Launched a scheduled ordering system to fix fragmented restaurant workflows, driving 53% adoption
Unifying customer ordering, staff management, and kitchen fulfillment into a seamless cross-platform experience
Role
Senior Product Designer
Contribution
Led end-to-end strategy and system-level time logic across multi-user, cross-platform experiences, with cross-functional collaboration and junior designer mentorship.
Duration
4 months
Company
iCHEF (Restaurant Tech/B2B SaaS)
CONTEXT
The ordering system did not support scheduled orders, leading to poor user experiences and restaurant complaints
In the early stages, our ordering system only supported ASAP orders. This created a significant gap between product capabilities and merchant needs, quickly becoming our most requested feature. This limitation resulted in off-hour revenue loss, inaccurate reporting for merchants, and fragmented experiences for both customers and staff.
SUMMARY
A scheduled ordering system to fix fragmented workflows and fulfill diverse restaurant operations across roles and platforms
This project integrated scheduled ordering into the existing system, replacing fragmented, workaround-based workflows across customers, staff, and restaurants. By designing a flexible architecture, the system fulfills diverse operational needs ranging from small cafes to complex full-service restaurants while reducing manual coordination and missed orders. Within the first month, 53% of restaurants adopted scheduled ordering, validating its impact on both adoption and day-to-day efficiency.
BUSINESS IMPACT
Driving adoption and revenue growth
Among the 2,000+ restaurants using the online ordering site at launch, 53% adopted scheduled ordering within the first month, driving a 1%+ increase in online revenue. UX improvements resulted in a 71% lift in daily activation, while structured cross-team alignment reduced development time by two weeks.
Adoption (First Month)
+53%
Online Revenue (First Month)
+1%
PROBLEM FRAMING
Defining scheduling failures across merchants and customers to guide cross-role and cross-platform decisions
Restaurant Research & Insights (B2B)
Restaurants have diverse workflows, but share the same core need: fewer missed orders, less effort
Through 100+ feedback entries and 8+ interviews, I learned that restaurant scheduling workflows vary by business type and operating model—such as same-day prep, shift-based handling, or inventory checks for large orders. Despite these differences, restaurants shared the same concern: missed orders and errors caused by unclear or fragmented scheduling information.
💰
Prevent Missed Orders
Protect revenue from scheduling mistakes, off-hour loss
👥
Reduce Staff Workload
Less manual coordination and follow-ups
🍽️
Maintain Brand & Quality
Prepare orders at the right time
Customer Research & Insights (B2C)
Customers want a clear and low-effort way to schedule orders and pick up on time
Through benchmarking and feedback analysis, I found that the lack of a clear scheduling system led to confusing experiences—manual pickup notes, unavailable options during off-hours, and low confidence that orders would be handled correctly. These issues increased hesitation and drop-offs when customers tried to place future orders.
Store Closed
Make scheduling availability visible upfront
Order Process
Make pickup time selection clear and predictable
Defining Key Design Goals
Design Pillars for Reliability
Based on these insights, I defined a set of design pillars to guide system-level decisions across merchant and customer experiences. These pillars directly shaped how scheduling logic, setup flows, and cross-role experiences were designed across the system.
Flexible configuration
Support different restaurant types and workflows without forcing a single operating model.
Error prevention over flexibility
Prioritize clear rules and system-driven logic to reduce mistakes, missed orders, and manual workarounds.
Lower operational load
Minimize manual coordination across staff, shifts, and kitchens to keep daily operations predictable.
Shared confidence across roles
Ensure customers, staff, and kitchens rely on the same scheduling logic and pickup time across platforms.
INTERACTION DESIGN
Designing an end-to-end experience by making deliberate trade-offs across roles, platforms, and mental models
I designed the scheduled ordering experience end to end, making deliberate trade-offs across roles and platforms based on user needs, mental models, and real workflows. The solution aligned shared system logic with role-specific interactions, reducing manual work and preventing missed orders.
Merchant Platform for Restaurant Owners
Easy, flexible setup for diverse restaurant workflows
Research showed most restaurants schedule pickups within business hours but need flexibility for different workflows. I designed a fast, flexible setup using one-click activation with configurable rules, letting restaurants adapt scheduling to their operations. To improve discoverability and future scalability, I added a dedicated Scheduled Orders entry—driving 71% higher activation and a 1% lift in online revenue in the first month.
POS for Restaurant Staff
A minimal-disruption workflow for fast order management
Restaurant staff rely on fast, familiar POS workflows. Early testing showed that large structural changes would slow down daily operations, so instead of redesigning the flow, I integrated scheduled orders into existing interaction patterns to minimize disruption.
To support diverse restaurant workflows and drive adoption, I made the Print Later action configurable and enabled by default for all restaurants—not just those using scheduled orders—so the feature stays visible and usable in daily operations, rather than hidden in settings.
Online Ordering Website for Customers
A clear scheduling experience built on a single source of truth
Previously, restaurants asked customers to note pickup times manually, while the system generated a different pickup time after checkout—causing errors and confusion. I unified pickup time into a single source of truth, enabling customers to clearly choose between ASAP and scheduled pickup, with the selected time applied consistently across ordering, POS, and kitchen workflows.
To further reduce misinterpretation, I replaced numeric dates with text-based dates, helping customers select the correct pickup time and avoid cultural or formatting misunderstandings.
SYSTEM LOGIC DESIGN
Define a single source of truth for pickup availability
I rebuilt the scheduling logic to eliminate conflicts between customer expectations and restaurant operations, establishing a single source of truth across roles and platforms.
Defining pickup time rules to ensure clarity and a single source of truth
Availability derived from pickup windows, not calendar dates
When restaurants limited advance booking, some dates appeared unavailable even though they were still accepting orders. I generated availability from configured pickup windows instead of calendar dates. This prevented missed sales, improved merchant satisfaction, and became a key sales differentiator.
ASAP is always the earliest valid option
To create a predictable experience and prevent lost orders, I enforced a clear rule: ASAP must always result in the earliest pickup time. Scheduled slots appear only after ASAP, preventing customers from seeing a scheduled option earlier than ASAP.
ASAP pickup time is calculated at order placement
Previously, ASAP time was calculated after staff acceptance, sometimes making it appear later than scheduled options. I moved the calculation to order placement, ensuring consistent timing across platforms. Although this slightly reduced preparation buffer time, 93% of orders were accepted within one minute, making the trade-off low risk.
The 10-minute auto-cancel rule conflicted with long-term scheduling and increased system complexity. I led a cross-functional review to assess operational impact and risk. After validating real scenarios, we removed the rule without negative impact, simplifying the system and reducing engineering overhead.
Validating the new logic across real operating models
I tested the new rules across diverse restaurant models, including split shifts, overnight operations, and large scheduled orders. This ensured rule consistency, aligned cross-functional understanding, and prevented downstream operational issues.
Validation Across Diverse Operating Models
Testing time logic across diverse operating models to ensure rule consistency and operational stability.
DESIGN PROCESS OVERVIEW
LEARNING