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Scheduled Ordering

Launched a restaurant scheduled-ordering and pickup system with 53% first-month adoption

Role

Sr. Product Designer

Contribution

Led end-to-end strategy, multi-user and cross-platform experiences, system-level time-logic integration, cross-functional collaboration, and mentorship for junior designers.

Duration

4 months

Company

iCHEF (Restaurant Tech/B2B SaaS)

THE PROBLEM

The system didn’t support scheduled future orders, causing revenue loss, poor user experiences, and staff overload

Our ordering system did not support scheduling, creating a clear gap with merchant needs and becoming our top-requested feature. This limitation led to off-hour revenue loss, inaccurate reporting for merchants, and poor experiences for both customers and staff.

BUSINESS OPPORTUNITY

A core lever in our online ordering growth strategy, expected to increase online GMV and POS subscriptions.

Scheduled ordering showed strong market pull. We assumed this feature would increase online GMV and strengthen POS subscription. It is a core infrastructure piece for our online ordering growth strategy, supporting future initiatives such as expanding payment revenue and enabling delivery features.

MAIN CHALLENGE

Aligning multi-role behaviors, time rules, and platform constraints into one system while minimizing workflow disruption

We needed to align four roles across multiple platforms under a unified time-logic system. Restaurant operations varied widely, workflows were fast and fragile to change, and a constrained legacy technical ecosystem with diverse scenarios and many edge cases required careful trade-offs to maintain consistency and reliability.

DESIGN SOLUTION

A scheduled pickup system designed for real restaurant workflows, reducing lost orders and operational friction

Merchant Platform for Restaurant Owners

Clear, low-effort setup through one-click activation and smart defaults

Trade-off: Embedded Existing IA vs. Dedicated Entry Point

Restaurant owners needed a low-effort way to enable scheduled ordering. I evaluated whether a dedicated onboarding flow would justify the development investment and chose a lean activation approach instead, using a single Scheduled Orders configuration page for both setup and adjustments.

I also evaluated whether the feature should follow the existing information architecture or be exposed as a side menu item. While following IA preserved system consistency, pilot testing revealed low discoverability, so I added a dedicated Scheduled Orders entry to the side menu, increasing activation by 71%.

❌ Ideation A: Reusing existing IA with low discoverability and high density
✅ Ideation B: A dedicated entry point designed for clarity and future scale
❌ Ideation A: Reusing existing IA with low discoverability and high density
✅ Ideation B: A dedicated entry point designed for clarity and future scale
❌ Ideation A: Reusing existing IA with low discoverability and high density
✅ Ideation B: A dedicated entry point designed for clarity and future scale
❌ Ideation A: Reusing existing IA with low discoverability and high density
✅ Ideation B: A dedicated entry point designed for clarity and future scale

POS for Restaurant Staff

A minimal-disruption workflow for fast order management

Trade-off: Structural Change vs. Workflow Continuity

Restaurant staff depend on fast, familiar POS workflows. Through early user testing, it became clear that larger structural changes would slow down daily operations, so I kept existing interaction patterns and worked scheduled-order needs into the current flow.

Based on research showing that restaurant operations vary widely, I made the Print Later button configurable and available by default to all restaurants, not just those using scheduled orders, to support adoption.

❌ Before
Misaligned with restaurant workflows, limiting usability and scalability
✅ After
A clear, workflow-aligned layout designed for long-term product expansion
Configurable slip-print behavior to support diverse restaurant workflows and maximize adoption
Layout Ideations
Designed and tested multiple UI concepts to evaluate trade-offs and determine the final direction
Button Ideations
Preserved familiar “Accept” behavior and added “Print Later” to support diverse workflows without retraining.
❌ Before
Misaligned with restaurant workflows, limiting usability and scalability
✅ After
A clear, workflow-aligned layout designed for long-term product expansion
Configurable slip-print behavior to support diverse restaurant workflows and maximize adoption
Layout Ideations
Designed and tested multiple UI concepts to evaluate trade-offs and determine the final direction
Button Ideations
Preserved familiar “Accept” behavior and added “Print Later” to support diverse workflows without retraining.
❌ Before
Misaligned with restaurant workflows, limiting usability and scalability
✅ After
A clear, workflow-aligned layout designed for long-term product expansion
Configurable slip-print behavior to support diverse restaurant workflows and maximize adoption
Layout Ideations
Designed and tested multiple UI concepts to evaluate trade-offs and determine the final direction
Button Ideations
Preserved familiar “Accept” behavior and added “Print Later” to support diverse workflows without retraining.
❌ Before
Misaligned with restaurant workflows, limiting usability and scalability
✅ After
A clear, workflow-aligned layout designed for long-term product expansion
Configurable slip-print behavior to support diverse restaurant workflows and maximize adoption
Layout Ideations
Designed and tested multiple UI concepts to evaluate trade-offs and determine the final direction
Button Ideations
Preserved familiar “Accept” behavior and added “Print Later” to support diverse workflows without retraining.

