01 · STRESS-TEST
Stress-test shortlisted systems
Compare vendors against the same demand, supply, and constraint scenarios before a long implementation.
Independent supply chain AI vendor evaluation
There is no “best” AI.
Only the right fit for you.
For retail, manufacturing, and distribution teams managing an active RFP, POC, or vendor selection.
Decision-behavior scorecard
Board-ready comparison
Example grades show how vendor decision behavior could be compared—not client results.
Vendor A
Disciplined and stable under volatilityVendor B
Governed and transparentVendor C
Fast to shift; weaker governanceIncumbent
Passive and slow to recognize changeConfidential PDF · 5–7 business days after scenario execution
The vendor evaluation blind spot
A successful demo does not prove operational fit.
Forecast accuracy, feature checklists, and polished demonstrations provide useful evidence. But they rarely show how a planning system behaves when demand shifts, data is incomplete, supply tightens, or operating constraints change.
Typical buying evidence
Forecast accuracyMAPE, bias, and benchmark metrics
Clean-data backtestsHistorical data without operational noise
Feature and workflow reviewsWhat the system is designed to do
Vendor-selected demosUseful evidence, but not the whole operating picture
What METRAI adds
Behavior under changing conditionsHow the system responds when demand and supply reality shifts
Response to imperfect inputsWhat happens when data is late, missing, or inconsistent
Constraint awarenessWhether recommendations remain executable under pressure
Comparable behavioral evidenceThe same relevant scenarios across shortlisted systems
The METRAI layer
Make the POC answer the question that matters.
A proof of concept can show whether a system works on a controlled slice of data. METRAI adds a different question: how does it behave when the conditions that matter to your operation change?
02 · SEPARATE
Separate feature fit from decision fit
Identify where a system performs well in a demo but behaves differently under operational pressure.
03 · DEFEND
Make the buying decision defensible
Give executives, technology teams, and planners evidence beyond vendor claims and theoretical KPIs.
METRAI complements—not replaces—your procurement process. Run the behavioral assessment alongside your RFP, IT and security review, workflow evaluation, and proof of concept.
What we evaluate
The same scenarios expose real behavioral differences.
Every assessment is built around the specific operational conditions that matter in your planning domain.
Domain 01
Demand Planning & Forecasting
Evaluate how planning systems behave when forecasts shift, promotions hit, new items launch, or data is incomplete. The question is not accuracy—it is whether the system's decisions hold up when reality diverges from the plan.
Real-world scenarios we evaluate
- Demand Spikes and Drops
- New Products with Little History
- Promotions and Lift
- Late or Messy Data
Demand Planning & Forecasting
Sample Question #1
Does the forecast overreact to short-term spikes, or does it separate noise from real demand changes?
Why it matters: Overreactive forecasts create bullwhip effects upstream. An AI that chases every blip can amplify volatility across your supply chain—costing you in excess orders, expediting fees, and planner overtime.
Domain 02
Replenishment
Evaluate how the system handles store-level replenishment and computer-assisted ordering under real operational constraints. The focus is on whether recommendations are executable at the shelf—not just optimal on paper.
Real-world scenarios we evaluate
- Store-Level Order Frequency and Nervousness
- Minimum Order Quantities and Case Pack Logic
- Shelf Capacity and Backroom Constraints
- Supplier Lead Time Variability
Replenishment
Sample Question #1
Does the system generate orders a store can actually receive, stock, and sell—or does it optimize for the warehouse while ignoring the shelf?
Why it matters: Computer-assisted ordering fails when the system ignores store-level realities: delivery windows, shelf space, backroom limits, and scan-based trading agreements. Recommendations that look clean in a dashboard can create chaos at the store.
Domain 03
Allocation
Evaluate how seasonal, fashion, and trend-driven inventory is allocated across channels and locations. The system's allocation logic must reflect how your product sells, not just how it was bought.
Real-world scenarios we evaluate
- Pre-Season Size and Color Curve Allocation
- In-Season Reallocation Based on Early Sales Signals
- End-of-Season Markdown and Clearance Positioning
- New Store and New Market Seeding
Allocation
Sample Question #1
When early sell-through signals diverge from the buy plan, does the system reallocate with conviction—or does it wait for the data to be clean enough?
Why it matters: Planning AI that cannot act on early signals misses the selling window. A system that over-allocates to weak doors while strong doors go out of stock can become a markdown generator.
Domain 04
Inventory Planning
Evaluate how the system manages DC-level inventory positioning, safety stock, and network-wide stock decisions. The system must balance inbound supply, outbound fulfillment, and network-wide service commitments simultaneously.
Real-world scenarios we evaluate
- DC Safety Stock Positioning Across Regions
- Inbound Variability and Supplier Reliability
- Cross-DC Transfers and Lateral Moves
- Replanning After Disruption or Supplier Failure
Inventory Planning
Sample Question #1
When a DC is short, does the system protect the right channels—or allocate on a first-come, first-served basis?
Why it matters: DC-level decisions have network-wide consequences. A system that cannot distinguish between high-value and low-priority channels will consistently underperform against a well-tuned manual process.
Independent by design
Evidence without a vendor incentive.
METRAI does not accept referral fees, commissions, or partnership revenue from software vendors. Our only client is the company commissioning the assessment.
Our operating principle
Comparable, business-relevant evidence about decision behavior under pressure.
What you receive
A decision record your team can use.
We turn comparable scenario results into an executive-ready view of vendor fit, operational behavior, and implications for selection and implementation.
Typical timing: 5–7 business days after scenario execution, depending on scope and vendor response.
Comparable scenario results
Decision-behavior scorecard
Vendor-by-vendor findings
Operational-fit observations
Executive-ready readout
Selection implications