Company
iCHEF
Time
3 months (From research to launch)
Team
2 Designers, 1 PM, 9 Engineers (FE, BE, iOS, QE)
Role
Sr. Product Designer (Led end-to-end UX, user research, product definition, and cross-functional execution)
I led the end-to-end UX for scheduled orders, aligning 4 roles and rebuilding time logic to ensure clarity and accuracy. This enabled restaurants to accept online scheduled-orders and reduce staff burden—resulting in 53% adoption within the first month.
My Impacts
Enabled 12,000+ Restaurants
53% Adoption in One Month
71% Boost in Daily Activation
2+ Weeks Saved in R&D
The Problem
No Scheduled Pickup: Top User Need, High Risk of Order Loss
iCHEF served 12,000+ restaurants across Asia, but its online ordering system only supported ASAP orders. We prioritized 100+ monthly merchant requests for scheduled pickup, as ASAP-only orders caused manual workarounds, errors, and incorrect performance metrics.
Business Goal
Increased Order Volume to Drive GMV and Subscription Upgrades
We prioritized scheduled pickup—the most requested core feature—based on a strategic hypothesis: increasing order volume would drive more merchants to upgrade their POS subscription plans and GMV. The feature targeted a critical gap in the ordering system and aligned with both user demand and infrastructure readiness, unlocking measurable growth in activations and GMV within the first month.
Main Challenge
Handled Cross-Role Flows and Time Logic Integration at Scale
We needed to support 4 distinct user types (operator, customer, staff, kitchen) with different tools, workflows, and habits. To ensure seamless adoption, we designed role-specific experiences and aligned them across the full order journey.
We also introduced a new time logic system to support scheduled pickups, requiring careful validation across timestamps, kitchen slips, and meal prep timing—ensuring new logic didn’t break business-critical operations.
Design Process
Design Plan
Shaped Problem Scope and Aligned Team Through Strategic Mapping
After receiving the project brief, I created a Design Plan and Service Plan based on the PRD, mapping out key considerations across business goals, user flows, and technical constraints. This helped define a clear problem scope, enabled team alignment early, and reduced miscommunication.
In parallel, I developed a strategic research plan focused on system limitations, restaurant workflows, and online ordering operations. This provided the domain knowledge and context we needed to make informed design decisions that addressed the needs of both staff and customers.
Initial Research Stage
Framed the Right Problem Through System-Level Discovery
After receiving the PRD, I created a design plan that outlined key assumptions, questions, and next steps. Building on that, we conducted research from three angles—restaurant staff, customers, and system behavior—to understand user mental models, motivations, and workflows. These insights shaped our design strategy and helped prioritize critical elements like timing logic and role-specific interactions.
Understanding Restaurant
01 | Why This Approach
Mixed-Methods Research to Uncover Restaurant Behaviors and Mental Models
I led and executed a strategic, layered research process to frame the problem space and uncover restaurant-specific constraints. This included building domain knowledge from user feedback, guiding in-depth interviews, and validating insights through behavioral data. I also mentored our junior designer throughout the process, aligning research execution with design strategy.
This approach helped us quickly align on real-world problems, reduce research blind spots, and prioritize the most impactful features for both staff and operations.
Step 1 | Feedback Analysis
Reviewed 84 feedback entries to identify recurring pain points and shape targeted interview questions.
Step 2 | Interview In Depth
Conducted 8 in-depth interviews and observations to uncover workflows, motivations, and edge cases across restaurant types.
Step 3 | Behavioral Data Analysis
Analyzed usage data to validate patterns and reveal system-level constraints not surfaced in interviews.
02 | Key Insights
❶ Restaurants Fear Missed Orders, Losing Revenue Opportunities
Customers often place orders during off-peak hours, especially at night—highlighting risks of missed revenue and operational gaps. By understanding how restaurants make scheduling decisions, I identified where design could reduce workload and increase revenue. This insight led me to address edge cases where restrictive pickup settings blocked orders, informing spec updates that improved conversion without disrupting operations.
Restaurant Motivation
❷ Manual Workflows Increase Risk and Staff Overload
Current workflows rely heavily on individual staff memory and manual coordination, making operations prone to miscommunication and prep errors. By mapping the end-to-end store journey, we pinpointed breakdowns in flow and identified system-level opportunities to streamline task ownership and reduce variability.
