video1.0<iframe src="https://www.loom.com/embed/cf6860fb6cd8442db6e64ded428d53c9" frameborder="0" width="2172" height="1629" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>16292172Loomhttps://www.loom.com16292172https://cdn.loom.com/sessions/thumbnails/cf6860fb6cd8442db6e64ded428d53c9-ac3f2204cec52be3.gif454.552This Loom explains why AutoScheduler is shifting from infinite configuration to an AI enablement platform built on customer data to capture warehouse and logistics nuance. It describes using six years of semantic knowledge and domain expertise to build applications on top of inventory, inbound and outbound, trailer, historical site activity, and optimization plan data, including shift labor planning with options like operational task breakdown and standard hours per person. The video highlights pre-built use cases such as inventory turn rate analysis with ABC and Pareto distributions, inventory health monitoring, and 30-day shift productivity trend analysis with root cause diagnostics that estimated about $2,000 in lost payroll from roughly 60 hours of unproductive time for one shift. It also shows a site-specific coaching application for pre-staging loads for automated systems, emphasizing building and iterating in days rather than months.