 0
END
), 0) as sold_after_end_date,
-- Calculate received during period (before end_date)
COALESCE(SUM(
CASE
WHEN po.received_date <= m.end_date THEN po.received
ELSE 0
END
), 0) as received_after_end_date,
p.cost_price
FROM
products p
CROSS JOIN
months m
LEFT JOIN
orders o ON p.pid = o.pid
AND o.canceled = false
AND o.date > m.end_date
AND o.date <= CURRENT_DATE
LEFT JOIN
purchase_orders po ON p.pid = po.pid
AND po.received_date IS NOT NULL
AND po.received_date > m.end_date
AND po.received_date <= CURRENT_DATE
GROUP BY
p.pid, m.year, m.month, m.end_date, p.stock_quantity, p.cost_price
)
SELECT
pid,
year,
month,
-- End of month quantity = current quantity - sold after + received after
GREATEST(0, current_quantity - sold_after_end_date + received_after_end_date) as end_of_month_quantity,
-- End of month inventory value
GREATEST(0, current_quantity - sold_after_end_date + received_after_end_date) * cost_price as end_of_month_value,
cost_price
FROM
monthly_inventory_calc
`);
processedCount = Math.floor(totalProducts * 0.40);
outputProgress({
status: 'running',
operation: 'Monthly inventory values calculated, processing time aggregates',
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)
}
});
// Initial insert of time-based aggregates
await connection.query(`
INSERT INTO product_time_aggregates (
@@ -75,76 +162,67 @@ async function calculateTimeAggregates(startTime, totalProducts, processedCount
WITH monthly_sales AS (
SELECT
o.pid,
EXTRACT(YEAR FROM o.date::timestamp with time zone) as year,
EXTRACT(MONTH FROM o.date::timestamp with time zone) as month,
EXTRACT(YEAR FROM o.date::timestamp with time zone)::INTEGER as year,
EXTRACT(MONTH FROM o.date::timestamp with time zone)::INTEGER 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,
SUM(COALESCE(o.costeach, 0) * o.quantity) as total_cost,
COUNT(DISTINCT o.order_number) as order_count,
AVG(o.price - COALESCE(o.discount, 0)) as avg_price,
CASE
WHEN SUM((o.price - COALESCE(o.discount, 0)) * o.quantity) > 0
THEN ((SUM((o.price - COALESCE(o.discount, 0)) * o.quantity) - SUM(COALESCE(p.cost_price, 0) * o.quantity))
THEN ((SUM((o.price - COALESCE(o.discount, 0)) * o.quantity) - SUM(COALESCE(o.costeach, 0) * o.quantity))
/ SUM((o.price - COALESCE(o.discount, 0)) * o.quantity)) * 100
ELSE 0
END as profit_margin,
p.cost_price * p.stock_quantity as inventory_value,
COUNT(DISTINCT DATE(o.date)) as active_days
FROM orders o
JOIN products p ON o.pid = p.pid
WHERE o.canceled = false
GROUP BY o.pid, EXTRACT(YEAR FROM o.date::timestamp with time zone), EXTRACT(MONTH FROM o.date::timestamp with time zone), p.cost_price, p.stock_quantity
GROUP BY o.pid, EXTRACT(YEAR FROM o.date::timestamp with time zone), EXTRACT(MONTH FROM o.date::timestamp with time zone)
),
monthly_stock AS (
SELECT
pid,
EXTRACT(YEAR FROM date::timestamp with time zone) as year,
EXTRACT(MONTH FROM date::timestamp with time zone) as month,
EXTRACT(YEAR FROM date::timestamp with time zone)::INTEGER as year,
EXTRACT(MONTH FROM date::timestamp with time zone)::INTEGER as month,
SUM(received) as stock_received,
SUM(ordered) as stock_ordered
FROM purchase_orders
GROUP BY pid, EXTRACT(YEAR FROM date::timestamp with time zone), EXTRACT(MONTH FROM date::timestamp with time zone)
),
base_products AS (
SELECT
p.pid,
p.cost_price * p.stock_quantity as inventory_value
FROM products p
)
SELECT
COALESCE(s.pid, ms.pid) as pid,
COALESCE(s.year, ms.year) as year,
COALESCE(s.month, ms.month) as month,
COALESCE(s.total_quantity_sold, 0) as total_quantity_sold,
COALESCE(s.total_revenue, 0) as total_revenue,
COALESCE(s.total_cost, 0) as total_cost,
COALESCE(s.order_count, 0) as order_count,
COALESCE(ms.stock_received, 0) as stock_received,
COALESCE(ms.stock_ordered, 0) as stock_ordered,
COALESCE(s.avg_price, 0) as avg_price,
COALESCE(s.profit_margin, 0) as profit_margin,
COALESCE(s.inventory_value, bp.inventory_value, 0) as inventory_value,
COALESCE(s.pid, ms.pid, mi.pid) as pid,
COALESCE(s.year, ms.year, mi.year) as year,
COALESCE(s.month, ms.month, mi.month) as month,
COALESCE(s.total_quantity_sold, 0)::INTEGER as total_quantity_sold,
COALESCE(s.total_revenue, 0)::DECIMAL(10,3) as total_revenue,
COALESCE(s.total_cost, 0)::DECIMAL(10,3) as total_cost,
COALESCE(s.order_count, 0)::INTEGER as order_count,
COALESCE(ms.stock_received, 0)::INTEGER as stock_received,
