Supply chain · Production

Blue Yonder September 2024 Release: ML demand planning, intelligent rebalancer, and computer-vision yard management

The problem

Supply chain teams need higher-quality demand forecasts, faster automated responses to supply disruptions, and reduced manual work in yard gate-check operations.

Workflow diagram · grounded in source
1
Demand data ingestion
trigger
“considering the various internal and external input sources that impact demand planning”
2
ML demand forecast generation
ai_action
“combining extensible machine learning (ML) and statistical techniques to generate a high-quality, mix-and-match forecast”
3
Collaborative consensus planning
human_review
“supporting collaborative consensus demand planning workflows across marketing, sales, and operations teams. This allows those teams visibility into numerous demand plan aspects, considering the various internal and external input sources…”
4
Supply disruption trigger
trigger
“swift, automatic order execution in response to supply and demand disruptions”
5
Near real-time inventory reallocation
ai_action
“in near real-time, reallocate inventory and fulfillment processes following a disruption. Our key value drivers are the ability to adjust order execution based on current data rather than planned needs, as well as the ability to prioriti…”
6
Computer-vision gate automation
ai_action
“computer-vision-based solution that uses cameras, object recognition and ML to automate gate-check activities. It helps logistics service providers, manufacturers, and retailers automate, monitor, and centralize yard operations”
7
Cross-system automatic update
integration
“enabling the linking of transportation equipment with shipment information in warehouse management during gate arrival and departure. The in-the-moment connection of Yard Management with Warehouse Management data automatically updates ea…”
Reported outcome

The September 2024 release delivers ML-powered demand forecasting for planner productivity and cost reduction, near-real-time inventory rebalancing after disruptions, and computer-vision yard automation promising greater throughput, fewer lost loads, decreased fees, and eliminated work hours.

Reported metrics
Planner productivityimproves the planner productivity
Supply chain costshelps reduce supply chain costs
Yard throughputgreater throughput
Lost loadsfewer lost loads
Show all 6 reported metrics
planner productivityimproves the planner productivity
supply chain costshelps reduce supply chain costs
yard throughputgreater throughput
lost loadsfewer lost loads
yard-related feesdecreased fees
work hourseliminating work hours
Reported stack
Cognitive Demand PlanningIntelligent RebalancerFulfillment Sourcing SimulatorWarehouse TaskingYard Management
Source
https://blueyonder.com/release-announcements/september-2024-release/details
Read source ↗

Frequently asked questions

What did this team achieve with this AI workflow?

The September 2024 release delivers ML-powered demand forecasting for planner productivity and cost reduction, near-real-time inventory rebalancing after disruptions, and computer-vision yard automation promising grea…

What tools did this team use?

Cognitive Demand Planning, Intelligent Rebalancer, Fulfillment Sourcing Simulator, Warehouse Tasking, Yard Management.

What results were reported?

Planner productivity: improves the planner productivity; Supply chain costs: helps reduce supply chain costs; Yard throughput: greater throughput; Lost loads: fewer lost loads (source-reported, not independently verified).

How is this supply chain AI workflow structured?

Demand data ingestion → ML demand forecast generation → Collaborative consensus planning → Supply disruption trigger → Near real-time inventory reallocation → Computer-vision gate automation → Cross-system automatic update.

Related supply chain cases