Add forecasting page

inventory-server/scripts/calculate-metrics.js

@@ -466,7 +466,88 @@ async function calculateLeadTimeMetrics(connection, startTime, totalProducts) {
     `);
 }
 
-// Update the main calculation function to include our new calculations
+// Add new function for category sales metrics
+async function calculateCategorySalesMetrics(connection, startTime, totalProducts) {
+    outputProgress({
+        status: 'running',
+        operation: 'Calculating category sales metrics',
+        current: Math.floor(totalProducts * 0.9),
+        total: totalProducts,
+        elapsed: formatElapsedTime(startTime),
+        remaining: estimateRemaining(startTime, Math.floor(totalProducts * 0.9), totalProducts),
+        rate: calculateRate(startTime, Math.floor(totalProducts * 0.9)),
+        percentage: '90'
+    });
+
+    await connection.query(`
+        INSERT INTO category_sales_metrics (
+            category_id,
+            brand,
+            period_start,
+            period_end,
+            avg_daily_sales,
+            total_sold,
+            num_products,
+            avg_price,
+            last_calculated_at
+        )
+        WITH date_ranges AS (
+            SELECT 
+                DATE_SUB(CURDATE(), INTERVAL 30 DAY) as period_start,
+                CURDATE() as period_end
+            UNION ALL
+            SELECT 
+                DATE_SUB(CURDATE(), INTERVAL 90 DAY),
+                CURDATE()
+            UNION ALL
+            SELECT 
+                DATE_SUB(CURDATE(), INTERVAL 180 DAY),
+                CURDATE()
+            UNION ALL
+            SELECT 
+                DATE_SUB(CURDATE(), INTERVAL 365 DAY),
+                CURDATE()
+        ),
+        category_metrics AS (
+            SELECT 
+                c.id as category_id,
+                p.brand,
+                dr.period_start,
+                dr.period_end,
+                COUNT(DISTINCT p.product_id) as num_products,
+                COALESCE(SUM(o.quantity), 0) / DATEDIFF(dr.period_end, dr.period_start) as avg_daily_sales,
+                COALESCE(SUM(o.quantity), 0) as total_sold,
+                COALESCE(AVG(o.price), 0) as avg_price
+            FROM categories c
+            JOIN product_categories pc ON c.id = pc.category_id
+            JOIN products p ON pc.product_id = p.product_id
+            CROSS JOIN date_ranges dr
+            LEFT JOIN orders o ON p.product_id = o.product_id 
+                AND o.date BETWEEN dr.period_start AND dr.period_end
+                AND o.canceled = false
+            GROUP BY c.id, p.brand, dr.period_start, dr.period_end
+        )
+        SELECT 
+            category_id,
+            brand,
+            period_start,
+            period_end,
+            avg_daily_sales,
+            total_sold,
+            num_products,
+            avg_price,
+            NOW() as last_calculated_at
+        FROM category_metrics
+        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 = NOW()
+    `);
+}
+
+// Update the main calculation function to include category sales metrics
 async function calculateMetrics() {
     let pool;
     const startTime = Date.now();
@@ -751,6 +832,7 @@ async function calculateMetrics() {
                                 SUM(CASE WHEN received >= 0 THEN received ELSE 0 END) as total_quantity_purchased,
                                 SUM(CASE WHEN received >= 0 THEN cost_price * received ELSE 0 END) as total_cost,
                                 MAX(date) as last_purchase_date,
+                                MIN(received_date) as first_received_date,
                                 MAX(received_date) as last_received_date,
                                 AVG(lead_time_days) as avg_lead_time_days,
                                 COUNT(*) as orders_analyzed
@@ -952,6 +1034,7 @@ async function calculateMetrics() {
                             inventory_value || 0,
                             purchases.avg_lead_time_days || null,
                             purchases.last_purchase_date || null,
+                            purchases.first_received_date || null,
                             purchases.last_received_date || null,
                             stock_status,
                             reorder_qty,
@@ -985,6 +1068,7 @@ async function calculateMetrics() {
                             inventory_value,
                             avg_lead_time_days,
                             last_purchase_date,
+                            first_received_date,
                             last_received_date,
                             stock_status,
                             reorder_qty,
@@ -1008,6 +1092,7 @@ async function calculateMetrics() {
                             inventory_value = VALUES(inventory_value),
                             avg_lead_time_days = VALUES(avg_lead_time_days),
                             last_purchase_date = VALUES(last_purchase_date),
+                            first_received_date = VALUES(first_received_date),
                             last_received_date = VALUES(last_received_date),
                             stock_status = VALUES(stock_status),
                             reorder_qty = VALUES(reorder_qty),
@@ -1067,6 +1152,12 @@ async function calculateMetrics() {
             });
             await calculateLeadTimeMetrics(connection, startTime, totalProducts);
 
+            // Add category sales metrics calculation
+            if (isCancelled) {
+                throw new Error('Operation cancelled');
+            }
+            await calculateCategorySalesMetrics(connection, startTime, totalProducts);
+
             // Calculate ABC classification
             if (isCancelled) {
                 throw new Error('Operation cancelled');