A supplier changes the delivery date. Which customer promises need attention?

A supplier moves a confirmed delivery date, and your planner loses the afternoon working out which customers will feel it. The better outcome is a reviewed worklist showing which commitments are at risk, which are covered, and which the data cannot yet vouch for.

Your planners can trace a proposed delay to the customer orders it touches before accepting it, once purchase-order lines, materials, production orders, inventory and customer commitments are connected in one business model. The test: when an order shows as unaffected, can you tell whether stock protects it or the dependency data simply misses it?

What a business ontology changes when a supplier slips

A business ontology is a shared model of your company's things and how they relate: suppliers, purchase-order lines, materials, production orders, stock, customer commitments, and the actions people may take on them. One documented approach builds it by mapping existing data sources into objects, properties and links, with governed actions on top. Functional AI Solutions builds that model from the ERP, spreadsheets and CRM you already run, then connects automation to it. For supplier changes, you gain:

• Less chasing. The chain from purchase-order line to production order to customer commitment is recorded once, not rebuilt from ERP screens and chat threads.
• Earlier answers for sales. Sales learns which promises are at risk while the supplier date is still negotiable.
• More value from your ERP. The ontology reads existing records and works alongside your planning engine.
• A reusable foundation. The same links serve the next automation, such as an agent that drafts customer updates for a planner to approve.

What did Microsoft announce on 23 September 2026?

Microsoft announced on 23 September 2026 that procurement agent impact analysis is in public preview in Dynamics 365 Supply Chain Management. It traces supplier-proposed purchase-order changes to affected sales, production and transfer orders, and to projected inventory.

The prerelease documentation, last updated 24 August 2026, covers later confirmed delivery dates, quantity decreases and cancellations, tracing through markings and peggings in the last-run dynamic plan. Its no-impact conditions include a purchase order with no linked downstream demand and inventory that stays above the item's minimum level. The review guide adds that projections use current planning data, and planning must run again once a change is accepted.

Whatever ERP you run, those two conditions frame your readiness question.

Why does an unaffected order need its own evidence?

An order with no recorded link and an order protected by real buffer can both come out as unaffected. Any dependency trace answers only for the links it holds: a promise falls outside the chain when it was entered after the last planning run, lives in a spreadsheet, or draws on a material shared with demand outside the traced scope. The remedy is a worklist that shows its evidence:

Worklist group	Evidence the planner sees
Affected	The chain from purchase-order line to production order and customer commitment, with the new supply date against the required date
Buffered	Usable stock above the agreed minimum, or supply still arriving before the required date, and when that data was recorded
Unverified	No traced dependency, a stale plan, or shared demand outside the pilot

Consider a Taiwan machine-parts maker

Consider an illustrative case: a Taiwan manufacturer whose bearing supplier pushes back a confirmed delivery for one spindle family.

Before: the planner checks in the ERP which production orders consume the bearing, compares stock in a spreadsheet, and asks sales in a chat group which customers were given dates. Every step depends on someone remembering where the answer lives.

After: the automation reads the connected records, sorts each customer commitment into the three groups and attaches the evidence. The planner starts with the unverified group, then decides whether to accept the supplier date. People authorised to revise customer promises make those changes.

Can your records support a one-family pilot?

Your records are ready if one recent supplier change can be traced end to end for a single product family:

1. Match the purchase-order line to the material identity production uses.
2. Find every production order needing that material, and the customer commitment each serves.
3. Check usable inventory, not only on-hand quantity.
4. Record when planning data was last refreshed, and agree how fresh it must be.
5. List materials shared with demand outside the family; include or flag that demand.
6. Agree which planning rules set revised dates. The ontology connects the records; your rules decide what is feasible.

Time how long identifying the affected orders takes by hand today, then with the worklist. That comparison is your pilot measure.

How FAIS builds the impact worklist

Functional AI Solutions models the purchase-order lines, stock and customer commitments already in your ERP, spreadsheets and CRM, and automates the reviewed worklist on that model. If your ERP already offers impact analysis, the evidence groups help planners read its results; if not, the ontology supplies the links such an analysis needs.

Take your most recent supplier date change through the six checks and note each point where your planner had to ask someone. Those gaps set the scope of your first ontology project.