Case Study — Product Design

Rye

Making efficient use of grocery inventory

Introduction

Rye is the product of a self-defined studio project, consisting of 5 weeks of research and insight generation plus 5 weeks of ideation and design iteration, culminating in a high-fidelity prototype, UI specification, and presentation.

My Role

I worked alongside my two teammates, Lan Vu and Kelly Hwu. I played a key role in research, ideation, storyboarding, style guides, and refining UI elements and design decisions.

Tools Used

Sketch
AdobeXD
Adobe InDesign

The Challenge

How might we develop a smart system that helps consumers efficiently manage food consumption?

The Problem

The theme for the studio involved finding design solutions to problems by changing human behavior within a smart city. As a team, we decided to address food waste within the concept of smart cities. Rather than addressing the problem of food waste on a systemic, infrastructural level, we pursued a solution to be implemented at the consumer level.

In industrialized countries, more than 40% of [food] losses happen at retail and consumer levels.

Every year, consumers in rich countries waste almost as much food (222 million tons) as the entire net food production of sub-Saharan Africa (230 million tons).

Per capita waste by consumers is between 95-115 kg a year in Europe and North America.

—Food and Agriculture Organization of the United Nations

These initial findings, coupled with the theme of changing human behavior, we set out to design a solution that helps consumers make better use of the food they purchase.

The Solution

Rye is a Skill for the Amazon Echo Show. With knowledge of your grocery purchases, Rye suggests and walks through recipes based on the most efficient usage of your inventory— preventing items from being forgotten, spoiled, or thrown away.

How It Works

Rye uses a Voice User Interface (VUI) and machine learning to help users decide how to use their grocery inventory at any time. Rye automatically adds purchases through Whole Foods or AmazonPrime / AmazonFresh to the user's virtual inventory. With this knowledge of inventory, Rye has the ability to determine the expiration dates of each item, thus suggesting recipes based on the most efficient usage of the user's inventory.

Virtual Pantry

Rye connects to your Amazon account and creates a virtual food pantry from Amazon Fresh and Whole Foods purchases. Rye uses your food preferences along with the estimated expiration dates of the items to efficiently manage your food consumption and inventory.

Rye addresses wasteful habits by not changing the way people purchase food, but rather how they use the foods they purchased.

Executing a Recipe

After suggesting recipes based on the most efficient use of your grocery inventory, Rye walks you through the recipe, step by step, until completion.

After the recipe is finished, Rye adjust the inventory based on the food that was used. To create a feedback loop and better understand the user's food preferences, Rye asks the user whether they enjoyed their meal.

But what about foods that aren't used in recipes?

Technically, Rye has no way of knowing whether a user consumes food on their own, outside of a recipe. To accommodate for this, Rye does the following:

Rye allows users to review the list of ingredients used before the recipe begins.

If an ingredient has been sitting in the virtual inventory for an extended period of time, Rye will automatically ask the user if they are sure they have that ingredient before starting a recipe.

If a user starts a recipe and realizes, mid-way through the recipe, that they do not have a required ingredient, they simply tell Rye that the ingredient is not available and Rye offers a substitution or continues the recipe, sans said ingredient. Either way, the recipe is adjusted in real time, preventing extra cognitive work on the part of the user.

The Process

Our initial approach, we tried to understand the scope of our design space - meetings in modern companies.  We explored this problem space through a variety of research methods– competitive assessments, surveys, secondary research, semi-structured expert and user interviews.

Research

Secondary Research

Through secondary research, we found that food waste inevitably and ubiquitously occurs at the consumer level— individuals and households are routinely wasting food due to things such as over-purchasing and throwing away food that has gone bad.

This led us to question how people viewed their own consumption and waste habits. To frame our subsequent research, we developed the following assumptions:

01

People prefer not to waste their food and desire a useful alternative that requires minimal effort.

02

People are willing to receive other people’s food that would otherwise be thrown away.

03

People are willing to exchange or trade their unwanted foods with community members.

In-City Intervention

We executed an "in-city intervention" to test our assumptions about exchanging unwanted foods. We installed a "neighborhood food pantry" in a residential area and in a community park. The neighborhood food pantry contained an assortment of opened, unopened, cooked, and uncooked foods.

As we passively observed from afar, we wanted to see if people would be willing to take or contribute their unwanted foods to the pantry.

