HED-G QuantEnterprise Agentic Simulator

Simulate your next move. Before you make it.

Connect research and ERP data to the economics of your business. HED-G Quant simulates the interplay of purchasing, inventory, and financing to help identify where costs can change.

Discuss your use caseExplore the case
Illustrative simulation structure

Enterprise operating data

R&D / demandPurchasing / stockLoans / capital
Ontology · connected business objects
HED-G QuantInteractions across economic entities
Purchase timingOrder volumeFunding terms

Compare the total cost of each choice

R&D → ONTOLOGY → SIMULATION01 — 04

01 / A problem from the field

The opportunity was beyond production.

~KRW 50bn annual revenueMetal manufacturer · anonymized case

A metal manufacturer was exploring 1–2% reductions in production costs per item. After introducing AI-IRIS in its laboratory, a combined analysis of research and ERP data exposed another cost driver.

The capital borrowed to buy materials. And the interest it carried.

We connected material demand, purchase schedules, and bank borrowing in an ontology. The analysis indicated that adjusting financing costs could offer a larger opportunity than incremental production savings.

  1. 01

    Research to demand

    Connect research data accumulated in AI-IRIS to material requirements

  2. 02

    Demand to funding

    Analyze purchasing, inventory, and borrowing records from ERP together

  3. 03

    Funding to decisions

    Compare purchasing and financing conditions through simulation

Historical backtests and ongoing forecasts are in progress. This case describes an identified opportunity, not realized cost savings.

02 / The decision model

One decision. Connected costs.

Buying earlier may secure a better price while increasing inventory holding time and interest costs. HED-G Quant is an enterprise simulator built to examine these connected effects.

Total cost

Purchasing

Price · volume · timing

Financing

Principal · rate · duration

Holding

Stock levels · storage time

Shortage

Delays · supply disruption

MEEAMulti-Economic Entity Agents

Model the behavior and interactions of economic entities across supply, finance, and logistics. Evaluate enterprise-data scenarios against historical records and forecast subsequent changes.

Illustrative cost dimensions for material procurement. Models and constraints are defined for each enterprise environment.

03 / Explore scenarios

Change the conditions. Reconsider the decision.

Select a decision to explore the data and cost relationships involved.

Buy now, or when needed?

PurchasingFinancingHolding

Conditions to change

  • Demand date
  • Expected purchase price
  • Material arrival schedule

Effects to examine

  • Pricing benefits of an early purchase
  • Inventory duration and financing interest
  • Delivery and supply reliability
The decision question

Does the price advantage offset the cost of holding materials longer?

Illustrative scenarios explaining the approach. These are not customer data or live simulation results.

04 / Deployment and expansion

From research records to business decisions.

The research and data foundation

AI-IRIS

Connect PMS, ELN, and ERP to capture research intent and operating records.

Explore AI-IRIS
A simulator for enterprise choices

HED-G Quant

Analyze cost structures and compare scenarios using data connected through an ontology.

Current / Simulation

Historical backtests and forecasts using MEEA logic are in progress.

Planned / Approval and execution

Human-approved ERP drafts and a connection to execution history are planned extensions.

Where could your business reduce costs?

Review your workflows, available data, and the decisions you need to make. Start with one operating question and define a practical simulation scope.

Request a meeting

Tell us your industry, current ERP, and the cost you want to examine.