Online Concierge Recipe Suggestion Based on Perishable Inventory
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Solution Overview
Problem
Customers who purchase perishable grocery items through online concierge systems often end up wasting these items due to forgotten recipes or unused portions, leading to environmental impact and resource inefficiency.
Innovation Solution
An online concierge system suggests recipes to customers based on available, candidate items, taking into account predicted perishability and usage. The system detects acquired items, identifies candidate available items, matches them with suitable recipes, and computes suggestion scores to rank recipes for customer suggestions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If customers purchase perishable grocery items in quantities that equal or exceed recipe requirements, then the recipe can be made successfully, but the items may spoil or go to waste if the customer forgets to make the recipe or has time constraints
Solution Approach 1:
The system performs preliminary actions by detecting acquired items in the customer's inventory, predicting their perishability, and proactively suggesting recipes before the items spoil. This allows customers to plan and make recipes in advance, ensuring recipe completion while preventing food waste from forgotten or unused perishable items.
Solution Approach 2:
The system implements feedback by continuously monitoring the customer's item inventory, tracking usage patterns, and providing dynamic recipe suggestions based on what items are currently available and approaching expiration. This feedback loop helps customers make informed decisions about what to cook next, improving recipe completion rates while reducing waste from unused perishable items.
2Productivity
If customers purchase larger quantities of grocery items to ensure sufficient ingredients for recipes, then the recipe can be made without additional trips, but unused portions may remain and go to waste
Solution Approach 1:
The system applies partial action by suggesting recipes that utilize specific portions of available ingredients rather than requiring entire quantities. By analyzing the customer's inventory and suggesting recipes that match the actual available amounts, the system enables efficient recipe preparation while minimizing unused portions that would otherwise go to waste.
Solution Approach 2:
The system changes parameters by adjusting recipe suggestions based on the specific quantities of ingredients available in the customer's inventory. Instead of suggesting fixed recipes, the system adapts recommendations to match the actual amounts of perishable items on hand, optimizing both recipe preparation efficiency and ingredient utilization to reduce waste.
3Adaptability or versatility
If the system tracks and analyzes customer item usage patterns to suggest recipes, then recipe suggestions become more personalized and effective, but the system complexity increases
Solution Approach 1:
The system applies self-service by automatically detecting items in the customer's inventory, tracking usage patterns, and generating personalized recipe suggestions without requiring active customer input or manual data entry. This automation provides high adaptability and personalization while managing system complexity through automated processes rather than manual intervention.
Solution Approach 2:
The system replaces mechanical tracking methods with automated detection and analysis mechanisms. By using computational algorithms to monitor inventory and usage patterns rather than manual tracking, the system achieves high personalization and adaptability in recipe suggestions while reducing the operational complexity for customers and streamlining the overall system process.
Data Source
AI summary
An online concierge system detects acquired items included among an inventory of a customer and identifies one or more candidate available items from the acquired items based on a predicted perishability of each item and a predicted amount of each item that was used. The system retrieves recipes, matches the item(s) likely to be available to a set of recipes based on their ingredients, and identifies any remaining items for each matched recipe not likely to be available. The system retrieves a set of attributes associated with the customer and the set of recipes and computes a suggestion score for each recipe based on the attributes. The system ranks the recipes based on their scores, identifies one or more recipes for suggesting to the customer based on the ranking, and sends the recipe(s) and any remaining items for each recipe to a client device associated with the customer.