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.
Enterprise operating data
Compare the total cost of each choice
01 / A problem from the field
The opportunity was beyond production.
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.
- 01
Research to demand
Connect research data accumulated in AI-IRIS to material requirements
- 02
Demand to funding
Analyze purchasing, inventory, and borrowing records from ERP together
- 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.
Purchasing
Price · volume · timing
Financing
Principal · rate · duration
Holding
Stock levels · storage time
Shortage
Delays · supply disruption
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?
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
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.
AI-IRIS
Connect PMS, ELN, and ERP to capture research intent and operating records.
Explore AI-IRISHED-G Quant
Analyze cost structures and compare scenarios using data connected through an ontology.
Historical backtests and forecasts using MEEA logic are in progress.
Human-approved ERP drafts and a connection to execution history are planned extensions.
ADOPTION / FAQ
HED-G Quant adoption FAQ
- What is it?
- HED-G Quant is an enterprise Agentic Simulator that connects research and ERP data through an ontology to compare interactions and total costs across purchasing, inventory, and financing.
- Who is it for?
- It is for enterprise purchasing, finance, and operations teams that need to consider purchase timing, order volume, inventory exposure, and borrowing terms together.
- What does it address?
- It addresses connected costs across departments, such as lower purchase prices leading to higher inventory holding or financing costs. Modeling interactions across supply, finance, and logistics supports scenario comparisons.
- How do you start?
- Review the current ERP and available research, purchasing, inventory, and financing data. Start with one decision and cost dimension, define the cost model and constraints, then scope historical backtests and forecasts.
Analysis scope
- Ontology connections across purchasing, inventory, and financing data
- Multi-economic entity simulation
- Purchase timing, order volume, and funding comparisons
- Historical backtests and forecasts
What data is needed for an adoption review?
Review research and demand, purchasing and inventory, and borrowing and financing data for the decision in question. Relevant fields such as timing, volume, interest rates, and payment terms, along with the historical period, are defined for each enterprise environment.
How are installation and external data policies confirmed?
An adoption review must confirm the systems, data connections, processing locations, and access permissions. This page does not establish universal HED-G Quant installation requirements or external transfer, retention, and model-training policies. Share your operating environment and security requirements during the consultation.
How does it connect to AI-IRIS and ERP?
The approach connects AI-IRIS research records and ERP data through an ontology to compare scenarios. Actual connections depend on your systems and permissions. Human-approved ERP drafts and a connection to execution history are planned extensions, not capabilities presented as currently shipped.
Where does a PoC or initial evaluation start?
Review the workflow and cost dimensions, then start with one question about purchase timing, order volume, or financing. Historical data, comparison conditions, evaluation criteria, and the schedule are defined during the adoption consultation.
Are simulation results realized savings?
Simulations and backtests examine savings opportunities under specified data, cost models, and constraints. Realized outcomes must be assessed separately through execution and measured results. The anonymized case on this page describes an identified opportunity and does not present realized savings.
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 meetingTell us your industry, current ERP, and the cost you want to examine.