Discrete Supply Chain Planning With Dynamic BOM Grouping
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Solution Overview
Problem
Process industries, such as meat processing, lack discretized planning, which prevents substitution and optimization of a supply chain plan, leading to undesirable outcomes like waste and inefficiency.
Innovation Solution
Implementing a supply chain planner that uses Bill of Materials (BOM) grouping to discretize products, model supply chain planning as a multi-objective hierarchical linear programming problem, and solve it to optimize business objectives like minimizing inventory and maximizing demand satisfaction, using clustering techniques to generate dynamic BOM groupings and optimize demand among coproducts and byproducts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If linear programming optimization is used for supply chain planning, then optimization efficiency is improved, but the ability to handle discrete planning and substitution is lost
Solution Approach 1:
The patent segments continuous supply chain planning into discrete time periods and discrete product categories. By dividing the planning horizon into specific time slots and categorizing products into discrete groups, the system enables substitution decisions between different products while maintaining computational efficiency through structured optimization models.
Solution Approach 2:
The patent transforms the optimization problem by changing parameters from continuous variables to discrete variables with specific constraints. This allows the linear programming model to handle substitution scenarios where discrete decisions about which products to produce or source are made, while still utilizing efficient LP solution methods.
2Adaptability or versatility
If discretized planning is implemented, then substitution and optimization capability is improved, but system complexity increases
Solution Approach 1:
The patent creates a universal discretized planning framework that can handle multiple product types, time periods, and substitution scenarios through a single integrated model. The BOM grouping methodology provides a multi-functional approach that works across different process industries and product categories, reducing the need for industry-specific customizations.
Solution Approach 2:
The patent implements dynamic BOM groupings that can be adjusted based on current supply chain conditions, demand patterns, and substitution opportunities. The system dynamically reconfigures product groupings and substitution relationships in response to changing parameters, enabling flexible optimization without requiring complete model redesign.
Data Source
AI summary
A system and method of supply chain planning of process industry production include a processor and memory and are configured to model a supply chain planning problem for two or more products of a process industry, wherein a coproduct is produced for at least one of the products, group the two or more products into groups, receive a weight and a yield for each raw material that produces each of the products in at least one of the groups, cluster each of the raw materials using weight-yield clustering, generate BOM grouping, and assign one BOM grouping to each of the raw materials of a single cluster.