Co trzeba umieć by pracować w logistyce jako analityk danych?

🟨 Analyst Community: https://kajodata.com/space/ 🟦 Courses - Excel, Power Query, SQL, PowerBI, Python, Tableau, Data Science: https://kajodata.com/kursy/ 🟥 Sign up for the newsletter and get FREE BONUSES: https://kajodata.com/newsletter/ 00:00 Why Logistics is a Great Field for an Analyst 01:29 Return Rate, or Returns That Can Eat Up Profitability 03:48 Lead Time, or How Long a Customer Waits for an Order 05:39 Order Cycle Time and Looking for Problems in the Warehouse 07:03 Fill Rate, or Are We Able to Fulfill Orders? 07:44 SKUs and Why Global KPIs Can Be Deceiving 08:42 Cost Per Shipment, or How Much Shipping Really Costs 10:30 What's Worth Considering in Logistics Costs 11:06 Summary: KPIs in Logistics Need to Be Break it down into details = 📈 In this episode, I show why logistics is such an interesting field for a data analyst. It quickly becomes clear that small data errors, incorrectly calculated delivery times, or overly general metrics can have very specific business consequences. 📈 I discuss the most common KPIs that appear in logistics: return rate, lead time, order cycle time, fill rate, and cost per shipment. I try to show not only what these metrics are, but also why simply calculating them is often not enough. The key is to be able to drill down and see where the problem really lies. 📈 This episode is especially useful if you want to better understand analytics in physical businesses, e-commerce, or warehouses. Because logistics isn't just about parcels, couriers, and trucks. It's also a great example of how an analyst can actually help a company save money, improve processes, and identify things that are completely invisible at first glance.