Automating Daily Menu Design

Can research improve user experience through optimizing operations?

Overview

For a meal subscription delivery company with hundreds of items, 50+ meals every day, and multiple categories and diet plans, menu design and generation is a long and tedious task. Multiple things go into consideration: meal ratings, meal composition, the status of the supply chain and what they're able to get and seasonal and new meals. It was also a manual process, and therefore, not scalable. Identifying that the best course of action is to automate this process, this research tackles do we get there.

Problem Statement

Meal subscription menus are more complex than restaurant menus. They require a balance between new and popular items, catering to both adventurous eaters and picky ones. With over 50 daily menu items, the process of generating menus has been successful, consistently receiving high ratings and minimal complaints about variety. However, the process is currently entirely manual and requires careful curation by the responsible individual, adhering to tried-and-tested Food R&D constraints. The problem is that this process has never been documented, making it difficult to expand the menu or automate the process. The question remains: how can we automate it?

Impact on user experience:

  • Automation allows the Food R&D team to focus on research and development

  • A unified, well-documented process makes it easier to trace feedback, isolate the issue, and make improvements

  • Reducing human error allows for a consistent pleasant experience.

Kick-off Meeting

This initial meeting serves as a platform to create a shared understanding of research goals and scope.

During this meeting, the product owner went over the following:

  • Orienting the team on the task at hand.

  • Breaking down the steps

Takeaways:

  • Setting success metrics

  • Understand the technical limitations

  • De-risking measures

User Interview - Subject Matter Experts

Understanding the manual menu generation process is crucial to building the foundation to automate it. The desired outcome of the interviews is complete documentation to build the foundation to automate the process.

A semi-structured contextual interview was conducted. The expert is the Food Operation Specialist overseeing the task of menu generation.

The outcome of this interview was a comprehensive decision tree with all the possible combinations to choose a specific meal for a slot on the menu.

Note: the decision tree nodes are redacted

Solution Workshop with the Team

Agenda

After the insights from the experts were turned into a decision tree, the team was gathered to workshop the best automation solution.

Participants

The Operation Squad → PM | Engineers | UX Designer

Food R&D → Food Ops Team

Technical limitations uncovered

The criteria the food team uses to design the menu are not the same criteria saved in the meals database.

Workaround

Import the data from the database to an external sheet

Re-write the criteria from the food team to meet the database identifier

Example

Before the workaround

select meal WHERE: type = breakfast, & diet = dietidentifier, & base = parfait OR pancake OR oats, & taste = sweet

After the workaround

select meal WHERE: type = breakfast, & diet = dietidentifier, & taste = sweet

First Iteration Release Plan

  • Release in the smallest market first

  • Allow the Food Ops team to make as many changes as necessary to the menu generated based on the commands

  • Document all changes and reasons for the change

  • Monitor any meals’ ratings changes and variations

  • Add the necessary identifiers generated from the changes the Food Ops team to the database

  • Make the changes necessary for the second iteration