231 lines
11 KiB
JavaScript
231 lines
11 KiB
JavaScript
const { outputProgress } = require('./utils/progress');
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const { getConnection } = require('./utils/db');
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async function calculateSalesForecasts(startTime, totalProducts, processedCount) {
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const connection = await getConnection();
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try {
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outputProgress({
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status: 'running',
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operation: 'Calculating sales forecasts',
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current: Math.floor(totalProducts * 0.98),
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total: totalProducts,
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elapsed: formatElapsedTime(startTime),
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remaining: estimateRemaining(startTime, Math.floor(totalProducts * 0.98), totalProducts),
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rate: calculateRate(startTime, Math.floor(totalProducts * 0.98)),
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percentage: '98'
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});
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// Calculate product-level forecasts
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await connection.query(`
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INSERT INTO sales_forecasts (
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product_id,
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forecast_date,
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forecast_units,
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forecast_revenue,
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confidence_level,
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last_calculated_at
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)
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WITH daily_sales AS (
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SELECT
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o.product_id,
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DATE(o.date) as sale_date,
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SUM(o.quantity) as daily_quantity,
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SUM(o.price * o.quantity) as daily_revenue
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FROM orders o
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WHERE o.canceled = false
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AND o.date >= DATE_SUB(CURRENT_DATE, INTERVAL 90 DAY)
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GROUP BY o.product_id, DATE(o.date)
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),
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forecast_dates AS (
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SELECT
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DATE_ADD(CURRENT_DATE, INTERVAL n DAY) as forecast_date
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FROM (
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SELECT 0 as n UNION SELECT 1 UNION SELECT 2 UNION SELECT 3 UNION SELECT 4 UNION
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SELECT 5 UNION SELECT 6 UNION SELECT 7 UNION SELECT 8 UNION SELECT 9 UNION
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SELECT 10 UNION SELECT 11 UNION SELECT 12 UNION SELECT 13 UNION SELECT 14 UNION
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SELECT 15 UNION SELECT 16 UNION SELECT 17 UNION SELECT 18 UNION SELECT 19 UNION
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SELECT 20 UNION SELECT 21 UNION SELECT 22 UNION SELECT 23 UNION SELECT 24 UNION
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SELECT 25 UNION SELECT 26 UNION SELECT 27 UNION SELECT 28 UNION SELECT 29 UNION
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SELECT 30
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) numbers
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),
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product_stats AS (
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SELECT
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ds.product_id,
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AVG(ds.daily_quantity) as avg_daily_quantity,
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STDDEV_SAMP(ds.daily_quantity) as std_daily_quantity,
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AVG(ds.daily_revenue) as avg_daily_revenue,
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STDDEV_SAMP(ds.daily_revenue) as std_daily_revenue,
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COUNT(*) as data_points,
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-- Calculate day-of-week averages
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AVG(CASE WHEN DAYOFWEEK(ds.sale_date) = 1 THEN ds.daily_revenue END) as sunday_avg,
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AVG(CASE WHEN DAYOFWEEK(ds.sale_date) = 2 THEN ds.daily_revenue END) as monday_avg,
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AVG(CASE WHEN DAYOFWEEK(ds.sale_date) = 3 THEN ds.daily_revenue END) as tuesday_avg,
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AVG(CASE WHEN DAYOFWEEK(ds.sale_date) = 4 THEN ds.daily_revenue END) as wednesday_avg,
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AVG(CASE WHEN DAYOFWEEK(ds.sale_date) = 5 THEN ds.daily_revenue END) as thursday_avg,
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AVG(CASE WHEN DAYOFWEEK(ds.sale_date) = 6 THEN ds.daily_revenue END) as friday_avg,
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AVG(CASE WHEN DAYOFWEEK(ds.sale_date) = 7 THEN ds.daily_revenue END) as saturday_avg
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FROM daily_sales ds
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GROUP BY ds.product_id
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)
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SELECT
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ps.product_id,
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fd.forecast_date,
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GREATEST(0,
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ps.avg_daily_quantity *
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(1 + COALESCE(
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(SELECT seasonality_factor
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FROM sales_seasonality
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WHERE MONTH(fd.forecast_date) = month
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LIMIT 1),
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0
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))
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) as forecast_units,
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GREATEST(0,
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CASE DAYOFWEEK(fd.forecast_date)
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WHEN 1 THEN COALESCE(ps.sunday_avg, ps.avg_daily_revenue)
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WHEN 2 THEN COALESCE(ps.monday_avg, ps.avg_daily_revenue)
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WHEN 3 THEN COALESCE(ps.tuesday_avg, ps.avg_daily_revenue)
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WHEN 4 THEN COALESCE(ps.wednesday_avg, ps.avg_daily_revenue)
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WHEN 5 THEN COALESCE(ps.thursday_avg, ps.avg_daily_revenue)
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WHEN 6 THEN COALESCE(ps.friday_avg, ps.avg_daily_revenue)
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WHEN 7 THEN COALESCE(ps.saturday_avg, ps.avg_daily_revenue)
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END *
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(1 + COALESCE(
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(SELECT seasonality_factor
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FROM sales_seasonality
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WHERE MONTH(fd.forecast_date) = month
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LIMIT 1),
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0
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)) *
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-- Add some randomness within a small range (±5%)
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(0.95 + (RAND() * 0.1))
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) as forecast_revenue,
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CASE
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WHEN ps.data_points >= 60 THEN 90
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WHEN ps.data_points >= 30 THEN 80
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WHEN ps.data_points >= 14 THEN 70
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ELSE 60
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END as confidence_level,
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NOW() as last_calculated_at
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FROM product_stats ps
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CROSS JOIN forecast_dates fd
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WHERE ps.avg_daily_quantity > 0
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ON DUPLICATE KEY UPDATE
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forecast_units = VALUES(forecast_units),