Restaurant Workflow Journey & Opportunity
❸ Pickup Timing Varies by Operating Model, Requiring Flexible Configuration
Business hours and operating models (e.g., split shifts, single shifts, 24/7) directly shape how pickups are managed. To reflect real-world workflows, the system needs flexible pickup logic—anchored to business hours and auto-adjusting during closures—to ease staff load and prevent missed pickups.
Typical Restaurant Operating Models
Understanding Customer
01 | Competitive Benchmarking
Grounding UX Decisions in Competitive Benchmarking to Drive Familiarity and Reduce Friction
To accelerate product decisions on this foundational feature, I conducted targeted benchmarking across regional leaders and adjacent platforms—given that scheduled ordering is a widely supported baseline capability.
Since over 80% of iCHEF’s subscribers are Taiwanese restaurants, I focused on the most adopted local ordering services—Foodpanda, Uber Eats, Ocard, and Nidin. These platforms revealed proven UX patterns for pickup flow, time formatting, and error prevention, as well as deep insights into scheduling behavior and store availability logic. This helped me anchor key specifications in industry-tested standards, align with local mental models, and reduce user friction and decision fatigue.
Selection Criteria
02 | Key Insights
Design Principle: Use Contextual Time Design to Reduce Friction and Improve Clarity
I adopted best-in-class UX patterns for time display, store status, and pickup slot formatting. This included combining date + time + label (e.g., “Now” or “Tomorrow”), showing next availability for closed stores, and using concise microcopy to clarify status—all to reduce ordering errors and support fast decision-making.
Benchmarking Highlights
Understanding System
01 | Why This Approach
Auditing System Dependencies to Prevent Conflicts and Scale Scheduled-Order Logic
To ensure the Scheduled Order feature didn’t conflict with existing logic, I conducted a system-level audit across all time-related dependencies. Scheduled Orders often required restaurants to confirm later—especially during off-hours or when prep review was needed—highlighting the need for a logic review beyond typical ASAP flows. This approach ensured the system remained scalable and conflict-free as we layered new functionality.
Mapped system dependencies to eliminate logic conflicts and support scalable scheduling.
02 | What the Audit Uncovered
Identified a Logic Conflict Causing Order Loss—And Removed It to Enable Long-Term Scheduling
One major conflict surfaced: the system auto-cancelled any order not accepted within 10 minutes—a rule originally designed for ASAP orders. I first investigated the rationale behind this behavior, then mapped its technical implications, evaluated trade-offs, and surfaced relevant user feedback. Many operators reported losing valid orders during peak hours due to this timeout. I ran a risk assessment and led stakeholder discussions to propose retiring the rule. After aligning with engineering and operations, we successfully removed it—reducing order loss risk and enabling sustainable Scheduled Order support.
Strategy Stage
Defined Strategy That Balanced Business Goals With Real-World Constraints
I defined the product strategy by translating goals into actionable design plans, rooted in real-world constraints across restaurants, customers, and backend systems.
This stage enabled early alignment with engineers and PMs, shaping a shared understanding of feasibility, scope, and user-centered priorities.
Business Alignment
Clarified UX Priorities by Deconstructing Business Goals
To align the team on what mattered most, I deconstructed top-level business goals into specific UX flows and impact areas. Instead of treating the scheduled-order feature as a standalone tool, I mapped how it could influence key growth metrics—specifically GMV and operational efficiency.
I worked backwards from business objectives to identify user flows that directly contributed to revenue lift, conversion, and order accuracy. This allowed me to prioritize design specs that balanced user value with engineering capacity—such as surfacing pickup times more clearly, supporting flexible order configurations, and simplifying setup for both new and senior users.
By translating abstract goals into concrete UX decisions, I helped the team stay focused on high-impact outcomes without expanding scope unnecessarily.
Transformed top-down goals into product decisions that improved outcomes without bloating scope.
MVP Feasibility Planning
Evaluate Feasibility Early to Prevent Scope Creep
Designing new features often starts with ambiguity. One of the hardest parts is defining what success should look like—and which specifications actually move us toward that goal. To avoid over-designing in the dark, I created a prioritization framework to help the team assess user needs and technical feasibility early on.
