Drink Production Simulation System for Consistent Blending
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Solution Overview
Problem
Business unit managers face challenges in developing consistent production plans for fruit-based drinks due to variable fruit supply and limited information, leading to inconsistent results and loss of institutional knowledge when personnel change positions.
Innovation Solution
A system that aggregates existing and predicted material information, production information, and consumer liking data to determine a production plan, using a processor to simulate drink production and optimize blend plans, considering constraints and attributes such as quality, cost, and demand.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a business unit manager uses intuition to develop a production plan, then the plan can be created quickly, but the results are inconsistent over time and across regions
Solution Approach 1:
The patent replaces the manual intuitive decision-making process with an automated computer-based optimization system. The system uses software algorithms to process material information, production constraints, and consumer demand data, generating consistent production plans without relying on individual manager intuition. This substitution of human judgment with automated computational logic ensures reliability and consistency across different regions and time periods.
Solution Approach 2:
The optimization system is designed to autonomously generate production plans by automatically processing input data and applying optimization algorithms. The system self-corrects and self-optimizes without requiring manual intervention, maintaining consistent decision-making criteria across all applications. This automated self-service capability eliminates variability introduced by different managers while preserving productivity.
2Ease of operation
If a business unit manager relies on limited information and intuition, then decision-making is simple, but institutional knowledge is lost when personnel change positions
Solution Approach 1:
The system captures and stores institutional knowledge in the form of optimized production plans, material databases, and consumer demand models within a computer-based platform. This digital copying of expertise allows the organization to retain critical knowledge independently of individual employees. When personnel change positions, the system continues to function using the stored knowledge base, preventing information loss while maintaining ease of operation through automated processes.
Solution Approach 2:
The optimization system serves multiple functions: it processes material information, applies production constraints, models consumer demand, generates production plans, and stores institutional knowledge. This multi-functional platform consolidates various decision-support activities into a single universal system that is both easy to operate and capable of retaining organizational knowledge across personnel changes.
3Manufacturing precision
If the system aggregates multiple types of information and models consumer liking, then production plan quality improves, but system complexity increases
Solution Approach 1:
The patent introduces a computer-based optimization system as an intermediary between raw input data (material information, production constraints, consumer preferences) and production decisions. This intermediary layer processes and integrates multiple information types through standardized algorithms, achieving high production plan quality while managing complexity through automated data processing and structured modeling approaches.
Data Source
AI summary
A system for simulating drink production. The system can include a processor configured to aggregate existing material information, receive predicted material information, aggregate production information, receive timing information of at least one drink product, model consumer liking of the at least one drink product, and determine a production plan based on the existing material information, the predicted material information, the production information, the timing information and the consumer liking. The existing material information can be associated with a beverage input of at least one drink product. The predicted material information can be associated with the beverage input of the at least one drink product. The production information is associated with production resources of the at least one drink product.