Traditional EIQ analysis was limited to static aggregations ("points") of historical data. In this system, by having machine learning AI models learn the fluctuations in destination counts (E) and item counts (I), EIQ analysis evolves into "AI prediction models" capable of dynamically simulating facility capacity and workload.
This system represents a next-generation simulation platform that algorithmizes 30 years of logistics consulting expertise. While conventional EIQ analysis merely aggregated past shipment results statically, this system acts as a "logistics digital twin" that dynamically forecasts capacity for new or consolidated facilities as the AI model learns fluctuations in delivery destinations (E) and item counts (I).

Because raw logistics field data often contains mixed units and errors, "automated classification conversion" standardizes shipping units (loose, case, etc.) to guarantee calculation accuracy. Additionally, automatically executing a "5-stage Pareto analysis (EIQ ranking)" enables the AI model to recognize which product groups drive center workloads as "features."
Built on VB.NET and Microsoft Access (database), this system is clearly modularized across processing phases. By adopting a design that completes entirely within the local environment (.accdb) without relying on external cloud services, it maintains strict confidentiality for sensitive shipping data while achieving high-speed batch processing in a desktop environment.

By simulating numerical outcomes under extreme conditions—such as "What if delivery destinations increase by 1.5x?" or "What if items are halved?"—the AI prediction model gains an accurate understanding of the facility's "growth margin" and "processing limits."

By simulating numerical outcomes under extreme conditions—such as "What if delivery destinations increase by 1.5x?" or "What if items are halved?"—the AI prediction model gains an accurate understanding of the facility's "growth margin" and "processing limits."

• Data Preprocessing (E1): Corrects shipping unit variations through "classification conversion" and automatically executes EIQ ranking via Pareto analysis, building high-quality training data that helps machine learning capture volume characteristics.
• Simulation (E2 & E3): Generates thousands of "virtual scenarios" varying E/I ratios between 50% and 150% beyond baseline shipping conditions. This teaches the AI model facility growth margins and processing limits accurately.
• Machine Learning (E4): Adopts "SDCA regression (Stochastic Dual Coordinate Ascent)," balancing training efficiency with predictive accuracy. Trains 160 individual target variables separately and exports lightweight prediction engines in .zip format.

The proprietary database table progression (expanding from T200 to T600) embodies core logistics expertise. Raw historical data in T000 is transformed into structured EIQ metrics in T200, and ultimately elevated into a 165-field machine learning AI model (T600).

• Data Preprocessing (E1): Corrects unit variations via classification conversion and runs automated EIQ ranking to prepare high-quality data.
• Simulation (E2 & E3): Generates thousands of virtual scenarios across E/I ratios from 50% to 150%. Enables the AI model to learn facility boundaries.
• Machine Learning (E4): Utilizes SDCA regression for fast, accurate model training across 160 parameters exported into zip models.

From the frontend UI to backend databases and machine learning AI prediction model integrations, the entire technology stack is unified under .NET Framework 4.8. This consistency minimizes library conflicts, ensures adaptability to OS updates, and delivers exceptional desktop responsiveness.

Raw Excel data evolves from "logistics data structuring" in T200 to "simulation space construction" in T600, culminating in an AI prediction model powered by machine learning. Through this sequence of database construction steps, unstructured shipment records become strategic assets supporting future facility engineering.