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Fix (probably) discrepancies and errors in import/calculate scripts

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This commit is contained in:
Matt
2025-02-02 00:01:46 -05:00
parent bd5bcdd548
commit 8a43da502a
10 changed files with 423 additions and 218 deletions
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67
inventory-server/scripts/metrics/sales-forecasts.js
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@@ -159,37 +159,72 @@ async function calculateSalesForecasts(startTime, totalProducts, processedCount,
confidence_level,
last_calculated_at
)
WITH daily_stats AS (
SELECT
ds.pid,
AVG(ds.daily_quantity) as avg_daily_qty,
STDDEV(ds.daily_quantity) as std_daily_qty,
COUNT(DISTINCT ds.day_count) as data_points,
SUM(ds.day_count) as total_days,
AVG(ds.daily_revenue) as avg_daily_revenue,
STDDEV(ds.daily_revenue) as std_daily_revenue,
MIN(ds.daily_quantity) as min_daily_qty,
MAX(ds.daily_quantity) as max_daily_qty,
AVG(ABS(ds.daily_quantity - LAG(ds.daily_quantity) OVER (PARTITION BY ds.pid ORDER BY ds.day_of_week))) as avg_daily_variance
FROM temp_daily_sales ds
GROUP BY ds.pid
HAVING AVG(ds.daily_quantity) > 0
)
SELECT
ds.pid,
fd.forecast_date,
GREATEST(0,
AVG(ds.daily_quantity) *
(1 + COALESCE(sf.seasonality_factor, 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,
2
)
) as forecast_units,
GREATEST(0,
COALESCE(
CASE
WHEN SUM(ds.day_count) >= 4 THEN AVG(ds.daily_revenue)
ELSE ps.overall_avg_revenue
END *
(1 + COALESCE(sf.seasonality_factor, 0)) *
(0.95 + (RAND() * 0.1)),
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,
CASE
WHEN ps.total_days >= 60 THEN 90
WHEN ps.total_days >= 30 THEN 80
WHEN ps.total_days >= 14 THEN 70
WHEN ds.total_days >= 60 AND ds.avg_daily_variance / NULLIF(ds.avg_daily_qty, 0) < 0.5 THEN 90
WHEN ds.total_days >= 60 THEN 85
WHEN ds.total_days >= 30 AND ds.avg_daily_variance / NULLIF(ds.avg_daily_qty, 0) < 0.5 THEN 80
WHEN ds.total_days >= 30 THEN 75
WHEN ds.total_days >= 14 AND ds.avg_daily_variance / NULLIF(ds.avg_daily_qty, 0) < 0.5 THEN 70
WHEN ds.total_days >= 14 THEN 65
ELSE 60
END as confidence_level,
NOW() as last_calculated_at
FROM temp_daily_sales ds
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, ps.total_days, sf.seasonality_factor
HAVING AVG(ds.daily_quantity) > 0
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),