Let me know your thoughts around IoT and the gaps still in the industry around it! What’s IoT & Why It Matters for Fulfillment IoT connects devices (sensors, scanners, machines) to the internet to collect and share data in real-time. For warehouses, IoT can: Boost efficiency: Track inventory instantly. Cut costs: Spot issues like equipment downtime early. Improve accuracy: Reduce picking errors. Add value: Deliver faster, happier customers. Example: A $50 IoT sensor on a conveyor belt can alert you when it’s jammed, saving hours of downtime. Getting Started with IoT (Low-Tech Friendly) No need for a tech overhaul! Here’s an executable plan: Start Small: Buy affordable IoT sensors (e.g., RFID tags, temperature monitors). Example: Use Bluetooth trackers for high-value inventory. Focus on Pain Points: Struggling with misplaced pallets? Use IoT GPS tags. High energy bills? Smart plugs monitor power usage. Use Plug-and-Play Tools: Platforms like Tuya or AWS IoT offer user-friendly dashboards. No coding needed—just connect and monitor! Train Your Team: Run a 1-hour workshop to show staff how to read IoT alerts. Keep it simple: “Red light = check machine.” Measure ROI: Track metrics like order accuracy or downtime reduction. Example: A small IoT setup can save 5-10 hours/week in manual checks. Affordable: Start with a $100-200 pilot project. Scalable: Add more devices as you grow. Competitive Edge: Real-time data = faster decisions, happier clients. Future-Proof: IoT preps you for automation down the road. Real-World Win: A small 3PL in Ohio used IoT temp sensors to ensure cold-chain compliance, winning a $50K contract with a pharma client.

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;

When you look at VCPU, what are you GO-TO triggers and check offs to make sure you are not eroding margins? How have you optimized it?

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;