Twin Series · Production & Material Control
PMC Twin
Connect demand, supply, and inventory data into a traceable PMC decision workflow.
PMC Twin is a Domain Twin™ for Production and Material Control (PMC). It connects demand, forecasts, supplier commitments, inventory, BOM, and existing supply data so production planners, material planners, and procurement teams can quickly see: Where is the risk, why is it happening, and what should we review next? Analysis criteria follow each company’s existing item, product, supplier, and process classifications. Formal procurement actions, changes to supplier commitments, and system changes remain governed by existing roles and workflows.
Supply-demand review
Which items need priority attention for supply-demand risk over the next few months?
PMC Twin first identifies the items and months that need attention based on existing company rules, without forcing teams to review the entire dataset.
The operational challenge
The challenge is turning scattered planning data into a clear view of supply-demand risk and its drivers.
Forecasts, supplier confirmations, inventory, and BOM data often sit across different systems and spreadsheets. When demand or supply changes, teams have to reconcile versions, reopen item-level detail, and rebuild the information needed for the next decision.
First: identify what needs attention
Use company-defined criteria such as month, ending balance, and company-defined thresholds to prioritize high-risk items.
Then: trace when the risk begins
Drill into demand, forecast, confirmed quantity, and beginning/ending balance for one item to see when the risk starts to form.
Finally: compare the comparable supply context
Compare items by supplier, product group, process, or other existing classifications, then review multi-month supply trends to see whether the issue is isolated or part of a broader shift.
PMC Twin’s role:Organize, calculate, compare, and explain the analysis. Formal procurement actions, changes to supplier commitments, and system changes remain in existing workflows.
PMC workflow
PMC Twin turns a supply-demand question into a review-ready result.
The workflow follows the way PMC teams work: prioritize risk, open the supporting detail, compare supply conditions, and prepare a result the team can review.
Identify | Prioritize risk
Use month, balance, company-defined thresholds, or other company-defined risk criteria to rank the items that need attention.
Common scenarios
Three common PMC questions, from supply-demand risk to traceable supporting detail.
Each scenario starts with a real operating question, then traces the data, comparison criteria, and output.
Item supply-demand risk & thresholds
Use the selected month, ending balance, and company-defined thresholds to identify high-risk items, then drill into monthly detail for a specific item.
- Rank high-risk items by month, ending balance, and company-defined thresholds
- Open demand, forecast, confirmed quantity, and beginning/ending balance
- Review how risk changes by month for a specific item
- Connect historical shipments with forward demand and supply into a multi-month trend
Configurable to your operating model:Fields, classifications, and calculation rules follow the company’s existing data model, such as product group, supplier, process, or other defined attributes.
Domain Twin™ architecture
The reusable asset is not just the report—it is the PMC decision logic behind it.
PMC Twin brings enterprise data, analysis rules, and agent tools into one governed workflow. A user asks a question in natural language; the agent queries, filters, calculates, and compares according to existing rules, then prepares tables and trends. Formal decisions and execution remain with authorized people and existing systems. AI Studio can further support agent collaboration and workflow integration.
Enterprise data & supply inputs
Demand, forecasts, supplier commitments, shipments, inventory, BOM, ERP, PLM, Excel, and other authorized enterprise data sources.
PMC Twin
The agent queries, filters, calculates, compares, and organizes trends using the company’s existing rules and classifications, creating a traceable PMC analysis workflow.
Human review & existing-system execution
PMC, production planning, material planning, procurement, and management teams review risk, supply conditions, and next steps. Formal procurement actions, changes to supplier commitments, and system changes remain governed by existing roles and workflows.
How to start
A PoC does not need every system connected. Start with one testable problem.
The best starting point is a recurring PMC question with available data and a clearly defined output.
Choose one recurring question
For example: high-risk item ranking, a monthly item gap, peer supply comparison, or forecast/inventory variance.
Align data and rules
Confirm required fields, data sources, versions, filters, top-N logic, balances, and other company-defined decision criteria.
Define a testable output
Agree on the risk list, monthly detail, comparison output, or trend view first, then validate whether it fits the daily workflow.
The test is not whether AI can produce an answer.It is whether data preparation is faster, manual reconciliation is reduced, and the team can complete daily reviews using the same analysis logic.
FAQ
Common questions before implementing PMC Twin
Clarify product scope, system boundaries, and the data needed for a PoC.
What is PMC Twin?
PMC Twin is a Domain Twin™ for Production and Material Control (PMC). It brings demand, forecasts, supplier commitments, inventory, BOM, and existing analysis rules into one working context so teams can identify supply-demand risk, trace monthly detail, compare supply conditions using company-defined classifications, and prepare the evidence for review.
Does PMC Twin replace ERP or procurement systems?
No. PMC Twin focuses on cross-source queries, calculations, comparisons, and analysis preparation. Formal procurement actions, changes to supplier commitments, and system changes continue through existing systems, roles, and approval workflows.
What do you need to start a PoC?
Start with one concrete question, the current data sources, key fields, and decision rules—for example forecast, actual demand, supplier commitments, inventory, BOM, or relevant spreadsheets—then define the output and acceptance criteria together.
PMC Twin PoC
Start with one real PMC problem.
Tell us what your team repeatedly analyzes today. We’ll help define a practical PMC Twin PoC starting point and acceptance criteria.
- Start with one high-frequency PMC problem and available data
- Reuse existing data sources and analysis rules without connecting every system first
- Keep human review in place; formal procurement and supply commitments remain in existing workflows
Talk to Profet AI
Official contact form
Tell us a little about your current PMC challenge. Our team will follow up to discuss a practical PoC starting point.