This approach ensured we solved the right problems without overcommitting limited resources. We evaluated specs based on fundamental requirements, user coverage, development cost, and availability of workarounds—so we could focus on must-haves rather than “nice-to-haves.”
After that, I broke down the specs into modular tasks. I partnered with our junior designer to co-assess task complexity, define ownership boundaries, and assign entry-level flows that matched both development goals and learning opportunities.
Transformed top-down goals into product decisions that improved outcomes without bloating scope.
Early Team Sync Up
Defined Scenario Scope to Align MVP and Surface Risks Early
To ensure alignment on what the MVP should support, I mapped all scheduled-order scenarios across roles and channels—identifying time-based conditions, state transitions, and system dependencies.
This helped define the boundaries of MVP coverage, clarify edge cases early, and reduce surprises during handoff. I used the flow as a shared reference to sync with PMs and engineers on scope, prioritize risk-prone paths, and streamline implementation planning.
Mapped use cases to reduce surprises and align MVP feasibility across teams.
UX Persona
Built Personas to Bridge Insights With Real User Behaviors and System Familiarity
Before moving into ideation, I translated early research findings into actionable personas grounded in real-world user behavior, system constraints, and familiarity levels. Rather than treating personas as abstract archetypes, I focused on practical usage patterns—capturing differences in experience level, mental models, and task expectations across roles.
These personas helped the team stay aligned on role-specific UX priorities and ensured we addressed both high-frequency workflows and onboarding clarity. They also provided a reference point across iterations, reducing design misalignment and helping us make confident, user-centered decisions.
Design Solution
Designed One Unified Experience Across Online and In-Store Operations
This solution bridged online behavior with in-store operations, redesigning not just the interface but also the timing logic to reduce friction and errors. One unified experience served three roles—operator, staff, and customers—solving for time, clarity, and trust across the order journey.
I led the design of the core flows, including role-based entry points and pickup-time logic. What follows highlights the decisions I drove and the systems I built to support a seamless cross-channel experience.
Merchant's Platform Solution 01
71% Growth in Daily Activation by Realigning Navigation with User Mental Models
The Situation
In the initial release, the feature was followed by IA, buried under “Order Settings,” creating a major discoverability gap. Early pilot testing showed that 3 out of 4 users navigated to the wrong location—causing confusion, blocking adoption, and risking long-term scalability.
Strategic Approach
Pilot test revealed a mismatch between IA logic and user mental models.I proposed a low-cost, high-impact fix—relocating the feature to the main menu with a clearer label. This reduced confusion and drove a 71% spike in daily activations without engineering overhead.
Strategic restructure based on user flow, not just surface-level UI.
My Learning
I initially over-indexed on IA structure without validating user expectations, which led to late-stage confusion and engineering rework. This experience reinforced a core insight: navigation clarity isn’t just UX polish—it’s critical to business growth. Going forward, I prioritize early validation through usability testing and partner proactively with PM and Engineering to surface friction points before committing to structure.
Merchant's Platform Solution 02
Faster Pickup Time Setup with Import Logic Based on User Insights
The Situation
The original UI for pickup time setup was outdated and overly manual—users had to create each time slot from scratch, leading to confusion and delays. A full redesign would have required significant engineering effort with limited impact, so I shifted focus to a more targeted question: How might we help restaurant owners complete setup efficiently using the existing legacy system?
Strategic Approach
To reduce friction without deep engineering effort, I identified a shared pattern in store behavior: most stores already aligned pickup hours with business hours. Based on this, I designed a one-click “Import Business Hours” solution and partnered with Education and Engineering to ensure smooth rollout across diverse stores.
My Learning
In this setup, I embedded hidden logic that restricted pickup time slots to store business hours, aiming to mirror real-world operations. However, this added design and engineering complexity, especially around error handling. Looking back, I realized strict, hidden constraints can backfire. In the future, I’d evaluate such rules more carefully against team cost.
Merchant's Platform Solution 03
Avoid Missed Orders by Smart Scheduling
The Situation
Some stores only accept pre-orders one day in advance and close on weekends, causing gaps when customers place orders on Saturdays—no valid pickup dates are returned. If the system strictly followed calendar days, stores risked missing weekend orders and losing revenue.