Online Ordering Website for Customers

A predictable, flexible pickup-time experience, avoiding confusion

Trade-off: Numeric Dates vs. Text-Based Dates

Customers often faced unclear store status and confusing pickup-time options, leading to friction and lost orders. To reduce learning cost, I reviewed patterns across major platforms and selected an interaction approach that felt familiar. One deliberate decision was how dates are presented. Numeric dates are compact but can be ambiguous across cultures, so based on cross-platform research across six major services, I chose text-based dates to reduce misinterpretation and increase confidence at the moment of order placement.

System Level Mechanism - Pickup Time

Aligning Pickup Time Logic with Customer Expectations to Prevent Lost Sales

Trade-off: Staff Acceptance Time vs. Order Placement Time for ASAP Calculation

I chose to calculate ASAP pickup time at order placement instead of staff acceptance, ensuring ASAP always appears earlier than scheduled options and matches customer expectations. While this can tighten preparation time, 93% of restaurants accept orders within one minute, and research showed restaurants prefer this risk over losing orders, with the flexibility to reject or adjust orders when needed.

At the same time, pickup options are generated from each restaurant’s configured pickup windows, so customers only see times the restaurant can realistically fulfill.

The UI: Customers see the final system-calculated ASAP pickup time at order confirmation
Trade-off: Staff acceptance vs. order placement for ASAP timing
To keep ASAP earlier than scheduled slots and prevent lost orders.
System Logic Behind Pickup Time Availability

When customers arrive on the ordering page, the system calculates available pickup options based on the restaurant’s configured pickup windows and slot intervals.
The UI: Customers see the final system-calculated ASAP pickup time at order confirmation
Trade-off: Staff acceptance vs. order placement for ASAP timing
To keep ASAP earlier than scheduled slots and prevent lost orders.
System Logic Behind Pickup Time Availability

When customers arrive on the ordering page, the system calculates available pickup options based on the restaurant’s configured pickup windows and slot intervals.
The UI: Customers see the final system-calculated ASAP pickup time at order confirmation
Trade-off: Staff acceptance vs. order placement for ASAP timing
To keep ASAP earlier than scheduled slots and prevent lost orders.
System Logic Behind Pickup Time Availability

When customers arrive on the ordering page, the system calculates available pickup options based on the restaurant’s configured pickup windows and slot intervals.
The UI: Customers see the final system-calculated ASAP pickup time at order confirmation
Trade-off: Staff acceptance vs. order placement for ASAP timing
To keep ASAP earlier than scheduled slots and prevent lost orders.
System Logic Behind Pickup Time Availability

When customers arrive on the ordering page, the system calculates available pickup options based on the restaurant’s configured pickup windows and slot intervals.

System Level Mechanism - Pickup Date

Pickup dates are driven by configured pickup windows, not calendar dates, to prevent missed sales

Trade-off: Calendar-Based Dates vs. Pickup-Window-Based Dates

Pickup dates are generated from configured pickup windows, not calendar dates. Research with the sales team showed that restaurants are most concerned about losing orders when availability appears unavailable. Without this approach, short advance booking limits would frequently result in “no available dates” and lost orders.

