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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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286
inventory-server/scripts/metrics/product-metrics.js
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@@ -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;