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.

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.

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.