ERP Industry Trends Engine for Throughput-Based Inventory Recommendations
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Business entities lack access to comprehensive industry-wide trends and analytics, leading to uncertainty in inventory management and sales projections due to limited insights from their own operational patterns and missing competitor data.
Innovation Solution
An industry trends engine integrated into an enterprise resource platform uses AI models to aggregate data from multiple entities, forecast throughput analytics, and generate graphical user interfaces providing real-time recommendations for inventory management based on predicted individual throughput and current inputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If dealers access only their own inventory and payment patterns to gather insight, then data privacy and security are maintained, but the comprehensiveness of industry trends insight is limited
Solution Approach 1:
The patent introduces an intermediary system (the trends insight service with AI models) that aggregates data from multiple dealers through secure connections to their ERP systems. This intermediary processes and anonymizes the data to generate industry trends insights without requiring direct access to individual dealer sensitive information, thus resolving the contradiction between information completeness and system complexity.
2Measurement precision
If AI models aggregate data from multiple entities to forecast throughput analytics, then the accuracy of industry trends prediction is improved, but the computational resources and processing time increase
Solution Approach 1:
The system performs preliminary actions by pre-aggregating and preprocessing data from multiple entities before the actual forecasting operation. The AI models are trained in advance on aggregated data from enrolled entities, and the system pre-establishes secure connections to ERP systems. This preliminary data preparation reduces the computational burden during real-time forecasting operations.
3Speed
If the system provides real-time recommendations through graphical user interface, then the responsiveness to dealer needs is improved, but the system operational complexity increases
Solution Approach 1:
The system implements self-service capabilities where the AI models automatically generate throughput analytics forecasts and recommendations without requiring manual intervention. The graphical user interface automatically retrieves data from connected ERP systems, processes it through the AI models, and delivers real-time recommendations to dealers. This automation reduces operational complexity despite the real-time responsiveness requirement.
Data Source
AI summary
Systems and methods are described herein for incorporating an industry trends engine into an enterprise resource platform. Such systems and methods may use an institution computing system to establish a connection with an embedded service within an enterprise resource of a first entity. After authenticating a user of the first entity accessing the embedded service, the system retrieves first data relating to other entities having one or more attributes corresponding to the attributes of the first entity. Using throughput analytics based on the first data and a count of second entities, the system determines an individual throughput for the first entity. The individual throughput is compared to a current input of the first entity, and a purchase recommendation based on the comparison is provided to the user via a user interface of the enterprise resource.