Strategic Approach
To mitigate order loss without adding user friction, I introduced intelligent fallback logic that automatically suggests the next valid pickup day. This decision was driven by operational reality across stores and validated through real-world sales scenarios to ensure both usability and revenue protection.
My Learning
This project reinforced the value of designing logic based on operational truth—not just system rules. By mapping store behavior early and aligning with sales reps, I avoided misalignment later in development. It taught me the value of building system resilience around business edge cases without overcomplicating the UI.
POS Solution 01
A Compact Interface for Quick Scanning and Fast Decisions
The Situation
Orders require quick, confident decisions—often within seconds—on pickup time, printing, or acceptance. However, the existing UI lacked visual hierarchy and flexibility to support real-time store operations across diverse workflows.
Strategic Approach
To support fast scanning under pressure, I prioritized reducing cognitive load without breaking habits. Drawing from usage data and store interviews, I refined the existing layout to highlight decision-critical info like time and price, while preserving button positions. This enabled smoother adoption across store types and improved operational clarity with minimal training overhead.
My Learning
Early in the process, I focused too heavily on redesigning and adding new features—without fully considering store-level behavior. Real-world testing revealed that minimizing layout changes while reinforcing familiar flows significantly improved confidence and efficiency. This experience reframed my design priorities: clarity and context over complexity. It also helped align cross-functional decisions around what truly mattered to operations, not just product aesthetics.
POS Solution 02
The “Accept (Print Later)” Button: Flexible for Diverse Store Operations
The Situation
User research showed a gap between system behavior and store workflows. Some merchants skipped the “Accept (Print Later)” button because it didn’t align with their print-review habits. This insight prompted a logic redesign to preserve operational efficiency while increasing flexibility.
Strategic Approach
To address friction without changing store behavior, I redesigned the logic and labeling to better reflect real-world usage. By keeping the default logic consistent but introducing an optional “Print Later” path, I supported multiple workflows without adding risk or engineering overhead.
My Learning
This case showed how a small UX detail—like a label—can impact behavior. Instead of forcing adoption through design, I aligned system defaults with existing workflows. This reinforced a key principle: flexibility and operational alignment often drive adoption more than feature additions.
Design Solution - Online Store 01
Time Format and Mechanism Redesign to Enhance User Understanding
The Situation
Inconsistent time formats and pickup logic across the UI caused user confusion and internal misalignment. Critically, the ASAP pickup time was calculated based on when the store accepted the order—leading to cases where scheduled orders appeared earlier than ASAP orders if the store accepted ASAP orders slowly. This confused and frustrated customers.
Strategic Approach
To fix the trust gap, I redesigned the logic to reflect real-time order behavior, shifting the calculation to the user’s order time. I also adopted text-based formats and created storyboards to align stakeholders and ensure teams clearly understood how pickup logic worked across edge cases—minimizing ambiguity without disrupting store flow.
My Learning
Redesigning a foundational system logic—like timing mechanisms—can introduce friction if not backed by evidence. I learned that leading this kind of change requires more than just UX rationale; it takes strategic storytelling, supporting data, and operational context to align cross-functional teams and mitigate risk to align cross-functional teams and ship confidently with user trust intact.
Pilot Test Stage
Pilot Test is the key to the new feature, it aims to identify and minimize any major issues that could disrupt restaurant operations.
Pilot Test
Identify key issues before Launch with 4 Pioneering Restaurants
We collaborated with 4 restaurants that had requested the feature—each with distinct operational models—to help uncover real-world issues. Their feedback, including concerns about navigation and wording, guided us in refining the experience through further iteration.
Cross-Function Collaboration
Streamlined Cross-Functional Alignment to Speed Up Delivery
Illustrated timeline to align order touch-points across PM, engineering, and QA teams.
❶ Concise Design Documentation
Simplified docs to improve team alignment and speed up development.
❷ Unified Time Format
Reduced inconsistency by standardizing time formats across 5 teams.
❸ Streamlined UX Writing Process
Identified translation gaps, implemented workflows with FE & QA, saving 1 week.
Leadership & Mentorship
