Machine Learning Model Predicting Transaction Likelihood
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Solution Overview
Problem
Large orders in enterprises are less predictable, leading to supply chain challenges and incorrect demand views due to the lack of timely information, resulting in extended lead times, order abandonment, and inventory wastage in conventional order management systems.
Innovation Solution
The implementation of machine learning techniques to automatically predict transaction likelihood and temporal information by processing historical data, training machine learning models, and performing automated actions based on predicted outcomes, thereby improving demand forecasting and inventory management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional order management approaches are used, then system simplicity is maintained, but prediction accuracy and demand forecasting reliability deteriorate
Solution Approach 1:
The patent replaces conventional mechanical/order-based management systems with machine learning algorithms and AI models to predict transaction likelihood and temporal information. This substitution enables automated, data-driven predictions that improve reliability while managing complexity through algorithmic processing rather than complex manual processes.
Solution Approach 2:
The system performs self-service by automatically training machine learning models on historical data and using them to generate predictions without requiring manual intervention for each prediction. The automated machine learning process handles the complexity internally, providing reliable predictions while keeping the user interface simple.
2Measurement precision
If machine learning models are trained on historical data, then prediction accuracy improves, but data processing complexity and computational requirements increase
Solution Approach 1:
The patent applies preliminary action by pre-processing and preparing historical data before training the machine learning models. The system performs data cleaning, feature engineering, and model training in advance, so that when predictions are needed, the models are ready to process new data efficiently. This pre-preparation reduces the complexity of real-time prediction operations.
Solution Approach 2:
The system creates copies of historical transaction data and uses these copies to train multiple machine learning models. By working with replicated historical data rather than original complex real-time data, the system can pre-compute patterns and relationships, reducing the computational complexity of subsequent predictions while maintaining high forecast accuracy.
3Speed
If automated actions are performed based on predictions, then response time improves, but system complexity and automation requirements increase
Solution Approach 1:
The patent implements feedback mechanisms where the system automatically executes actions based on predictions and monitors outcomes, using this feedback to refine future predictions. The automated feedback loop enables rapid response to changing conditions while managing automation complexity through continuous learning and adaptation rather than rigid pre-programmed rules.
Solution Approach 2:
The system applies dynamics by making the automation rules flexible and adaptive rather than static. The machine learning models continuously learn from new data and changing patterns, allowing the automated actions to adapt to new conditions. This dynamic approach enables fast response times while reducing the need for complex rigid automation infrastructure.
Data Source
AI summary
Methods, apparatus, and processor-readable storage media for automatically predicting transaction likelihood information and transaction-related temporal information using machine learning techniques are provided herein. An example computer-implemented method includes obtaining historical data pertaining to completed transactions within an enterprise system; determining, for at least one of the completed transactions, a set of multiple transaction-related features by processing at least a portion of the obtained historical data; training at least one machine learning model using at least a portion of the set of multiple determined transaction-related features; predicting transaction likelihood information and transaction-related temporal information associated with input data attributed to at least one pending transaction within the enterprise system by processing at least a portion of the input data using the at least one machine learning model; and performing one or more automated actions based on one or more of the predicted transaction likelihood information and the predicted transaction-related temporal information.