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Section 5-2 : Case Study ERP Custom App

How EasyManage AI provided Proactive Insights and Inventory Optimization for custom ERP app​

AI Case Study​

This tutorial is based on case study, where AI data solution is used as in a real-world example of a customer using it.

Please review important aspects and key considerations on How To Use AI Data Solutions in previous chapter.

ERP Custom App Case Study​

Of: ERP Custom App for Electronics Manufacturer

Brief Synopsis​

Leverage opportunities for AI data solutions such as AI-driven insights & trends and workflow automation for ERP Custom App used by an electronics manufacturer.

Steps​

Following steps were taken towards implementing solutions using EasyManage AI.

  • Import Tables
    • App Tables from MySQL/mariaDB cloud database imported into EasyManage.
  • Build/Generate MCP Server REST with APIs REST.
  • Run APIs.
  • Configure MCP Server in Cursor.

Results from AI analysis is given in response docs below.

AI Model's Analysis​

Presented below and listed are few analysis and solutions being implemented. Rest of them are under privacy for Confidential Business Information.

Get API Schema​

  • Chat input
easymanage get all api schema
  • Chat input
easymanage get and study all table schema and suggest what insights and trends can be derived from tables
  • AI model response saved as chat export in workspace:
    • chat-02-asked-insights-and-trends-org.md

Ask AI What AI Workflows Can Implement​

  • Chat input
easymanage get and study all table schema and suggest what AI workflows can be implemented based on tables
  • AI Model found previous insights recommendations response doc and enhanced the same for workflow suggestions:
    • chat-02-asked-insights-and-trends-org.md

Response For AI Insights And AI Workflows​

Inventory Reorder Level Optimization​

  • Chat input
what queries I can give for inventory reorder level optimization