STEALTH
The fashion industry is built on gambling. Demand planners have to place orders on rapidly evolving trends months in advance. By talking to CEOs of mid-sized fashion brands, we realized brands need to automate the planning process to be adaptive in real time, or die trying.
I joined a stealth-mode pre-seed start-up for 4-months to transform complex data layers into intuitive insights for its 40+ year old users, working with the engineers and users to refine AI dashboard prototypes and MVPs.
Team
Me (Contractual Design Lead)
Christopher Hugentobler (Founder)
Jimmy Chu (Engineer)
Problem
The UI and explanations honored the mission to make demand-planning lightweight
The new dashboard metrics transformed insights from passive to actionable and verifiable. This saved clients weeks of manual excel work to identify and act on supply chain shocks.
Size-level drop-down eliminates unnecessary information at dashboard level but makes product health at granular size level accessible when needed
Intuitive and humanist explanations upon hover helps the user transition to the new product during onboarding period.
Low actionability
Users had to navigate cluttered ui to determine and act on risky products, slowing execution due to manual due diligence.
Low verifiability
Users couldn’t verify AI prediction sources, and so are less likely to adopt insights, making the product less effective.
Risk became our north star
f(risk) = abnormal*movement
By creating a user journey, the founder and I isolated the key pain to be manual monitoring risks across the season
From a data perspective, we had narrowed down what data to manage and display in a logic map
Process
Working questions for refining data-viz
How to best distill complex size, time and inventory-level data at first glance?
which information is most essential to display first?
How do we streamline onboarding to prevent low user adoption?
Self-imposed Constraints
Keeping data unbiased by eliminating convoluted, unclearly structured data, or dark patterns early
Standardized taxonomy - This builds trust and buy-in from demand planners by embedding familiar concepts
Scalable mvp that is buildable across rows and columns
At a glance insights to reduce mental load
Allow for direct feedback for the design and forecast accuracy
Breaking task down
Pivotal Crossroad: Grid Versus Rows
We had to accurately forecast, identify and assign actions for products tied to risk levels.
Independent widgets and charts caused unnecessary engineering and user context-switching.
A fully integrated dashboard enabled scalable columnar data, row-by-row product comparison, and a seamless user flow.
So we abandoned the widget concept, but it informed the final time-series chart design
Visualizing data over time
To show abnormality over time, I experimented with various time series charts, this informed the product feed.
By asking the customers to explain the chart to us and how they reached their conclusion, we were able to narrow down which types of visualizations directions were most intuitive to scan regularly.
Development
Halfway through development, the team collectively decided to launch the final design as a responsive google sheets MVP. I adapted the Figma design accordingly using spark-lines.
This enabled the client to onboard directly onto our product, play with our data, and integrate it their google-sheet sheet-heavy work-flow without investing front-end development costs up-front.
I worked closely with the engineer to make this google sheet a home for him to programmatically updated live data onto
After shipping the redesign, we were able to increase engagement amongst existing customers and engage clients worth over 1M ARR
50% of existing customers found this intuitively easy to use without an onboarding
50% of existing customers included this in their weekly SIOP meetings
Reduce time to act (from weeks to days) by 40% through actionable CTAs
Cut support and onboarding queries by 25% (simplified, granular workflow)
Qualified 2 trial customers to consider premium subscription worth 1M ARR for demand planning needs
Soft signs of traction
Users readily adopted alongside existing system
Users pulled up the sheet during board meetings
This contractual project was a rare chance to collaborate closely with product, engineering and users to ship live changes via weekly sprints.
“Yes. This is well-designed. I don’t even know how you did this.” Christopher Hugentobler (Founder) on seeing the Google sheet dashboard
No such thing as too many variations
I committed to ideating a new component or data concept daily
Through rigorous comparison and analysis with the founder, we identified optimal solutions and articulated why certain hierarchies or components outperform others to prevent over-engineering.
This iterative approach also helped me detach from early ideas, ensuring design excellence
Mindful Design
Tools that allow for manual monitoring is the worst waste of human attention and intelligence
Something designed once ends up used a thousand times over. We should be mindful of how a product is used and the deeper implications for users.