❌ Before: Calendar-based pickup dates often showed no availability and led to lost orders.
✅ After: Pickup dates driven by configured pickup windows to prevent unavailable dates and lost orders.
Model Validation
Validating scheduling mechanisms across real restaurant operating models
❌ Before: Calendar-based pickup dates often showed no availability and led to lost orders.
✅ After: Pickup dates driven by configured pickup windows to prevent unavailable dates and lost orders.
Model Validation
Validating scheduling mechanisms across real restaurant operating models
❌ Before: Calendar-based pickup dates often showed no availability and led to lost orders.
✅ After: Pickup dates driven by configured pickup windows to prevent unavailable dates and lost orders.
Model Validation
Validating scheduling mechanisms across real restaurant operating models
❌ Before: Calendar-based pickup dates often showed no availability and led to lost orders.
✅ After: Pickup dates driven by configured pickup windows to prevent unavailable dates and lost orders.
Model Validation
Validating scheduling mechanisms across real restaurant operating models

System Level Legacy Sunset

Sunsetting the legacy 10-minute auto-cancel rule to support real restaurant workflows

Trade-off: System Safeguards vs. Real Restaurant Workflows

The legacy 10-minute auto-cancel rule conflicted with scheduled ordering and real kitchen workflows, often causing unnecessary cancellations for large or delayed-acceptance orders.

Through system-level analysis and cross-functional reviews with PM, Engineering, and Ops, I led the decision to sunset this rule. Removing the constraint reduced operational friction and allowed scheduled orders to function reliably in real-world restaurant workflows.

PROCESS & STRATEGY

1. Restaurant Research
Layered research to uncover real restaurant workflows
Based on 100+ merchant feedback entries, 8 interviews, and 2 data analyses, I found that merchants aim to grow revenue while keeping daily operations manageable. Workflows differ widely by restaurant type and operational models, such as breakfast shops, takeout-focused restaurants, and full-service bistros. These differences led to a key insight: the design must remain flexible enough to support multiple restaurant models without adding extra work for staff.
1. Customer Research
Efficient validation through market benchmarks
To validate customer needs efficiently, I benchmarked 4 mature delivery and pickup platforms. This confirmed that customers primarily value clear and accurate interfaces that help them place orders smoothly and with confidence, rather than complex or unfamiliar interactions.
1. System-Level Research
Map system constraints and time-related dependencies across platforms
I analyzed time-related logic across the Merchant Platform, POS, and Online Ordering to surface conflicts and constraints. This revealed a legacy mechanism that conflicted with scheduled ordering and was a frequent source of merchant complaints. Based on these research findings, I proposed sunsetting the mechanism, and aligned the decision through stakeholder reviews to simplify the time model and improve overall system consistency and user experience.
1. Design Principles
Easy to Learn
Efficiency
Confidence
Design principles to minimize disruption and increase clarity
I defined cross-platform design principles to guide decisions and reduce operational risk. The principles emphasized preserving familiar restaurant workflows, keeping the system easy to learn, enabling efficient setup, and providing a clear, confident ordering experience so customers could trust the system.
1. Product Strategy
Drive a high-impact release strategy through business goal breakdown and feasibility alignment
I translated business goals and user insights into a lean, high-impact strategy for the first release by working closely with PM and Engineering to assess feasibility and prioritize MVP scope. Since the primary goal was to increase GMV, the strategy focused on maximizing adoption, which led me to prioritize a low-friction setup flow and align the design with common restaurant operating patterns.
1. Pilot Test
Validate workflow safety through pilot testing before launch
I conducted pilot testing with selected restaurants to validate that scheduled ordering worked smoothly in real operations. The results surfaced only minor copy tweaks and navigation issues, with no critical workflow breaks or hotfix-level problems, giving us confidence to proceed with a safe, low-risk launch.

