Multi-store mesh network of beverage robots and associated systems and methods
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Solution Overview
Problem
Existing beverage production systems face challenges such as manual preparation leading to inconsistencies, limited beverage options, and inefficiencies in ingredient management and order fulfillment.
Innovation Solution
A mesh network of beverage robots that share recipe information and adjust recipes based on ingredient availability, allowing for consistent flavor profiles across different locations and enabling customers to choose the fastest or most convenient location for beverage pickup.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual beverage preparation is used, then beverage variety and customization are improved, but preparation time and labor costs increase
Solution Approach 1:
The beverage robot autonomously prepares beverages without human intervention. The system self-manages ingredient selection, dosage control, mixing, and dispensing operations, eliminating the need for manual preparation while maintaining beverage variety through programmable recipes and customization options.
Solution Approach 2:
The system uses controllable parameters such as ingredient dosages, mixing speeds, temperatures, and dispensing rates to create diverse beverage variations. By adjusting these parameters programmatically, the robot can produce different beverage types and customizations without manual intervention, resolving the contradiction between beverage variety and preparation speed.
2Productivity
If automated beverage production is implemented, then preparation speed and consistency are improved, but system complexity and maintenance requirements increase
Solution Approach 1:
The beverage robot is designed as a multi-functional system that can prepare multiple types of beverages using a single integrated platform. The system universally handles ingredient storage, dosing, mixing, heating, cooling, and dispensing functions, reducing the need for multiple separate devices and simplifying overall system management despite the automated capabilities.
Solution Approach 2:
The system incorporates sensors and control systems that continuously monitor ingredient levels, beverage preparation progress, and equipment status. This feedback mechanism enables automatic adjustments and alerts for maintenance needs, managing system complexity through intelligent monitoring rather than requiring complex manual intervention systems.
3Loss of information
If centralized ingredient management is used, then inventory tracking is improved, but ingredient freshness and contamination risk worsen
Solution Approach 1:
The ingredient management system is segmented into multiple independent storage containers, each dedicated to specific ingredient types. This segmentation allows for better inventory tracking of individual ingredients while reducing cross-contamination risks. Each container can be independently sealed, monitored, and replaced, maintaining both inventory visibility and ingredient freshness.
Solution Approach 2:
The system uses automated dispensing mechanisms and sealed transfer systems as intermediaries between ingredient storage and the beverage preparation area. These intermediaries maintain ingredient isolation, prevent contamination during storage and transfer, while enabling centralized inventory management through digital tracking of ingredient levels and usage.
4Manufacturing precision
If single-location beverage production is maintained, then recipe consistency is improved, but customer convenience and order fulfillment speed worsen
Solution Approach 1:
The beverage robot system replicates the same standardized recipes across multiple location units. Each robot is equipped with identical or compatible preparation capabilities and accesses the same digital recipe database, ensuring consistent beverage quality across different locations while providing customer convenience through multi-location availability and ordering options.
Data Source
AI summary
A mesh network of food and/or beverage robots across multiple store locations, and associated systems and methods, are disclosed herein. For example, a method of operating the mesh network can include receiving an order for a beverage from a first beverage robot in a first store location and identifying one or more beverage robots that are available to prepare the beverage. The second robots can be identified as available based on one or more ingredients required by a recipe for the beverage. The method can also include, for each individual beverage robot identified, determining an estimated reception time at the individual beverage robot based on when a user would receive the order from the individual beverage robot. The method can then include presenting estimated reception times to the user, receiving selection of a beverage robot, and sending the order to the selected beverage robot.