AI MRP scenarios — model the disruption before it happens.
V5 runs MRP scenarios in parallel with production: model a demand spike, a supplier failure, a capacity outage or a shelf-life exposure and see the downstream impact on run schedule, safety stock and customer OTIF — with an AI narrative of the recommended plan.
What breaks without this.
Scenario planning is done in Excel
A one-off model for one question that nobody trusts a week later.
Trade-offs stay implicit
Do we buy more raw? Reschedule the line? Contact the customer? The plan is in someone's head.
The next disruption is a fire drill
No standing scenario library, no learned response, no time to think.
Records-by-execution. Compliance, by design.
Scenario as a first-class object
Named, versioned, sharable, comparable — not a spreadsheet in an email.
Runs against live MRP data
BOMs, routings, on-hand, on-order, open sales, shelf-life — the model is your actual state, not a copy.
Multi-variable modelling
Combine demand, supplier, capacity and shelf-life shocks in one scenario.
AI narrative recommendation
'Under scenario A, expedite PO-4421, reschedule campaign C to line 3, and pre-notify customer 12 of a 5-day slip.' Cited to the driving constraint.
One-click promote to plan
The scenario the team picks becomes the actual plan with an audit trail of the decision.
What to look for when you're buying.
Scenario planning tools succeed when they run against live data and produce an actionable recommendation. Criteria to test.
Live data or extract
What it tests: Do scenarios run against live MRP state?
Why it matters: Extracts drift.
V5: Live.
Multi-variable
What it tests: Can demand / supplier / capacity / shelf-life shocks be combined?
Why it matters: Real disruptions are multi-variable.
V5: Native combination.
Narrative recommendation
What it tests: Is the recommendation plain-English and cited?
Why it matters: Numbers without recommendations get argued forever.
V5: AI narrative recommendation.
Named, versioned scenarios
What it tests: Are scenarios first-class objects?
Why it matters: Spreadsheets can't be compared apples-to-apples.
V5: First-class.
One-click promote
What it tests: Does the chosen scenario become the plan with audit trail?
Why it matters: Decision provenance matters.
V5: One-click promote with signed decision.
Scheduled runs
What it tests: Can scenarios run on a schedule against tomorrow's plan?
Why it matters: Standing scenarios beat one-off models.
V5: Scheduled runs.
Spreadsheet vs legacy QMS vs V5.
AI scenario planning vs Excel modelling and vs a legacy S&OP module.
| Capability | Spreadsheet | Legacy QMS | V5 Ultimate |
|---|---|---|---|
| Live data | Snapshot | Extract | Live |
| Multi-variable | Painful | Limited | Native |
| Named scenarios | File names | Sometimes | First-class |
| Narrative | None | None | AI, cited |
| Promote to plan | Copy-paste | Manual | One-click, signed |
The clauses, verbatim — and how V5 answers each.
Scenario planning intersects business-continuity and quality risk management.
Management responsibility.
V5: Scenarios support management review with live evidence.
Quality risk management.
V5: Supply and capacity scenarios feed QRM.
Business continuity strategies and solutions.
V5: Scenarios are the continuity artefact.
S&OP body of knowledge.
V5: Cadence and process align with CPIM S&OP.
Step by step on the floor.
Scenario planning goes live in weeks.
- 1Week 1
Baseline calibrated
MRP baseline validated against current plan.
- 2Week 2
Standing scenarios defined
Library of standing scenarios (demand spike, supplier failure, capacity outage) defined.
- 3Week 3
Scheduled runs
Overnight runs against tomorrow's plan; morning brief carries results.
- 4Ongoing
Decision loop
Chosen scenario becomes plan with signed decision.
The math, with the assumptions visible.
ROI is measured in OTIF protected and inventory optimised.
OTIF impact of disruptions
Standing scenarios pre-position responses.
Time to plan for a disruption
Model is standing, not built on the fly.
Safety-stock right-sizing
Right-size by simulated exposure.
Sites recover the module cost quickly through OTIF-driven revenue protection.
What changed on the floor.
Setting
A pharma network with 5 producing sites and 400 finished SKUs.
Before
Every disruption was a fire drill; safety stock set by rule-of-thumb.
After
One year on V5 scenarios: three major disruptions absorbed with zero OTIF impact; safety-stock reduced measurably on covered SKUs while service improved.
Proof points
- Scenarios run against live MRP state
- Multi-variable — demand, supplier, capacity, shelf-life
- AI narrative surfaces the trade-off
- Chosen scenario becomes the plan with decision trail
Built to satisfy
- ICH Q10 §3.2 (management responsibility)
- ICH Q9(R1) (quality risk management)
- ISO 22301 §8.3 (business continuity)
- APICS CPIM (S&OP body of knowledge)
Related on V5
Frequently asked questions
Can we run scenarios overnight against tomorrow's plan?+
Yes — scheduled scenarios run against the current MRP state; the morning brief carries the outputs.
Does the AI ever change the plan on its own?+
No. AI proposes; the planner picks. Promotion to plan is a signed action.
How is this different from S&OP?+
S&OP is the monthly forum; V5 scenarios are the daily what-if that feeds it — same data spine, different cadence.
See V5 on your own line.
Free trial, no card. Live in 7 days with guided onboarding.