Jayga · Product Decisions, Team of 8
A flexible inventory management system for fragmented warehouse workflows
A warehouse field visit surfaced the insight that shaped the business model: unstacked inventory and inconsistent rack usage were quietly cutting revenue per square metre. It included field research which informed product and business decisions together. I owned product decisions for a team of eight: strategy, research, workflow architecture, design direction, and cross-functional delivery, from that first field observation to shipped system.
3 days → 2–3 hrs
Order processing time
3×
Revenue potential from storage optimisation
8
Product decisions owned across the team
Shipped faster
From owning product decisions directly
Problem
The opportunity was operational fragmentation, not just missing software.
Inventory and warehouse workflows were managed through spreadsheets, manual logs, and disconnected processes that couldn't scale with growing retail and ecommerce operations. Orders took days to process, warehouse space was under-optimized, and users had poor visibility across storage, billing, and fulfillment.
The product hypothesis: a design-driven operating system could unify these workflows into a clearer, more scalable experience — treating storage, payments, and fulfillment as one model instead of isolated modules.
Field visit — warehouse aisle observation during active operations
Research
Personas, cost-demand modelling, surveys, and field visits found the right wedge.
The early work mapped potential user segments — ecommerce sellers, event organizers, NGOs, distributors, agricultural businesses, raw-material suppliers — then narrowed through a cost-demand matrix scoring item value, maintenance burden, quantity, storage accessibility, and growth potential.
Surveys and field visits then validated what users needed most: flexible storage models, better inventory visibility, and workflows that reflected real warehouse constraints.
Proto personas across target segments
Cost-demand scoring matrix
NGOs and raw-material manufacturers emerged as stronger fits. Unstacked goods and inconsistent rack usage were directly reducing warehouse efficiency and revenue — a field observation that directly shaped the product model.
Survey findings — cost-effectiveness, proximity, security, and retrieval were the strongest decision factors
Key Decisions
Four decisions shaped how the product became a real operating system.
The product direction was intentionally design-led — meaning architecture, delivery model, and UX were developed together.
Field research uncovered the pricing model
- —A cost-demand matrix scored potential segments on item value, maintenance burden, quantity, storage accessibility, and growth potential
- —NGOs and raw-material manufacturers emerged as stronger fits than generic ecommerce warehousing
- —Field visits surfaced the constraint that mattered most: unstacked goods and inconsistent rack usage were quietly cutting revenue per square metre
Grid-based storage model, priced from that finding
- —Warehouse space was broken into assignable grids with pricing driven by location, rack condition, and security
- —This was the direct answer to the field observation: pricing had to account for how usable a rack actually was
- —Physical operations became structured product data: grid IDs, item quantities, and verification status
Design-led delivery cadence
- —Instead of a complete design system upfront, the team was aligned around consistent principles and flow clarity as implementation constraints
- —A project charter, Notion-based Kanban, and lean sprints was used for faster iteration and alignment
- —Owning product decisions directly reduced ambiguity and got the MVP to market faster
Platform split by user context
- —Web handled request submission, billing, and admin oversight — designed for decision-makers and client-facing workflows
- —Mobile handled warehouse execution and item verification — optimised for field staff working in physical space
- —Keeping the platforms distinct prevented UI compromises that would have hurt both user groups
Design-led delivery map — showing how stakeholder feedback, design, and execution were kept aligned
How It Works
The end-to-end flow was mapped before any screen was designed.
The system had to coordinate request submission, warehouse intake, grid assignment, item verification, billing, and delivery — across web and mobile contexts. Those dependencies were turned into flowcharts and process maps first, then used to guide screens, backend coordination, and UI patterns.
Web handled request handling, billing, and operational oversight. Mobile handled warehouse execution and item verification. The result was a more legible operational surface for admins, clients, and warehouse managers — each seeing only what mattered to their context.
End-to-end order process — from storage request through payment, intake, grid assignment, and invoice generation
Mobile verification — item-to-grid assignment with quantities and verification status made explicit
Admin dashboard — active grid requests across warehouse, user, item, quantity, duration, and status
Outcomes
The product improved operational speed and created a clearer business model for storage.
3 days → 2–3 hrs
Order processing moved from multi-day lag to a few hours
3× revenue potential
Storage optimisation and rack-based pricing decisions increased revenue per square metre
Reduced time to ship
Owning product decisions directly cut ambiguity without cutting scope
Operational clarity
Users gained a visible, traceable workflow across request submission, storage, and fulfillment
Jayga shows a different side of technical product design than Zevian: less AI and more operational systems thinking, service design, field research, and workflow architecture. The throughline is the same — product direction, system design, and execution quality developed together instead of as separate tracks.