AI-Powered Warehouse Task Assignment System
Role: UX & AI Research Lead | Client: Multi-Site Logistics Operations
Brief:
Developed and implemented an AI-driven warehouse task allocation system designed to optimize labor efficiency, reduce idle time, and align workforce distribution with real-time operational demands. Leveraged machine learning models to assign tasks based on skill set, workload, location, and priority while ensuring transparency, fairness, and compliance with labor guidelines.
Product Framing & Success Criteria
Product Goal: Improve labor efficiency and operational throughput by replacing manual task assignment with a transparent, AI-driven system that dynamically balances workload, skill fit, and fairness.
Primary Users: Warehouse supervisors, shift leads, and frontline warehouse employees executing assigned tasks.
Business Constraints: Labor regulations, fairness and bias concerns, workforce trust, real-time operational variability, and limited tolerance for opaque automation.
Success Metrics: Task allocation time, workforce utilization, productivity lift, employee satisfaction, and auditability of AI-driven task decisions.
Key Objectives
Replace manual task assignment with a predictive AI model to dynamically optimize workforce utilization.
Reduce idle time and minimize bottlenecks in warehouse operations.
Ensure AI task allocation complies with operational governance and labor fairness standards.
Provide real-time visibility of task assignments to supervisors and workers via dashboard and mobile application.
Process & Responsibilities
Conducted observational studies and time-motion analysis to document existing task allocation workflows.
Partnered with AI engineers to design a task assignment algorithm factoring in skill level, proximity, current workload, and task urgency.
Led usability testing sessions with warehouse teams to ensure AI recommendations were transparent and trusted.
Designed the human-in-the-loop override system for supervisors to adjust AI recommendations without breaking workflow integrity.
Developed compliance tracking modules for labor hour distribution and fair allocation governance reporting.
Key Product Decisions & Tradeoffs
Fairness & Transparency vs. Pure Optimization: Balanced efficiency gains with explainable task assignment logic to ensure perceived fairness and compliance with labor guidelines.
Human Oversight vs. Full Automation: Implemented supervisor override functionality to maintain trust, accountability, and operational flexibility.
Algorithm Complexity vs. Adoption: Prioritized interpretable assignment factors (skill, proximity, workload) over more complex models to support user understanding and adoption.
Scalability vs. Local Context: Designed a configurable system that supported site-level nuances while maintaining consistent governance and reporting standards.
Outcome
Reduced task allocation time by 80% compared to manual methods.
Increased overall workforce productivity by 25% across tested sites.
Improved employee satisfaction scores due to perceived fairness and workload balance.
Established a replicable governance framework for AI-based operational decision-making.
