Timeline
Nov 2023 - Dec 2023
My Role
UX Designer
Project Type
University of California Irvine's MHCID School Project
Overview
I designed an app called Fresh Tracker to assist in minimizing food waste at retail stores by closely monitoring item status and inventory through various IoT sensors and RFIDs. The target users for this app are grocery store staff responsible for inventory management, as research indicates that effective inventory management plays a crucial role in reducing food waste.
Discover
Problem Statement
How might we minimize food waste at retail stores by effectively managing food stocks, streamlining orders, and monitoring the status of food items?
Discover
Challenges & Solutions at Wholesale & Retail
I discovered that effective inventory and food quality management significantly reduces food waste in retail stores. Addressing the food waste problem can be facilitated through the implementation of IoT and AI technologies.
Challenges
Due to inaccurate ordering & forecasting demand
Lack of cold storage/interruption of the cold chain
Failure to comply with minimum food safety standards
Packaging defects resulting in product damage
Solution
Smart Management System with IoT
RFID Tags • Sensors • Artificial Intelligence
Priefer et al., 2016, Fan et al., 2019, Condea et al., 2012, Caro et al., 2019
Discover
Features
I found features that can efficiently reduce food waste in retail stores using IoT and AI technologies.
Fan et al., 2019, Condea et al., 2012, Caro et al., 2019
Define
Empathy Map
George has managed a grocery store for five years, overseeing food supply orders and inventory. His daily routine involves meticulously checking items like vegetables, fruits, meats, and snacks to ensure shelves are stocked.
Desires a simpler, more efficient stock management method.
Aims for accurate customer demand forecasts to order suitable quantities.
Strives to extend the freshness of perishable foods.
Spends excessive time checking every aisle.
Faces challenges in forecasting, leading to over-ordering.
Struggles to detect spoiled food promptly.
"Relying on gut feeling for orders is risky; predictive methods are lacking."
“At times, I end up ordering more items because I wasn't aware that those items were available in the backroom.”
"Finding expired items on shelves is frustrating."
"Current stock monitoring software often provides inaccurate data."
"AI forecasting for food stock can be useful, but I struggle to trust automated systems."
Places orders based on stock levels.
Adjusts orders for holidays like Thanksgiving.
Discards expired or spoiled items.
When an item is sold out, he restocks it from the backroom.
Feels guilty discarding food.
Experiences anxiety about over-ordering and excess stock.
Feels good when promotional items sell out.
Riesenegger et al., 2022
Develop
System Flow Chart
This system flowchart illustrates how RFIDs and various types of sensors operate. This chart also indicates the specific factors for which the system notifies users.
Develop
Smart Environment Visualization
The smart environment visualization illustrates the locations for RFIDs and sensors placement, outlining the specific information they will gather and the functions they will perform.
Develop
UI Design
Home
The home screen displays two sections: tasks and the weekly report. The task section provides information on items to order, damaged items, expired and soon-to-be expired items, and the sections where mold or bugs are detected. By clicking each card, the user can check detailed information.
Expiration Data
The expiration data page displays items that are either expired or soon-to-be expired, providing their location and quantity. This allows the user to easily locate the items.
Order Management
Users can view items that need to be ordered and their order history. The AI-powered system suggests the recommended quantity to order by analyzing previous data.
When users click the "Place order" button, they can access to the order placement page where they can modify item quantities and add more items. I included this modification feature because users prefer having control and are cautious about completely relying on autonomous systems.
Store Section
The store section page shows sections where mold or bugs have been detected on top, while below, users can view all sections in the store along with their temperature, humidity, and status.
When users click on a list, it allows them to access a detailed information page. Each section provides an explanation regarding its status, temperature, and humidity levels. Additionally, it displays a store map to assist users in easily locating the respective section.
Item Management
The item page shows damaged items at the top and all items in the store at the bottom. In the damaged items section, users can view the item, location, and its image. So the user can easily find the items.
Notifications
Finally, the notification page showcases alerts triggered by sensors and RFIDs regarding specific factors. Clicking on a notification enables users to access the respective page, where they can review detailed information.
Develop
Prototype
This interactive prototype showcases the main pages and primary flows of the app.
Reflection
I discovered solutions to tackle the food waste issue in grocery stores through secondary research, driven by the time constraints of my school project. Nevertheless, I aim to conduct user research involving interviews and observations because delving into user needs through this method will uncover their underlying requirements.
References
Forbes, H., Quested, T., & O’Connor, C. (2021, March 4). UNEP Food Waste Index Report 2021. UNEP. https://www.unep.org/resources/report/unep-food-waste-index-report-2021
Buzby, J. C., Wells, H. F., & Hyman, J. (2014, February). The estimated amount, value, and calories of postharvest food losses at the retail and consumer levels in the United States. USDA ERS. https://www.ers.usda.gov/publications/pub-details/?pubid=43836#:~:text=In%20the%20United%20States%2C%2031,%24161.6%20billion%20using%20retail%20prices.
Priefer, C., Jörissen, J., & Bräutigam, K.-R. (2016). Food waste prevention in Europe – A cause-driven approach to identify the most relevant leverage points for action. Resources, Conservation and Recycling, 109, 155–165. https://doi.org/10.1016/j.resconrec.2016.03.004
Fan, H. (2019). Theoretical Basis and System Establishment of China Food Safety Intelligent Supervision in the Perspective of Internet of Things. IEEE Access, 7, 71686–71695. https://doi.org/10.1109/ACCESS.2019.2919582
Condea, C., Thiesse, F., & Fleisch, E. (2012). RFID-enabled shelf replenishment with backroom monitoring in retail stores. Decision Support Systems, 52(4), 839–849. https://doi.org/10.1016/j.dss.2011.11.018
Caro, F., & Sadr, R. (2019). The Internet of Things (IoT) in retail: Bridging supply and demand. Business Horizons, 62(1), 47–54. https://doi.org/10.1016/j.bushor.2018.08.002
Riesenegger, L., & Hübner, A. (2022). Reducing Food Waste at Retail Stores—An Explorative Study. Sustainability (Basel, Switzerland), 14(5), 2494-. https://doi.org/10.3390/su14052494