Machine Learning Bid Reservation System for Revenue Optimization
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Solution Overview
Problem
Businesses face challenges in optimizing revenue from perishable and capacity-constrained inventory due to uncertain demand and the need for near real-time decision-making, as they must evaluate bids individually without knowing future bids or inventory availability, especially in scenarios where inventory becomes worthless if not sold in time.
Innovation Solution
A machine learning algorithm is used to evaluate bids in near real-time, trained to make accept/reject decisions for perishable and capacity-constrained inventory, utilizing artificial neural networks to predict optimal revenue generation by considering various input features and adjusting weights based on historical data and feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If bids are evaluated individually in near real-time without knowing future bids, then response time is reduced and near real-time decision-making is achieved, but revenue optimization becomes more difficult
Solution Approach 1:
The system performs preliminary actions by training machine learning models on historical bid data before actual bid evaluation. The model learns optimal acceptance/rejection patterns in advance, enabling fast real-time decisions without needing to analyze future bids. This preliminary training phase stores learned patterns that guide rapid individual bid evaluations.
Solution Approach 2:
The system implements feedback loops where actual bid outcomes and revenue results are fed back into the machine learning model. The model continuously learns from past decisions and their consequences, improving its ability to optimize revenue for individual bid evaluations. This feedback mechanism allows the system to refine its decision-making accuracy over time while maintaining fast evaluation speeds.
2Measurement precision
If machine learning algorithms are used to evaluate each bid on a standalone basis, then revenue optimization is improved, but system complexity increases
Solution Approach 1:
The machine learning model serves itself by automatically learning from historical data and making decisions without human intervention. Once trained, the model independently evaluates bids and determines acceptance/rejection based on learned patterns. This self-service capability reduces the need for complex manual evaluation systems while maintaining high revenue optimization accuracy.
Solution Approach 2:
The system replaces complex mechanical or manual bid evaluation processes with machine learning algorithms. Instead of using intricate rule-based systems or human judgment, the patent employs ML models that automatically process bid data and generate decisions. This substitution simplifies the overall system architecture while improving decision accuracy through data-driven insights.
3Ease of operation
If inventory is sold at fixed prices, then pricing simplicity is maintained, but revenue optimization is limited when demand is uncertain
Solution Approach 1:
The system transitions from static fixed pricing to dynamic bid-based pricing. Instead of setting unchangeable prices, the patent implements a dynamic system where prices are determined through customer bids. The machine learning model dynamically decides whether to accept or reject each bid based on learned patterns, enabling revenue optimization while maintaining operational simplicity through automated decision-making.
Solution Approach 2:
The system changes the pricing parameter from fixed values to variable bid amounts. Customers propose different prices based on their willingness to pay, and the machine learning model adjusts acceptance decisions based on these varying parameters. This parameter change enables the system to capture more revenue by accepting higher bids while rejecting lower ones, optimizing overall revenue without complex manual price setting.
Data Source
AI summary
A method for evaluating bids for the purchase of inventory items to optimize bid-generated revenue. The method comprises receiving a bid for an inventory item, the bid comprising a bid price, a specific inventory item that is to be purchased, and a number of inventory items to be purchased. The bid is input into a machine learning algorithm as are features associated with the inventory item and information associated with the bid. The machine learning algorithm analyzes the bid and generates an output to accept or decline the bid. Responsive to that output the bidder is advised whether the bid was accepted or declined.