Rye

Making most efficient use of your grocery inventory

September–October 2017
‍In collaboration with Lan Vu and Kelly Hwu
Product Design, User Interface Design, User Testing, VUI/CUI Design

Rye, a Skill for the Amazon Echo Show, suggests recipes based on the most efficient usage of your inventory - preventing edible foods from being forgotten, spoiled, or thrown away.

Design Challenge

Smart Cities

The theme for the studio involved finding solutions to problems within a "smart city." 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 decided to pursue a solution to be implemented at the consumer level.

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

Our Solution

How it works

Rye connects to your Amazon account and creates a virtual food pantry from Amazon and Whole Foods purchases. Rye uses your food preferences along with the estimated expiration dates of your food to optimally 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.

Why Rye?

We found that people have trouble managing their personal and household food consumption which can lead to food waste. Despite these wasteful habits, there is not a widely-used, or effective solution to tackle food waste at the consumer level.

Through our literature review, we found that in most Americans cities, waste at the consumer level is sorted into three categories: landfill, recycle, and compost. We questioned what was being discarded and saw compost as an opportunity space to explore, especially in the area of expiring food. We made three assumptions about unwanted food and probed further into people’s food waste behavior.

"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)."

—Food and Agriculture Organization of the United Nations

Secondary Research + Assumptions

Through secondary research, we found that in most Americans cities, waste is sorted into three categories: landfill, recycle, and compost. However, this isn't always practiced on the consumer level.

This led us to question how people viewed their own consumption and waste habits. We developed the following assumptions:

Assumption #1

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

Assumption #2

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

Assumption #3

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

Testing Assumptions

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 the needy.

“I actually would like to, but I have the means to afford what I need.”

We also learned that most people found exchanging opened or cooked foods undesirable.

“I would just be dumping my problem somewhere else...I mean who wants half-eaten food? I don’t think anyone.”

Concept Exploration

After synthesizing our findings and insights from the intervention, we developed storyboards to address different scenarios. 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.

Our hi-fi storyboard focused on a VUI device to assist people with meal prepping based on their schedule, grocery purchases, and food inventory.

Prototype exploration

While we found through our "in-city intervention" that the food sharing network was rather ineffective, we did not want to completely dismiss the idea. We proceeded with three paper prototype concepts:

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.

Insight #1

Users feel guilty about wasting foods but desire a solution that requires minimal effort.

Insight #2

Users did not understand that the recipes were based on their food inventory or in order of efficient food usage.

Insight #3

Users preferred to make efficient use of their food before it goes bad instead of giving away unwanted or expired foods.

Down Selection + Iteration

We found that all of our participants participated in wasteful habits with their food. Additionally, each participant expressed feelings of “sorry” or “guilt” for their forgotten, spoiled, or thrown away food items. 

"I wish there was something else I can do, but what do I do with bad food?"

With these insights from testing, we decided to pivot from a food sharing network to a food management system. 

Pivoting

Voice User Interface

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.

However, during testing we noticed several times that the user would ask “Alexa” to repeat a direction or phrase. To address this cognitive overload, especially in a kitchen environment where multitasking and interruptions can be common, we decided that a visual aid is needed.

While testing Echo devices at the Amazon Bookstore, we interacted with the Echo Show. We were drawn to the Echo Show because the screen complemented the main voice functionalities, but gave the user agency to refer back to the screen for reference.

As smart systems rapidly grow and integrate into our home environment, we felt that the Amazon Echo Show was the right platform to initiate widespread change in food consumption and food waste.

Interaction Flows

Logic is important. Things need to make sense. Here are the interaction flows I developed for our three use cases.

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

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

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

Hi-Fi Prototypes

We designed our HiFi prototypes for the Echo Show and conducted a second round of user testing. During this round, our users stressed the importance of seeing the ingredients for each recipe step. They also wanted the flexibility to see previous and future steps. We took their feedback into consideration and created the final design for Rye.

Style Guides

Amazon's color and typography influenced Rye's design. We were also inspired by our interaction with an Amazon Echo Show and its AllRecipes application. For the conversation interactions, we explored Amazon Alexa Voice Design Guide and tailored it to Rye.

Next Steps

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.

Takeaways

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.