SELECT p.item_id, i.item_name AS item_description, SUM(p.planned_qty) AS planned_quantity, SUM(p.picked_qty) AS picked_quantity, SUM(p.shipped_qty) AS shipped_quantity, SUM(CASE WHEN p.status = 'SHORTED' THEN p.planned_qty ELSE 0 END) AS shorted_quantity FROM warehouse.pick_details p LEFT JOIN warehouse.item_catalog i ON i.warehouse_id = p.warehouse_id AND i.item_id = p.item_id WHERE CAST(p.created_at AS DATE) >= '2025-06-08' -- since Sunday AND p.item_id IN ('04637336') GROUP BY p.item_id, i.item_name ORDER BY p.item_id;

SELECT o.order_id, o.order_date, c.client_name, o.channel, COUNT(oi.sku) AS sku_count, SUM(oi.quantity) AS total_units, SUM(oi.item_cost + oi.labor_cost + oi.packaging_cost + oi.carrier_cost) AS total_cost, ROUND(SUM(oi.item_cost + oi.labor_cost + oi.packaging_cost + oi.carrier_cost) / SUM(oi.quantity), 2) AS cpu, CASE WHEN o.order_date BETWEEN '2024-11-01' AND '2025-01-15' THEN 'Peak' ELSE 'Off-Peak' END AS season FROM orders o JOIN order_items oi ON o.order_id = oi.order_id JOIN clients c ON o.client_id = c.client_id GROUP BY o.order_id, o.order_date, c.client_name, o.channel ORDER BY cpu DESC;

This identifies batchable low-SKU orders—a great starting point for a smart batching run SELECT order_id, COUNT(DISTINCT sku) AS sku_count, COUNT(*) AS total_lines FROM order_items WHERE order_date = CURRENT_DATE GROUP BY order_id HAVING sku_count <= 2;

I’ll kick it off with a simple SQL query I use to identify slow-moving inventory: SELECT sku, qty, last_moved_date FROM inventory WHERE last_moved_date < DATEADD(month, -3, GETDATE()) ORDER BY last_moved_date ASC;