We quickly learned through this experiment and user interviews that our food sharing network did not fit with most people’s mental models. Instead of a food sharing network, many people saw the Neighborhood Food Pantry as another form of food donation for those in need.

Findings

01
People did accept foods, but this was dependent on
their socioeconomic status.

02
Overall, people were more inclined to provide unwanted items rather than take items they may need.

These findings verified the assumption that people prefer not to waste their food and desire a useful alternative that requires minimal effort. However, the findings challenged the assumption that people are willing to exchange or trade their unwanted foods with community members. While people are likely to donate their unwanted items, they were unlikely to take items they may need, thus preventing the continuous mutual benefit of a food exchange system within a community.

Ideation and Evaluation

Concept Generation

After synthesizing our findings and insights from the intervention, we redefined our approach. Our goal was not to address food waste directly— instead, we wanted to capitalize on peoples' existing behaviors and implement a solution that extended the life of purchased foods.

We sketched out 30 concepts total. From the 30 initial concepts, we narrowed down to 3 concepts to move forward with prototyping.

FoodMatch
A mobile app that encourages neighbors to exchange unwanted foods within their community.

Meal Prep Assistant
A mobile app that encourages neighbors to exchange unwanted foods within their community.

Meal Prep GUI + CUI
A personal assistant that uses a graphical user interface and a conversational user interface to help people efficiently meal prep.

Prototyping

What We Wanted to Understand

How our target users would interact with the prototypes.

What concepts users were generally drawn to.
Down Selection Criteria and Rationale

After narrowing down to three concepts, we developed a paper prototype for each. We went on to test each concept with three separate potential users. At the beginning of each test, we asked the user to describe their current behavior around food waste. All six users mentioned that they would often waste food and felt “bad” or “guilty” about it, but they didn’t know what else to do with expiring foods.

After testing the paper prototypes, we reached three insights to drive our final design direction.

01
People are open to an alternative way to reduce their food waste outside
of composting.

02
Time is a significant factor for users in determining whether or not they
would engage in alternative activities to reduce food waste.

03
Underlying factors such as effort level and security that impact active participation.

High Fidelity Prototyping

During our initial round of user testing, more than half of our users were drawn to Meal Prep because of the hands-free cooking experience. Users also felt that the interactivity of the device would help them manage their food waste better because it held them accountable for their food inventory. Through user testing and feedback, we decided to pursue a VUI for our final concept. With this knowledge, we decided that the next design iteration needed to:

Incorporate a visual aid to mitigate cognitive overload.

Implement a solution into an existing technology such as Amazon Echo Show.

Final Prototype

Interaction Flows

To comprehensively represent the product and showcase the main features, we developed three interaction flows.

Onboarding

This flow outlines Rye’s onboarding scenario for a first-time user. The onboarding flow is designed to familiarize the user through key features such as the home screen, the sidebar navigation, the inventory, and the recipe steps and overview.

View Prototype

Recipe + Feedback

This flow depicts the user’s journey when realizing they do not have an ingredient after starting a recipe. The flow begins at the start of the recipe and examines the ingredient substitution suggestion, recipe steps, and recipe feedback.

View Prototype

Low Confidence

This flow outlines an instance when a user chooses a recipe outside of the recommended recipes. Therefore, Alexa has “low confidence” in this ingredient because it was either purchased a long time ago, has not been used in awhile, or has expired. 

View Prototype

Style Guide and Visual Specifications

"Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum."

Final Thoughts

Beyond Amazon

Future iterations of Rye would ideally include grocery purchases from other stores. While creating Rye, we experimented with the idea of tracking non-Amazon purchases through store rewards cards or credit cards. However, we need to further explore these tracking techniques as well as other methods to find an effective solution.

What we learned

Throughout the design process of Rye, I learned that it is important to quickly adapt to change and proactively learn new skills.

One of the challenges that we faced is our lack of familiarity with the Echo Show. Initially, we read a lot about the Echo Show through the developer guide and secondary research. However, we did not fully understand the Echo Show's voice interactions or style guidelines until we interacted with one. When designing for an existing interface it is crucial to interact with the device early on in the design process because it can lead to better design decisions.

Despite this challenge, we quickly learned about VUIs and prototyped a working model with limited resources. We also learned new software such as Adobe XD which gave us more flexibility in creating Hi-Fi prototypes of the Echo Show and SaySpring which allowed us to create multi-turn VUI interactions. Ultimately, our flexibility during the design process and eagerness to learn helped us create a successful project.