When exploring forecast charts, my main motivation was Josh (user), a 40+ year old demand planning veteran. Our dashboard saved him hours every week staring at excel sheets by making data easier to monitor and act on.
AI ethics
Disclosures and estimated accuracy encourage further investigation and prevent overlying on AI-generated insights to inform multi-million dollar purchase decisions.
I enjoyed refining accuracy metrics with the users to reach the more verifiable dashboard we have today.
Discovery
On-demand Planning
By talking to different fashion CEOs of mid-sized brands, we came to terms that in today’s hyper-season market, brands need to be adaptive in real time. With today’s micro-trends, demand changes every day, while brands need to make orders months in advance.
How can we demand planners with tools to make demand planning frictionless?
I first outlined the user journey across various jobs to be done throughout the season, then the information architecture unifies the team across various timelines.
The logic flow and 2x2 matrix prompt suggestive actions across different complexities and contexts.
All forecasting pointed to a function of risk
f(risk) = abnormal*movement
Working questions
How to best distill complex data at first glance?
How can we simplify information to what is absolutely essential?
How can we embed confidence levels in a way that is interactive and makes the user feel engaged?!
How do we seamlessly onboard end-users to reduce churn-rate after earning buy-in from leadership?
Considerations
Keeping data unbiased
Eliminate convoluted, unclearly structured data, or dark patterns early
Usable and scalable MVP
Standardized taxonomy - This builds trust and buy-in from demand planners by embedding familiar concepts that are fond to them.
Buildable over time
At a glance insights
↓ Number of clicks → ↓ interaction cost → ↓ mental load
Feedback Pathway
Provide feedback for the design as well as the accuracy of the forecasts and explanations themselves
With this new design goal in mind, I got to work exploring visualizations of the forecast data:
Data Visualisations
To show abnormality spring up over time, I experimented with various time series charts, this informed the product feed and card.
Some of hundreds of variations - had to pull out my dusty Econ book for this one!
Development
Halfway through development, I adapted the final design into a responsive MVP using google sheets and sparklines. Back-end data would programmatically update into the client's sheet every day. This decision enabled the user to interact with the data, and onboard it into their sheet-heavy work-flow more seamlessly, while reducing development costs for such a lean team.
I recreated the platform by honoring type systems, using dynamic charts and spark-lines to code the data directly into the sheet as graphs.
Size-level Details
Break down size upon pressing the collapse to show the health by different sizes ensures this holistic detail is accessible and comparable on how the product is performing as a whole while eliminating necessary noise at the dashboard level.
Details on Hover
As users reported some confusion about the header copy when onboarding onto the new dashboard, we modified the note feature on google sheet to provide intuitive explanations upon hover.
In order to show it to users in real-time, we would develop the sheet one column at a time, from left to right. This enabled us to ensure the data was being interpreted smoothly, and iron out any miscommunications from data to final explanation.
This contractual role was a fantastic opportunity to engage in weekly sprints, ideate directly with product and engineering teams, and ship live changes based on real-time user feedback:
No such thing as too many variations
During weekly sprints, I committed to ideating a new component or data concept daily. By rigorously comparing options, we identified optimal solutions—a process highlighting why human designers remain critical in an AI-driven world—by articulating why certain hierarchies or components outperform others. This iterative approach also helped me detach from early ideas, ensuring only the strongest designs progressed to the final product.
Design Against Waste
Everything is designed once, then used thousands of times
When exploring all the different ways to draw one forecast chart, the one thing that kept me going was the idea of James (name changed for privacy), a 40+ year old demand planning veteran, spending 8 hours every day staring at an excel sheet. Because of the start up's dashboard, he can save time by designing the data he needs to monitor in a way that is easier to find and interpret into actionable insights. So much of this world takes so long just to learn how to use, and it is the worst kind of waste.
AI ethics
The process of working with James and demand planners like him also touched on ethical designs for AI - where disclosures and notes needed to be in place to prevent overlying on AI-generated insights to inform multi-million dollar purchase decisions. I enjoyed narrowing down choices with James to reach the more intuitive dashboard we have today.
