B2B SaaS Foodservice Supply Chain Management:
End-to-End Limited Time Offer Management & Reporting
Most people know about and get excited about limited-time offers from their favorite fast-food restaurant.
A day in the life of a Promotion Manager is focused on checking inventory on thousands of items across potentially hundreds of Distribution Centers and Stores in spreadsheets. Calling or emailing Distribution Centers to flag potential inventory issues. Tracking what stores have sold and how that aligns with the forecast in more spreadsheets. Their job is focused on pre-emptively finding issues, and they are constantly digging through tons of data to do it.
Our goal is to create a dashboard that surfaces issues faster so they can spend less time looking for problems and more time solving them.
ArrowStream: Foodservice Supply Chain Management
Company and Users Overview
Arrowstream is a B2B Supply Chain Management SaaS product. It is a multi-persona platform with Brands, Distributors, and Suppliers utilizing different products. It is complex and data-heavy, ingesting and mapping data from the distribution center, supplier feeds, and user uploads.
It helps users make critical decisions in their supply chain with focused reporting.
Foodservice supply chain management is a complex domain. Due to that complexity, users often have worked in their roles for a long time and get attached to certain workflows and processes. Each brand has its own workflows and required data. This is a field that runs on interpersonal relationships, and a personal phone call or email to a distribution center or supplier is how most communication is handled over automated notifications.
Project Context
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Promotion Managers work at Brands to manage Limited Time Offers
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These offers use a mix of promotion-specific and general items
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Users are focused on:
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Building up a stock of these items before a promotion launches
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Maintaining inventory levels during a promotion
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Tapering off inventory as a promotion ends to avoid overstock and financial exposure
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This project is an additional paid for product offering as an add-on to our core product
The Team, My Role, Timeline, Constraints
The Team
Pod consisting of a Product Manager, Product Marketing Manager, 5 engineers, 2 QA testers, and me
My Role
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End-to-end design
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Qualitative user research
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Collaborative scoping of product requirements
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Product design strategy
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Experience design
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Prototyping and Usability Testing
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Dev Handoff
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UAT and Beta Validation
Timeline
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Q1 on Product Market Fit, Discovery, and User Research
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Q2 on Ideation, Scoping, Design, and User Validation
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Q3 on Development, QA, and UAT
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Q4 Beta program
Constraints
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Scope did not include forecast creation or post-promotion analysis
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Data ingestion was a manual upload using a template spreadsheet for V1
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Diversity in brands' internal processes means some processes (Franchisee commitments, etc) happen outside the system for V1
We saw an opportunity to improve brands' processes and reporting for LTOs, and it worked!
Faster Responses
Money Saved
Brands could see potential stock out issues and respond faster.
Saving them money during and after a promotion ended.
Saving Time for
More Productive Work
Daily time spent on promotion inventory management decreased by 30%
Time spent on report creation during promotions decreased by 20% due to easy system exports.
Growing Industry Thought Leadership
Through our research for this product we helped companies ideate on "what a successful promotion looks like". We created sessions for users from different brands to share insights, struggles, and best practices with each other.
UX Research
I organized external qualitative interviews and materials, as well as facilitated sessions with brands.
I also attended internal meetings around our data structure.
10 External Brand Interviews
Goal: Understand brands' promotion forecasting, setup, management, and tracking processes
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At what level are brands forecasting and how are they generating that forecast?
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Once a promotion starts, what does daily management look like?
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What are key indicators of concern during a promotion?
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During and after a promotion, what kind of reports are generated and what is the audience for those reports?
12 Internal Meetings
Goal: Understand how AS receives data from Distribution Centers, the limitations of our data streams, and the creation of metrics that accurately portray the indicators brands are interested in
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What is the cadence of data received?
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If we are unable to directly answer a question with data, is there another way we can indicate something?
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What are the thresholds to pay attention to in our metrics?
