video1.0<iframe src="https://www.loom.com/embed/16825152f2414fe686dd2126190505fc" frameborder="0" width="1730" height="1297" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>12971730Loomhttps://www.loom.com12971730https://cdn.loom.com/sessions/thumbnails/16825152f2414fe686dd2126190505fc-10699e8792d67a97.gif169.094In this video, I walk through a time and transit model using a retailer with two warehouses and about 2.5 million annual shipments as an example. I demonstrate the power of our machine learning model compared to static TNTs, highlighting how it accounts for various factors like package sizes and seasonalities. We also explore the potential impact of adding a new warehouse in Tennessee and a new carrier in the northeast. I encourage you to think about how these changes could influence your operations and consider the benefits of modeling out different scenarios.