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Concept AI Product Retail Ops

Uniqlo DWS: Intelligent Replenishment

A conceptual redesign of Uniqlo’s internal DWS app, proposing an AI-assisted replenishment and planning system to reduce lost sales and free supervisors from manual, low-value work. Based on real operational challenges observed while working in-store, not on internal company data or an official initiative.

Client

Personal project

Industry

Retail / Fashion

Role

Product Designer (concept)

Duration

1–3 weeks

At a glance

The challenge

Manual, reactive workflows in Uniqlo’s internal DWS app left staff without real-time priorities and supervisors buried in low-value planning and troubleshooting, contributing to lost sales on best-selling items.

The outcome

A concept redesign proposing three connected systems (AI-assisted replenishment, automated scheduling, and centralized alerts) reframing DWS from a passive record-keeping tool into a proactive decision-support system.

Core pillars

Driven alerts

Concept

Speculative redesign

Overview

During a year working in a Uniqlo store, I used DWS, the internal system for stock management and task scheduling, every day. It worked well as a system of record: inventory counts, task lists, and customer feedback all lived there. But none of it translated into a next action. A count of “150 units” said nothing about whether the shelf was actually empty, and task lists didn’t change when the floor did.

This concept started from that gap, not from a formal research engagement or access to internal data, but from direct, first-hand observation of daily store operations, combined with product thinking about what a system like DWS could reasonably do with the data it already had. It is a speculative redesign, unaffiliated with Uniqlo. The interface concepts shown here were designed and prototyped with Claude.

01 / Challenge

The core challenge was a reactive, manual system that created problems on two levels: on the floor, and in the back office.

◆ Inventory shown without context: “150 units in the system,” with no signal that the shelf was already empty

◆ Static task lists that didn’t reflect real-time store priorities

◆ Supervisors spending hours a day building schedules by hand

◆ Supervisors patrolling the floor to catch problems the system should have surfaced on its own

◆ Task and schedule changes communicated informally, with no confirmation or context

02 / Solution

The proposal centers on three connected systems, reframing DWS from a passive record-keeping tool into a proactive decision-support system.

Intelligent Replenishment System (IRS)

An AI-assisted layer that reads real-time sales and inventory signals to generate and prioritize restocking tasks automatically, turning “150 units in the system” into “Shelf empty, restock 15 units now.”

Intelligent Planning Module

A supervisor tool designed with a target of automating around 80% of schedule creation from sales patterns and staffing rules, so review and approval take minutes instead of hours. This is a design target for the concept, not a measured result.

Centralized Communication

A high-priority notification layer that surfaces task and schedule changes with confirmation and context, closing the gap left by informal, undocumented communication.

The Intelligent Replenishment System drafts both the task and the best assignee from live sales and stock signals, but nothing moves until a person approves it.

One reallocation updates the live floor, the schedule, and each person’s rotation at once: the planning layer keeping everything in sync from a single decision.

Centralized communication with built-in trust: every task explains itself, every skip teaches the AI, and late work escalates on its own.

Challenges along the way

Because this is a concept rather than an implemented product, the open question isn’t technical feasibility, it’s trust. Would staff act on an AI-generated task without double-checking the shelf themselves? The design leans on transparency, showing why a task was generated, not just what to do, as the main lever for adoption.

03 / Key learnings

Improving employee tools has outsized impact

B2E changes ripple directly into customer experience and revenue. Internal efficiency is the foundation of external excellence.

Data without context is noise

The shift from “150 units” to “Shelf empty, restock 15 now” is where design turns a data point into a decision.

Automate the repeatable, protect the human

The goal isn’t replacing the supervisor, it’s freeing their time from repetitive tasks so they can focus on training and the sales floor.

Observation has limits

Grounded in real, lived problems, but without access to DWS data or user testing with other staff, it remains a hypothesis. The natural next step would be validating the IRS logic and the planning module with actual users.

04 / Result

Scale

Two user groups addressed directly: sales-floor staff and store supervisors

Three integrated systems: replenishment, planning, and communication

Grounded in real day-to-day retail operations, not a hypothetical brief

Mobile-first, sized for phone up to tablet, where store work actually happens

Impact & recognition

Faster response to low stock on best-sellers, where delays become lost sales

Supervisors reviewing and approving a schedule instead of building one

Clear, prioritized tasks replacing static, context-free lists

Fewer miscommunications around last-minute task or schedule changes

The same system from the staff side: clock in, work the rotation, stay break-compliant, and get a summary at clock-out. The second of two audiences.

DWS taught me that the highest-leverage design isn’t always customer-facing. A companion piece to “UX Is Folding Shirts Too,” this concept comes from the same year behind the counter, arguing that the tools we hand employees quietly shape everything a customer feels.

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