Auction Price Forecasting for Commodity Distribution Profit
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Solution Overview
Problem
The remarketing industry faces challenges in maximizing profits due to the variability in auction prices of commodity products, which are influenced by factors such as model year, attributes, economic conditions, and auction site location, as well as constraints on shipments and capacities.
Innovation Solution
A commodity product distribution plan is developed that forecasts auction prices by considering various factors, using a system with modules for regional trend analysis, seasonality analysis, price elasticity computation, and usage depreciation analysis, and employs a genetic algorithm to optimize distribution plans for maximizing profit.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If commodity products are distributed to auction sites without optimized forecasting, then distribution is simple, but profit maximization cannot be achieved
Solution Approach 1:
The system performs preliminary actions by forecasting auction prices before distribution decisions are made. The forecasted prices are used to optimize the distribution plan in advance, allowing the system to allocate products to auction sites based on predicted profitability rather than reacting to actual auction outcomes after the fact.
Solution Approach 2:
The system incorporates feedback mechanisms by using actual auction results and market data to refine and update the forecasting models. This continuous feedback loop allows the distribution system to learn from past performance and improve its accuracy over time, balancing the complexity of the system with improved decision-making capability.
2Measurement precision
If multiple factors are considered in auction price forecasting, then price prediction accuracy improves, but system complexity increases
Solution Approach 1:
The forecasting system segments the complex task of price prediction into distinct analytical modules. Each module handles a specific factor such as model year, attributes, economic conditions, and location. This segmentation allows the system to manage complexity by breaking down the forecasting process into manageable, independent components that can be optimized separately.
Solution Approach 2:
The system dynamically adjusts and weights different parameters based on their relative importance in different market conditions. By changing the emphasis on various factors like model year, attributes, economic conditions, and auction site location, the system can maintain high prediction accuracy across varying market scenarios without requiring all factors to be treated equally, thus managing system complexity.
3Productivity
If distribution plans are optimized using complex algorithms, then profit maximization is achieved, but computation time increases
Solution Approach 1:
The optimization system applies partial action by focusing computational resources on the most impactful factors and auction sites. Rather than exhaustively optimizing every possible distribution scenario, the system identifies and optimizes the critical decisions that yield the highest profit returns, achieving satisfactory optimization results with reduced computation time.
Solution Approach 2:
By performing preliminary forecasting of auction prices, the system establishes a foundation for optimization that reduces the computational burden during the actual optimization phase. The forecasted prices serve as input parameters that guide the optimization algorithm, allowing it to focus on allocating products rather than calculating potential revenues from scratch, thus reducing overall computation time.
Data Source
AI summary
A commodity product distribution plan is used to instruct source sites as to how commodity products are to be distributed among target sites. Where the commodity products are to be sold at auction, a wide range of auction prices can be expected due to mixed models, model years, commodity attributes such as color or optional features, economic conditions, and the auction site location itself. Additional factors that contribute to realized auction prices include depreciation and interest rate costs as well as constraints on shipments and auction site capacities. The present invention provides forecast auction prices for the commodity products, taking these various factors into consideration. In this way, an optimized distribution planned aimed at maximizing the potential profit for the commodity products to be sold at auction is generated.