Transforming Grocery Management
Food waste is a significant global issue, with households being one of the largest contributors. This project aims to develop a solution that empowers users to track, manage, and utilise their groceries more efficiently, reducing household food waste and promoting sustainable consumption habits.
As a UX Designer, I conducted research with Unicorn Grocery Store to identify household food waste challenges. I designed an inclusive solution with grocery tracking, expiration reminders, and personalised recipes, promoting sustainability and community engagement. This scalable concept bridges technology and food waste reduction, with potential for real-world implementation.
Home cooks struggle with tracking perishables, meal variety, and overall food waste due to time constraints and inefficient management. Asha applies a human-centered design approach, using AI-driven pantry tracking and personalised recipes to streamline meal planning and promote sustainability.
Image of key challenges for Asha, highlighting time constraints, environmental impact, limited culinary variety, and insufficient food management as core issues to address in the project.
I applied the design thinking approach by leverage human-centered practices to create a solution for grocery sustainability.
Nudge Theory: I applied Ebbinghaus’ Forgetting Curve and Spaced Repetition to the approach to implement information retention and habit formation - reinforcing memory will help to reduce food waste effectively.
Image of the design thinking approach for Asha, outlining the process from empathising through research, defining insights, ideating solutions, prototyping, testing, and finally evolving through algorithmic solutions.
Enable users to discover nearby group runs, tailored to their preferences with customizable filters.
Highlighted that runs are displayed within a 5-mile radius, refined filter designs based on insights from A/B testing.
Conversation levels are defined clearly and socialization opportunities available during and before/after runs.
Designed to be welcoming, intuitive, and mission-driven, guiding users toward sustainable food choices effortlessly.
Seamlessly connects users with personalised recipes, pantry tracking, and meal planning for a more sustainable approach to food management.
The recipe generator empowers users to create personalised meals based on their cravings, dietary needs and on-hand pantry ingredients.
The page provides an interactive cooking experience, integrating sustainability insights and ingredient flexibility.
Enable users to upload FASTQ files (DNA sequencing text based files) to initiate fusion detection directly from the home screen.
Users can filter and sort through files and create folders to organize the data for more streamlines upload-to-results pipeline with realtime updates and clear progress.
Allow users to customize key analysis parameters prior to running the pipeline.
Parameters are clearly labeled with helpful default values to support novice users, while advanced users can fine-tune settings to fit specific research needs.
Provide users with real-time visibility into each stage of the fusion detection pipeline through a clearly segmented progress tracker.
Each module, from read alignment to fusion calling, is labeled and dynamically updated as steps complete.
Display fusion detection results in a concise, user-friendly table immediately after analysis completion.
Designed to support quick interpretation and downstream decision-making, with the ability to scroll, review, and export data for further analysis.
Enable users to configure essential app settings such as file storage location, cache management, and update preferences.
Users can change default settings of parameters in the settings to personalize the fusion detections settings based on their needs.
Allow users to personalize their profiles to share content on profile based on privacy preferences.
Agency to expand their network by connecting with compatible runners through friend requests.
Empower users to design runs by setting location, pace, and distance while fostering community through nearby runner participation.
Enhanced customisation options for greater control, including socialisation preferences and granular interest matching. Added seamless run-sharing across social media.
Keep a track of all runs—upcoming, hosted, and saved for later—organised in one place, along with related notifications like join requests, their statuses, and friend requests.
Updates were informed by user insights to align seamlessly with the overall experience and ensure consistency throughout the app.
Enable users to set running and social preferences like pace, experience, and interests.
Added a product overview with engaging wording and playful icons.
Introduced onboarding questions for personalisation and an app walkthrough for seamless navigation.
I conducted user research to understand food waste challenges through secondary research and user interviews.
Infographic showing how expert interviews, feedback sessions, and usability tests led to four design goals: improved navigation, clearer workflow, better result interpretation, and a cohesive design system.
A comparison of the current (first image) and revised (second image) Nanopore App flows, showing a shift from linear navigation to a more organized, expert-informed layout with improved sorting, navigation, and tab structure.
A stakeholder map for BioDepot Workflow Builder (Bwb) showing concentric layers of end users, direct and indirect stakeholders.
This journey map illustrates the user flow of the mobile app, from onboarding and pantry management to recipe generation, helping users track ingredients and reduce food waste through personalised meal planning.
A journey map outlining user steps, pain points, and design opportunities in the Nanopore app workflow. Starting from launching the app to editing workflows.
Image of usability testing and prototypes for Nanopore, showcasing home, parameters, progress and results screens, focusing on usability of legacy and revised versions.
Creating this design system ensured consistency and efficiency by defining typography, colours, icons, and components. This streamlined framework enables faster iteration, better accessibility, and a cohesive user experience, making the interface intuitive and polished.
Image of a design system, including logotypes, colours, typography, icons, UI components, and customised elements, ensuring consistency and scalability of Nanopore.
Usability testing revealed key areas for improvement, including clearer navigation, better visibility of expiry dates, a more intuitive recipe selection process, and a less cluttered onboarding experience.
Images of usability testing findings for a recipe app, highlighting user feedback on meal selection, onboarding, pantry organization, and home screen improvements.
Users may struggle to manually add pantry items, especially produce. To address this, I collaborated with a Machine Learning Engineer to develop a classification algorithm for categorising fruits and vegetables. This approach can enhance the app by automating item categorisation and generating recipes based on available ingredients.
The image shows Python code for constructing a CNN model using TensorFlow and Keras, featuring convolutional, max pooling, flatten, and dense layers, compiled with the Adam optimiser and categorical crossentropy loss.
Working on the Asha project provided valuable insights into understanding and designing sustainable solutions. Here are the key takeaways:
