Next-generation logistics simulation powered by Logistics AI "EIQ_AI". Training machine learning models on shipping data to build highly accurate predictive models for logistics center capacity.
Logistics AI "EIQ_AI" is a breakthrough logistics simulation system combining EIQ analysis and machine learning based on historical shipping data. By creating a baseline data model from a single shipping dataset and applying machine learning to 100–200 model cases keyed on Order Count (E) and Item Count (I), it predicts 164 ranked variable items (line count, piece count, case count, pallet count, volume, weight, etc.) with high precision and automatically generates EIQ matrix tables.
This innovative data model construction method and the distribution center logistics simulation process are explained through the interface of free software (Tera Calculation EIQ_AI).
If you are new to this system, follow these steps to smoothly understand how our logistics simulation works:
* If you downloaded the free software, please also check the Folder Usage Guide.
The EIQ_AI software is an educational and learning support tool designed for logistics practitioners and students to practically study "building data models using Logistics AI" and "distribution center logistics simulation." A prototype version has been pre-introduced in lectures and research at two universities, with plans for a full public release as completely free software.
Additionally, this system is an AI-expanded version of the "Tera Calculation (Tera Calculation)" logistics analysis series proposed by our institute, visualizing and automating the specific calculation processes of EIQ analysis along with required floor area and staffing requirements.
The late Dr. Shin Suzuki proposed that "distribution centers are fractals." This means shipping data also exhibits fractal properties.
Just as a mulberry leaf maintains the same self-similar shape whether large or small, shipping data maintains its compositional ratio across individual elements regardless of volume fluctuations.
In summary, even as shipping volume varies based on Order Count (E) and Item Count (I), shipping data preserves the composition ratio of the EIQ matrix table derived from EIQ analysis.
Our system leverages this characteristic by performing actual calculations while varying the E and I parameters of the data model, training the Logistics AI on the aggregated results via machine learning to achieve exceptionally high prediction accuracy.
Combining 30 years of logistics consulting expertise with advanced machine learning algorithms (Microsoft.ML), our software instantly derives optimal data models for logistics design from actual shipping data. Using minimal key parameters—Order Count (E) and Item Count (I)—it automatically generates capacity prediction models for logistics simulation covering detailed ranked variables including line count, piece count, case count, pallet count, volume, and weight.
Loads arbitrary shipping data and executes machine learning using SDCA regression models. Generates a data model serving as the "Logistics AI brain" capable of predicting capacity across 164 ranked variables.
Uses the created data model to instantly output 6 predictive matrices reflecting different characteristics (frequency, weight, volume, etc.), visually evaluating and simulating distribution center capacity.
For practitioners improving facilities or designing logistics centers, as well as students researching Logistics AI and machine learning data models, we offer a free software version (Tera Calculation EIQ_AI) for practical capacity prediction from your shipping data.
This portal adopts "ML.NET" to build and integrate custom machine learning data models natively within the .NET development environment. Without relying on external languages like Python, it realizes advanced Logistics AI capacity predictions entirely within C#.