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forecast_revenue = VALUES(forecast_revenue),
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confidence_level = VALUES(confidence_level),
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last_calculated_at = NOW()
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`);
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// Calculate category-level forecasts
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await connection.query(`
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INSERT INTO category_forecasts (
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category_id,
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forecast_date,
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forecast_units,
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forecast_revenue,
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confidence_level,
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last_calculated_at
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)
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WITH category_daily_sales AS (
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SELECT
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pc.category_id,
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DATE(o.date) as sale_date,
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SUM(o.quantity) as daily_quantity,
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SUM(o.price * o.quantity) as daily_revenue
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FROM orders o
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JOIN product_categories pc ON o.product_id = pc.product_id
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WHERE o.canceled = false
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AND o.date >= DATE_SUB(CURRENT_DATE, INTERVAL 90 DAY)
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GROUP BY pc.category_id, DATE(o.date)
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),
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forecast_dates AS (
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SELECT
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DATE_ADD(CURRENT_DATE, INTERVAL n DAY) as forecast_date
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FROM (
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SELECT 0 as n UNION SELECT 1 UNION SELECT 2 UNION SELECT 3 UNION SELECT 4 UNION
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SELECT 5 UNION SELECT 6 UNION SELECT 7 UNION SELECT 8 UNION SELECT 9 UNION
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SELECT 10 UNION SELECT 11 UNION SELECT 12 UNION SELECT 13 UNION SELECT 14 UNION
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SELECT 15 UNION SELECT 16 UNION SELECT 17 UNION SELECT 18 UNION SELECT 19 UNION
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SELECT 20 UNION SELECT 21 UNION SELECT 22 UNION SELECT 23 UNION SELECT 24 UNION
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SELECT 25 UNION SELECT 26 UNION SELECT 27 UNION SELECT 28 UNION SELECT 29 UNION
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SELECT 30
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) numbers
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),
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category_stats AS (
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SELECT
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cds.category_id,
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AVG(cds.daily_quantity) as avg_daily_quantity,
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STDDEV_SAMP(cds.daily_quantity) as std_daily_quantity,
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AVG(cds.daily_revenue) as avg_daily_revenue,
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STDDEV_SAMP(cds.daily_revenue) as std_daily_revenue,
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COUNT(*) as data_points,
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-- Calculate day-of-week averages
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AVG(CASE WHEN DAYOFWEEK(cds.sale_date) = 1 THEN cds.daily_revenue END) as sunday_avg,
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AVG(CASE WHEN DAYOFWEEK(cds.sale_date) = 2 THEN cds.daily_revenue END) as monday_avg,
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AVG(CASE WHEN DAYOFWEEK(cds.sale_date) = 3 THEN cds.daily_revenue END) as tuesday_avg,
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AVG(CASE WHEN DAYOFWEEK(cds.sale_date) = 4 THEN cds.daily_revenue END) as wednesday_avg,
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AVG(CASE WHEN DAYOFWEEK(cds.sale_date) = 5 THEN cds.daily_revenue END) as thursday_avg,
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AVG(CASE WHEN DAYOFWEEK(cds.sale_date) = 6 THEN cds.daily_revenue END) as friday_avg,
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AVG(CASE WHEN DAYOFWEEK(cds.sale_date) = 7 THEN cds.daily_revenue END) as saturday_avg
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FROM category_daily_sales cds
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GROUP BY cds.category_id
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)
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SELECT
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cs.category_id,
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fd.forecast_date,
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GREATEST(0,
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cs.avg_daily_quantity *
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(1 + COALESCE(
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(SELECT seasonality_factor
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FROM sales_seasonality
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WHERE MONTH(fd.forecast_date) = month
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LIMIT 1),
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0
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))
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) as forecast_units,
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GREATEST(0,
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CASE DAYOFWEEK(fd.forecast_date)
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WHEN 1 THEN COALESCE(cs.sunday_avg, cs.avg_daily_revenue)
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WHEN 2 THEN COALESCE(cs.monday_avg, cs.avg_daily_revenue)
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WHEN 3 THEN COALESCE(cs.tuesday_avg, cs.avg_daily_revenue)
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WHEN 4 THEN COALESCE(cs.wednesday_avg, cs.avg_daily_revenue)
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WHEN 5 THEN COALESCE(cs.thursday_avg, cs.avg_daily_revenue)
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WHEN 6 THEN COALESCE(cs.friday_avg, cs.avg_daily_revenue)
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WHEN 7 THEN COALESCE(cs.saturday_avg, cs.avg_daily_revenue)
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END *
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(1 + COALESCE(
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(SELECT seasonality_factor
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FROM sales_seasonality
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WHERE MONTH(fd.forecast_date) = month
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LIMIT 1),
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0
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)) *
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-- Add some randomness within a small range (±5%)
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(0.95 + (RAND() * 0.1))
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) as forecast_revenue,
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CASE
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WHEN cs.data_points >= 60 THEN 90
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WHEN cs.data_points >= 30 THEN 80
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WHEN cs.data_points >= 14 THEN 70
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ELSE 60
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END as confidence_level,
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NOW() as last_calculated_at
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FROM category_stats cs
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CROSS JOIN forecast_dates fd
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WHERE cs.avg_daily_quantity > 0
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ON DUPLICATE KEY UPDATE
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forecast_units = VALUES(forecast_units),
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forecast_revenue = VALUES(forecast_revenue),
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confidence_level = VALUES(confidence_level),
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last_calculated_at = NOW()
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`);
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return Math.floor(totalProducts * 1.0);
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} finally {
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connection.release();
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}
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}
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module.exports = calculateSalesForecasts;
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