AI-Assisted Backorder Processing Setup From Natural Language
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Solution Overview
Problem
The implementation and execution of Backorder Processing (BOP) in existing ERP systems is challenging due to the need for users to navigate through multiple applications and manually translate intentions into selection criteria and confirmation strategies, leading to inefficiencies and incorrect setups.
Innovation Solution
An AI-powered BOP engine automates the setup and execution of BOP by integrating a large language model (LLM) that interprets natural language user prompts and translates them into actionable commands, simplifying the process and reducing the need for specialized expertise.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual configuration and navigation through multiple applications is used for BOP setup, then users can access detailed control options, but the process becomes time-consuming and requires specialized expertise
Solution Approach 1:
The patent introduces an AI assistant as an intermediary between the user and the complex BOP configuration system. The AI assistant translates natural language user intentions into the required technical parameters and configuration steps, eliminating the need for users to manually navigate through multiple applications and understand complex BOP concepts. This mediator approach directly resolves the contradiction by making the system easier to operate while maintaining access to detailed control options.
Solution Approach 2:
The system enables self-service by allowing users to configure BOP through simple natural language prompts rather than requiring manual navigation through complex interfaces. The AI assistant autonomously interprets user intent, determines appropriate BOP parameters, and executes the configuration, thereby reducing both the time required and the expertise needed for BOP setup.
2Reliability
If manual translation of user intentions into selection criteria and confirmation strategies is required, then precise control over BOP parameters is achieved, but the process becomes complex and error-prone
Solution Approach 1:
The patent replaces the manual mechanical process of translating user intentions into BOP parameters with an AI-based cognitive system. Instead of users manually configuring selection criteria and confirmation strategies through complex interfaces, the AI assistant autonomously performs this translation by interpreting natural language prompts and generating the appropriate technical configurations, thereby reducing complexity while maintaining or improving accuracy.
Solution Approach 2:
The AI assistant serves as an intelligent intermediary that bridges the gap between user intent and system configuration. It accurately translates natural language requirements into precise BOP parameters, selection criteria, and confirmation strategies, eliminating the need for users to manually navigate complex configuration options while ensuring accurate implementation of the desired BOP behavior.
3Adaptability or versatility
If users navigate through multiple applications to configure BOP, then comprehensive control over order fulfillment is possible, but the process becomes inefficient and difficult to use
Solution Approach 1:
The patent merges the functionality of multiple applications into a unified AI-assisted interface. Instead of requiring users to navigate through separate applications for different BOP configuration tasks, the AI assistant consolidates these functions into a single interaction model where users can configure and execute BOP through natural language prompts, thereby maintaining comprehensive control capability while dramatically improving ease of operation.
Solution Approach 2:
The AI assistant provides universal access to all BOP configuration and execution functions through a single interface. It can handle diverse BOP scenarios, interpret various user intentions, and execute appropriate configurations without requiring users to switch between multiple applications, thereby maintaining versatility while simplifying the user experience.
Data Source
AI summary
A computer-implemented method for improved backorder processing (BOP) in an enterprise resource planning system is disclosed. The method can receive one or more user prompts from a user interface and create a BOP segment using a large language model. The BOP segment selects a subset of a plurality of order requirements using one or more filters determined based on the one or more user prompts. A filter is defined by an attribute, an operator, and one or more attribute values. The method can create a BOP variant using the large language model. The BOP variant defines a confirmation scheme for the BOP segment based on the one or more user prompts. The method can further execute the BOP variant using the large language model, including batch processing the subset of the plurality of order requirements using the confirmation scheme.