1. Restaurant Research
Layered research to uncover real restaurant workflows
Based on 100+ merchant feedback entries, 8 interviews, and 2 data analyses, I found that merchants aim to grow revenue while keeping daily operations manageable. Workflows differ widely by restaurant type and operational models, such as breakfast shops, takeout-focused restaurants, and full-service bistros. These differences led to a key insight: the design must remain flexible enough to support multiple restaurant models without adding extra work for staff.
1. Customer Research
Efficient validation through market benchmarks
To validate customer needs efficiently, I benchmarked 4 mature delivery and pickup platforms. This confirmed that customers primarily value clear and accurate interfaces that help them place orders smoothly and with confidence, rather than complex or unfamiliar interactions.
1. System-Level Research
Map system constraints and time-related dependencies across platforms
I analyzed time-related logic across the Merchant Platform, POS, and Online Ordering to surface conflicts and constraints. This revealed a legacy mechanism that conflicted with scheduled ordering and was a frequent source of merchant complaints. Based on these research findings, I proposed sunsetting the mechanism, and aligned the decision through stakeholder reviews to simplify the time model and improve overall system consistency and user experience.
1. Design Principles
Easy to Learn
Efficiency
Confidence
Design principles to minimize disruption and increase clarity
I defined cross-platform design principles to guide decisions and reduce operational risk. The principles emphasized preserving familiar restaurant workflows, keeping the system easy to learn, enabling efficient setup, and providing a clear, confident ordering experience so customers could trust the system.
1. Product Strategy
Drive a high-impact release strategy through business goal breakdown and feasibility alignment
I translated business goals and user insights into a lean, high-impact strategy for the first release by working closely with PM and Engineering to assess feasibility and prioritize MVP scope. Since the primary goal was to increase GMV, the strategy focused on maximizing adoption, which led me to prioritize a low-friction setup flow and align the design with common restaurant operating patterns.
1. Pilot Test
Validate workflow safety through pilot testing before launch
I conducted pilot testing with selected restaurants to validate that scheduled ordering worked smoothly in real operations. The results surfaced only minor copy tweaks and navigation issues, with no critical workflow breaks or hotfix-level problems, giving us confidence to proceed with a safe, low-risk launch.

1. Restaurant Research
Layered research to uncover real restaurant workflows
Based on 100+ merchant feedback entries, 8 interviews, and 2 data analyses, I found that merchants aim to grow revenue while keeping daily operations manageable. Workflows differ widely by restaurant type and operational models, such as breakfast shops, takeout-focused restaurants, and full-service bistros. These differences led to a key insight: the design must remain flexible enough to support multiple restaurant models without adding extra work for staff.
1. Customer Research
Efficient validation through market benchmarks
To validate customer needs efficiently, I benchmarked 4 mature delivery and pickup platforms. This confirmed that customers primarily value clear and accurate interfaces that help them place orders smoothly and with confidence, rather than complex or unfamiliar interactions.
1. System-Level Research
Map system constraints and time-related dependencies across platforms
I analyzed time-related logic across the Merchant Platform, POS, and Online Ordering to surface conflicts and constraints. This revealed a legacy mechanism that conflicted with scheduled ordering and was a frequent source of merchant complaints. Based on these research findings, I proposed sunsetting the mechanism, and aligned the decision through stakeholder reviews to simplify the time model and improve overall system consistency and user experience.
1. Design Principles
Easy to Learn
Efficiency
Confidence
Design principles to minimize disruption and increase clarity
I defined cross-platform design principles to guide decisions and reduce operational risk. The principles emphasized preserving familiar restaurant workflows, keeping the system easy to learn, enabling efficient setup, and providing a clear, confident ordering experience so customers could trust the system.
1. Product Strategy
Drive a high-impact release strategy through business goal breakdown and feasibility alignment
I translated business goals and user insights into a lean, high-impact strategy for the first release by working closely with PM and Engineering to assess feasibility and prioritize MVP scope. Since the primary goal was to increase GMV, the strategy focused on maximizing adoption, which led me to prioritize a low-friction setup flow and align the design with common restaurant operating patterns.
1. Pilot Test
Validate workflow safety through pilot testing before launch
I conducted pilot testing with selected restaurants to validate that scheduled ordering worked smoothly in real operations. The results surfaced only minor copy tweaks and navigation issues, with no critical workflow breaks or hotfix-level problems, giving us confidence to proceed with a safe, low-risk launch.

IMPACT

Scalable Impact

Rolled out seamlessly to 16k+ restaurants serving 2M+ users, unlocking new GMV opportunities.

