Hide Batch Optimization Across Tanneries for Grade Homogeneity
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Solution Overview
Problem
Transformation industries face challenges in supplying batches of transformed raw products that meet diverse buyer criteria, including topological quality, volume, and complementary parameters, especially when dealing with products of random quality from multiple distant factories.
Innovation Solution
A multi-site parametric optimization method that sets a unified topological quality norm, dynamically grades products, and optimizes batch composition to meet buyer criteria, allowing for the pooling of products from multiple factories to create homogeneous batches.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If products are pooled from multiple distant factories to meet buyer volume requirements, then the volume parameter is satisfied, but the topological quality homogeneity deteriorates due to random quality variations from different factories
Solution Approach 1:
The system segments the batch composition process into multiple optimization stages: first selecting factories based on quality parameters, then selecting specific products within those factories. This segmentation allows independent optimization of quality homogeneity and volume fulfillment, resolving the contradiction between pooling products for volume while maintaining quality standards.
Solution Approach 2:
The system dynamically changes selection parameters based on buyer requirements. For each purchase request, it adjusts the optimization criteria to prioritize either quality homogeneity or volume fulfillment depending on the specific request, allowing flexible adaptation to different buyer needs while maintaining both quality and volume requirements.
2Measurement precision
If a unified topological quality norm is imposed on all factories, then quality measurement precision is improved, but the adaptability to diverse buyer criteria deteriorates
Solution Approach 1:
The system implements a universal quality norm that serves multiple functions: it provides a common measurement standard for all factories, enables objective quality comparison across different sources, and simultaneously supports diverse buyer requirements through parametric optimization. The same norm adapts to different buyer preferences by adjusting selection weights and optimization criteria.
Solution Approach 2:
While maintaining a unified global quality norm, the system allows local adaptation in the optimization process by adjusting selection parameters based on specific buyer requests. Different factories and products can be weighted differently based on their local quality characteristics relative to the buyer's specific needs, preserving both measurement precision and adaptability.
3Ease of operation
If manual quality control and batch constitution methods are used, then ease of operation is maintained, but productivity and economic efficiency deteriorate
Solution Approach 1:
The system implements self-service automation where the optimization algorithm autonomously performs batch constitution based on buyer requirements and factory inventory data. The system automatically grades products, selects optimal combinations, and generates purchase orders without manual intervention, dramatically improving productivity while maintaining ease of use through automated decision-making.
Solution Approach 2:
The system replaces manual mechanical quality control and batch assembly processes with automated digital processing. Computer algorithms perform quality assessment, optimization calculations, and batch constitution that were previously done manually, significantly increasing processing speed and efficiency while reducing operational complexity through automation.
4Adaptability or versatility
If factories maintain large stock to satisfy diverse buyer requests, then adaptability to buyer needs is improved, but loss of time in stock turnover deteriorates
Solution Approach 1:
The system performs preliminary actions by pre-grading and categorizing all available products according to the unified quality norm before actual sales occur. Factory inventories are pre-processed and tagged with quality parameters, enabling rapid matching with buyer requirements without manual inspection or lengthy processing, thus reducing stock turnover time while maintaining adaptability.
Solution Approach 2:
The system implements feedback mechanisms where buyer purchase patterns and quality preferences are continuously analyzed. This feedback optimizes future batch constitutions and factory production planning, allowing the system to anticipate buyer needs and reduce both stock levels and turnover time while maintaining high adaptability to actual demand.
Data Source
AI summary
A Tannery System (DT), for providing a Batch (LOkr) of Hides (1ij) with a homogeneous Grade (Gkr) from multiple Tanneries (Fi), intended to undergo a Transformation Stage (Se); (i) whose size is greater than the number of Hides having a Grade (Gkr) of each Tannery; and (j) that minimizes or maximizes the statistical numeric Constraint Parameter (PN) of a global statistical technical characteristic of all Hides in the Batch. It includes (a) a Computer Network (RL) that connects an online Platform (CL) to at least two Tanneries and their two Digitizing Scanners (19i); (b) Means for Filtering (25) by the Grade (G) all the Hides (1ij) available, to select the Combined-Subset (SCkr) of the Complying Fractions (FCi) of Hides from the Tanneries having the Grade (Gkr); (c) Means of Batch Optimization (26) for (i) performing Selections of Collections of combined Complying Sub-Fractions (Sim) of Complying Fractions, (ii) for determining for each Selection, the reached value of the Numerical-constraint Parameter, and, (iii) for constituting the optimal Batch by the Selection which maximizes or minimizes the Numerical Constraint Parameter.