Target-Driven Charting for Flat Sheet Industries
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Solution Overview
Problem
Conventional charting approaches in flat sheet industries fail to achieve a delicate balance between various business objectives, such as minimizing charting loss, inventory utilization, and production efficiency, due to their focus on single objectives like charting loss minimization, leading to suboptimal results and lack of flexibility in handling irregular customer demands and varying product sizes.
Innovation Solution
A target-driven charting method that sets specific targets for business objectives, such as charting yield, inventory utilization, and equipment throughput, by determining the bounds of various business goals and using these targets to prioritize objectives for each charting batch or run, enabling a robust, efficient, and flexible charting protocol.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of substance
If conventional charting approaches focus on minimizing charting loss, then charting yield is improved, but other business objectives like inventory utilization and production efficiency are not optimized
Solution Approach 1:
The system dynamically adjusts charting patterns and objectives based on real-time inputs including customer order priorities, inventory status, and equipment availability. The optimization model allows dynamic weighting of different business objectives (charting loss, inventory utilization, production efficiency) rather than static single-objective optimization, enabling adaptive response to changing business conditions
Solution Approach 2:
The invention changes the optimization parameters from single-objective to multi-objective by introducing weighted parameters for different business goals. The system varies parameters such as objective function weights, constraint thresholds, and priority levels to balance charting loss minimization with inventory utilization and production efficiency, allowing flexible adjustment based on business needs
2Productivity
If standard sizes are used to improve charting yield, then charting efficiency is improved, but production schedule is affected due to time consumed in producing standard sizes
Solution Approach 1:
The system performs preliminary analysis of customer order requirements and inventory availability before generating charting patterns. By pre-identifying which standard sizes can be produced without delaying confirmed customer orders, the system prepares optimization constraints in advance, allowing efficient use of standard sizes while protecting production schedules
Solution Approach 2:
The optimization model dynamically determines the extent to which standard sizes should be produced based on real-time priorities. The system adjusts the weighting and constraints for standard size production versus confirmed customer orders, allowing flexible balancing of charting efficiency gains against production schedule requirements based on current business conditions
3Adaptability or versatility
If multiple business objectives are optimized simultaneously, then overall business performance is improved, but system complexity increases
Solution Approach 1:
The system segments the multi-objective optimization into distinct modules: customer order management module, inventory management module, charting pattern generation module, and optimization calculation module. Each module handles specific functions independently, reducing overall system complexity while maintaining multi-objective optimization capability through structured information flow between segments
Solution Approach 2:
The invention introduces an intermediary optimization model that translates multiple business objectives into a unified mathematical framework. This intermediary layer processes diverse inputs (customer priorities, inventory status, equipment constraints) and converts them into optimized charting patterns, simplifying the complexity by providing a structured mediation layer between business requirements and production execution
4Reliability
If customer order priorities are incorporated into charting optimization, then customer service level is improved, but charting loss may increase due to reduced flexibility in pattern selection
Solution Approach 1:
The system uses parameter changes by introducing weighted objectives in the optimization function that balance customer service requirements with material efficiency. By adjusting the weight parameters for different customer order priorities and incorporating them into the objective function, the system finds optimal charting patterns that satisfy high-priority orders while minimizing overall charting loss through mathematical optimization rather than rigid constraint enforcement
Data Source
AI summary
Optimal charting patterns for charting of raw rolls/sheets from flat sheet industry are produced with a processing system and includes the steps of: (a) receiving user selected business objectives; (b) receiving user selected business preferences; (c) setting targets for user selected business preferences; (d) establishing charting constraint sets; (e) generating charting patterns based on user selected business objectives and targets for user selected business preferences; and (f) selecting charting patterns based on targets for user selected business preference using an objective function, wherein the objective function includes terms related to the user selected business objectives. Target driven charting assists users in knowing the bounds (upper and/or lower) of the values for various business objectives for an individual charting run. The analysis of bounds allows for explicit understanding of the trade-offs between various business objectives and enables users to prioritize their business goals separately for each charting batch or run.