5 Design Feedback
Goal: Receive brand feedback on mocks and show work completed in previous sprints
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Validate work and processes
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Open up the discussion to different employee roles
From our research, we broke the promotion process into 3 distinct phases with their own needs and goals.
Phase 1:
Planning &
Setup
Phase 2:
Management
Tracking
Phase 3:
Complete
Analysis
A "successful" promotion has no inventory outages during and little to no obselete inventory after.
1. Planning & Setup
Users are inputting forecasts, setting up data collection streams, and beginning to track/ship physical products to Distribution Centers and Stores
Clear data setup and definitions are critical
2. Management Tracking
A promotion is live in stores, and users are managing their inventory at both the Distribution Center and Stores
Problem Indicators and remedial actions are critical
3. Complete Analysis
A promotion has ended. Obsolete inventory must be managed, and the data gathered will be used for future promotion planning.
Obsolete inventory management and the ability to export data for reporting and planning is critical.
Zooming in on Phase 2, we focused on solving 3 main user goals.
Inventory Alerts
Goal: Keep stock levels at Distribution Centers and Stores at levels that meet demand with no outages and avoid overstock.
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Know how many Days On Hand of stock are on hand for each item in the promotion. Special attention is paid to items that are only used for the promotion.
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If there is a potential for shortage or overstock, be able to see the surrounding Distribution Centers to arrange transfers
Financial Exposure
Goal: Understand your potential financial exposure for the promotion
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If your promotion ended today, know how much obsolete inventory you would be left with and the base cost of it.
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Calculate based on only promotion items for some brands and for all items for other brands.
Data Slicing/Reporting
Goal: Create internal reports for different business needs to share promotion progress and analysis
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Slice the data in different ways based on attributes like region, distribution centers, and stores
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Easily zoom in and out in reports based on the internal audience and their needs
Experience Design and Prototyping
To fulfill our users' goal of Inventory Alerts, I designed a dashboard view that surfaces immediate issues.
Available during Phase 2, all this data can be exported into Excel
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Inventory Alerts are for Item/DC and Item/Store groupings. The issues are sorted to the top, and are categorized by:
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Out of Stock (Red) where the On Hand is less than or equal to zero
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High Risk (Orange) where the estimated Run Out Date is before the Next Purchase Order Delivery Date
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At Risk (Yellow) where there is either No Purchase Order placed or the estimated Run Out Date is before the 2nd or 3rd Purchase Order Delivery Date
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No Alerts (Green): none of the conditions above are met
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Count of days until estimated Run Out: Clicking on this gives you a deeper inventory view at that DC Location/Item Combo
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Filter: A user can filter by promotional only items, statuses, and DCs
To fulfill our users' goal of Financial Exposure, I designed a dashboard view that tracks purchase orders and inventory.
Available during Phase 2, all this data can be exported into Excel
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Total Exposure at all Distribution Centers across all Items if the promotion ended today, and Forecasted Leftover if the promotion continues performing against the forecast
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Total Exposure at the listed Distribution Center across all Items based on Cases on Hand, Cases on Order, and the Cost Per Case.
To fulfill our users' goal of Data Slicing and Reporting, all DCs can be drilled into further. I also created a Dashboard to surface DCs underperforming their forecasts to quickly surface outliers.
Available during Phase 2, all this data can be exported into Excel
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Key Insights generated by AI on which DCs are underperforming and potential remedial actions
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Expected vs Actual Sales and Variance
Beta Release
We launched a guided Beta with 8 brands and generated 3 early adopter upsells!
This makes my morning check in so much faster! I love being able to download the reports to send to the team to keep them up to date.
Early Adopter
I really being able to tell what is current data and where I need to talk more to DCs to fill in gaps.
Beta User
Sales identified a $1-3 million potential ARR based on our current brand customers.
Key Learnings
Balancing available data, different workflows, and user needs.
Definitions drive expectation. Creating a consistent mutual language with brands, users, and our system is crucial.
Users in foodservice will always say yes to more data. Focusing on clearly defining use cases allows common goals to be prioritized.