Simultaneous Price and Inventory Optimization for Manufacturing Profit
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Solution Overview
Problem
Current price optimization solutions in manufacturing fail to consider optimal inventory allocation of both finished goods and their component parts, replenishment of both finished goods and component parts, and do not account for supply chain constraints, leading to sub-optimal profit maximization.
Innovation Solution
A method and system for simultaneous price optimization and asset allocation that determines optimal price points, expected demand values, and supply-side constraints, including time-phased inventories and capacities, while ensuring demand-side constraints are met, to maximize manufacturing profits.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If price optimization is performed separately without consideration of inventory, then price decisions can be made independently, but the solution fails to consider inventory replenishment and supply-side constraints leading to sub-optimal profits
Solution Approach 1:
The patent merges price optimization and inventory allocation into a single integrated optimization model. The objective function simultaneously optimizes prices for finished goods and component parts while allocating inventory resources, rather than treating them as separate sequential decisions. This integration ensures that price decisions account for inventory constraints and replenishment capabilities.
2Device complexity
If existing price optimization solutions consider only finished goods inventory, then the complexity is reduced, but component part inventory allocation and replenishment are ignored leading to incomplete optimization
Solution Approach 1:
The patent segments the inventory optimization into two distinct but integrated components: finished goods inventory and component parts inventory. Each segment has its own allocation and replenishment decisions, but both are optimized simultaneously within the same mathematical framework. This segmentation allows the model to handle the complexity of multi-level inventory while maintaining complete optimization coverage.
3Productivity
If supply chain constraints are not considered in price optimization, then the optimization process is simpler, but capacity constraints and supply chain limitations lead to infeasible or sub-optimal solutions
Solution Approach 1:
The patent incorporates dynamic supply chain constraints into the optimization model, including capacity limitations, lead times, and replenishment rates. These constraints are expressed as mathematical inequalities that dynamically adjust the feasible region of the optimization based on current inventory levels, production capacities, and demand forecasts. The model adapts to changing supply chain conditions while maintaining feasibility.
Data Source
AI summary
Systems and methods in accordance with various embodiments of the present invention provide for a system and method for simultaneous price optimization and asset allocation to maximize manufacturing profits. In one embodiment, a set of price points for the item and a set of expected demand values for each price point are determined. A supply-side constraint which models inventory, replenishment, and capacities associated with replenishment and a joining constraint, which requires that the set of expected demand values be equal to a planned supply of the item, are determined. A demand-side constraint is determined. Further, an objective function to maximize profits is determined, based on the set of price points, the set of expected demand values, and subject to the supply-side, joining, and demand-side constraints. Based on the objective function, an optimal price profile for the item is provided.