Discrete Supply Chain Planning for Coproduct 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 inefficiencies and waste.
Innovation Solution
Implementing a supply chain planner that uses Bill of Materials (BOM) grouping to discretize products, model production as a multi-objective hierarchical linear programming (LP) problem, and solve it to optimize demand satisfaction and minimize inventory, using clustering techniques to group raw materials and dynamically assign BOM groupings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If linear programming optimization is used for process industries, then complex business goals can be modeled and solved efficiently, but discretized planning is lacking which prevents substitution and optimization of supply chain plan
Solution Approach 1:
The patent segments continuous process industry products into discrete units by introducing virtual products and discretization variables. This allows the supply chain plan to treat process products as substitutable discrete items, enabling optimization across different production paths and sources while maintaining the continuous nature of actual production.
Solution Approach 2:
The patent introduces virtual products as intermediaries between raw materials and finished goods in the supply chain model. These virtual products serve as mediators that enable substitution and optimization by representing potential supply paths without requiring physical changes to the production process.
2Adaptability or versatility
If discretized planning is implemented in process industries, then substitution and optimization of supply chain plan become possible, but the complexity of modeling increases
Solution Approach 1:
The patent creates a universal discretization framework that can be applied across different process industries and supply chain configurations. The virtual product concept and discretization variables serve multiple functions: enabling substitution, maintaining continuity, facilitating optimization, and providing a standardized modeling approach that reduces overall complexity despite the detailed discretization.
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.