COALESCE(ms.stock_ordered, 0)::INTEGER as stock_ordered,
COALESCE(s.avg_price, 0)::DECIMAL(10,3) as avg_price,
COALESCE(s.profit_margin, 0)::DECIMAL(10,3) as profit_margin,
COALESCE(mi.end_of_month_value, 0)::DECIMAL(10,3) as inventory_value,
CASE
WHEN COALESCE(s.inventory_value, bp.inventory_value, 0) > 0
AND COALESCE(s.active_days, 0) > 0
THEN (COALESCE(s.total_revenue - s.total_cost, 0) * (365.0 / s.active_days))
/ COALESCE(s.inventory_value, bp.inventory_value)
WHEN COALESCE(mi.end_of_month_value, 0) > 0
THEN (COALESCE(s.total_revenue, 0) - COALESCE(s.total_cost, 0))
/ NULLIF(COALESCE(mi.end_of_month_value, 0), 0)
ELSE 0
END as gmroi
END::DECIMAL(10,3) as gmroi
FROM (
SELECT * FROM monthly_sales s
UNION ALL
SELECT
ms.pid,
ms.year,
ms.month,
pid,
year,
month,
0 as total_quantity_sold,
0 as total_revenue,
0 as total_cost,
0 as order_count,
NULL as avg_price,
0 as profit_margin,
NULL as inventory_value,
0 as active_days
FROM monthly_stock ms
WHERE NOT EXISTS (
@@ -153,50 +231,40 @@ async function calculateTimeAggregates(startTime, totalProducts, processedCount
AND s2.year = ms.year
AND s2.month = ms.month
)
UNION ALL
SELECT
pid,
year,
month,
0 as total_quantity_sold,
0 as total_revenue,
0 as total_cost,
0 as order_count,
NULL as avg_price,
0 as profit_margin,
0 as active_days
FROM temp_monthly_inventory mi
WHERE NOT EXISTS (
SELECT 1 FROM monthly_sales s3
WHERE s3.pid = mi.pid
AND s3.year = mi.year
AND s3.month = mi.month
)
AND NOT EXISTS (
SELECT 1 FROM monthly_stock ms3
WHERE ms3.pid = mi.pid
AND ms3.year = mi.year
AND ms3.month = mi.month
)
) s
LEFT JOIN monthly_stock ms
ON s.pid = ms.pid
AND s.year = ms.year
AND s.month = ms.month
JOIN base_products bp ON COALESCE(s.pid, ms.pid) = bp.pid
UNION
SELECT
ms.pid,
ms.year,
ms.month,
0 as total_quantity_sold,
0 as total_revenue,
0 as total_cost,
0 as order_count,
ms.stock_received,
ms.stock_ordered,
0 as avg_price,
0 as profit_margin,
bp.inventory_value,
0 as gmroi
FROM monthly_stock ms
JOIN base_products bp ON ms.pid = bp.pid
WHERE NOT EXISTS (
SELECT 1 FROM (
SELECT * FROM monthly_sales
UNION ALL
SELECT
ms2.pid,
ms2.year,
ms2.month,
0, 0, 0, 0, NULL, 0, NULL, 0
FROM monthly_stock ms2
WHERE NOT EXISTS (
SELECT 1 FROM monthly_sales s2
WHERE s2.pid = ms2.pid
AND s2.year = ms2.year
AND s2.month = ms2.month
)
) s
WHERE s.pid = ms.pid
AND s.year = ms.year
AND s.month = ms.month
)
LEFT JOIN temp_monthly_inventory mi
ON s.pid = mi.pid
AND s.year = mi.year
AND s.month = mi.month
ON CONFLICT (pid, year, month) DO UPDATE
SET
total_quantity_sold = EXCLUDED.total_quantity_sold,
@@ -214,7 +282,7 @@ async function calculateTimeAggregates(startTime, totalProducts, processedCount
processedCount = Math.floor(totalProducts * 0.60);
outputProgress({
status: 'running',
operation: 'Base time aggregates calculated, updating financial metrics',
operation: 'Base time aggregates calculated',
current: processedCount,
total: totalProducts,
elapsed: formatElapsedTime(startTime),
@@ -234,45 +302,9 @@ async function calculateTimeAggregates(startTime, totalProducts, processedCount
processedPurchaseOrders: 0,
success
};
// Update with financial metrics
await connection.query(`
UPDATE product_time_aggregates pta
SET inventory_value = COALESCE(fin.inventory_value, 0)
FROM (
SELECT
p.pid,
EXTRACT(YEAR FROM o.date::timestamp with time zone) as year,
EXTRACT(MONTH FROM o.date::timestamp with time zone) 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, EXTRACT(YEAR FROM o.date::timestamp with time zone), EXTRACT(MONTH FROM o.date::timestamp with time zone), p.cost_price, p.stock_quantity
) fin
WHERE pta.pid = fin.pid
AND pta.year = fin.year
AND pta.month = fin.month
`);
processedCount = Math.floor(totalProducts * 0.65);
outputProgress({
status: 'running',
operation: 'Financial metrics updated',
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)
}
});
// Clean up temporary tables
await connection.query('DROP TABLE IF EXISTS temp_monthly_inventory');
// If we get here, everything completed successfully
success = true;
@@ -298,6 +330,12 @@ async function calculateTimeAggregates(startTime, totalProducts, processedCount
throw error;
} finally {
if (connection) {
try {
// Ensure temporary tables are cleaned up
await connection.query('DROP TABLE IF EXISTS temp_monthly_inventory');
} catch (err) {
console.error('Error cleaning up temporary tables:', err);
}
connection.release();
}
}