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Inventory Case Study

From manual spreadsheets to realtime, multi‑location inventory (MVP shipped Dec 2024)

+8% product adoption (10 months)
+18% recurring revenue

My role: Lead Product Designer - problem framing, IA, interaction design, high fidelity prototyping, usability testing, and design QA.
Team & cadence: Myself, 2 PMs, and India-based 10 engineers; collaborated on Figma/Teams (3×/week).

Problem

Users: When planning or purchasing for a project, we need inventory that’s accurate without manual reconciliation, so we avoid delays and rework
Stakeholders: Enterprise prospects required integrated inventory

Goals

Reduce manual tracking time and errors
Automate alerts and reorders
Integrate with purchasing/projects
Boost adoption and recurring revenue

Research Highlights → Design Implications

Methods: 6 user interviews, workflow analysis, competitive review
Time sink: 2–3 hours/day lost to reconciling stock; 15–20% manual error rate
Market signal: Integration required by all large prospects

Solution Journey

Problems

Users frequently missed reordering thresholds due to spreadsheet blind spots
Users felt error-prone manual reconciliation led to project delays and emergency purchases

How Might We

How might we automatically resurface critical inventory risks and eliminate manual reconcilliation?

Solutions

Surface risk: Visual low-stock and incoming PO components on the dashboard
Automation: PO-driven stock updates and status changes across locations

Key decisions & tradeoffs

Realtime vs. batch updates: Chose real-time inventory updates to ensure immediate accuracy for all locations. This required careful coordination across the purchasing, receiving, and project allocation flows, trading added technical complexity for instant visibility and higher user trust.
Unified inventory model: Combined project and stock inventory into a single shared system. This eliminated confusion and duplication but demanded intensive early collaboration with engineering to design a flexible data structure.
Manual vs. automated reordering: For MVP, launched with manual reorder and alerting to move quickly and validate workflows. Delayed full auto-PO generation and advanced analytics to Phase 2, recognizing that usability with core flows mattered more than immediate automation.
Deep integration: Opted for native integration with Cloud purchasing and project management tools over a simple standalone inventory module. This increased initial scope but met enterprise needs and reduced long-term risk of redundant or competing tools.
Clarity vs. feature volume: Prioritized a streamlined, transparent experience—single dashboard, intuitive alerts, clear allocation—over adding advanced features that could distract or confuse, especially during onboarding.

Dashboard builder with drag-and-drop widgets — Product detail: Tracks movement history, allocations, and reorder settings.

AI recommendations panel suggesting metrics with accept/edit controls — Surfaces low stock, incoming POs, and bulk actions, with a modal for precise asset management.

Prototyping & validation

High‑fidelity Figma prototypes tested repeatedly with users/stakeholders (Teams 3×/week). Validated receiving, transferring, allocating, and reordering flows for clarity and error reduction.

What shipped

Multiple location inventory, real‑time PO-tied updates, barcode‑assisted check‑ins, low‑stock alerting; integration with Cloud ordering and projects.

Quality & handoff

Documented edge/case states for all components
Design tokens/component in Figma, reviewed with engineering
Data model alignment for multi‑location tracking and allocations

Business impact

Reduced material waste and improved project accuracy - one customer uncovered “thousands of dollars of unused equipment.” Drove +8% adoption and +18% recurring revenue in the first year.

Lessons & next

MVP shipped earliest “PO received” loop for confidence - automation to follow
Next: auto‑PO creation from thresholds, mobile scanning, and stock analytics (velocity, dead stock).