User Growth

53% merchant adoption in the first month—demonstrating immediate value and strong product-market fit.

Optimized UX

Improved daily activation by 71% through targeted UX and navigation enhancements.

Team Efficiency

Reduced development time by 2+ weeks via cross-team alignment and structured mentorship.

Learnining

❶ Evaluate real workflows before adding system rules
I enforced “pickup time must be within business hours” assuming it reflected real restaurant operations. But this rule added days of extra design and engineering work, while restaurants could already self-check and correct their settings.
I learned to only enforce system-level safeguards when mistakes create major, irreversible impact. Real workflows and actual risk must guide whether a rule is necessary.
❷ Question existing structures instead of assuming they work
I initially placed the feature based on the platform’s existing information architecture, assuming users would locate it easily. Pilot testing showed they could not, which led to rework. This taught me to challenge inherited designs and validate user behavior early, especially for core flows that directly affect activation and business outcomes.
❸ Preserve familiarity when improving fast-paced workflows
Restaurant operations move quickly and rely heavily on muscle memory. Even small changes in layout or flow can slow staff down or introduce errors during busy periods. This taught me that clarity is important, but maintaining familiar patterns is equally critical. When designing for fast-paced environments like restaurants, improvements must enhance understanding without disrupting the flow people already depend on.
❶ Evaluate real workflows before adding system rules
I enforced “pickup time must be within business hours” assuming it reflected real restaurant operations. But this rule added days of extra design and engineering work, while restaurants could already self-check and correct their settings.
I learned to only enforce system-level safeguards when mistakes create major, irreversible impact. Real workflows and actual risk must guide whether a rule is necessary.
❷ Question existing structures instead of assuming they work
I initially placed the feature based on the platform’s existing information architecture, assuming users would locate it easily. Pilot testing showed they could not, which led to rework. This taught me to challenge inherited designs and validate user behavior early, especially for core flows that directly affect activation and business outcomes.
❸ Preserve familiarity when improving fast-paced workflows
Restaurant operations move quickly and rely heavily on muscle memory. Even small changes in layout or flow can slow staff down or introduce errors during busy periods. This taught me that clarity is important, but maintaining familiar patterns is equally critical. When designing for fast-paced environments like restaurants, improvements must enhance understanding without disrupting the flow people already depend on.
❶ Evaluate real workflows before adding system rules
I enforced “pickup time must be within business hours” assuming it reflected real restaurant operations. But this rule added days of extra design and engineering work, while restaurants could already self-check and correct their settings.
I learned to only enforce system-level safeguards when mistakes create major, irreversible impact. Real workflows and actual risk must guide whether a rule is necessary.
❷ Question existing structures instead of assuming they work
I initially placed the feature based on the platform’s existing information architecture, assuming users would locate it easily. Pilot testing showed they could not, which led to rework. This taught me to challenge inherited designs and validate user behavior early, especially for core flows that directly affect activation and business outcomes.
❸ Preserve familiarity when improving fast-paced workflows
Restaurant operations move quickly and rely heavily on muscle memory. Even small changes in layout or flow can slow staff down or introduce errors during busy periods. This taught me that clarity is important, but maintaining familiar patterns is equally critical. When designing for fast-paced environments like restaurants, improvements must enhance understanding without disrupting the flow people already depend on.
❶ Evaluate real workflows before adding system rules
I enforced “pickup time must be within business hours” assuming it reflected real restaurant operations. But this rule added days of extra design and engineering work, while restaurants could already self-check and correct their settings.
I learned to only enforce system-level safeguards when mistakes create major, irreversible impact. Real workflows and actual risk must guide whether a rule is necessary.
❷ Question existing structures instead of assuming they work
I initially placed the feature based on the platform’s existing information architecture, assuming users would locate it easily. Pilot testing showed they could not, which led to rework. This taught me to challenge inherited designs and validate user behavior early, especially for core flows that directly affect activation and business outcomes.
❸ Preserve familiarity when improving fast-paced workflows
Restaurant operations move quickly and rely heavily on muscle memory. Even small changes in layout or flow can slow staff down or introduce errors during busy periods. This taught me that clarity is important, but maintaining familiar patterns is equally critical. When designing for fast-paced environments like restaurants, improvements must enhance understanding without disrupting the flow people already depend on.

