Empowering Warehouse Operations and Management through Artificial Intelligence (AI)

Details

A practical, tool-agnostic program connecting AI (LLMs/copilots, optimization, computer vision, predictive analytics) with core warehouse processes and systems (WMS/WES/ERP/TMS). Participants build prompt workflows, design slotting and pick-path optimizers, set up anomaly detection for inventory, and establish governance for safe, auditable AI use.

Target Group

Target Group

• Warehouse/operations managers and supervisors
• Inventory control leaders
• Industrial/process engineers
• Logistics and fulfillment planners
• Quality/HSE leads
• IT/OT or analytics partners supporting WMS/WES initiatives.

Goals

• Spot high-impact AI use-cases across inbound, inventory control, fulfillment, and shipping with measurable KPIs.
• Clean and structure operational data for AI (master data, transaction logs, IoT/telematics, vision feeds).
• Improve planning through demand-driven labor scheduling, dynamic slotting, and replenishment triggers.
• Reduce errors and cycle time using computer vision, anomaly detection, and rules-plus-ML decisioning.
• Generate clear operations briefs, dashboards, and exception queues with traceable explanations.
• Establish governance for safety, data security, model validation, and continuous improvement.
  

Target Competencies

Target Competencies

• AI-enabled process optimization and exception management
• Slotting, replenishment, and pick-path design using data and ML
• Computer vision for accuracy, safety, and quality assurance
• Labor planning, cost-to-serve analysis, and KPI storytelling
• Governance, data quality, and audit-ready evidence management
  

Outlines

AI Strategy for Warehouse Excellence

• Value mapping: where AI moves dock-to-stock, pick rate, accuracy, OTIF, and cost per order
• Readiness checklist: item masters, locations, units of measure, process timestamps, and data lineage
• Copilot patterns for SOPs, receiving checklists, exception notes, shift handovers, and KPI narratives
• Human-in-the-loop controls for decisions affecting safety, inventory accuracy, and service levels
• Quick-win portfolio and baseline metrics to demonstrate ROI early

Inbound, Receiving & Put-Away Intelligence

• Smart capture: ASN/label/OCR, damage detection, and count verification with computer vision
• Dynamic dock assignment and appointment scheduling to reduce waiting and dwell
• AI-assisted put-away: rules + learning for proximity, velocity, and compatibility
• Early quality signals: supplier scorecards, defect clustering, and auto-escalation paths
• Continuous improvement loops from inbound exceptions to master-data fixes