What’s New:
Centralized Warehouse Orchestration (Multi-Site) – Pilot Launched
Large customers running multiple facilities often struggle to balance workload between sites — one site is slammed while another sits idle. Multi-site orchestration lets planners see all sites at once and shift work or resources in real time. This new engineering is a critical foundation for the expanded scope of upcoming features.
Unloads Based on Code Date – Live
Food & beverage customers told us they were wasting labor unloading pallets that wouldn’t be needed for days while time-sensitive products sat in trailers. This update prioritizes unloads based on product code date, keeping high-priority SKUs moving and reducing spoilage risk.
Coming Soon:
Power BI Integration
Operations leaders told us they want direct access to the data unlocked by AutoScheduler’s ability to harmonize across warehouse systems. At the same time, operations teams asked for simplified views of the plan, watch-outs, and immediate tasks. Soon, those tasks — along with KPIs like dock utilization, labor allocation, and on-time performance — will update automatically in Power BI, bringing the optimized plan closer to the people who make the buildings run.
Rules Engine
Every site has its quirks — customer-specific dock rules, priority load types, special handling. The Rules Engine will let customers set these preferences directly, so AutoScheduler’s orchestration follows their exact playbook.
AI Agent in Action
Our AI Agent — the warehouse assistant powered by AutoScheduler — is now in the hands of power users and select key customers.
Why we’re building it: Coordinators often understand AutoScheduler’s optimized plans, but sometimes want help turning those plans into immediate actions and staying better informed about the day’s activities. The Agent bridges this gap by translating the nuances of optimization into plain, actionable guidance coordinators can use while executing.
What’s being added: The Agent is learning to recognize and explain activities related to inbounds and outbounds, door usage, and labor and equipment allocation.
Where it’s headed: Once the Agent can effectively converse on the “what to do,” the next step is the “why” — explaining cuts, dock scheduling challenges, bottlenecks, capacity constraints, door compliance, and labor imbalances. Soon, it won’t just suggest actions — it will explain the reasoning in context, making the schedule a conversation instead of just a plan.
About the Author:
Kunj Pandya – Head of Product at AutoScheduler.ai
https://www.linkedin.com/in/kunj-pandya/