Curiosity & Strategy

Designing real-world impact across UX, systems, and teams.

Let's Talk

More Projects

Curiosity & Strategy

Designing real-world impact across UX, systems, and teams.

Let's Talk

More Projects

Curiosity & Strategy

Designing real-world impact across UX, systems, and teams.

Let's Talk

More Projects

Scheduled Ordering

Launched a restaurant scheduled-ordering and pickup system with 53% first-month adoption

The system didn’t support scheduled future orders, causing revenue loss, poor user experiences, and staff overload

A core lever in our online ordering growth strategy, expected to increase online GMV and POS subscriptions.

Aligning multi-role behaviors, time rules, and platform constraints into one system while minimizing workflow disruption

A scheduled pickup system designed for real restaurant workflows, reducing lost orders and operational friction

Clear, low-effort setup through one-click activation and smart defaults

❌ Ideation A: Reusing existing IA with low discoverability and high density

✅ Ideation B: A dedicated entry point designed for clarity and future scale

A minimal-disruption workflow for fast order management

Misaligned with restaurant workflows, limiting usability and scalability

A clear, workflow-aligned layout designed for long-term product expansion

Configurable slip-print behavior to support diverse restaurant workflows and maximize adoption

Designed and tested multiple UI concepts to evaluate trade-offs and determine the final direction

Preserved familiar “Accept” behavior and added “Print Later” to support diverse workflows without retraining.

A predictable, flexible pickup-time experience, avoiding confusion

Aligning Pickup Time Logic with Customer Expectations to Prevent Lost Sales

The UI: Customers see the final system-calculated ASAP pickup time at order confirmation

Trade-off: Staff acceptance vs. order placement for ASAP timingTo keep ASAP earlier than scheduled slots and prevent lost orders.

System Logic Behind Pickup Time AvailabilityWhen customers arrive on the ordering page, the system calculates available pickup options based on the restaurant’s configured pickup windows and slot intervals.

Pickup dates are driven by configured pickup windows, not calendar dates, to prevent missed sales

❌ Before: Calendar-based pickup dates often showed no availability and led to lost orders.

✅ After: Pickup dates driven by configured pickup windows to prevent unavailable dates and lost orders.

Model Validation

Validating scheduling mechanisms across real restaurant operating models

Sunsetting the legacy 10-minute auto-cancel rule to support real restaurant workflows

Layered research to uncover real restaurant workflows

Efficient validation through market benchmarks

Map system constraints and time-related dependencies across platforms

Design principles to minimize disruption and increase clarity

Drive a high-impact release strategy through business goal breakdown and feasibility alignment

Validate workflow safety through pilot testing before launch

Layered research to uncover real restaurant workflows

Efficient validation through market benchmarks

Map system constraints and time-related dependencies across platforms

Design principles to minimize disruption and increase clarity

Drive a high-impact release strategy through business goal breakdown and feasibility alignment

Validate workflow safety through pilot testing before launch

Layered research to uncover real restaurant workflows

Efficient validation through market benchmarks

Map system constraints and time-related dependencies across platforms

Design principles to minimize disruption and increase clarity

Drive a high-impact release strategy through business goal breakdown and feasibility alignment

Validate workflow safety through pilot testing before launch

Scalable Impact

User Growth

Optimized UX

Team Efficiency

❶ Evaluate real workflows before adding system rules

❷ Question existing structures instead of assuming they work

❸ Preserve familiarity when improving fast-paced workflows

Curiosity & Strategy

Designing real-world impact across UX, systems, and teams.

Curiosity & Strategy

Designing real-world impact across UX, systems, and teams.

Curiosity & Strategy

Designing real-world impact across UX, systems, and teams.

Trade-off: Staff acceptance vs. order placement for ASAP timing
To keep ASAP earlier than scheduled slots and prevent lost orders.

System Logic Behind Pickup Time Availability

When customers arrive on the ordering page, the system calculates available pickup options based on the restaurant’s configured pickup windows and slot intervals.