Fighting Hunger Through
Smarter
Logistics
Designing a decision-support tool for Nine13 Logistics — a nonprofit delivering food to thousands of Indianapolis families who can't reach a pantry on their own.
ROLE: Led UX thinking from research to mid-fidelity, translating complex findings into clear product direction. Defined system structure, key interaction flows, and decision-support concepts based on real-world logistics needs.
TIMELINE: September 2025 - Present
TEAM: Capstone project completed as part of a four-member product design team - Shruti R., Agastya D., Shraddha K.
Access, made easier
What looks like a simple delivery is often the difference between access and absence. Nine13 Logistics helps make that access possible.
Overview
The Last Mile is the Hardest Mile
Nine13 Logistics started as a youth cycling nonprofit. During COVID-19, it pivoted into something more urgent — a community logistics operator delivering food directly to seniors, people with disabilities, and families across Central Indiana. At peak pandemic response, Nine13 was reaching up to 1,000 Marion County residents per week.
Operates with a small fleet and a “white glove” approach, delivering every order with care and dignity.
As they scaled, planning became complex, raising questions about when to add trucks, drivers, or routes.
Existing tools handled routing but couldn’t support these decisions, creating a gap our team addressed.
The Problem
Growing on Instinct Isn't a Strategy
Nine13's COO, Ashley, has an extraordinary ability to look at a 7-hour route plan and immediately know it will actually take 8 hours and 15 minutes. That institutional knowledge is invaluable — and completely unscalable. When routes go wrong, when a pantry volunteer doesn't show, when a call box is broken and a recipient can't be reached — the system bends on the strength of one person's experience.
"What if we add another truck, van, or pantry — how does that actually change our cost and capacity?"
-Tom Hanley, CEO & Founder, Nine13 Logistics
Purdue University's engineering team had already built a powerful mathematical model capable of running thousands of "what-if" simulations. But it was deeply technical — dense with outputs, built for engineers, not for a nonprofit operations coordinator juggling drivers and email chains at 8 AM.
Our task was to bridge that gap: translate a rigorous backend model into a human-centered decision tool that Tom and Ashley could actually use.
What Nine13 Needed
View case
What Was Already There
View case
User Research
Getting Off the Laptop and Into the Truck
We didn't start with wireframes. We started with field visits, ride-alongs, and long conversations with the people who actually run these routes. Two studies shaped the entire project.
Study 1 — Stakeholder Interviews
We conducted semi-structured interviews with Tom Hanley (CEO) and Ashley Acuff (COO). These weren't brief check-ins — Tom's site tour ran nearly 90 minutes. We used qualitative coding to pull themes from the transcripts, clustering statements around recurring patterns: delivery delays, scaling anxiety, the gap between digital routes and real-world conditions.
Study 2 — Ride-Along Field Observations
One team member joined the box truck route on a Wednesday morning — observing the full arc from loading pallets at Gleaners Food Bank to delivering to four pantry sites. Another shadowed Ashley on a Monday van route delivering produce boxes door-to-door across Marion County. What we saw confirmed everything we'd been told, and surfaced things no interview could have revealed.
Research Insights
What the Field Taught Us
Key Findings
Five Truths About Last-Mile Food Delivery
Defining Scope
Two Layers of Problems, One Deliberate Choice
Operational Challenges
At the operational layer — broken call boxes, failed deliveries logged only in Ashley's memory, scheduling done over email, Midwest Food Bank capacity unknown until the truck is already loading.
Strategic Challenges
At the strategic layer — no way to model what adding a truck, a driver, or ten new pantry contracts would actually cost or deliver.
Both matter. But this project was built on top of Purdue's engineering model, which was designed specifically to answer strategic scaling questions — not to track individual delivery exceptions. Trying to solve both with one tool would have stretched the model beyond what it was built for, and produced something that did neither well.
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We made a deliberate call: scope this phase to the strategic decision-support layer — giving Tom and Ashley a way to explore growth decisions with real data behind them.
The operational gaps address logging, real-time tracking, scheduling coordination are real, documented, and the right problem for a future phase. This case study covers the strategic layer.
Competitive Analysis
Learning From What Already Exists
Strong conceptual fit
$$$ — not nonprofit-friendly
Strong simulation logic
Complex, enterprise-grade
UX inspiration only
Affordable, browser-based
The Gap We Found
No tool in the market combines strategic what-if modeling with an interface simple enough for a nonprofit coordinator. AIMMS has the logic but not the accessibility. EasyCargo has the usability but not the scope. Our opportunity was to bring these together — in plain language, for non-technical users, at zero enterprise cost.
User Models
Two Perspectives, One Shared Problem
Tom Hanley
Ashley Acuff
CEO & Founder
The Strategic Thinker
"If we add another truck, van, or pantry — how does that actually change our cost and capacity?"
-Tom Hanley, Nine13 Logistics
Tom leads Nine13 at the strategic level. He cares deeply about growth without losing humanity — the "white glove" mission. He has a strong community instinct but lacks the data infrastructure to make confident expansion decisions.
Scenario Comparison
Impact reporting
Growth forecasting
Plain-language output
Task Analysis
Mapping Where Work Breaks Down
We built hierarchical task analyses for both delivery types — documenting ideal flows (how things should work) and failure flows (what actually happens when they don't). This gave us a precise picture of the decision points, failure modes, and recovery behaviors that any future tool must account for.
Box Truck — Pantry Delivery
Gleaners routes use pre-packed pallets and fixed stops, with failures when pantries are closed or unstaffed. Midwest Food Bank routes rely on on-site selection, with failures when chosen loads exceed truck capacity mid-route.
Van — Home Delivery
Van routes involve pre-packed boxes and 20+ stops with personal handoffs, where stop times vary widely and failures include no answer, wrong address, or access issues. These routes require constant improvisation and generate minimal structured data.
The Critical Insight: These Are Two Different Use Cases
Pantry routes focus on coordination and schedules, while home deliveries involve personal interaction and handling exceptions—requiring the system to treat them as related but distinct.
From Research to Design
What Is This Tool Actually Measuring?
Before designing screens, we defined what “better” means for Nine13—captured by two key outputs from the Purdue model for every scenario.
The problem was that this model lived in an engineering environment — dense outputs, no plain language, built for analysts. Tom and Ashley couldn't use it. Our job was to design an interface that surfaces this tradeoff clearly, for non-technical users, in the middle of a busy workday.
Turning Findings Into Direction
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Information Architecture
Structuring a Tool That Thinks Like a Planner
The IA needed to support two modes — Tom's strategic exploration and Ashley's operational planning. We organized the product around five top-level areas, each mapped to a distinct user need.
We're currently translating this structure into high-fidelity screens. Check back soon.