Spot Market Profit Optimization via Demand Elasticity
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Solution Overview
Problem
Conventional business planning tools fail to maximize profits as they do not adequately consider supply and demand factors, including demand elasticity and varying profit margins, when predicting product quantities for sale in a spot market.
Innovation Solution
A system using an optimization model that determines optimal prices and quantities for products in a spot market, taking into account demand elasticity, profit margins, and business rules, ensuring a feasible production plan from available parts, while managing risk and maintaining business reputation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional forecasting models are used to predict product quantities, then the planning process is simple, but profit maximization is not achieved due to ignoring demand elasticity and profit margins
Solution Approach 1:
The patent combines demand forecasting, supply chain constraints, demand elasticity analysis, and profit margin optimization into a single integrated optimization model. This merging of previously separate functions enables profit maximization while accounting for all relevant factors, resolving the contradiction between simplicity and effectiveness.
Solution Approach 2:
The optimization model incorporates demand elasticity parameters and profit margin parameters as variable inputs that can be adjusted based on market conditions. By making these parameters dynamic rather than fixed, the system adapts to changing market conditions to maximize profits while maintaining a manageable planning framework.
2Productivity
If spot market pricing is used to maximize immediate profits, then revenue increases, but production feasibility may be compromised due to insufficient time to obtain parts from supply chain
Solution Approach 1:
The optimization model performs preliminary assessment of production feasibility by evaluating available inventory and supply chain lead times before determining spot market prices. This advance planning ensures that pricing decisions are made with full knowledge of production constraints, preventing scenarios where high prices are set for products that cannot be produced in time.
Solution Approach 2:
The system dynamically adjusts pricing and production plans based on real-time inventory levels and supply chain capacity. By making the optimization model responsive to current conditions rather than static, the system can maximize revenue while adapting to production feasibility constraints as they change.
3Reliability
If business rules such as minimum price differences between product grades are enforced, then business reputation is maintained, but profit optimization flexibility is reduced
Solution Approach 1:
The optimization model incorporates business rules as parameter constraints rather than rigid restrictions. By formulating minimum price differences and other business rules as adjustable parameters, the system can maintain business reputation while allowing the optimizer to find profitable solutions within the constrained parameter space.
Data Source
AI summary
A profit optimization system takes account of supply-side and demand-side factors in optimizing profit for an organization. The profit optimization system uses an optimization model to optimize profit in a spot market. The model takes into account which parts the organization uses to assemble various products. Demand curves are used to characterize the quantity of each product that will be demanded as a function of price on the spot market. Supply model data is used to determine which mix of products can be sold in view of parts availability. Using the demand model and supply model data, the optimization model can recommend a set of prices to use for selling the organization's products. The model ensures that the organization has sufficient resources available to produce the products and enforces user-supplied business rules and other constraints.