Drink Blend Planning Using Consumer-Liking and Material Variability
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Solution Overview
Problem
Business unit managers face challenges in developing production plans for fruit-based drinks due to the variability in fruit quantity, cost, and quality, leading to inconsistent results and loss of institutional knowledge when managers change positions, necessitating improved systems for optimizing drink blends.
Innovation Solution
A system that aggregates material and production information, models consumer liking, and provides plan information for controlling production resources, utilizing a processor to optimize drink blends by integrating data from suppliers, inventory, production resources, and consumer feedback to create efficient blend plans.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a business unit manager relies on intuition to develop production plans, then the process is simple and quick, but the results are inconsistent and institutional knowledge is lost when managers change positions
Solution Approach 1:
The patent replaces the manual, intuition-based mechanical process of production planning with an automated computer-based system that uses algorithms and data models to generate production plans, eliminating human subjectivity and ensuring consistent results
Solution Approach 2:
The system enables the organization to capture and utilize its own historical production data and material information automatically, allowing the system to self-optimize production plans based on accumulated institutional knowledge stored in databases
2Reliability
If the system captures and stores material information and production data, then institutional knowledge is preserved, but the system complexity increases
Solution Approach 1:
The computer system performs multiple functions including data collection, storage, analysis, and production plan generation within a single integrated platform, reducing overall system complexity while capturing institutional knowledge
Solution Approach 2:
The system introduces databases as intermediary components that automatically store and manage material information and production data, serving as a bridge between various system modules without requiring complex manual management
3Manufacturing precision
If the system analyzes multiple scenarios with varying demand and raw material attributes, then blending decisions are optimized, but the computational time and resources increase
Solution Approach 1:
The system pre-processes and stores material information and historical production data in databases before actual production planning, allowing rapid scenario analysis by retrieving pre-organized data rather than processing raw information in real-time
Solution Approach 2:
The system efficiently handles varying raw material attributes by using parameter-based data structures and algorithms that can quickly adapt to different material properties, demand scenarios, and production constraints without requiring complete re-analysis
Data Source
AI summary
A system for optimizing blending for mass production produced products. The system may include a processor configured to aggregate material information, aggregate production information, model consumer liking of the at least one product, and provide plan information for controlling production resources based on the material information, the production information, and the consumer liking. The material information can be associated with a product input of the at least one product. The production information can be associated with the production resources of the at least one product.