LogistX

1. Project Overview

LogistX is a SaaS Warehouse Management System (WMS) designed to simplify, automate, and optimize supply chain operations. Developed for real-world warehouse environments, it supports modular workflows for inbound, put-away, outbound, inventory, trips, and more.

It is tailored to meet the dynamic needs of logistics teams and operations managers. The goal of LogistX is to replace outdated Excel-based workflows and legacy systems with a seamless, intuitive, and scalable platform that enhances visibility, improves accuracy, and empowers decision-makers with real-time data.

2. Discovery & Alignment

The Discovery phase focused on clarifying the business vision and aligning all stakeholders on the core challenges and goals. Through stakeholder workshops with the GHOEX team, warehouse supervisors, and operational leads, I gathered key information about their workflow limitations and future aspirations. The major goals outlined included improving space utilization, minimizing human errors, and centralizing cargo tracking. Problem statements helped define our hypothesis and refine the scope.

"we lose track of inventory across zones"

"driver assignments are chaotic"

I also identified KPIs, including reduced request processing time, fewer QC errors, and improved user satisfaction scores. The goal of this phase was to understand the business vision, define the problem, and align with key stakeholders from logistics, warehousing, and technical operations.

3. Research & Data Gathering

To build empathy and understand the system's end users, I conducted extensive qualitative and quantitative research. I held interviews with key user types (admin, warehouse manager, supply chain head, and drivers) and surveyed 10+ users to assess pain points in trip planning and inventory tracking.

A competitive audit revealed that most logistics teams still rely on Excel or legacy WMS systems. Reviewing analytics and historical logs highlighted inconsistencies in QC processes and a lack of trip assignment visibility. I found that 70% of driver assignments were still handled via WhatsApp or phone, and irregularities were often reported manually.

4. Data Analysis & Synthesis

After collecting the data, I used affinity mapping and thematic analysis to uncover patterns and insights. I created personas to represent key user types:

• Admins

• Warehouse Managers

• Drivers

Their journeys through the system were mapped to reveal inefficiencies, especially in inbound quality control, trip dispatch, and cross-warehouse visibility. These insights helped me define clear pain points and identify opportunities to introduce automation, commodity grouping, and improved dashboards to aid faster decision-making.

Maya is a 34 years old Operations Manager with +8 years of experience in logistics and supply chain.

5. Ideation & Information Architecture

With user needs and insights in hand, I began generating and evaluating potential solutions. Brainstorming sessions helped shape concepts for critical modules like trip planners, inbound/outbound dashboards, and inventory grouping by commodity. I defined the platform’s content structure and user flows, mapping how various user roles would navigate the system. This resulted in a clear information architecture that supported both functional depth and ease of use, balancing admin control with warehouse operator speed.

6. Design & Prototyping

I moved from wireframes to interactive, high-fidelity prototypes using Figma. Early sketches helped validate layouts and navigation before progressing to complete design systems. I introduced both Kanban and list views for inbound and outbound requests, dedicated screens for shippers and vehicles, and consistent modal flows for exports, irregularities, and confirmation actions. Special attention was given to space management with hierarchical visualizations and flexible commodity filters.

Real-time dashboard for monitoring key metrics and operational insights.

Drag-and-drop Kanban board for managing inbound requests with effortless status updates.

Structured list view for managing inbound requests with inline status updates and sorting.

Assigned users can set requests as 'Not Suitable' and log irregularities directly.

Smart Scan uses AI to automatically extract and fill outbound details from hand-written packing lists, reducing manual entry and ensuring shipment accuracy.

A hierarchical chart visualizes inventory distribution across zones, shelves, and bins.

View customer-specific inventory details directly within their profile.

Manage platform users through a structured list with roles, statuses, and access controls.

A web app for drivers to manage deliveries, cash collections, payouts, and history.

7. Validation & Testing

To validate my designs, I conducted moderated usability testing with warehouse staff. The feedback confirmed the usefulness of features like grouped commodities, intuitive trip assignment, and real-time scanning status. Users appreciated the clarity of label printing flows and the visibility into driver assignments. Based on this feedback, I iterated on button placements, label content, and modal step-by-steps to ensure smoother interactions.

8. Handoff & Continuous Improvement

With design finalized and tested, the development handoff was completed via Figma Dev Mode, Jira tickets, and documented flows in Notion. Post-launch, dashboards were monitored to collect data on usage patterns, and feedback loops were established for iterative improvement. Features like label printing, thermal printer compatibility, and export scheduling were closely tested. The case study reflects my commitment to building a data-backed, user-centered logistics platform that evolves over time.